From 62a3f35bb5fe8b4d0972e6f971a29a071043608f Mon Sep 17 00:00:00 2001 From: Iris-1004 Date: Sat, 10 May 2025 12:21:23 +0900 Subject: [PATCH 01/10] Create Exploratory Data Analysis --- Exploratory Data Analysis/.gitignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 Exploratory Data Analysis/.gitignore diff --git a/Exploratory Data Analysis/.gitignore b/Exploratory Data Analysis/.gitignore new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Exploratory Data Analysis/.gitignore @@ -0,0 +1 @@ + From 453c4351e2c3f8a373bf3f743c0c1112bc4d4072 Mon Sep 17 00:00:00 2001 From: Iris-1004 Date: Sat, 10 May 2025 12:21:55 +0900 Subject: [PATCH 02/10] Add EDA --- Exploratory Data Analysis/EDA.ipynb | 4470 +++++++++++++++++++++++++++ 1 file changed, 4470 insertions(+) create mode 100644 Exploratory Data Analysis/EDA.ipynb diff --git a/Exploratory Data Analysis/EDA.ipynb b/Exploratory Data Analysis/EDA.ipynb new file mode 100644 index 0000000..56db031 --- /dev/null +++ b/Exploratory Data Analysis/EDA.ipynb @@ -0,0 +1,4470 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kOdo4PjMhphy", + "outputId": "fc5f1159-0f93-440e-ed66-4a50061b1049" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.font_manager as fm\n", + "import matplotlib\n", + "import warnings\n", + "import logging\n", + "import seaborn as sns\n", + "from IPython.display import display\n", + "from sklearn.feature_selection import mutual_info_classif\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from scipy.stats import mannwhitneyu\n", + "from scipy.stats import chi2_contingency\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import StratifiedKFold, cross_val_score\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import winsorize\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "warnings.filterwarnings('ignore')\n", + "logging.getLogger('matplotlib.font_manager').setLevel(logging.ERROR)" + ], + "metadata": { + "id": "JlYuUHP3llLS" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!apt-get -y install fonts-noto-cjk > /dev/null\n", + "!fc-cache -fv > /dev/null\n", + "!rm -rf ~/.cache/matplotlib\n", + "\n", + "font_path = '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'\n", + "fontprop = fm.FontProperties(fname=font_path)\n", + "matplotlib.rcParams['font.family'] = fontprop.get_name()\n", + "plt.rcParams['axes.unicode_minus'] = False" + ], + "metadata": { + "id": "h8U0pbsWoxZ1" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 데이터 불러오기\n", + "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", + "train_df = pd.read_csv(train_src)\n", + "X = train_df.drop(['id','shares','y'], axis=1)\n", + "y = train_df['y']\n", + "df = train_df.copy() # 원본 보존\n", + "'''df_missing_indicators # Missing Indicator 추가한 df (결측 여부와 성능 관계 확인)'''" + ], + "metadata": { + "id": "HsiO6qYOifwD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "b5529aa9-6cee-445e-e2ca-75ae7743ebd1" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'df_missing_indicators # Missing Indicator 추가한 df (결측 여부와 성능 관계 확인)'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "train_df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 256 + }, + "id": "-UOFcOtI8CWN", + "outputId": "b077b553-8fad-4f76-edfb-4541a776a266" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id n_tokens_title n_tokens_content n_unique_tokens n_non_stop_words \\\n", + "0 17335 9.0 409.0 0.501326 1.0 \n", + "1 21242 10.0 317.0 0.725086 1.0 \n", + "2 9448 11.0 447.0 0.584091 1.0 \n", + "3 36797 13.0 143.0 0.638298 NaN \n", + "4 29504 17.0 576.0 0.524412 1.0 \n", + "\n", + " n_non_stop_unique_tokens num_hrefs num_self_hrefs num_imgs num_videos \\\n", + "0 0.587361 23.0 2.0 11.0 0.0 \n", + "1 0.830097 6.0 3.0 NaN 21.0 \n", + "2 0.730104 NaN 1.0 1.0 1.0 \n", + "3 0.787500 3.0 2.0 NaN 0.0 \n", + "4 0.714286 8.0 3.0 NaN 0.0 \n", + "\n", + " ... min_negative_polarity max_negative_polarity title_subjectivity \\\n", + "0 ... -0.1875 -0.1 0.50 \n", + "1 ... -0.6000 NaN 0.00 \n", + "2 ... -0.5000 -0.1 0.75 \n", + "3 ... 0.0000 0.0 1.00 \n", + "4 ... -1.0000 -0.1 0.00 \n", + "\n", + " title_sentiment_polarity abs_title_subjectivity \\\n", + "0 0.500 0.00 \n", + "1 0.000 0.50 \n", + "2 0.125 0.25 \n", + "3 NaN 0.50 \n", + "4 0.000 0.50 \n", + "\n", + " abs_title_sentiment_polarity data_channel weekday shares y \n", + "0 0.500 Lifestyle NaN 801 0 \n", + "1 0.000 Lifestyle Tuesday 426 0 \n", + "2 0.125 World Friday 2400 1 \n", + "3 0.500 Social Media NaN 1100 0 \n", + "4 0.000 World Wednesday 1500 1 \n", + "\n", + "[5 rows x 49 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idn_tokens_titlen_tokens_contentn_unique_tokensn_non_stop_wordsn_non_stop_unique_tokensnum_hrefsnum_self_hrefsnum_imgsnum_videos...min_negative_polaritymax_negative_polaritytitle_subjectivitytitle_sentiment_polarityabs_title_subjectivityabs_title_sentiment_polaritydata_channelweekdaysharesy
0173359.0409.00.5013261.00.58736123.02.011.00.0...-0.1875-0.10.500.5000.000.500LifestyleNaN8010
12124210.0317.00.7250861.00.8300976.03.0NaN21.0...-0.6000NaN0.000.0000.500.000LifestyleTuesday4260
2944811.0447.00.5840911.00.730104NaN1.01.01.0...-0.5000-0.10.750.1250.250.125WorldFriday24001
33679713.0143.00.638298NaN0.7875003.02.0NaN0.0...0.00000.01.00NaN0.500.500Social MediaNaN11000
42950417.0576.00.5244121.00.7142868.03.0NaN0.0...-1.0000-0.10.000.0000.500.000WorldWednesday15001
\n", + "

5 rows × 49 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "train_df" + } + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "source": [ + "def resumetable(df):\n", + " print(f'데이터 셋트 형상: {df.shape}')\n", + " summary = pd.DataFrame(df.dtypes, columns=['데이터 타입'])\n", + " summary = summary.reset_index()\n", + " summary = summary.rename(columns={'index': '피처'})\n", + " summary['고유값 개수'] = df.nunique().values\n", + " summary['결측값 개수'] = df.isnull().sum().values\n", + " summary['결측 비율(%)'] = df.isnull().mean().values * 100\n", + " summary['첫 번째 값'] = df.loc[0].values\n", + " summary['두 번째 값'] = df.loc[1].values\n", + " summary['세 번째 값'] = df.loc[2].values\n", + "\n", + " return summary\n", + "\n", + "resumetable(train_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "FqJFDoA_8TpZ", + "outputId": "fede3863-0b05-4efa-8d8d-cc4e739b21d7" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "데이터 셋트 형상: (22200, 49)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 피처 데이터 타입 고유값 개수 결측값 개수 결측 비율(%) \\\n", + "0 id int64 22200 0 0.000000 \n", + "1 n_tokens_title float64 17 2200 9.909910 \n", + "2 n_tokens_content float64 2023 2146 9.666667 \n", + "3 n_unique_tokens float64 15806 2258 10.171171 \n", + "4 n_non_stop_words float64 1301 2183 9.833333 \n", + "5 n_non_stop_unique_tokens float64 13993 2256 10.162162 \n", + "6 num_hrefs float64 114 2233 10.058559 \n", + "7 num_self_hrefs float64 52 2280 10.270270 \n", + "8 num_imgs float64 79 2214 9.972973 \n", + "9 num_videos float64 47 2294 10.333333 \n", + "10 average_token_length float64 16678 2213 9.968468 \n", + "11 num_keywords float64 10 2245 10.112613 \n", + "12 kw_min_min float64 16 2216 9.981982 \n", + "13 kw_max_min float64 1005 2179 9.815315 \n", + "14 kw_avg_min float64 11194 2229 10.040541 \n", + "15 kw_min_max float64 842 2215 9.977477 \n", + "16 kw_max_max float64 24 2232 10.054054 \n", + "17 kw_avg_max float64 17230 2160 9.729730 \n", + "18 kw_min_avg float64 9044 2151 9.689189 \n", + "19 kw_max_avg float64 11957 2182 9.828829 \n", + "20 kw_avg_avg float64 19917 2177 9.806306 \n", + "21 self_reference_min_shares float64 1106 2206 9.936937 \n", + "22 self_reference_max_shares float64 975 2174 9.792793 \n", + "23 self_reference_avg_sharess float64 5210 2246 10.117117 \n", + "24 LDA_00 float64 19870 2224 10.018018 \n", + "25 LDA_01 float64 19775 2202 9.918919 \n", + "26 LDA_02 float64 20010 2153 9.698198 \n", + "27 LDA_03 float64 19685 2243 10.103604 \n", + "28 LDA_04 float64 19925 2187 9.851351 \n", + "29 global_subjectivity float64 18022 2254 10.153153 \n", + "30 global_sentiment_polarity float64 18044 2231 10.049550 \n", + "31 global_rate_positive_words float64 8680 2234 10.063063 \n", + "32 global_rate_negative_words float64 6874 2249 10.130631 \n", + "33 rate_positive_words float64 1683 2219 9.995495 \n", + "34 rate_negative_words float64 1684 2242 10.099099 \n", + "35 avg_positive_polarity float64 14979 2150 9.684685 \n", + "36 min_positive_polarity float64 31 2242 10.099099 \n", + "37 max_positive_polarity float64 36 2167 9.761261 \n", + "38 avg_negative_polarity float64 8269 2284 10.288288 \n", + "39 min_negative_polarity float64 49 2274 10.243243 \n", + "40 max_negative_polarity float64 46 2203 9.923423 \n", + "41 title_subjectivity float64 470 2272 10.234234 \n", + "42 title_sentiment_polarity float64 572 2233 10.058559 \n", + "43 abs_title_subjectivity float64 376 2266 10.207207 \n", + "44 abs_title_sentiment_polarity float64 456 2249 10.130631 \n", + "45 data_channel object 6 2147 9.671171 \n", + "46 weekday object 7 2113 9.518018 \n", + "47 shares int64 1230 0 0.000000 \n", + "48 y int64 2 0 0.000000 \n", + "\n", + " 첫 번째 값 두 번째 값 세 번째 값 \n", + "0 17335 21242 9448 \n", + "1 9.0 10.0 11.0 \n", + "2 409.0 317.0 447.0 \n", + "3 0.501326 0.725086 0.584091 \n", + "4 1.0 1.0 1.0 \n", + "5 0.587361 0.830097 0.730104 \n", + "6 23.0 6.0 NaN \n", + "7 2.0 3.0 1.0 \n", + "8 11.0 NaN 1.0 \n", + "9 0.0 21.0 1.0 \n", + "10 4.596577 4.624606 4.961969 \n", + "11 10.0 7.0 NaN \n", + "12 217.0 217.0 -1.0 \n", + "13 3400.0 831.0 1300.0 \n", + "14 712.7 431.142857 482.333333 \n", + "15 NaN 720.0 0.0 \n", + "16 617900.0 69100.0 843300.0 \n", + "17 90770.0 36531.428571 NaN \n", + "18 1226.666667 720.0 0.0 \n", + "19 3979.625442 2903.5 4360.0 \n", + "20 3056.007739 2292.299174 2113.994414 \n", + "21 2900.0 1800.0 2400.0 \n", + "22 2900.0 22800.0 2400.0 \n", + "23 2900.0 12300.0 2400.0 \n", + "24 0.139798 0.028627 0.028573 \n", + "25 0.020023 0.028671 0.028573 \n", + "26 0.020008 0.320833 0.885708 \n", + "27 0.020092 0.593291 0.028573 \n", + "28 NaN 0.028577 0.028573 \n", + "29 0.521154 0.50532 0.484134 \n", + "30 0.365865 0.060027 0.151472 \n", + "31 0.05379 0.037855 0.040268 \n", + "32 0.007335 0.047319 0.024609 \n", + "33 0.88 0.444444 0.62069 \n", + "34 0.12 0.555556 0.37931 \n", + "35 0.495455 0.5425 0.431742 \n", + "36 0.1 0.1 0.1 \n", + "37 1.0 1.0 1.0 \n", + "38 -0.129167 -0.303815 -0.202652 \n", + "39 -0.1875 -0.6 -0.5 \n", + "40 -0.1 NaN -0.1 \n", + "41 0.5 0.0 0.75 \n", + "42 0.5 0.0 0.125 \n", + "43 0.0 0.5 0.25 \n", + "44 0.5 0.0 0.125 \n", + "45 Lifestyle Lifestyle World \n", + "46 NaN Tuesday Friday \n", + "47 801 426 2400 \n", + "48 0 0 1 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
피처데이터 타입고유값 개수결측값 개수결측 비율(%)첫 번째 값두 번째 값세 번째 값
0idint642220000.00000017335212429448
1n_tokens_titlefloat641722009.9099109.010.011.0
2n_tokens_contentfloat64202321469.666667409.0317.0447.0
3n_unique_tokensfloat6415806225810.1711710.5013260.7250860.584091
4n_non_stop_wordsfloat64130121839.8333331.01.01.0
5n_non_stop_unique_tokensfloat6413993225610.1621620.5873610.8300970.730104
6num_hrefsfloat64114223310.05855923.06.0NaN
7num_self_hrefsfloat6452228010.2702702.03.01.0
8num_imgsfloat647922149.97297311.0NaN1.0
9num_videosfloat6447229410.3333330.021.01.0
10average_token_lengthfloat641667822139.9684684.5965774.6246064.961969
11num_keywordsfloat6410224510.11261310.07.0NaN
12kw_min_minfloat641622169.981982217.0217.0-1.0
13kw_max_minfloat64100521799.8153153400.0831.01300.0
14kw_avg_minfloat6411194222910.040541712.7431.142857482.333333
15kw_min_maxfloat6484222159.977477NaN720.00.0
16kw_max_maxfloat6424223210.054054617900.069100.0843300.0
17kw_avg_maxfloat641723021609.72973090770.036531.428571NaN
18kw_min_avgfloat64904421519.6891891226.666667720.00.0
19kw_max_avgfloat641195721829.8288293979.6254422903.54360.0
20kw_avg_avgfloat641991721779.8063063056.0077392292.2991742113.994414
21self_reference_min_sharesfloat64110622069.9369372900.01800.02400.0
22self_reference_max_sharesfloat6497521749.7927932900.022800.02400.0
23self_reference_avg_sharessfloat645210224610.1171172900.012300.02400.0
24LDA_00float6419870222410.0180180.1397980.0286270.028573
25LDA_01float641977522029.9189190.0200230.0286710.028573
26LDA_02float642001021539.6981980.0200080.3208330.885708
27LDA_03float6419685224310.1036040.0200920.5932910.028573
28LDA_04float641992521879.851351NaN0.0285770.028573
29global_subjectivityfloat6418022225410.1531530.5211540.505320.484134
30global_sentiment_polarityfloat6418044223110.0495500.3658650.0600270.151472
31global_rate_positive_wordsfloat648680223410.0630630.053790.0378550.040268
32global_rate_negative_wordsfloat646874224910.1306310.0073350.0473190.024609
33rate_positive_wordsfloat64168322199.9954950.880.4444440.62069
34rate_negative_wordsfloat641684224210.0990990.120.5555560.37931
35avg_positive_polarityfloat641497921509.6846850.4954550.54250.431742
36min_positive_polarityfloat6431224210.0990990.10.10.1
37max_positive_polarityfloat643621679.7612611.01.01.0
38avg_negative_polarityfloat648269228410.288288-0.129167-0.303815-0.202652
39min_negative_polarityfloat6449227410.243243-0.1875-0.6-0.5
40max_negative_polarityfloat644622039.923423-0.1NaN-0.1
41title_subjectivityfloat64470227210.2342340.50.00.75
42title_sentiment_polarityfloat64572223310.0585590.50.00.125
43abs_title_subjectivityfloat64376226610.2072070.00.50.25
44abs_title_sentiment_polarityfloat64456224910.1306310.50.00.125
45data_channelobject621479.671171LifestyleLifestyleWorld
46weekdayobject721139.518018NaNTuesdayFriday
47sharesint64123000.0000008014262400
48yint64200.000000001
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"resumetable(train_df)\",\n \"rows\": 49,\n \"fields\": [\n {\n \"column\": \"\\ud53c\\ucc98\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 49,\n \"samples\": [\n \"kw_max_min\",\n \"data_channel\",\n \"shares\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\ub370\\uc774\\ud130 \\ud0c0\\uc785\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"int64\",\n \"float64\",\n \"object\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uace0\\uc720\\uac12 \\uac1c\\uc218\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8032,\n \"min\": 2,\n \"max\": 22200,\n \"num_unique_values\": 49,\n \"samples\": [\n 1005,\n 6,\n 1230\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uacb0\\uce21\\uac12 \\uac1c\\uc218\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 538,\n \"min\": 0,\n \"max\": 2294,\n \"num_unique_values\": 44,\n \"samples\": [\n 2284,\n 2224,\n 2202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uacb0\\uce21 \\ube44\\uc728(%)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.424933873485474,\n \"min\": 0.0,\n \"max\": 10.333333333333334,\n \"num_unique_values\": 44,\n \"samples\": [\n 10.288288288288289,\n 10.018018018018019,\n 9.91891891891892\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uccab \\ubc88\\uc9f8 \\uac12\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 40,\n \"samples\": [\n 3056.00773865,\n 90770.0,\n 617900.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\ub450 \\ubc88\\uc9f8 \\uac12\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 43,\n \"samples\": [\n -0.6,\n 0.320833490645,\n 0.593291435874\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uc138 \\ubc88\\uc9f8 \\uac12\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 37,\n \"samples\": [\n 0.0285732771793,\n 4360.0,\n 0.99999999654\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "source": [ + "train_df.drop(columns = 'id').describe()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "id": "X-lnJIiNm94J", + "outputId": "255f08d7-d6c0-4bad-f6e2-cb36d0c3a470" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " n_tokens_title n_tokens_content n_unique_tokens n_non_stop_words \\\n", + "count 20000.000000 20054.000000 19942.000000 20017.000000 \n", + "mean 10.387150 543.920614 0.530611 0.970175 \n", + "std 2.105585 460.694982 0.138709 0.170108 \n", + "min 3.000000 0.000000 0.000000 0.000000 \n", + "25% 9.000000 246.000000 0.471005 1.000000 \n", + "50% 10.000000 409.000000 0.539683 1.000000 \n", + "75% 12.000000 715.750000 0.609865 1.000000 \n", + "max 19.000000 7413.000000 1.000000 1.000000 \n", + "\n", + " n_non_stop_unique_tokens num_hrefs num_self_hrefs num_imgs \\\n", + "count 19944.000000 19967.000000 19920.000000 19986.000000 \n", + "mean 0.672893 10.840337 3.303263 4.473431 \n", + "std 0.155004 11.024976 3.944523 8.092647 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.626836 4.000000 1.000000 1.000000 \n", + "50% 0.691228 7.000000 3.000000 1.000000 \n", + "75% 0.754545 14.000000 4.000000 4.000000 \n", + "max 1.000000 186.000000 116.000000 108.000000 \n", + "\n", + " num_videos average_token_length ... max_positive_polarity \\\n", + "count 19906.000000 19987.000000 ... 20033.000000 \n", + "mean 1.248669 4.552209 ... 0.755952 \n", + "std 4.087507 0.840299 ... 0.248262 \n", + "min 0.000000 0.000000 ... 0.000000 \n", + "25% 0.000000 4.479499 ... 0.600000 \n", + "50% 0.000000 4.665148 ... 0.800000 \n", + "75% 1.000000 4.857716 ... 1.000000 \n", + "max 91.000000 8.041534 ... 1.000000 \n", + "\n", + " avg_negative_polarity min_negative_polarity max_negative_polarity \\\n", + "count 19916.000000 19926.000000 19997.000000 \n", + "mean -0.259972 -0.523223 -0.107647 \n", + "std 0.127974 0.289365 0.096059 \n", + "min -1.000000 -1.000000 -1.000000 \n", + "25% -0.329167 -0.700000 -0.125000 \n", + "50% -0.253718 -0.500000 -0.100000 \n", + "75% -0.187500 -0.300000 -0.050000 \n", + "max 0.000000 0.000000 0.000000 \n", + "\n", + " title_subjectivity title_sentiment_polarity abs_title_subjectivity \\\n", + "count 19928.000000 19967.000000 19934.000000 \n", + "mean 0.284772 0.070169 0.342966 \n", + "std 0.327151 0.264686 0.188498 \n", + "min 0.000000 -1.000000 0.000000 \n", + "25% 0.000000 0.000000 0.166667 \n", + "50% 0.144444 0.000000 0.500000 \n", + "75% 0.500000 0.138600 0.500000 \n", + "max 1.000000 1.000000 0.500000 \n", + "\n", + " abs_title_sentiment_polarity shares y \n", + "count 19951.000000 22200.000000 22200.000000 \n", + "mean 0.155305 3459.794865 0.495676 \n", + "std 0.225603 12767.254516 0.499993 \n", + "min 0.000000 22.000000 0.000000 \n", + "25% 0.000000 948.000000 0.000000 \n", + "50% 0.000000 1400.000000 0.000000 \n", + "75% 0.250000 2800.000000 1.000000 \n", + "max 1.000000 843300.000000 1.000000 \n", + "\n", + "[8 rows x 46 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_tokens_titlen_tokens_contentn_unique_tokensn_non_stop_wordsn_non_stop_unique_tokensnum_hrefsnum_self_hrefsnum_imgsnum_videosaverage_token_length...max_positive_polarityavg_negative_polaritymin_negative_polaritymax_negative_polaritytitle_subjectivitytitle_sentiment_polarityabs_title_subjectivityabs_title_sentiment_polaritysharesy
count20000.00000020054.00000019942.00000020017.00000019944.00000019967.00000019920.00000019986.00000019906.00000019987.000000...20033.00000019916.00000019926.00000019997.00000019928.00000019967.00000019934.00000019951.00000022200.00000022200.000000
mean10.387150543.9206140.5306110.9701750.67289310.8403373.3032634.4734311.2486694.552209...0.755952-0.259972-0.523223-0.1076470.2847720.0701690.3429660.1553053459.7948650.495676
std2.105585460.6949820.1387090.1701080.15500411.0249763.9445238.0926474.0875070.840299...0.2482620.1279740.2893650.0960590.3271510.2646860.1884980.22560312767.2545160.499993
min3.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.000000-1.000000-1.000000-1.0000000.000000-1.0000000.0000000.00000022.0000000.000000
25%9.000000246.0000000.4710051.0000000.6268364.0000001.0000001.0000000.0000004.479499...0.600000-0.329167-0.700000-0.1250000.0000000.0000000.1666670.000000948.0000000.000000
50%10.000000409.0000000.5396831.0000000.6912287.0000003.0000001.0000000.0000004.665148...0.800000-0.253718-0.500000-0.1000000.1444440.0000000.5000000.0000001400.0000000.000000
75%12.000000715.7500000.6098651.0000000.75454514.0000004.0000004.0000001.0000004.857716...1.000000-0.187500-0.300000-0.0500000.5000000.1386000.5000000.2500002800.0000001.000000
max19.0000007413.0000001.0000001.0000001.000000186.000000116.000000108.00000091.0000008.041534...1.0000000.0000000.0000000.0000001.0000001.0000000.5000001.000000843300.0000001.000000
\n", + "

8 rows × 46 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" + } + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# y분포 확인\n", + "train_df['y'].value_counts(normalize=True) # (11196, 11004)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "2YNzHoaIm901", + "outputId": "2051e6b7-c555-4919-d7c0-694e91d0fa7c" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "y\n", + "0 0.504324\n", + "1 0.495676\n", + "Name: proportion, dtype: float64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
proportion
y
00.504324
10.495676
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 결측치 처리\n", + "가장 기본적인 방법으로 먼저 처리했습니다.\n", + "- 수치형 변수: 평균으로 대체\n", + "- 범주형 변수: 최빈값으로 대체\n", + "\n", + "이후 피처 엔지니어링 단게에서 다양한 기법으로 처리할 수 있습니다.\n", + "- 제거: 결측치가 적을 때\n", + "- 단순 대체: 빠른 탐색\n", + "- 확률적 대체: 분포 왜곡 최소화(분포 보존)\n", + "- 다중 대체(MICE): 변수 간 관계 유지\n", + "- 모델 기반 대체: 예측 모델 활용\n", + "- 그룹별 대체: 그룹 특성 반영" + ], + "metadata": { + "id": "eH2-_3QuMO9B" + } + }, + { + "cell_type": "code", + "source": [ + "# 수치형/범주형 컬럼 정의\n", + "num_cols = X.select_dtypes(include=['int64','float64']).columns.tolist()\n", + "cat_cols = ['data_channel', 'weekday']\n", + "\n", + "# # Missing Indicator\n", + "# missing_flags = df[num_cols + cat_cols].isnull().astype(int).add_suffix('_was_missing')\n", + "# df_missing_indicators = pd.concat([df, missing_flags], axis=1)\n", + "\n", + "# 수치형 결측치 → 평균 대체\n", + "num_imputer = SimpleImputer(strategy='mean')\n", + "X[num_cols] = num_imputer.fit_transform(X[num_cols])\n", + "\n", + "# 범주형 결측치 → 최빈값 대체\n", + "cat_imputer = SimpleImputer(strategy='most_frequent')\n", + "X[cat_cols] = cat_imputer.fit_transform(X[cat_cols])\n", + "\n", + "# 확인\n", + "print(X[num_cols].isnull().sum())\n", + "print(X[cat_cols].isnull().sum())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "7A5ZcZE3Em-Q", + "outputId": "d0948e28-28e1-41b8-a350-effa8f54f542" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "n_tokens_title 0\n", + "n_tokens_content 0\n", + "n_unique_tokens 0\n", + "n_non_stop_words 0\n", + "n_non_stop_unique_tokens 0\n", + "num_hrefs 0\n", + "num_self_hrefs 0\n", + "num_imgs 0\n", + "num_videos 0\n", + "average_token_length 0\n", + "num_keywords 0\n", + "kw_min_min 0\n", + "kw_max_min 0\n", + "kw_avg_min 0\n", + "kw_min_max 0\n", + "kw_max_max 0\n", + "kw_avg_max 0\n", + "kw_min_avg 0\n", + "kw_max_avg 0\n", + "kw_avg_avg 0\n", + "self_reference_min_shares 0\n", + "self_reference_max_shares 0\n", + "self_reference_avg_sharess 0\n", + "LDA_00 0\n", + "LDA_01 0\n", + "LDA_02 0\n", + "LDA_03 0\n", + "LDA_04 0\n", + "global_subjectivity 0\n", + "global_sentiment_polarity 0\n", + "global_rate_positive_words 0\n", + "global_rate_negative_words 0\n", + "rate_positive_words 0\n", + "rate_negative_words 0\n", + "avg_positive_polarity 0\n", + "min_positive_polarity 0\n", + "max_positive_polarity 0\n", + "avg_negative_polarity 0\n", + "min_negative_polarity 0\n", + "max_negative_polarity 0\n", + "title_subjectivity 0\n", + "title_sentiment_polarity 0\n", + "abs_title_subjectivity 0\n", + "abs_title_sentiment_polarity 0\n", + "dtype: int64\n", + "data_channel 0\n", + "weekday 0\n", + "dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 이상치 탐지\n", + "1. 단일 변수 분포 탐색\n", + " - 변수별 히스토그램 + 박스플롯으로 분포와 이상치 위치 파악\n", + "2. 이상치 개수 및 비율 계산\n", + " - 가장 간단한 IQR(사분위수) 기준으로 이상치 개수 정량적으로 확인\n", + " - 이후 사용할 수 있는 탐지 기법:\n", + " - 통계 기반 (IQR(사분위수), Z-Score, MAD, ESD)\n", + " - 모델 기반 (Isolation Forest, LOF, One-Class SVM)\n", + " - 시각화 기반 (박스플롯, 산점도)\n" + ], + "metadata": { + "id": "-O1tgg22MRoF" + } + }, + { + "cell_type": "code", + "source": [ + "# 단일 변수 분포 탐색\n", + "for col in num_cols:\n", + " plt.figure(figsize=(11,4))\n", + " plt.subplot(1,2,1)\n", + " sns.histplot(X[col], bins=30, kde=True)\n", + " plt.title(f'{col} 히스토그램', fontproperties=fontprop)\n", + "\n", + " plt.subplot(1,2,2)\n", + " sns.boxplot(x=X[col])\n", + " plt.title(f'{col} 박스플롯', fontproperties=fontprop)\n", + " plt.show()\n", + "\n", + "\n", + "for col in cat_cols:\n", + " plt.figure(figsize=(8,4))\n", + " sns.countplot(x=X[col], order=X[col].value_counts().index)\n", + " plt.title(f'{col} 빈도 그래프 (Countplot)', fontproperties=fontprop)\n", + " plt.xlabel(col)\n", + " plt.ylabel('빈도', fontproperties=fontprop)\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "AqaSMY0XBsqa", + "outputId": "d672dc8d-6aa7-4312-fcad-6e2037e3748f" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAGGCAYAAABsREzJAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAdu1JREFUeJzt3Xd4VGXaBvD7zExm0ia9EQgCEkA6gtFIaIEVVAQ/FVzEwtJEFhcQFFCqUlxXWdmliSJYYHURpYm6RKpUFQJIC0WBtElCeptkZt7vjzCHTAokMJkzk9y/6zqXmfOe8pzDyMuTt0lCCAEiIiIiIiIiB1MpHQARERERERE1TExIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSInIqb3wwgtYtWqVzb7i4mJIkoQ//vhDmaDsbMCAAVi7dq3SYRARUQM2ffp0jBgxQukwqAFiQkpUA71798bUqVOVDqNBMhgMyM3NrfV5n332GVq3bl0HEdXOuHHjEBQUZLMlJyfj3XffZcVPRA0a61bHGjVqFBYuXGizz9vbGxcvXqzxNWpSt44dOxZNmjSBVquFv78/mjRpgiZNmsDHxwceHh5o0qQJ3nrrLZtz3nrrLfm4iltISAj8/PxqHCO5Hiak1KBkZGRAkiTk5+crHYpLqKv3NWHChJv+I6RVq1bw8/ODn58f4uLiMHPmTPnz+PHja3SP8+fP4+rVqzAajdUe89tvv0GSJPnaVW2bN2+2OWfgwIHw9vaucnN3d0fLli1tjl+5ciUyMjLk7dq1a7BYLDV6BiIiV8C6tXaUqluNRiNKSkps9hUUFEAIUeN71KRuXbVqFRITE3Hvvffin//8JxITE5GYmIhXXnkFgwcPRmJiImbNmmVzzqxZs+TjKm7bt2+vcXzkmjRKB0BEVNHp06erLZMkCaWlpTc932QyYfPmzTCbzfj+++8xePDgao9Vq9XIzs6ucWzbtm2rtmz79u2YOHFija9FRETkSFu2bEFiYuJtnVuburW2Zs6cieXLlyMgIKBSmcViQXh4uN3uRc6HLaTkUnr37o1PP/0Uc+fORYsWLRAeHo7Vq1fX6Ny1a9ciODgYAKDX623GIF68eBEDBw6En58f2rRpg3/96183bcUaOXIkoqOj5d80pqWl4c9//jMCAgIQGRmJFStWyL9xXLt2LR566CH8+OOP6NGjB3x9fTF06FAUFhbK1/v000/RsmVL+Pj4oH///jhx4kSN38k333yDrl27Qq/Xo3v37jh48KBctmPHDkRFRUGv1yMqKgo//vijzbmSJOHEiRN4/vnnERQUhHbt2uHIkSO3fF+XLl3Cww8/DB8fH7Rv3x7ffPONfM25c+di7Nix+OKLL9CtWze5VdP6Pps1a4Zly5bhvffegyRJVXZb1Wg0yMzMxNixY9GiRQt06NABCxYsgMVigVqtvun7EELgtddeg7u7O7788kuMHj36pmNNzWbzTVtIa/MbbKPRCJ1OZ7Nv3rx56Ny5s7yV9/nnn8Pb2xs7duyo8T2IiOyNdWtl9bFuBYDw8HB069ZN3iqy1kvvvfeezf7a1K2TJ09GkyZNcPToUfnnJk2aYPHixdi8ebP8c3kmkwlPPPEELly4UGm7dOnSTX9RTfWAIHIhvXr1Er6+vmLWrFni1KlTYurUqUKn04lr167d8tzCwkKxceNGAUBcuHBBpKSkCJPJJDIzM0VISIgYOXKkOHnypPj6669FcHCwePvtt23uO2XKFCGEEKtXrxYhISEiMTFRCCFESUmJ6NChg5g0aZI4e/as2LFjhwgPDxf/+9//hBBCrFmzRnh6eorY2Fjx008/iZ07dwqdTieWLVsmhBDiypUrQqVSiYULF4rTp0+Lf/7zn+LHH3+s0fvYsGGD0Gq1YsmSJeLs2bNi+fLlYteuXUIIIX766Sfh5uYmFi9eLM6cOSPee+89odFoxIEDB+TzAYiIiAjxySefiDNnzojevXuL++6776bvKzs7WzRq1Ei8/fbbIiEhQXz99dfC19dXnD59WgghxJw5c4RerxdPPvmk+OWXX8R///tfAUB8++23Qggh0tLSRHR0tBg3bpxISUkR2dnZlZ7LYrGImJgYMWbMGJGeni7Onz8vHnzwQTFjxgwhhBBFRUUCgPj9999tzktJSRH/93//JyIjI8Xly5eFEEK89dZbIjAwUPz3v/8VZrPZ5viTJ08KtVpdo3ddE//5z39Ely5dbPaNGjVKvPrqqyI9PV0YDAZx6dIlkZycLP7xj3+IF154QQghRP/+/cWaNWvsFgcRUW2wbrVVX+vW4cOHizlz5tjsAyDOnz8vhBBi2rRpcr1UXm3r1tsxbdo04e7uLkJDQ6vdTp48ecf3IefEhJRcSq9evcTYsWPlz+fOnRMAxL59+2p0/q5duwQAkZeXJ++bP3++aNmypTCZTPK+NWvWCC8vL1FSUiLfd8qUKeL48ePC29tbrpiEEGL9+vUiKipKWCwWed/ChQvFX/7yF/laer1eFBUVyeXR0dFi1KhRQgghTp8+LQCIc+fO1eJNlGnVqpWYPn16lWX9+vUTI0eOtNn3wgsviIceekj+DECsX79e/vzBBx8IjUYjVy5Vva+FCxeKoUOH2lx37NixYt68eUKIskqzTZs28jUsFosICwsTb731lnx8+X+EVOWPP/4QHh4ewmg0yvsOHDggmjZtKoSoOiEdP3680Ol0YsyYMSIjI8Pmeps2bRLNmjUTTZs2FUePHpX32zsh/cc//iEeffRRm32jRo0SXl5ewt/fX3h7e4uWLVuKIUOGMCElIqfButVWfa1bhw8fLnQ6nfD19ZW3WyWkta1bn3jiCZvr32x76aWXqo31rrvuEjt27Ki2nOoXjiEll6PX6+WfrRPI1GYMYEXx8fHo2bOnTVfQ2NhYFBQU4MKFC7jnnnsAALm5uXjqqacQFhaGmJgY+dijR4/i119/tYmrtLQUvXv3lj9rtVq4u7vbxG2NuU2bNnj99dfx4IMP4rnnnsPUqVPRuHHjW8adn5+PhIQEDBgwoNrnev755232xcbG4tVXX7XZV/F9mkwmFBYWwtvbu8rrHj16FJs3b7YpNxqNGDlypPzZy8sLKlXZiABJkhAZGVmrPyOz2QxJkuRrAICbmxtMJlO154wbNw6zZ89GaGhopbLBgwdj4MCB+Omnn9ChQwd5vyRJ0Gg0CAoKAgB5bKqbm5t8zLx58/DXv/61RnFfvHgRzZo1s9n3wQcf4IMPPqjU1fjdd9+Vf/7LX/6Ctm3b1ugeRER1gXVrmfpct37++ef4/PPPqy2Pjo6uNKt9bevWjRs31jgeK2sdXF5mZiaeeOIJaLVam/3//Oc/8dxzz9X6HuTcmJCSSyufsNwucZPZ5cqPdfn4448xZMgQHDt2DMuWLZMnrxFCICoqCp9++qnNuV5eXtVet3zckiRhwYIFGDduHN555x3cc889+OCDDzBs2LCbxm2NTZKkWj3Xzcbv1OR9CiEwePBgLFq0yGa/r6/vHV23vObNm6Nt27Z49dVX8dZbbyEnJwfTpk3D008/Xe055RPNqqjVavTq1ctmX7t27VBcXCx/njp1KrKzs/HRRx/VKl6rpKQk9OzZs9J9qzJ+/HiYzWYAuOlzERE5GuvW+lm33kpVkxTdTt26bds2TJo06abnbd++Ha1atQJQNutwRdu2bUO3bt0QFhZ2i6ipPuCkRtSgaDRlv4MpP+lB586dsW/fPjk5AIBdu3bB09NT/ssSADp27IjVq1dj8eLFmD17NgwGAwCgU6dOOHv2LEJCQtCyZUt5a9SoUa1ii4iIwL///W9MmTIF//jHP255vI+PD+66665KkymUf649e/bY7Nu1axe6dOlS45iqel+dOnXCyZMn0axZM5vntU7SUNPrlr9mRZIk4ZtvvsHVq1fRtGlT3HfffejcuTMWLFhQ43soYcuWLXjllVeqLGvWrBm8vLzkyZLCw8MREREBPz8/aDQarF271rHBEhHZCetW16hbAWDGjBnVTuKn0+nssj72wIEDq5ycyLqlpqZWWn6mvLS0NAwaNAiffPLJHcdCroEJKTUozZs3hyRJ+PDDD3Hu3DkUFRVh/PjxyM7Oxrhx43D69Gls2bIFr732GmbOnGnTdfORRx6Bp6cnHn74YTzwwAOYPn06gLLWrUaNGuGJJ57AkSNHcObMGbz99tu4fPlyjWJas2YNRowYgQMHDuDnn3/G999/j6ZNm9bo3FmzZuGdd97BqlWrkJCQgDVr1uD9998HAMyZMweffvoplixZgnPnzuH999/HunXrMGfOnDt6XxMmTEBmZiaef/55xMfH4/jx45g9ezby8vJqfN2WLVsiLi4Ox48fx6VLl6o8pkmTJvjqq6+QmZmJ5ORkvPfee/Dw8Kjy2LVr11a7NmhV2+0KDAy85bV9fHzg7e1dZbeozZs3Izs7u9JWvpsaEZGrYd3qOnXrokWLqqyHsrOzMXnyZJtjb7du/fbbbxEUFIQ2bdpUuXl7e8tJeUWFhYUYMWIEevXqhcWLF+Pnn3+u8fOT62JCSg1K48aNMX/+fCxevBh9+/bFmTNnEBAQgP379yMpKQnR0dGYOnUqpk+fjmnTplV5DUmSsHjxYnz++ec4ePAgtFotdu7cidDQUDz22GPo1asXjh07VuOYBg8eDC8vLzz77LPo27cvAgMD8e9//7tG544cORJLly7FkiVL0LVrV3z88cfyNO49evTA1q1b8dlnn6Fr1674/PPPsW3bNnTv3r3GsVX1vvz9/XHw4EHk5eUhNjYWjz76KFJSUm76286Kpk2bBj8/P3Tv3t0urZ4jRoxAfn5+jTcAGDVqVKXKdOXKlfjiiy8q7X/44YcBANeuXavxPZ599tk7fi4iIlfAupV1a/kl0kpLS9GkSROcPXu2yi01NRVt2rSRj7dYLDh8+DAWLVoktwp/9913WL58OR599FE8//zz2LJlC7Kysu74mcg5SeJmnfyJiJxQcXExPDw88Pvvv1eaRMhZNWvWDKmpqVX+VrioqAirV6+2S1cpIiKi2zF9+nSkpqbe8RCSTZs2YejQoQgPD6/2mDFjxuCNN94AUDZ+9oUXXkDHjh0xePBgREZGysfl5+fj+++/x+bNm/HXv/4VDzzwwB3FRs6JCSnVG1u3br3pZAWJiYnw8/NzXEB3aNmyZdX+JtnX1xdJSUkOjoiIiBoa1q1EVNeYkFK9kZ+fj9TU1GrLmzdvXu2Mp84oOzu7ypnngLKZ9Vq0aOHgiIiIqKFh3UpEdY0JKRERERERESmCkxoRERERERGRIpiQEhERERERkSKYkBIREREREZEiql6VtoGzWCxITk6GXq+HJElKh0NERAoRQiAvLw/h4eFQqfg73JpgHUpEREDN61AmpFVITk5GRESE0mEQEZGTuHr1Kpo0aaJ0GC6BdSgREZV3qzqUCWkV9Ho9gLKX5+Pjo3A0RESklNzcXERERMj1At0a61AiIgJqXocyIa2CtYuRj48PK1MiImLX01pgHUpEROXdqg7lgBgiIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJShKIJaUFBAcaNG4egoCCEh4fjtddeg8lkksuGDx+OwMBAREVF4dChQzbnJiQkoGfPnvD398fAgQORlpZmU75p0yZ07NgRoaGhmDhxonxdIiIiIiIicg6KJqSzZ8/G0aNH8eOPP+Lzzz/HJ598ghUrVgAARo4ciQsXLiAuLg79+/dH//795aTTaDSib9++aN26Nfbs2QNJkjB48GD5usePH8fQoUMxfvx4bN26FVu2bMGsWbMUeUYiIiIiIiKqmiSEEErdvFOnTpg3bx4ef/xxAMBrr72Gc+fO4YMPPkCTJk2wb98+REdHQwiB1q1bY/z48Zg0aRI2btyIv/zlL0hPT4dOp0NSUhKaNGmCY8eOoXPnznj55ZeRkJCAH374AQCwfv16TJw4EUlJSdBqtbeMKzc3F76+vsjJyYGPj09dvgIiInJirA9qj++MiIiAmtcHiraQtmnTBufPn5c/e3h4oHXr1ti/fz88PDwQFRUFAJAkCbGxsdi1axcAYPfu3ejRowd0Oh0AoHHjxmjdurVNeb9+/eTrxsbGIiMjA6dOnXLUo92Wo1eysPh/52A0mZUOhYiIiIiIqM5plLz5q6++iocffhilpaUYPnw4vvrqK3z11VfYtWsXQkJCoFar5WPDw8MRHx8PADAYDAgLC7O5Vnh4OAwGQ5XlISEhUKlUcnlFRqMRRqNR/pybm2uvR6yVeVtP4/jVbAR66/DCg80UiYGIiIiIiMhRFG0hbdGiBVq0aIF169ahRYsWiImJwT333IOsrCzo9XqbY/V6PTIzMwGg1uUqlQre3t5yeUWLFi2Cr6+vvEVERNjzMWvEbBE4m1KWCG85nuzw+xMRERERETmaYgmpyWRCbGwspk6dipMnT2Ljxo3YunUr5syZg4CAAOTl5dkcn5ubi8DAQACodbnFYkFeXp5cXtGMGTOQk5Mjb1evXrXno9bI5WsFMJosAIBfL2chMavQ4TEQERERERE5kmJddvfs2YOMjAw89dRTkCQJjz/+OFQqFZ566il89tlnMBgMMJvNcrfd5ORkuRtuWFiYzdjTqspTUlLkMoPBACFEpW6+VjqdTh6PqpRzqbYJ9rYTKRjX626FoiEiIiIiIqp7irWQFhQUQKvVovwkv40aNUJpaSliY2NhNBpx+PBhAIAQAjt37kRsbCwAoE+fPti3b5887jMxMREJCQk25XFxcfJ1d+7ciZCQELRt29ZRj1drZ68npN66st8RbIlnt10iIiIiIqrfFEtIe/TogaKiIrz00ks4c+YMfvnlF0yePBmxsbEIDg7GkCFDMHnyZMTHx2P27NlIT0/HsGHDAACPPPIIAgICMGHCBJw4cQLjxo1DTEwMOnToAAAYPXo09uzZg5UrV+LIkSOYOXMmxo4dCzc3N6Ue95asLaQjHmwGlQScTslFep7xFmcRERERERG5LsUSUn9/f8TFxSExMRHR0dEYOHAgIiMj8cUXXwAAPvzwQ9x9992IjY3F999/jx9++AFBQUEAAK1Wi7i4OJw7dw49e/YEAHz99dfytdu3b48NGzZg2bJlGDhwIAYNGoS5c+c6/Blr45yhLCF9oEUggvVl3YdTcoqUDImIiIiIiKhOKbrsS7t27fDtt99WWebl5YX169dXe25kZCT27t1bbfmgQYMwaNCgO47REYpKzPjjWgEAoHWYHmE+7jDkGpGaU4yOTao/TwiBo1eyEBmqh4+787b+EhERERERVUXRZV+ozPm0PAgBBHppEazXIdTHHQBguEWX3b3nM/DkioOYtek3R4RJRERERERkV0xInYB1QqPWYWVrp8oJaU7xTc/7I6OsVfXgxWt1GB0REREREVHdYELqBC6llyWWkSHeAIAw3+sJae7NE9LcolIAQFpeWfdeIiIiIiIiV8KE1AnkFJUAAAK9yyYzCrk+qVHqrRLS4lL55xOJ2XUTHBERERERUR1hQuoEcotMAABfj7KJiWreQmqSfz6RmFNH0REREREREdUNJqROwNrS6eNRNulxmHUMae7NJzXKKSrXQprEhJSIiIiIiFwLE1InYB0Lal26JeR6QppTVIriUnP155XrsnsyMRtCiDqMkoiIiIiIyL6YkDoBa0unz/Uuuz7uGni4qQHcvNtu+YQ0q7AUiVlFdRglERERERGRfWmUDoCA3GLbMaSSJCHUR4c/rhUiNacYdwV6VX3e9TGkbmoJpWaBE4k5iAjwdEzQRERETsZgMCAnx35DWHx9fREaGmq36xERUWVMSBUmhKjUZRcoW4v0j2uFN51p19qy2rGJH369nIWL6fl1GywREZGTMhgMePa551FacvP5F2rDTavD5599yqSUiKgOMSFVWFGpGSZL2dhP66RGwI2ZdtOqmdjIYhHIu95lt9H1Y2823pSIiKg+y8nJQWmJEUUtesHi7lvtcaqibHj8vhdFzXvC4uFX/XHFOcClPcjJyWFCSkRUh5iQKszayummluRxo0BZCylQ/VqkBSUmXM9jEaK3JqSWOoyUiIjI+VncfWHxCrr1cR5+NTqOiIjqFic1Uph1HKiPuxskSZL3h/rcfC1S67hTrUYljz0tNrGFlIiIiIiIXAcTUoXdWIPUzWZ/qI8OQPUJaU7hjXGn7m5lf4zssktERERERK6ECanCbiSWtr2nw+QW0qrHkFoTWV8PDdyvd/U1sssuERERERG5ECakCquuhTTASwsAyCooqfq8cmuX6jRsISUiIiIiItfDhFRh5RPL8qwJaZ7RhBJT5ZbPnKLyXXbLWkg5hpSIiIiIiFwJE1KFWScnKr8GqfWz6vocR9lFlVtJ5fM8bowhZZddIiIiIiJyJUxIFSa3dHrYjiFVqST4eVq77ZZWOk9uWXXXQMcWUiIiIiIickFMSBVmTSx9K3TZBQB/z7J9mVWMI70xqZEb3DXXE1K2kBIRERERkQthQqoweVIj98oJqTyxUWHlhDSn3NhTLvtCRERERESuiAmpwnKLbowFrcj/epfdKltIi26MPdWxhZSIiIiIiFwQE1KF5dy0y271S7/cWC5GU25SI7aQEhERERGR62BCqrAbXXY1lcr85S67N5vUiMu+EBERERGRa2JCqrDq1iEFgACvsn1VjSHNtRlDWpaQlpoFzBZRV6ESERERERHZlWIJ6dq1ayFJUqVNoylrKTQYDHj00Ufh5+eHXr164fz58zbnHzhwAFFRUQgMDMSzzz6LgoICm/JVq1YhMjISERERmD9/PoRwvkTNYhHIM1a9DilwizGk19ch9S03qREAGNlKSkRERERELkKxhPTpp59GSkqKzTZgwABMnjwZQggMGjQIarUae/fuRWRkJPr164eSkrLELDU1FQMGDMCAAQMQFxeHhIQEjB49Wr729u3b8fLLL+Ptt9/GJ598gvfeew+rVq1S6lGrlWc0wZonV1yHFKh+ll2T2YJ8OZHVyJMaAZzYiIiIiIiIXEflLMhBPDw84OHhIX/et28fTp8+jQ0bNuDo0aM4cuQIkpKSEB4ejmXLliEoKAjbt2/H448/jnXr1iE8PBzz5s2DJEl4//330atXLyxZsgQhISFYsWIFRowYgSeffBIA8Oqrr2LFihV48cUXlXrcKlm73bq7qWySSiu/alpIrckoAOjd3aBWSXBTSyg1Cy79QkRERERELsNpxpDOmDEDM2fOhLe3N3bv3o22bdsiPDwcAKDT6dC9e3fs2rULALB792707dsXkiQBAKKioqDVarF//365vF+/fvK1Y2Njcfz4cWRlZVV5b6PRiNzcXJvNEW62BilQroW0QkJqnZnXw00Nrabsj9BdXvqFCSkREREREbkGp0hIz507h6NHj2Lo0KEAysaPhoWF2RwTHh4Og8FQZblGo0FoaCgMBgMKCgqQn59vU25NbK3nV7Ro0SL4+vrKW0REhF2frzo3W4MUAAKut5AWlJhtxoZWtVSMzo1rkRIRERERkWtxioR03bp1ePjhh+Hr6wsAyMrKgl6vtzlGr9cjMzPzluXZ2dny5/JlAOTzK5oxYwZycnLk7erVq3Z5rlvJu95Cqq9iyRfrfrWqrBU4u9zSL9af/TxvJKTWiY249AsREREREbkKp0hIDx48iHvvvVf+HBAQgLy8PJtjcnNzERgYeMvygIAAALApt3bBtZ5fkU6ng4+Pj83mCEXXu9d6aiuPHwUAlUqC3/VW0PLjSKtqIbUu/WJkCykREREREbkIxRNSIQSOHTuGtm3byvvCwsKQkpJic1xycrLcDbdiuclkQlpaGsLCwuDh4QEfHx+b8uTkZABAaGhoXT5KrVnHe3q4VZ2QAoB/FeNIs6tMSNlCSkRERERErkXxhDQvLw/Xrl2Tx3kCQJ8+fXDmzBkkJSUBAIqLi7F//37ExsbK5XFxcfLaoocPH0ZpaSliYmJsyq127tyJe++9F35+fg56qpopKilLHt1vkpBax5Fmllv6xTo7b/kuu9ZZeo2c1IiIiIiIiFyE4glpfn4+ANgsAdO5c2dER0dj3LhxOHHiBCZMmIDg4GAMGDAAAPDMM8/AYDBgzpw5iI+Px+TJk/H000/LXXJfeuklfPLJJ/j666+xc+dOvPvuuxg/frzjH+4Wiq53r715C2lZ0mnTQno9Oa2yhZRddomIiIiIyEU4ZUIKAJs2bYLZbEbPnj1x/vx57NixA25uZQlYSEgIvv/+e3z33XeIjY1Fq1at8MEHH8jn9u/fH0uXLsVrr72GF154AVOnTsXIkSMd91A1ZB1D6lHNGFKg3NIv5SY1ypFbSLXyPi77QkRERERErqbq6V0dqFWrVnLX2/JCQkKwffv2as+Ljo7Gzz//XG35mDFjMGbMGLvEWFesyePNuuxak85MmxbS6+uXVjGpERNSIiIiIiJyFYq3kDZkNRlDGni9hfRaFbPs+tmsQ2qd1IhddomIiIiIyDUo3kLakBVVMctufn4+CgsL5c9aixEAkHwtD2lpaQCAa3lFAACdZJKP47IvRERERETkathCqqAby76U/THk5+fjrmbNERoaKm9jn38aAHDo+Gl539nfrwIAhg99Uh6Dq9Nw2RciIiIiInItbCFVUHGFSY0KCwuReS0Dr63aBi/fAABAdrEZX5/OgWdwU8z78gAA4NP4TJgsQHbqVRQWFsLb25tjSImIiIiIyOUwIVVQUTWTGnn5BkDvX7aEjdZkBpCDUouAu48/JAkwWTIBAJbifPmcG7PssssuERERERG5BnbZVZB1UqObrUOqVaugUUkAgAKjyWaMqMV4Y6ypdR1SI1tIiYiIiIjIRTAhVVDR9eTyZuuQSpIEL11ZQ3aB0Sx3ydWpJQA3lsuRu+xyDCkREREREbkIJqQKKq5ilt2qeF1PWAtKTDBeX9ZFq5FsjrG2kLLLLhERERERuQqOIVVQTdYhBVCuhdQkd98tayG9QXd9DKmRLaREREREROQi2EKqoKIKs+xWx0t7PSEtMcstpBUTUraQEhERERGRq2FCqqCiGnbZ9dRdXxbGaJK7+Wo1tn90Oi77QkRERERELoYJqULMFoGS662dtxxDqrvRQlpcXQuphgkpERERERG5FiakCimfON5yDKl1UiOjSV7WRcdJjYiIiIiIyMUxIVVIUbmEVKe5+R/DjRZSU/UtpG6c1IiIiIiIiFwLE1KF3JhhVwWVSrrpsdZJjYpLLSg0mgBUMYZUwxZSIiIiIiJyLUxIFVLTNUiB60nr9Zw1s7AEAFtIiYiIiIjI9TEhVUhNZ9gFAEmS4Gld+sV4fQxpNQlpqVnAbBH2DJWIiIiIiKhOMCFViNxl9xZrkFp5Xx9HauXhZvtH517uM2faJSIiIiIiV8CEVCG1aSEFAB+PsoRUq1ahV6tg+OgqJKSaG9dhQkpERERERK5Ac+tDqC5YJx+qaUL64N1BCNG7o02YHl46DfKyrtmUq1QStGoVSswWeSZeIiIiIiIiZ8aEVCHypEY17LLr6+GGrnf53/QYneZ6QsoWUiIiIiIicgHssqsQa5dd9xq2kNaE7vq1mJASEREREZErYEKqEOukRjXtslsT1omNjOyyS0RERERELoAJqUJqO6lRTbizhZSIiIiIiFyI4gnpoUOH0L17d/j4+OD+++/Hrl27AAAFBQUYPnw4AgMDERUVhUOHDtmcl5CQgJ49e8Lf3x8DBw5EWlqaTfmmTZvQsWNHhIaGYuLEiTCZTA57ppqo7RjSmtBp2EJKRERERESuQ9GE9NixY4iNjcWgQYNw5MgRjB07Fnv27AEAjBw5EhcuXEBcXBz69++P/v37y0mn0WhE37590bp1a+zZsweSJGHw4MHydY8fP46hQ4di/Pjx2Lp1K7Zs2YJZs2Yp8ozVsXbZ1bnZ749Aez0hLWFCSkRERERELkDRWXYXLlyIZ555BtOmTQMAtGnTBgCQmpqKjRs3Yt++fejSpQs6d+6ML7/8EuvXr8ekSZOwbds25OTkYOnSpdDpdFi5ciWaNGmC+Ph4dO7cGR999BH69OmDcePGAQAWLFiAiRMnYt68edBqtYo9b3l10WWXLaRERERERORKFGshNZvN2LRpE5555plKZfv374eHhweioqIAAJIkITY2Vu7Ou3v3bvTo0QM6nQ4A0LhxY7Ru3dqmvF+/fvL1YmNjkZGRgVOnTtX1Y9VY3SSkZdcycgwpERERERG5AMUS0pSUFJhMJkiShMceewyNGzfGkCFDYDAYYDAYEBISArX6RrIWHh4Og8EAADAYDAgLC7O53s3KQ0JCoFKp5PKKjEYjcnNzbba6VhdjSOUuu2a2kBIRERERkfNTLCFNTEwEAEycOBHPPfccvvzySyQkJODFF19EVlYW9Hq9zfF6vR6ZmZkAUOtylUoFb29vubyiRYsWwdfXV94iIiLs9pzVsY4htes6pNYuu6VMSImIiIiIyPkplpBaE8Zly5Zh6NChiImJwdtvv41t27bBx8cHeXl5Nsfn5uYiMDAQABAQEFCrcovFgry8PLm8ohkzZiAnJ0ferl69arfnrE5ddtllCykREREREbkCxSY1srZCuru7y/uaNWsGs9mMkJAQGAwGmM1mudtucnKy3A03LCwM58+ft7lexfKUlBS5zGAwQAhRqZuvlU6nk8ejOkrR9VZMeyakWraQEhERERGRC1GshdTHxwfdunWTl3kBytYW9fb2RmxsLIxGIw4fPgwAEEJg586diI2NBQD06dMH+/btg9FoBFDW/TchIcGmPC4uTr7uzp07ERISgrZt2zrq8W6puKQu1yHlpEZEROTaiouLkZCQgOLiYqVDcYiG9rxERFaKrkM6bdo0LFy4ENu2bcOvv/6K119/HS+++CKCg4MxZMgQTJ48GfHx8Zg9ezbS09MxbNgwAMAjjzyCgIAATJgwASdOnMC4ceMQExODDh06AABGjx6NPXv2YOXKlThy5AhmzpyJsWPHws3NTcnHtWHtslsXY0i5DikREbm6K1euYOzYsbhy5YrSoThEQ3teIiIrRdchfeqpp5Cbm4tXX30VaWlpGDZsGObPnw8A+PDDDzFmzBjExsbi7rvvxg8//ICgoCAAgFarRVxcHEaNGoWePXsiJiYGX3/9tXzd9u3bY8OGDXjjjTdgMBgwbNgwzJ07V4lHrBbXISUiIiIiooZO0YQUAEaOHImRI0dW2u/l5YX169dXe15kZCT27t1bbfmgQYMwaNAgu8RYF+pi2Rfd9eSWLaREREREROQKFO2y25AV10ELqVbNMaREREREROQ6mJAqoNRsQalZALBzl103dtklIiIiIiLXoXiX3YbI2joK3Egib1d6err8s7GwAACQV1iEtLS0Ssd6enrC29v7ju5HRERERERkL0xIFVB8fZ1QSboxEVFtGYsLAUlC+/bt5X1ebXsj6LGp2LFzN0Jf7FHpnIDAIFz+43cmpURERERE5BSYkCrAOsZTp1FBkqTbukapsRgQAhOWbEBwWGMAwO9ZJdj1ez6atb8Pf33igM3xBTmZeGfsQBQWFjIhJSIiIiIip8CEVAHWFlKd5s7Hj3r5+EPvHwgA8DblA8gHVGp5HxERERERkbPipEYKKN9Cak9qVVlrq8ki7HpdIiIiIiKiusCEVAHWWXDvdEKjijSqsuuZmZASEREREZELYEKqAKMdu+yWZ20hZUJKRERERESugAmpAqxddt3t3EIqd9k1MyElIiIiIiLnx4RUAfac1Kg8jfp6C6lgQkpERERERM6PCakC6mxSI4lddomIiIiIyHUwIVWAPKlRHc2ya7YICLaSEhERERGRk2NCqoAbCamdu+xeT0gBdtslIiIiIiLnx4RUAcbSup3UCGC3XSIiIiIicn5MSBVQVy2kTEiJiIiIiMiVMCFVgLWFVGfnFlJJkuSJjUxMSImIiIiIyMkxIVVAXU1qBNhObEREREREROTMmJAqoK667AJMSImIiIiIyHUwIVVAXa1DCtxISNlll4iIiIiInB0TUgUYS8taSN3d7N9CqmELKRERERERuQgmpAooNtXNpEYAu+wSEREREZHrYEKqAGsLKSc1IiIiIiKihowJqQLqclIjjTyG1GL3axMREREREdkTE1IFOGJSI7aQEhERERGRs1M0IR0xYgQkSbLZ5s6dCwAoKCjA8OHDERgYiKioKBw6dMjm3ISEBPTs2RP+/v4YOHAg0tLSbMo3bdqEjh07IjQ0FBMnToTJZHLUY92StYW0LiY1YkJKRERERESuQvEW0qeffhopKSnyNnXqVADAyJEjceHCBcTFxaF///7o37+/nHQajUb07dsXrVu3xp49eyBJEgYPHixf8/jx4xg6dCjGjx+PrVu3YsuWLZg1a5Yiz5efn4+0tDSbraCoBABQmJ9rsz89Pf2O76dRlf2RctkXIiIiIiJydhqlA4iIiEBYWJjNvtTUVGzcuBH79u1Dly5d0LlzZ3z55ZdYv349Jk2ahG3btiEnJwdLly6FTqfDypUr0aRJE8THx6Nz58746KOP0KdPH4wbNw4AsGDBAkycOBHz5s2DVqt12LPl5+fjrmbNkXktw2Z/+NhVcPMPx5OPPwZj0tlK55WWltz2PdlCSkRERERErkLxhDQoKKjSvv3798PDwwNRUVEAAEmSEBsbi127dmHSpEnYvXs3evToAZ1OBwBo3LgxWrdujV27dqFz587YvXs3nn/+efl6sbGxyMjIwKlTp9ClSxfHPBiAwsJCZF7LwGurtsHLN0De/8XJLBSWCoydvwpBnjf+CNKuXsKyqc+itPT2uxczISUiIiIiIleheJfdbdu2oX379oiMjMSsWbNQUlICg8GAkJAQqNU3xliGh4fDYDAAAAwGQ6VW1ZuVh4SEQKVSyeUVGY1G5Obm2mz25OUbAL1/oLxZUJY0+vj62ez39PG743up5Vl2mZASEREREZFzU7SF9E9/+hO6dOmCnj174ujRo5g0aRLUajXc3Nyg1+ttjtXr9cjMzAQAZGVlITw8/Kbl5c9XqVTw9vaWyytatGgR5s2bZ89Huylr66VGbf/fB2jYQkpERERERC5C0YR0+PDh8s9dunTBlStXsH79ekyePBl5eXk2x+bm5iIwMBAAEBAQUGV527Ztqyy3WCzIy8uTz69oxowZeOWVV2yuFRERcWcPdxPW1ktr8mhP7LJLRERERESuQvExpOW1bdsWSUlJCAsLg8FggNlslrvtJicny91ww8LCcP78eZtzK5anpKTIZQaDAUKISt18rXQ6nTweta6ZLQLieq6oZkJKREREREQNmGJjSE0mEwoKCmz2xcfHo02bNoiJiYHRaMThw4cBAEII7Ny5E7GxsQCAPn36YN++fTAajQCAxMREJCQk2JTHxcXJ1925cydCQkLkFlQllU8U66KFVCOPIbXY/dpERERERET2pFhCun79etx///348ssvkZCQgDVr1mDJkiV47bXXEBwcjCFDhmDy5MmIj4/H7NmzkZ6ejmHDhgEAHnnkEQQEBGDChAk4ceIExo0bh5iYGHTo0AEAMHr0aOzZswcrV67EkSNHMHPmTIwdOxZubm5KPa6sfKLIFlIiIiIiImrIFOuy+9xzz6GgoAArVqzA0aNH0bhxY6xYsQJDhw4FAHz44YcYM2YMYmNjcffdd+OHH36Ql4jRarWIi4vDqFGj0LNnT8TExODrr7+Wr92+fXts2LABb7zxBgwGA4YNG4a5c+cq8ZiVWBNFtSRBkpiQEhERERFRw6VYQipJEl566SW89NJLVZZ7eXlh/fr11Z4fGRmJvXv3Vls+aNAgDBo06I7jtDfrhEZqtf2TUQDQqFQ29yEiIiIiInJWiq9D2tCY63CGXYAtpERERERE5DqYkDqYyXy9hZQJKRERERERNXBMSB2srltINUxIiYiIiIjIRTAhdTDrLLtsISUiIiIioobO7glpamqqvS9Zr9xoIa2b3wWo5XVImZASEREREZFzu62sSK1WIy0trdL+06dPo0ePHnccVH1mYpddIiIiIiIiALeZkAohqlxD8+eff0ZGRsYdB1Wf1fWyL+yyS0RERERErqJW65AGBwdDkiRIkoR77rkHqnLdTouLi1FQUIDx48fbPcj6xGx2zLIv1rGqREREREREzqpWCen3338PIQSioqIwe/Zs+Pr63riQRoOWLVvi/vvvt3uQ9QknNSIiIiIiIipTq4S0a9euAIA5c+Zg9OjR8PT0rJOg6jNrolhXCal1siSLACxCQFVF12oiIiIiIiJnUKuE1GrOnDn2jqPBMNXxLLuacmNTTWYBrYYJKREREREROafbSkgvXbqE119/HUePHkV2dnal8qpm4KUyZgfNsgsApWYLtBouNUtERERERM7pthLSZ555Bvn5+RgyZAhatmxpM7kR3ZypjrvsSpIEN7WEUrPgWqREREREROTUbishTUhIwE8//YS2bdvaO556zzqpUV21kJZdW4VSsxklJs60S0REREREzuu2mjZ79+6NCxcu2DuWBsFcx+uQApC76XLpFyIiIiIicma31UL63nvvYdCgQQgLC4O3t3elcracVu/GOqR1183ZOrERW0iJiIiIiMiZ3VZCes8996CkpAQPPPCAvE+SJAghIEkSzGaz3QKsb0x1PKkRAGjV1hZSjiElIiIiIiLndVsJ6blz5+wdR4NR15MaATdaSEvZQkpEROT00tPTMXbsWACQ/1tfqVQquLm5wWKxwGQyQYiqf3muVquh1+uRk5MDIQTUajXUajVUKhUsFgskSYKHhwciIyORmZmJ9PR0FBUVwc3NDWFhYTbnBgQEwN/fH0FBQUhMTERJSQnCwsIghMCFCxfg7e2NoUOHokuXLjh58iSOHTuG1NTUKmOTJAkhISHQ6/UoKCiAEAI+Pj7w8fHBmTNncO3aNXh6eqJfv35QqVSIj49HWloagoOD4evrCz8/P+Tm5sLPzw9BQUFo164dTp06hYyMDGRnZ8PPzw8BAQEAgMzMTGRmZiIrKwsJCQly3P3798e9994LtVp9y/dtNptx4sQJZGZmws/PDwDk+1h/DggIkOPIzMxEQEAAOnbsWKPrK6GkpASbN29GcnIywsPDMXjwYGi1WkVjKv+elXp/9opBiWe5rYT0rrvusnccDUZdL/sC3GghLTWzhZSIiMiZDRw4EPn5+UqH4TAWiwVGo/GWx5nNZpulBc1mc6UeeEajET///LPNvtLSUly6dMlm3+XLl295v19//fWWx9TGjh07anScWq2uVc/C3377DXFxcfD09MT06dPRs2fPao/du3cvli9fjtTU1FrHERYWhvHjx9/0+kpYuXIlNmzYYBPrypUrMWTIEIwbN06RmKp6z45+f/aKQalnua2BjJ9++ulNN6qedaKhum0hvZ6QclIjIiIip9XQklFnZW0ttAdr62ZF7u7uNp8bNWoEANBoytqGIiMjMWjQoJteOygoSP65sLAQs2fPxt69e6s8du/evZgzZw5atGiBMWPGAAA6dOiApk2bAgCaNm2KDh06yMf7+PhAkiS88cYbWLZsGVq0aIE5c+ZUe30lrFy5El988QV8fHwwdepUbNy4EVOnToWPjw+++OILrFy50uExlX/Py5Ytw/bt2x3+/uwVg5LPclsJ6ZQpUypt48ePx5QpU/DZZ5/ZO8Z6xeyALrtu7LJLRETk1NLT05mMKiwoKAhubm7Izs6u1OXTzc0Nbm5ukKSyf1NZ/1te+X16vR46nc6mVdeabGq1Wvj6+sLf3x9ubm7QarVIS0uDn58fTCYT/Pz8kJeXh8OHD+OBBx6ATqezuc/999+P6OhoaDQaPPDAA9BqtfK9ly9fXqmF1Ww2Y/ny5YiOjsa8efOwdetWPPjgg1i8eDFKSkrg7++PkpIS/OMf/4BOp4NOp8MXX3yB6OhofPzxx2jTpg3mz5+P6OhorFixwinmhikpKcGGDRvg7++PDRs2YODAgQgMDMTAgQNt9peUlDgspvLvef78+WjXrh08PT3Rrl07h70/e8Wg9LPcVkKanp5eabt8+TLatGmDN998094x1ismB8yy6ya3kLLLLhER1S2j0Yjc3FybzZ4uX76MhISEW2416ZZZl/ev7aZU90K6ISMjA+3btweASolMaWkpSktL5XGkVY0nLb/v4YcfhtFohKVc7zRrC2RJSQkMBgNGjRqF0tJSlJSUwGw2o3PnzjCbzXjooYeQmpoKg8GA+++/v1KX5gceeADPPvssUlNTcf/996OkpES+d2pqKk6cOGFz/IkTJ5Camorhw4fjt99+q/TzqFGjkJqaiq1bt8JoNMJoNOL06dMYPnw4UlJScOLECahUKpvPStu8eTPMZjNGjRolJ/pWGo0GI0eOhNlsxubNmx0WU/n3rKrw73pHvT97xaD0s9zWGNKqBAYGYvbs2Xjttdewb98+e1223pFn2a3DdUjlhJQtpEREVMcWLVqEefPm1dn1FyxYUGfXdoX7U90KDAy0y3XCw8Nvee3o6Gibz9ZuvOXPrdg6aj2uefPm1ZZnZmZW+bl58+Y4ePBgpZ+tcSQnJ9ucY91f/vyqrq8Ea6wV36FVVc9U1yq+p4oc8f7sFYPSz2K3hBQAiouLcfLkSXtest6xjiGty0mN5C67HENKRER1bMaMGXjllVfkz7m5uYiIiLDb9d94440aTaZ4+fLlOkkea3r/2poxYwauXbtm9+tS7djrz6CqRKjita0JoVVxcXGlc6ua8Km4uBi///57teUVx61aP//+++9V/myNo3wiHBAQIN+j/DlVXV8J1lgPHjyIgQMHViqv6pnqWvn31K5du0rljnh/9opB6We5rYR06NChNp8tFgsuXryIU6dOVSojW3KXXbUDuuya2GWXiIjqlnUMWl2566670KpVqzq7vlL3t84MSsoJCgrCb7/9BqBsnGf5brtubm4AIC9NI0lSpW675fd999130Ol0KC0tlbvtWhtptFot/P39sXr1anlcqtlsRnx8PNRqNf73v//Jy9AcPnwYOp3OJvE8dOgQjhw5grCwMBw+fBharVbuThwWFoaOHTvaxNWxY0eEhYVh3bp1mDdvXqWfV69ejbCwMDz22GP46KOPAABt27bFvHnz0KhRI3Ts2BEWiwXr1q2TPytt8ODBWLlyJVavXo0BAwbYdNs1mUz4+OOPoVarMXjwYIfFVP49z58/36arq6Pen71iUPpZbisr8vLystn0ej169OiBDz74AKtXr6719UpKStC6dWs0a9ZM3mcwGPDoo4/Cz88PvXr1wvnz523OOXDgAKKiohAYGIhnn30WBQUFNuWrVq1CZGQkIiIiMH/+/GrXuXIkIcSNLrt12kLKWXaJiIicWXBwMLy9vZUOo0HLyMhAaWkp/Pz87ngMaV5eHoxGo82MvSaTCUDZv3NzcnKQlZUljyENCQlBdnY2NBoNsrOzodfrcf/99+PQoUOVWkEPHz6MgwcPwmQy4dChQzZjSMePH19pjUi1Wo3x48fj4MGDmDNnDgYOHIgDBw7glVdegVarRVZWFrRaLV599VV5DOmf//xnHDx4ECNHjsTZs2cxc+ZMHDx4EC+99JJTrEeq1WoxZMgQZGVlYciQIdi6dSsyMjKwdetWm/2OXI+0/HueOXMmTp06hcLCQpw6dcph789eMSj9LLfVQrpmzRq7BrFs2TIkJyfLfe2FEBg0aBBCQ0Oxd+9e/Otf/0K/fv1w/vx5aLVapKamYsCAAZg0aRL+7//+Dy+++CJGjx6N//znPwCA7du34+WXX8b69evh7++PJ598EsHBwXjxxRftGndtmctNMlS3Y0g5yy4REZGz27ZtG5d+cQLlZ8a9U9WNsbN2z7VKSUkBcCNpPX/+fKXGl4oyMjLkn728vDBt2rRq14bs2bMn5s2bh+XLl+PAgQMAYDOs7sqVKzbH5+bmQgghd3tv1KgR5s2b51TrkFonAtuwYQPee+89eb9arcaf//xnRSYKK/+e//rXv8r7Hfn+7BWDks9yR2NIDx8+jPj4eFgsFtx77724//77a32N9PR0zJs3DxMmTJATyqNHj+LIkSNISkpCeHg4li1bhqCgIGzfvh2PP/441q1bh/DwcMybNw+SJOH9999Hr169sGTJEoSEhGDFihUYMWIEnnzySQDAq6++ihUrViiekJrKJ6ScZZeIiKjB27ZtG9LT0xtM912VSgU3NzdYLBa5O2xV1Go19Ho9cnJyIISAWq2GWq2GSqWCxWKBJEnw8PBAZGQkMjMzkZ6ejqKiIri5uSEsLMzm3ICAAPj7+yMoKAiJiYkoKSmRu8heuHAB3t7eGDp0KLp06YKTJ0/i2LFjSE1NrTI2SZIQEhICvV6PgoICCCHg4+MDHx8fnDlzBteuXYOnpyf69esHlUqF+Ph4pKWlITg4GL6+vvDz80Nubi78/PwQFBSEdu3a4dSpU8jIyEB2djb8/PzkcXqZmZnIzMxEVlYWEhIS5Lj79++Pe++995atVT179kT37t1x4sQJZGZmyq231vtYfw4ICJDjyMzMREBAADp27OgULaMVjRs3DiNHjsTmzZuRnJyM8PBwDB482KEtoxVVfM9KvD97xaDUs9xWQlpQUIAhQ4bghx9+kLvZ/vHHH+jfvz82bNgALy+vGl9r9uzZ6NKlCx566CE5Id29ezfatm0rD0zW6XTo3r07du3ahccffxy7d+9G37595TWYoqKioNVqsX//fvzf//0fdu/ejY8//li+R2xsLN544w1kZWXB39+/UgzW7gpW9p6y3sqakEpSHa9DquIsu0RERK4iODgYq1atwtixY7Fq1SpFx8w2dF27dkXXrl1v69yHH3640r777rvvlud16dLltu5XE2q1usbXr8s47MnafdeZ1OY9O3sMSjzLbTXTTZs2DdeuXcOFCxdw8eJFXLx4ERcuXEBmZiamTZtW4+ucPHkSa9euxfLly20WFzYYDAgLC7M5Njw8HAaDocpyjUaD0NBQGAwGFBQUID8/36bcmthaz69o0aJF8PX1lTd7zg5Ynslc9zPsAoCbhrPsEhERERGR87uthHTTpk3497//bbNWTfPmzbFkyRJ88803NbqGEAKTJk3ClClTcM8999iUZWVlQa/X2+zT6/Vyv/yblVvHAZQvt/5cXb/+GTNmICcnR96uXr1ao2eorRsTGtVdd12As+wSEREREZFruK0uu9bprytS1SLR2rx5M37//Xds3bq1UllAQAAuXbpksy83N1ee9CggIAB5eXlVllv73Zcvt3bBrW7x47qest7qxpIvddxCWm6W3er+rIiIiIiIiJR2W011gwcPxt/+9jebGbquXLmCSZMm1Xj9H+vMuk2bNkVQUBAGDx6MK1euICgoCD4+PvLsY1bJyclyN9ywsDCbcpPJhLS0NISFhcHDw6PS+dYFh0NDQ2/nce3GZHFQl93rCa8QtjP7EhEREREROZPbSkjfeecd+Pj44O6770ZkZCQiIyNx9913w8vLC++8806NrrF+/XpcuHAB8fHxiI+Px5w5cxAeHo74+Hj069cPZ86cQVJSEoCyqbL379+P2NhYAECfPn0QFxcnz352+PBhlJaWIiYmxqbcaufOnbj33ntt1oZSgtxlV13HXXbLtVRzpl0iIiIiInJWt9Vl19vbGz/88AP279+P48ePQwiBzp07o02bNjVe5Dk4ONjmc0BAADQaDZo0aYImTZogOjoa48aNw4IFC/Cvf/0LwcHBGDBgAADgmWeewZw5czBnzhw88cQTmDx5Mp5++mm5S+5LL72EQYMGYcCAAfDz88O7776Ld99993Ye1a7kLrt13EKqUklQqySYLQKlJgs83Jxv2m4iIiIiIqIaN9VZWy4t5WZu7d69O8aPH4+//vWvuPvuu9GqVSscP37cLoFt2rQJZrMZPXv2xPnz57Fjxw64ubkBAEJCQvD999/ju+++Q2xsLFq1aoUPPvhAPrd///5YunQpXnvtNbzwwguYOnUqRo4caZe47oSjuuwCN7rtlpo50y4RERERETmnGreQzpo1C71796524qKwsDBMnjwZb7zxBrZt21brQEaMGIERI0bIn0NCQrB9+/Zqj4+OjsbPP/9cbfmYMWMwZsyYWsdRl25MalS3XXaBsomNikstKDWzyy4RERERETmnGmdGBw4cwBNPPHHTYx5//HEcPnz4joOqr24s++KIFtLrM+2yhZSIiIiIiJxUjRPS0NBQpKam3vSY9PR0+Pv733FQ9ZXpenJY18u+AOyyS0REREREzq/GCWm/fv0wf/58mM3mKstNJhPefvtt9OnTx27B1Tc3Wkgd02UXALvsEhERERGR06pxZrRw4UIkJyejW7duWL9+Pc6ePYvMzEycOXMG69atQ1RUFC5dulTjZV8aInbZJSIiIiIiuqHGkxp5e3vj8OHDWLhwIcaNG4f8/HxIkgQhBDw9PfHiiy/i9ddfh6+vb13G69LYZZeIiIiIiOiGWq1D6uvri7///e/4+9//juTkZCQmJqJx48YIDw+HJNV9kuXqFOmya2GXXSIiIiIick61SkjLCw8PR3h4uD1jqfduLPviwC67JraQEhERERGRc6r7pjqSmSzXu+w6ZAxp2T1MnNSIiIiIiIicFBNSB1Kiy24Jx5ASEREREZGTYkLqQEp02TUxISUiIiIiIifFhNSBlOiyyxZSIiIiIiJyVkxIHehGC6njuuxyDCkRERERETkrJqQOdGMMqeO67LKFlIiIiIiInBUTUgeSu+w6YAyph1YNACgsMdf5vYiIiIiIiG4HE1IHkrvsOmCWXS85ITVBCHbbJSIiIiIi58OE1IEc2WXX2kJqEYDRxG67RERERETkfJiQOogQAmaL45Z90ahU0GnK/ngLjKY6vx8REREREVFtMSF1EGvrKOCYLrsA4KXVAOA4UiIiIiIick5MSB3ENiGt+xZSAPDkxEZEREREROTEmJA6iOn68isqCVA5KiHVlSWkBSXssktERERERM6HCamD3JjQyHGv3JNddomIiIiIyIkxIXUQeckXB0xoZFV+6RciIiIiIiJnw4TUQUyWsi67jho/CpRrITWyhZSIiIiIiJwPE1IHudFC6sAuuxxDSkRERERETkzRhHTHjh3o3bs39Ho97rnnHqxbt04uMxgMePTRR+Hn54devXrh/PnzNuceOHAAUVFRCAwMxLPPPouCggKb8lWrViEyMhIRERGYP38+hBBQ0o0xpI7ssssxpERERERE5LwUS0izsrIwYsQIDB06FL/88gteeuklPPfcczh8+DCEEBg0aBDUajX27t2LyMhI9OvXDyUlJQCA1NRUDBgwAAMGDEBcXBwSEhIwevRo+drbt2/Hyy+/jLfffhuffPIJ3nvvPaxatUqpRwVwY5Zdx3bZLWshLSoxw6JwQk5ERERERFSRRqkb+/v748KFC/Dw8AAAtG7dGh9++CF27NgBjUaDI0eOICkpCeHh4Vi2bBmCgoKwfft2PP7441i3bh3Cw8Mxb948SJKE999/H7169cKSJUsQEhKCFStWYMSIEXjyyScBAK+++ipWrFiBF198UanHvdFC6sAuux5uZQmpAGA0MSElIiIiIiLnomiXXWsyCgAWiwX5+fnw9vbG7t270bZtW4SHhwMAdDodunfvjl27dgEAdu/ejb59+0KSylobo6KioNVqsX//frm8X79+8rVjY2Nx/PhxZGVlOerRKlGiy65KJclJaWGpxWH3JSIiIiIiqgnFJzUSQiAlJQWvvPIKioqK8Mwzz8BgMCAsLMzmuPDwcBgMBgCoVK7RaBAaGgqDwYCCggLk5+fblFsTW+v5FRmNRuTm5tps9iZ32XXgsi8A4HV9YqMiExNSIiIiIiJyLoonpFOmTEF4eDhWr16NDRs2ICQkBFlZWdDr9TbH6fV6ZGZmAsBNy7Ozs+XP5csAyOdXtGjRIvj6+spbRESEvR5PdqOF1LGv3Lr0S1Epu+wSEREREZFzUTwhffXVV7F79268/PLLeOSRR/Djjz8iICAAeXl5Nsfl5uYiMDAQAG5aHhAQAAA25dYWT+v5Fc2YMQM5OTnydvXqVbs9n5W87IsDu+wC5SY2YpddIiIiIiJyMopNamTVqFEjNGrUCL169UJeXh7efPNNPP7440hJSbE5Ljk5GW3btgUAhIWF2ZSbTCakpaUhLCwMHh4e8PHxsSlPTk4GAISGhlYZg06ng06ns/ej2TBZFOqya136hQkpERERERE5GcVaSEtLS1FYWGizz8/PD4WFhejTpw/OnDmDpKQkAEBxcTH279+P2NhYAECfPn0QFxcnry16+PBhlJaWIiYmxqbcaufOnbj33nvh5+fngCermlJddv283AAAWcVci5SIiIiIiJyLYgnp559/jvvvvx///e9/cf78eWzcuBFLly7FkCFD0LlzZ0RHR2PcuHE4ceIEJkyYgODgYAwYMAAA5ImP5syZg/j4eEyePBlPP/203CX3pZdewieffIKvv/4aO3fuxLvvvovx48cr9agAynXZdXALabB3WctvVhETUiIiIiIici6KddkdMWIE8vPzsXTpUhw7dgwhISGYNm0apk6dCgDYtGkTRowYgZ49e6JTp07YsWMH3NzKWvtCQkLw/fff429/+xuWLl2KRx55BB988IF87f79+2Pp0qV47bXXYDQaMXXqVIwcOVKR57QquT7LrtaB65ACQKCXFhKAYpOA2svfofcmIiIiIiK6GcUSUkmS8PLLL+Pll1+usjwkJATbt2+v9vzo6Gj8/PPP1ZaPGTMGY8aMueM47aXk+rIrWo1jE1KNWgU/TzdkFZbCLaS5Q+9NRERERER0M4rPsttQKJWQAje67WqZkBIRERERkRNhQuogckLq4C67ABCoZ0JKRERERETOhwmpg1jHkOoUbCF1C2ZCSkREREREzoMJqYMo2WU3yFsLAHALbAKjieuREhERERGRc2BC6gBCiBuz7CqQkHrrNNCqJUgqNX6/VuTw+xMREREREVWFCakDlJZrlFRiDKkkSQj0UAMAThsKHH5/IiIiIiKiqjAhdYBSswAAqCRArZIUiSHUu2yFn6NX8xS5PxERERERUUVMSB2g5HpCqtWoIEnKJKSN9G4AgGOJeRBCKBIDERERERFReUxIHaDUrNySL1bBXhoIUynSC0rxx7VCxeIgIiIiIiKyYkLqACWWGy2kStGoJBhTzgEADl26plgcREREREREVkxIHaDUXPZfJRNSACi+chIAE1IiIiIiInIOTEgdwDqpkU6jVjQO4/WE9PClTI4jJSIip9a0aVOsWrUKTZs2VToUh2hoz0tEZMWE1AHkSY0UHEMKAMbks3BTS0jNLcalDC7/QkREzsvd3R2tWrWCu7u70qE4REN7XiIiKyakDlDqBGNIAUCYStC5sR4AsOdcuqKxEBERERERMSF1gPLLvigtupkPAGBPAhNSIiIiIiJSlvIZUgNQ6iRddgEgupkfgLKJjYqtsy0REREREREpQPkMqQEodaIW0haB7mjk6w6jyYLDv2cqHQ4RERERETVgymdIDYAzrENqJUkSercOBsBxpEREREREpCzlM6QGwJm67AJAr1ZlCemuc2lc/oWIiIiIiBTjHBlSPVcir0PqHK87JjIYWrUKv2cU4EJavtLhEBERERFRA+UcGVI950xjSAHAW6dBTGQQAOCHU6kKR0NERERERA2Vc2RI9ZyzrENaXv92oQCA75mQEhERERGRQpwnQ6rHSpxsDCkA9LsnFCoJ+C0pF4lZhUqHQ0REREREDZDzZEj1lOTmLv/sTC2kgd463NcsAADwwymDwtEQEREREVFD5DwZUj2l0nkCACQJ0KgkhaOxNaB9GABg+8kUhSMhIiIiIqKGSNGEdNeuXejfvz98fX3RqVMnfPfdd3JZQUEBhg8fjsDAQERFReHQoUM25yYkJKBnz57w9/fHwIEDkZaWZlO+adMmdOzYEaGhoZg4cSJMJpNDnqkiSVuWkGrVKkiS8glpeno60tLSkJaWhqhGbpAA/Ho5C8cvXJX3W7f8fM7AS0REREREdUexhPT48eN46qmn8NRTT+Hw4cMYOHAgHn/8cVy8eBEAMHLkSFy4cAFxcXHo378/+vfvLyedRqMRffv2RevWrbFnzx5IkoTBgwfbXHvo0KEYP348tm7dii1btmDWrFmKPKe1hVTp7rrG4kJAktC+fXuEhoYiNDQU7e+OQNHl4wCAns9Olvdbt7uaNWdSSkREREREdUaj1I07duyIX375Bc2bNwcAzJ8/H59//jm2bduGp59+Ghs3bsS+ffvQpUsXdO7cGV9++SXWr1+PSZMmYdu2bcjJycHSpUuh0+mwcuVKNGnSBPHx8ejcuTM++ugj9OnTB+PGjQMALFiwABMnTsS8efOg1Wod+pzOkpCWGosBITBhyQYEhzWW95/LKMb+K4W4++HRePyVKfL+gpxMvDN2IAoLC+Ht7a1EyEREREREVM8pliVJkiQno9bP/v7+yM3Nxf79++Hh4YGoqCi5LDY2Frt27QIA7N69Gz169IBOpwMANG7cGK1bt7Yp79evn3zt2NhYZGRk4NSpU456PJnqepddnZPMsOvl4w+9f6C8tW8WBpUEZBaZUarVy/u9fAOUDpWIiIiIiOo558iSABQVFeHMmTPo0KEDDAYDQkJCoFar5fLw8HAYDGWzwRoMBoSFhdmcf7PykJAQqFQqubwio9GI3Nxcm81eJJ0XAMDNiWbYLc/dTY27AstiPJeap3A0RERERETUkDhNlrRixQoEBgZiwIAByMrKgl6vtynX6/XIzMwEgFqXq1QqeHt7y+UVLVq0CL6+vvIWERFht+dSe/oAADzd1Lc4UjmtQsu65J4z5EEIoXA0RERERETUUDhFQpqUlISFCxdi9uzZcHd3R0BAAPLybFvrcnNzERgYCAC1LrdYLMjLy5PLK5oxYwZycnLk7erVq3Z7NrWXPwDAU6vYcN1bahHkDY1KQk5RKdLyjEqHQ0REREREDYTiCWlJSQmGDBmC6OhovPjiiwCAsLAwGAwGmM1m+bjk5GS5G25YWBhSUmzXzrxZucFggBCiUjdfK51OBx8fH5vNXtSefgAAT63ztpBqNSo0DyrrtptgYLddIiIiIiJyDEUTUrPZjJEjRyI7OxuffPKJvE5nTEwMjEYjDh8+DAAQQmDnzp2IjY0FAPTp0wf79u2D0VjWmpeYmIiEhASb8ri4OPk+O3fuREhICNq2bevIxwMAqLx8AQAeTpyQAkDrsLIuzgmGfHbbJSIiIiIih1AsIbUmo3v27MFXX32FkpISpKamIjU1FcHBwRgyZAgmT56M+Ph4zJ49G+np6Rg2bBgA4JFHHkFAQAAmTJiAEydOYNy4cYiJiUGHDh0AAKNHj8aePXuwcuVKHDlyBDNnzsTYsWPh5ubm8Od0hRZSALgr0BNajQr5RhOSs4uVDoeIiIiIiBoAxRLS//73v/j000+RmJiIdu3aoVGjRvIGAB9++CHuvvtuxMbG4vvvv8cPP/yAoKAgAIBWq0VcXBzOnTuHnj17AgC+/vpr+drt27fHhg0bsGzZMgwcOBCDBg3C3LlzHf6MAKD28gPg3GNIAUCjUqFl8I3JjYiIiIiIiOqaYlnSsGHD5BbPqnh5eWH9+vXVlkdGRmLv3r3Vlg8aNAiDBg26oxjvlNkioPK4Psuuk7eQAmWz7Z5OycX5tDx0DfFVOhwiIiIiIqrnFJ/UqD7LKTZBUpUloh5OvOyLVYS/Jzzc1CgutSApt1TpcIiIiIiIqJ5jQlqHMgvKkjp3jQSVSlI4mltTqSREXl+T9FJWicLREBERERFRfceEtA5lFpkAAO4a13nNrUPLZtu9kl0CSaNVOBoiIiIiIqrPXCdTckHWFlIPjfO3jlo18nWH3l2DUgvg0aKb0uEQEREREVE9xoS0DmUWXu+y6+Y6r1mSJLS63krq2baXwtEQEREREVF95jqZkgvKKnS9FlLgRrddz7vvQ77RrHA0RERERERUXzEhrUOZhWVjSD1cqIUUAIK8tfDVqSBptNhzMUvpcIiIiIiIqJ5yrUzJxWTKLaSu9ZolSUKLAB0A4H9nMxWOhoiIiIiI6ivXypRcjDyG1MW67AJAC/+yGXaPXM7BtXyjwtEQEREREVF9xIS0Drlql10A8HVXw5h6AWYBbP8tVelwiIiIiIioHnK9TMlFCCFcdlIjq8IzewAAW48nKxwJERERERHVR0xI60ie0YQSswDgWsu+lFdwZh8A4Oc/MpGSU6RwNEREREREVN+4ZqbkAjLyysZdWoyF0Khcs4XUnJeBzo29IQSw7XiK0uEQEREREVE9w4S0jripVXikbSAKz+1XOpQ78lCbQADAFnbbJSIiIiIiO2NCWkciAjwxd0ALXPtuidKh3JG+kf5QqyScTMrB7xkFSodDRERERET1CBNSuil/TzfEtAwCAGyJZyspERERERHZDxNSuqXHOoUDALYcT4IQQuFoiIiIiIiovmBCSrfUv10otBoVLqYX4ExKntLhEBERERFRPcGElG5J7+6G2NYhADi5ERERERER2Q8TUqqRQZ3Luu1uPZ7MbrtERERERGQXTEipRmLbhMBbp0FSdhGOXslSOhwiIiIiIqoHNEoHQM4tPT1d/rlHC198d+Yavjh4EREepkrHenp6wtvb25HhERERERGRC2NCSlUyFhcCkoT27dvL+9xbdEXokHn4Yv85vPvMA4Cw2JwTEBiEy3/8zqSUiIiIiIhqhAkpVanUWAwIgQlLNiA4rDEAwCIE/nMiG0Yvf4xdtQuNfdzk4wtyMvHO2IEoLCxkQkpERERERDWi6BjS9PR0zJw5E02bNkW3bt1sygoKCjB8+HAEBgYiKioKhw4dsilPSEhAz5494e/vj4EDByItLc2mfNOmTejYsSNCQ0MxceJEmEyVu5jSrXn5+EPvHwi9fyB8A4IQGaYHAFwtkOT9ev9AePkGKBwpERERERG5GkUT0qtXr+LChQvw8fGpVDZy5EhcuHABcXFx6N+/P/r37y8nnUajEX379kXr1q2xZ88eSJKEwYMHy+ceP34cQ4cOxfjx47F161Zs2bIFs2bNcthz1WetQ8sS0gvp+TBZLLc4moiIiIiIqHqKJqT33nsvvvjiCzz11FM2+1NTU7Fx40a8//776NKlC958802EhoZi/fr1AIBt27YhJycHS5cuRceOHbFy5UocOnQI8fHxAICPPvoIffr0wbhx4xAVFYUFCxbgo48+QklJiaMfsd5p7OcBb50GJSYLLl8rVDocIiIiIiJyYU657Mv+/fvh4eGBqKgoAIAkSYiNjcWuXbsAALt370aPHj2g0+kAAI0bN0br1q1tyvv16ydfLzY2FhkZGTh16pSDn6T+kSQJkaFlY0TPpeYpHA0REREREbkyp0xIDQYDQkJCoFar5X3h4eEwGAxyeVhYmM05NysPCQmBSqWSyysyGo3Izc212ah694SVdbG+kJ6P7EK2OhMRERER0e1xyoQ0KysLer3eZp9er0dmZuZtlatUKnh7e8vlFS1atAi+vr7yFhERYc/HqXeC9TrcFegJIYBfLmcpHQ4REREREbkop0xIAwICkJdn2x00NzcXgYGBt1VusViQl5cnl1c0Y8YM5OTkyNvVq1ft+Tj10v3Ny2bVPZOSi9yiUoWjISIiIiIiV+SUCWlYWBgMBgPMZrO8Lzk5We6GGxYWhpSUFJtzblZuMBgghKjUzddKp9PBx8fHZqOba+TrgaYBnrAI4PDvVbc8ExERERER3YxTJqQxMTEwGo04fPgwAEAIgZ07dyI2NhYA0KdPH+zbtw9GoxEAkJiYiISEBJvyuLg4+Xo7d+5ESEgI2rZt6+Anqd8eaHGjlTSriOu8EhERERFR7SiakGZmZiI1NRX5+fkoLS1Famoq0tPTERwcjCFDhmDy5MmIj4/H7NmzkZ6ejmHDhgEAHnnkEQQEBGDChAk4ceIExo0bh5iYGHTo0AEAMHr0aOzZswcrV67EkSNHMHPmTIwdOxZubm5KPm6908jXAy2DvSEA/JxUpHQ4RERERETkYjRK3vyJJ57Anj175M+NGjXCXXfdhT/++AMffvghxowZg9jYWNx999344YcfEBQUBADQarWIi4vDqFGj0LNnT8TExODrr7+Wr9O+fXts2LABb7zxBgwGA4YNG4a5c+c6+vEahAdbBuJSRj4Sc0vh3rSj0uEQEVEDpyrOuXl5UbbNf2/3OkREZB+KJqS7d++utszLywvr16+vtjwyMhJ79+6ttnzQoEEYNGjQnYRHNeDvqUWHxr44npgDv95/gUUIpUMiIqIGyNfXF25aHXBpz60PBuDxe/X/hrBy0+rg6+t7p6EREdFNKJqQUv0Q1TwAp5NzgEaR2HEuE8+FhiodEhERNTChoaH4/LNPkZNjv5ZNX19fhLJOIyKqU0xI6Y55ajXoEOqBoylFWP5TIoY+2Ao6jVrpsIiIqIEJDQ1lAklE5GKccpZdcj3tQ91hyruGlNwS/OfwFaXDISIiIiIiF8CElOxCo5KQs/8/AICluy6gwMhlYIiIiIiI6OaYkJLd5J/cgca+OmTkl2DtgT+UDoeIiIiIiJwcE1KyH4sZLz7YGACwYvdFGHKLFQ6IiIiIiIicGRNSsquH2gSgU4Qf8o0mLPj2jNLhEBERERGRE2NCSnalkiTMH9weKgnYcjwZP53PUDokIiIiIiJyUkxIya7S09MRqjXiyU4hAICJXxzFbxcTkZaWZrPl5+crHCkRERERESmN65CSXRiLCwFJQvv27QEAktYDYc/+A9fQDH+atwGG/0yHMJXIxwcEBuHyH7/D29tbqZCJiIiIiEhhTEjJLkqNxYAQmLBkA4LDyiY2yjWasfVsLhDeClFztqDf3d7QqlUoyMnEO2MHorCwkAkpEREREVEDxi67ZFdePv7Q+wdC7x+IxmEheKxzY2jVKqTmm/D9xUJY3H3g5RugdJhEREREROQEmJBSnWrs54GnujaBp1aNjPwS/OfIFVzNKbn1iUREREREVO8xIaU6F6zX4c/3RSDMxx1GkwU7LubDr8dzMFuE0qEREREREZGCmJCSQ+jd3fBU1ybo1MQXAOD74NN4eeM5ZOQbFY6MiIiIiIiUwoSUHEatktC7dQh6N/OCpaQIv1zNw6P/2ocDF7lWKRERERFRQ8SElByuRYAOKZ9ORvMAdxhyjXjmw8OYtek35BSWKh0aERERERE5EBNSUoTpWiLWPNMWzz7QFADw2aHL6PXuLny07xIKjCaFoyMiIiIiIkfgOqSkmIKcTPztwVA80MQDi3ddwaVrRZj/7Rn8Ky4BT3YOwdDOoQj0cgMAeHp6cs1SIiIiIqJ6hgkpOZyxuBCQJLRv3/7GTkkF7w794HP/U8gNCMeawyn4eP9lFF44gsJz++FRlI5LJ4/AR69XLnAiIiIiIrIrJqTkcKXGYkAITFiyAcFhjW3KLELgSnYpTqYVIb1AC682MfBqEwMAiP7HT2jTyBfdmvnjobZh6BLhB5VKUuIRiIiIiIjIDpiQkmK8fPyh9w+stL9DANC+uUB6nhEJhnz8np6La3nFKIAbfr2chV8vZ+GDPZfQLMAdQ7uE4tG2gfBwU9tcg118iYiIiIicHxNSckqSJCHExx0hPu5o412EhSMfg5t/OLShd8OjRTd43H0f/sgE3vnxMhZ9ewoFJ+NQeG4/jMnnAGFBQGAQLv/xO5NSIiIiIiInxoSUnF6psRgwm/DirPfkLr4lZoHz14w4nV6MPHjD577H4XPf43BTAf5a4OLB7Vi55yLuiQhBRIAHIvw94efpBkliF18iIiIiImfBhJRcRsUuvoFBwP2tBH6/VoCE1Hz8fq0AJSYL0ooBfZdHsHRfIoDEG+drVWjko0O4rw7hPjqE+2rlnyPD/RHs76vAUxERERERNVz1OiEVQuCtt97Chx9+CA8PD7z22msYPXq00mGRHUmShBZB3mgR5A2LReBaQQkuJSbjh6/WQeMbCrVfKDS+odB4B6CgxIILGUW4kFFU6TrCYkLnCH88GBmC+5sHoG0jHwTrdWxRJSIiIiKqQ/U6IV25ciXef/99bNy4EdeuXcPw4cPRpEkTDBgwQOnQqA6oVBKC9TqYPEzI3veZzSy+JotAvtGCvBIz8kssyDNakF9iRp7RgjyjGSXQ4HhSHo4n5WHF7osAAE83FSL83dHU3x1N/dzR1F9X9jnYF42D/KBWSZAkQEJZYlz2XzCJJSIiIiKqoXqbkAohsHz5crz66qvo06cPAOB///sfVq5cyYS0gajYxde/muMyUq7i738bDveI9nBv2h66xu2g8QtFYSlwLq0Q59IKb+v+GpUEPw8NAr21CNZ7INBbiwAvLfTubtDrNPDSaeClU8Nbp4GHmxpuGhXc1Cpo1Sp46dTw0mngrdNAp1ExySUiIiKieqneJqSZmZn47bff0K9fP3lfbGwsXnzxRQWjImdUaiyGOceAv7y5VG5RNVsE8kosyC02I8doRq7RgpxiM7ILS1FUaoakvvX/OiaLQEZBKTIKSnHOUHDb8alVErzcVPDUquGpVUOtAtRSWeus9b8qlQQVJKisP0uASrrxX0kCVNdbc1WqstZc1fUk16aVt9x5akmCVusGD50WKkkqu69KJd9frVIBAAQELAKAECg2lqCktBRCCJhF2TswWwRMFgGTWQAAdBoV3N1UcNeo4OPlDn9vD3hoNXB3U8EiAItFwCLKziv7b9n6tEIICJSVX7/d9f+K688hyc9ofRabluvr78f6M6zvxloulX8nN96R9f1Y72kRwua+ZXEIWCw34hFlL6bcsWXHlI9ZiBvXKPszk6BWSVBLElQqCRpV2X/V15+rIlHFd0VUsVNUcWTF46q+VlV77XfP6s+tWSxVRlfFzn5tQxHgpa3qaCIiInIC9TYhNRgMAICwsDB5X3h4OHJzc1FUVAQPDw95v9FohNFolD/n5OQAAHJzc+8ohry8PABAZmoiigtvnZBkpyUDALLSkqCq+p9bt3Wssx3vTLGUP76kuNDmz8kdgLsWCNEC0AOAGhlJV/DhzLH48+v/hl9gCIAq/g18PemwAMjMvIZv16+C2l0Placv1B56qLSegJs7VDpPqLTukNw8IGm0kNSask2jhaTRQaUt+45aAGQXAdm3fBIiqug/Y+5HhyZ+t32+tR6oaYJON97VndahRETk2mpah0qintay+/fvR0xMDLKysuDn5wcAOHbsGO69914kJSUhPDxcPnbu3LmYN2+eQpESEZGzu3r1Kpo0aaJ0GC4hMTERERERSodBRERO4lZ1aL1NSM+cOYO2bdviypUrcsW4Z88e9O7dG0VFRXB3d5ePrdhCarFYkJmZicDAwDsau5ebm4uIiAhcvXoVPj4+t/8wCnDl2AHXjt+VYwdcO35Xjh1g/HVBCIG8vDyEh4dDdb2bOt2cxWJBcnIy9Ho961AXjB1w7fhdOXbAteN35dgBxl8XalqH1tsuu9auuikpKXJCmpycDD8/P5tkFAB0Oh10Op3NPmurqj34+Pg4zRejtlw5dsC143fl2AHXjt+VYwcYv735+nKN4tpQqVR2bU12tu9Dbbhy7IBrx+/KsQOuHb8rxw4wfnurSR1ab3/d6+/vj06dOiEuLk7et3PnTsTGxioYFREREREREVnV2xZSABg/fjymTZuG6OhoZGZm4tNPP8W2bduUDouIiIiIiIhQzxPSMWPGwGAw4LnnnoOHhweWL1+OP/3pTw67v06nw5w5cyp1B3YFrhw74Nrxu3LsgGvH78qxA4yf6hdX/j64cuyAa8fvyrEDrh2/K8cOMH4l1dtJjYiIiIiIiMi51dsxpEREREREROTcmJASERERERGRIpiQEhERERERkSKYkNYRIQTefPNNREREoFWrVvjoo48UiyU9PR0zZ85E06ZN0a1bN5uygoICDB8+HIGBgYiKisKhQ4dsyhMSEtCzZ0/4+/tj4MCBSEtLsynftGkTOnbsiNDQUEycOBEmk8nu8e/atQv9+/eHr68vOnXqhO+++85l4t+xYwd69+4NvV6Pe+65B+vWrZPLDAYDHn30Ufj5+aFXr144f/68zbkHDhxAVFQUAgMD8eyzz6KgoMCmfNWqVYiMjERERATmz5+PuhwOXlJSgtatW6NZs2YuE/+IESMgSZLNNnfuXADO/72xOnToELp37w4fHx/cf//92LVrl0vEv3bt2krvXpIkaDRl8+g5+3eHlMc61H5Yhyr/9wjrUNahtdEg61BBdWL58uXC399f7Ny5U2zYsEFotVrx3XffKRLLr7/+Kp5++mnRrl070bVrV5uyoUOHiqioKHH06FExc+ZM4ePjIwwGgxBCiOLiYtGkSRMxevRocfz4cTFw4EDxwAMPyOfGx8cLNzc3sWLFCnH48GHRrFkzMX36dLvGHh8fLwICAsSqVavEmTNnxOuvvy60Wq24cOGC08efmZkpwsPDxbJly8TZs2fFkiVLhCRJ4tChQ8JisYioqCjx2GOPiePHj4tRo0aJpk2bCqPRKIQQIiUlRej1ejFr1ixx9OhRcd9994k///nP8rW//fZbodVqxVdffSV+/PFH4efnJ1auXGm32CtavHix8Pb2FnfddZcQQrhE/C+88IJ4+umnRUpKirzl5eUJIZz7e2N19OhR4eHhId5++21x5swZ8dFHH4k5c+a4RPyFhYU27z0lJUUMGDBATJ061SW+O6Q81qH2wTrUOf4eYR3KOrQ2GmIdyoS0DlgsFtG+fXuxcOFCed+YMWPE4MGDlQtKCDFnzhybyjQlJUWo1Wpx4MABIURZ3JGRkeKf//ynEEKIr776Suj1elFcXCyEECIxMVEAEMeOHRNCCDFhwgTx0EMPyddbt26dCAoKkv+nsAeLxSIuXbpk87lp06bi/fffd4n4CwsLbT63b99evPXWW+KXX34RAERSUpIQouwvQG9vb/HNN98IIYR49913RevWrYXFYhFCCLF//36h0WjkvzAHDhwoxo4dK193wYIFolOnTnaLu7y0tDTh6+srpk+fLlemrhD/Cy+8IKZOnVppvyt8b4QQ4qmnnhKjRo1y2fjL27t3r2jatKnIy8tzie8OKYt1KOtQK9ahrENvF+tQ5b/7tcEuu3UgMzMTv/32G/r16yfvi42NlbsKOIv9+/fDw8MDUVFRAABJkmzi3L17N3r06CGvZ9S4cWO0bt3aprziM2ZkZODUqVN2i1GSJDRv3tzms7+/P3Jzc10ifg8PD/lni8WC/Px8eHt7Y/fu3Wjbti3Cw8MBlK0d1b17d5vY+vbtC0mSAABRUVHQarXYv39/tbEfP34cWVlZdovdavbs2ejSpQseeugheZ+rxB8UFFRpnyt8b8xmMzZt2oRnnnnGJeOvaMaMGZg5c6bLffdJGaxDWYdasQ5lHXo7WIcq/92pLSakdcBgMAAAwsLC5H3h4eHIzc1FUVGRUmFVYjAYEBISArVaLe8LDw+X4zcYDDbPcKvykJAQqFQqubwuFBUV4cyZM+jQoYPLxC+EQEpKCl555RUUFRXhmWeeqXVsGo0GoaGhMBgMKCgoQH5+fqXvl/U8ezp58iTWrl2L5cuXy3+5VRWfs8a/bds2tG/fHpGRkZg1axZKSkpc4nuTkpICk8kESZLw2GOPoXHjxhgyZAgMBoNLxF/euXPncPToUQwdOvS24lPqu0PKYR3KOrQ81qGsQ2uLdajy353aYkJaB6y/ZdDr9fI+689K/waivKysLJsYgbI4MzMzb6tcpVLB29tbLq8LK1asQGBgIAYMGOAy8U+ZMgXh4eFYvXo1NmzYgJCQkDuKPTs7W/5cvgyAXWMXQmDSpEmYMmUK7rnnHpsyV4j/T3/6E5566il89tlnmD59Ot5//30sXLjQJb43iYmJAICJEyfiueeew5dffomEhAS8+OKLLhF/eevWrcPDDz8MX1/f24qvfLmjvjukLNahrEPLYx2qTPysQ5WLv7yGUodqFL17PRUQEAAAyMvLg5+fHwAgNzfXpswZBAQEIC8vz2Zfbm4uAgMDb1retm3bKsstFgvy8vLk8+0tKSkJCxcuxPz58+Hu7u4y8b/66qsYPHgwfvjhBzzyyCPYtGkTAgICcOnSpVrHHhgYaPP9Kl8GwK6xb968Gb///ju2bt1aqcwV4h8+fLj8c5cuXXDlyhWsX78ekydPdvrvjbWCWLZsGXr06AEAePvtt/HYY4+hb9++Th9/eQcPHkTv3r3lz67w3SFlsQ5lHVoe61Bl4mcdqlz85TWUOpQtpHXA2hSekpIi70tOToafnx/c3d2VCquSsLAwGAwGmM1meV9ycrIcf1hYmM0z3KrcYDBACFGpK4E9lJSUYMiQIYiOjsaLL77oUvE3atQIvXr1wsKFCzFixAi8+eabtY7NZDIhLS0NYWFh8PDwgI+PT6XvFwCEhobaLe5ly5YhOTkZTZs2RVBQEAYPHowrV64gKCio0v2dMf6K2rZti6SkJJf43kRERACAzd8XzZo1g9lsRkhIiNPHbyWEwLFjx+SK/Hbic4bvDjkW61DWoeWxDnWOvwdZh7IOrUtMSOuAv78/OnXqhLi4OHnfzp07ERsbq2BUlcXExMBoNOLw4cMAyr745ePs06cP9u3bB6PRCKCsC0RCQoJNecVnDAkJsfkfxx7MZjNGjhyJ7OxsfPLJJ/I4DGePv7S0FIWFhTb7/Pz8UFhYiD59+uDMmTNISkoCABQXF2P//v2VYhPX14Y6fPgwSktLERMTU23s9957r9yaYA/r16/HhQsXEB8fj/j4eMyZMwfh4eGIj49Hv379nDp+k8lUad2t+Ph4tGnTxum/NwDg4+ODbt26Yc+ePfK+hIQEeHt7IzY21unjt8rLy8O1a9fkMSrW+zvzd4eUxzqUdSjAOpR16O1jHeqCdahjJvNteD744APh5+cndu7cKb766iuh1WrF//73P0ViuXbtmkhJSRFTpkwRHTt2FCkpKSItLU0IIcSwYcNEVFSUOHbsmJg5c6bw9fUV6enpQgghjEajaNq0qbwW06OPPipiYmLk6548eVJotVqbtZhmzpxp19hNJpN4/vnnRZMmTcSpU6ds1mRy9vg//vhj0b59e/Hll1+KhIQE8dVXXwk/Pz/x97//XQghRHR0tBg4cKC8jlSzZs1ESUmJEEIIg8EgfHx8xKxZs8SxY8fEfffdJ5599ln52t9//73QarVi48aN8jpSH330kd1ir8qaNWvkKeudPf5PPvlEtGvXTnzxxRfi3Llz4uOPPxYeHh7iiy++EEI49/fGasOGDcLf319s3bpV/PLLL6Jt27ZiypQpLhO/EEIkJSUJAOL48eM2+535u0POgXWofbAOdZ6/R1iHlmEdWnMNqQ5lQlpHLBaLePPNN0Xjxo1Fy5YtFf3D7tWrlwBgs1n/UszPzxfDhg0T/v7+olu3buLQoUM25yYkJIgePXoIX19f8eijj8qVsNXmzZtF+/btRXBwsPjb3/4mTCaTXWNfv359pditm7PHb7FYxL/+9S/Ro0cP4e3tLVq0aCEWLVokzGazEKLsL42HH35Y+Pr6ip49e4rz58/bnH/gwAHRrVs34e/vL4YPHy4KCgpsyletWiXuvvtu0aRJEzF//nx5zam6UrEydeb4LRaLWL58uejVq5fQ6/WiTZs2Yu3atfI9nPl7U97q1atFmzZtREBAgPjrX/8qioqKXCr+c+fOCQAiISHBZr8zf3fIObAOtQ/Woc7z9wjr0BtYh9ZMQ6pDJSGut+kSERERERERORDHkBIREREREZEimJASERERERGRIpiQEhERERERkSKYkBIREREREZEimJASERERERGRIpiQEhERERERkSKYkBIREREREZEimJASERERERGRIpiQErmI3r17Y+rUqUqHQURE5HJYhxI5LyakRA6WkZEBSZKQn5+vdCguoa7e14QJE/iPEyIiF8M6tHZYh5IrYEJKREREREREimBCSnQbevfujU8//RRz585FixYtEB4ejtWrV9/yvLVr1yI4OBgAoNfrIUkS/vjjDwDAxYsXMXDgQPj5+aFNmzb417/+BYvFUu21Ro4ciejoaJSUlAAA0tLS8Oc//xkBAQGIjIzEihUrIISQ7/vQQw/hxx9/RI8ePeDr64uhQ4eisLBQvt6nn36Kli1bwsfHB/3798eJEydq/D6++eYbdO3aFXq9Ht27d8fBgwflsh07diAqKgp6vR5RUVH48ccfbc6VJAknTpzA888/j6CgILRr1w5Hjhy55fu6dOkSHn74Yfj4+KB9+/b45ptv5GvOnTsXY8eOxRdffIFu3brBz88P48ePl99ns2bNsGzZMrz33nuQJAkjRoyo8bMSEdGdYR1qi3UoNXiCiGqtV69ewtfXV8yaNUucOnVKTJ06Veh0OnHt2rWbnldYWCg2btwoAIgLFy6IlJQUYTKZRGZmpggJCREjR44UJ0+eFF9//bUIDg4Wb7/9ts09p0yZIoQQYvXq1SIkJEQkJiYKIYQoKSkRHTp0EJMmTRJnz54VO3bsEOHh4eJ///ufEEKINWvWCE9PTxEbGyt++uknsXPnTqHT6cSyZcuEEEJcuXJFqFQqsXDhQnH69Gnxz3/+U/z44481ehcbNmwQWq1WLFmyRJw9e1YsX75c7Nq1SwghxE8//STc3NzE4sWLxZkzZ8R7770nNBqNOHDggHw+ABERESE++eQTcebMGdG7d29x33333fR9ZWdni0aNGom3335bJCQkiK+//lr4+vqK06dPCyGEmDNnjtDr9eLJJ58Uv/zyi/jvf/8rAIhvv/1WCCFEWlqaiI6OFuPGjRMpKSkiOzu7Rs9KRER3jnXoDaxDiYRgQkp0G3r16iXGjh0rfz537pwAIPbt23fLc3ft2iUAiLy8PHnf/PnzRcuWLYXJZJL3rVmzRnh5eYmSkhL5nlOmTBHHjx8X3t7ecoUlhBDr168XUVFRwmKxyPsWLlwo/vKXv8jX0uv1oqioSC6Pjo4Wo0aNEkIIcfr0aQFAnDt3rpZvQohWrVqJ6dOnV1nWr18/MXLkSJt9L7zwgnjooYfkzwDE+vXr5c8ffPCB0Gg0wmw2CyGqfl8LFy4UQ4cOtbnu2LFjxbx584QQZZVpmzZt5GtYLBYRFhYm3nrrLfn48v84ISIix2EdegPrUCIh2GWX6Dbp9Xr555YtWwIAsrOzb+ta8fHx6NmzJ9RqtbwvNjYWBQUFuHDhgrwvNzcXTz31FMLCwhATEyPvP3r0KH799Vfo9Xp4e3vD29sbc+fORVJSknyMVquFu7u7TczWeNu0aYPXX38dDz74ICZPnmxz3s3k5+cjISEBAwYMqPa5evfubbMvNjYW8fHxNvsqvkuTyWTTFaqio0eP4ptvvpGf1dvbGx9//LFN3F5eXlCpyv6KkyQJkZGRt/3nQ0RE9sU6lHUokRUTUiI7sP6lfbvE9XEqVSk/Bubjjz9G165doVarsWzZMpvzo6KiEB8fL2+nTp3C2rVraxSzJElYsGABjh07BpPJhHvuuQf/+c9/bhm3NTZJkmr1XDcb11OTdymEwODBg22e98yZM5g/f/4dXZeIiByPdSjrUGrY+O0icjCNRgMANr+97Ny5M/bt2wez2Szv27VrFzw9PdGqVSt5X8eOHbF69WosXrwYs2fPhsFgAAB06tQJZ8+eRUhICFq2bClvjRo1qlVsERER+Pe//40pU6bgH//4xy2P9/HxwV133VVpkoXyz7Vnzx6bfbt27UKXLl1qHFNV76tTp044efIkmjVrZvO81skbanrdm/0GmYiInA/rUNahVP8wISVysObNm0OSJHz44Yc4d+4cioqKMH78eGRnZ2PcuHE4ffo0tmzZgtdeew0zZ86Em5ubfO4jjzwCT09PPPzww3jggQcwffp0AMDTTz+NRo0a4YknnsCRI0dw5swZvP3227h8+XKNYlqzZg1GjBiBAwcO4Oeff8b333+Ppk2b1ujcWbNm4Z133sGqVauQkJCANWvW4P333wcAzJkzB59++imWLFmCc+fO4f3338e6deswZ86cO3pfEyZMQGZmJp5//nnEx8fj+PHjmD17NvLy8mp83ZYtWyIuLg7Hjx/HpUuXanweEREph3Uo61Cqf5iQEjlY48aNMX/+fCxevBh9+/bFmTNnEBAQgP379yMpKQnR0dGYOnUqpk+fjmnTplV5DUmSsHjxYnz++ec4ePAgtFotdu7cidDQUDz22GPo1asXjh07VuOYBg8eDC8vLzz77LPo27cvAgMD8e9//7tG544cORJLly7FkiVL0LVrV3z88cfo1q0bAKBHjx7YunUrPvvsM3Tt2hWff/45tm3bhu7du9c4tqrel7+/Pw4ePIi8vDzExsbi0UcfRUpKijx9f01MmzYNfn5+6N69OxYsWFDj84iISDmsQ1mHUv0jiZt1vCciIiIiIiKqI2whJbKjrVu32sxaV3FztRnqli1bVu2zNG7cWOnwiIioHmEdStQwsYWUyI7y8/ORmppabXnz5s1tpqV3dtnZ2cjIyKiyTKVSoUWLFg6OiIiI6ivWoUQNExNSIiIiIiIiUgS77BIREREREZEimJASERERERGRIpiQEhERERERkSKYkBIREREREZEimJASERERERGRIpiQEhERERERkSKYkBIREREREZEimJASERERERGRIv4fi5BoaCCVgtYAAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6wAAAGGCAYAAAB/kww9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZBRJREFUeJzt3Xl8VOXZ//HvzCSZhEz2kIQQFtlFBFEbRVkkoFBl8UHQn6JVURDRVqlbURBwQZ/W9rG1Km4tblSLWlS0iMhaBFwAFyoCIgokTICQPZkkM+f3R5gDk40QJpkZ8nm/nFcy5z7nzHXOSby5cm8WwzAMAQAAAAAQZKyBDgAAAAAAgLqQsAIAAAAAghIJKwAAAAAgKJGwAgAAAACCEgkrAAAAACAokbACAAAAAIISCSsAAAAAICiRsAIAAAAAghIJK4Bmc/311+v555/32VZeXi6LxaLdu3cHJig/GzlypBYsWBDoMAAArdzvfvc73XDDDYEOA/A7ElYgxK1atUoWi0VVVVV+PW9RUZEuu+wyRUdHa9iwYU06h9PpVGFh4Qkf9+qrr6pnz55N+kx/mjp1qpKTk31e2dnZeuKJJ/hHAQCc4oKtfr3ppps0b948n20Oh0M//PBDo8/RmPp1ypQpysjIUEREhBISEpSRkaGMjAzFxsYqKipKGRkZevjhh32Oefjhh839ar5SUlIUHx/f6BiBmsICHQCA4PTqq6/qk08+0YoVK5SWltbo43r06KHc3FxJUnFxsVatWqVHHnlEknTNNdfoT3/603HPsWPHDu3Zs0cul0t2u73Ofb799ludeeaZiouLq/c8L7/8ssaOHWu+HzVqlFatWlXnvlVVVcrIyNDOnTvNbfPnz9f8+fPN9xaLRR6P57jxAwBQn6bWry6XSxUVFT7bSkpKZBhGo8/RmPrV2zPq/PPP19SpU80/0M6ZM0fbtm3TG2+8UeuYWbNmadasWXWe74svvtDw4cMbHSNQEwkrgDrl5uaqd+/euuCCC07ouP/+97/1llksFlVWVjZ4fFVVld5991253W4tXbrUJ+GsyWazKT8/v9GxLVmypN6yDz/8UHfccUejzwUAQFM0tX6VpPfee0979+5t0ueeSP16ombOnKlnnnlGiYmJtco8Ho/S09P99llofegSDJyAiy66SK+88ormzJmjLl26KD09XS+99JJZvmDBAiUnJ/scM378eJ/uo507d9ayZcs0ZcoUtW3bVhdeeKG2bt2qDRs2aMiQIYqPj9fNN998wl2QvvnmG40YMUJxcXG6+OKLzVZO72euXr1aN9xwgxISErR161ZJ0scff6xzzjlHMTExGjJkiL799ltJ1X9FnTt3rjZv3iyLxaIFCxaotLRU119/vRITE9WxY0fdddddKikpqRVHWFiY8vLyNGXKFHXp0kVnnnmmHn30UXk8HtlstgavwTAM3XvvvYqMjNSbb76pm2++ucGxrm63W/Hx8fW+iouLG33/6vpr89y5c3XWWWeZr2O99tprcjgc+vjjjxv9GQCAulG/Hr9+laT09HSde+655qsmb930xz/+0Wf7idSv06dPV0ZGhjZt2mR+n5GRoT/96U969913ze+PVVVVpXHjxmnnzp21Xrt27Wrwj9nA8ZCwAifoN7/5jTwej5YsWaKJEyfqtttuU15e3gmd49prr1X//v21Zs0aud1ujR07Vvfee6/mzZun1157TS+99JLef//9EzrnLbfcorvvvluffPKJvvnmGz3xxBM+5ZMnT1aXLl20fv169ezZUxs2bNA111yjGTNmaPPmzRo5cqRGjx4tl8ulu+++W3fddZf69u2rnJwcXXXVVXryySe1bNkyvfPOO3rnnXdksVjqjMMwDF1xxRUKCwvTpk2b9P7772vZsmWaM2dOg/Hv379fV1xxhZYsWaJFixbp8ssv1x133KFzzz1XixYtqrMrrreFtb6Xw+Fo9P1zuVyKiIjw2bZnzx5dcsklWr58uZYtW6Zdu3aZSfe1116r4uJiXXzxxY3+DABA/ahfG65fJencc8/V1KlTzVdd119cXKy77rrL3Hai9ev//d//ae/evaqoqNDhw4e1d+9e7d27V4WFhSorK9PevXv129/+ttZnv/7660pLS6v35U3agRNmAGi0IUOGGFOmTDHff//994YkY+3atYZhGMbf//53IykpyeeYK664wrj++uvN9506dTLmzZtnvn/iiScMSUZhYaG5rVu3bsYDDzzQqJhWrlxpSDKys7PNbVdffbUxbNgwn88cM2aM4fF4zG2XXHKJ8cwzz5jvPR6P0aNHD2P16tWGYRjG7NmzjXPOOccsv/fee40LL7zQcLvdDcaze/duIyoqynC5XOa2Tz/91OjYsaNhGIZRVlZmSDJ+/PFHs3zatGmG3W43Jk+ebBw8eNDnfIsXLzY6d+5sdOzY0di0aZO5/ZtvvjFsNluDsZyIP/zhD8Zll13ms+2mm24yoqOjjYSEBMPhcBjdunUzJkyYYPzhD38wn+mIESOMv//9736LAwBaI+rX49evEydONOx2uxEXF2e+JBk7duwwDMMw7rvvPp/7YRgnXr+OGzfO5/wNvW699dZ6Y+3UqZPx8ccfN3g9QGPRwgqcoJiYGPP7bt26SdIJjaOseY7u3bvXue1kztmtW7dax1988cU+f7XdtGmTfvvb38rhcMjhcCgmJkY7duzQvn376jz/nXfeKcMw1KdPH/3tb3+rdyyq2+2WxWKR1Xr0fy/h4eENdsGaOnWqfvrpJz3//PNKSkryKRs7dqx27typV155RWeeeaa53WKxKCwszJy9Ny4uTnFxcT4z+j799NP1fmZNP/zwgzp37uyz7bnnnlNBQYHy8vJUVFSkHTt26J///KfPPjfeeKPOOeecRn8OAKBu1K8N16+vvfaaysvLfXoSGYZh3qsBAwbUmnX4ROvXt99+u8GeS8e+nnnmGUmqNZt+cnKyfv75Z40bN67W9ldffbURdxzwxaRLwEk4NinzOtFZZOs6R13bTvacNRmGoYcffliXX365z/b6Zixs166d/vOf/+jjjz/WQw89pMcff1xr1qyptf9pp52m3r1765577tHDDz+sgoIC3XfffbrqqqvqjeXYRLQuNptNQ4YM8dl2xhlnqLy83Hx/9913Kz8/Xy+++GKD56rPvn37NHjw4FqfW5dp06bJ7XZLUoPXBQBoGurX2vXr8dQ1iVJT6tclS5bozjvvbPC4Dz/8UD169JAkHTx4sFb5kiVLdO65557wNQB1oYUV8KOYmBgdPnzYHHPjdruVnZ0d4Kjq1q9fP23btk3dunXzeTU07tNiseiSSy7R6tWr5fF4arU2evf517/+pT179qhjx476xS9+obPOOkuPPvpoc17OSXvvvffqHJMjVU+qER0dbU7mlJ6erg4dOig+Pl5hYWFasGBBywYLAK0M9Wu1GTNm1DvRoN1u98sa4aNGjapz8iTva//+/bWW1zlWbm6uxowZo5dffvmkYwEkWlgBv+rXr58k6amnntIll1yiv/zlL9qzZ4/5V8hgMnfuXF100UXq0qWLrrjiCh04cEBr167VjBkzau1rGIbGjRunrKwsDRs2TFu3btXu3bvVsWPHOs+dkZGht956q1FxLFiwQLfffnuj4z6RmX+PlZSUJJfL1ah958+fr2uvvdZn27vvvlvnOnIXXXRRk+IBADQe9Wu1xx57TI899lidZb/73e+0f/9+831T69cPPvhA119/fa1Zmb0cDofCwupOIUpLS3XDDTdoyJAh+tOf/qSsrCz94he/aHQMQF1IWAE/6tatmx5//HH94Q9/0D/+8Q/99re/Ve/evfXDDz8EOrRaBg4cqKVLl+qBBx7QvHnz1L59e11//fXyeDy1ujxZLBbde++9mj17tmbNmqXo6GjNmjXLL2u43XDDDSf8F+GbbrpJb775Zp1lNRc0HzRokP7973/r0KFDTQ0RABBg1K8nrin1qyRVVlYqIyNDW7ZsadT+Ho9Hn3/+uVasWKGnnnpKF198sZ577jl98MEHuuyyyzRy5EiNHz9egwYNUkJCwgnHA1gMwzACHQSA1qO8vFxRUVH68ccfa01yFKw6d+6s/fv31/kX5bKyMr300kt+6YYFAEBTeVtYT3aYyuLFi3XllVcqPT293n0mT56sBx54QFJ1K/H111+vvn37auzYseZkV1J1q+3SpUv17rvv6rbbbtP5559/UrGhdSJhBYJYfn6+MjIy6i3/xz/+odGjR7dgRAAAhD7qVyB0kLACQcztduvHH3+stzwtLa3BSRwAAEBt1K9A6CBhBQAAAAAEJZa1AQAAAAAEJRJWAAAAAEBQImEFAAAAAAQl1mFtAo/Ho+zsbMXExMhisQQ6HABAgBiGoaKiIqWnp9daXxF1ow4FAEiNr0NJWJsgOztbHTp0CHQYAIAgsWfPngaXyMBR1KEAgGMdrw4lYW2CmJgYSdU3NzY2NsDRAAACpbCwUB06dDDrBRwfdSgAQGp8HUrC2gTeLkyxsbFUtgAAuraeAOpQAMCxjleHMuAGAAAAABCUSFgBAAAAAEGJhBUAAAAAEJQCmrAeOHBAM2fOVMeOHXXuuef6lJWUlGjixIlKSkpSZmamNmzY4FO+fft2DR48WAkJCRo1apRyc3N9yhcvXqy+ffsqNTVVd9xxh6qqqswywzD00EMPqUOHDurRo4defPHF5rtIAAAAAECTBDRh3bNnj3bu3FnnpAuTJk3Szp07tXz5co0YMUIjRowwk1KXy6Vhw4apZ8+eWr16tSwWi8aOHWse+9VXX+nKK6/UtGnT9P777+u9997TrFmzzPL58+frySef1CuvvKJ58+bptttu09KlS5v/ggEAAAAAjWYxDMMIdBBz5szRkiVL9MUXX0iS9u/fr4yMDK1du1YDBgyQYRjq2bOnpk2bpjvvvFNvv/22brzxRh04cEB2u1379u1TRkaGNm/erLPOOku//vWvtX37dn300UeSpIULF+qOO+7Qvn37FB4err59++qaa67RjBkzJElTpkxRbm6uFi9e3Kh4CwsLFRcXp4KCAmY4BIBWjPrgxHHPAABS4+uDoBzDum7dOkVFRSkzM1NS9VTHWVlZWrlypSRp1apVGjRokOx2uySpffv26tmzp0/58OHDzfNlZWXp4MGD2rp1q/Ly8vTtt9/WKvceWxeXy6XCwkKfFwAAAACgeQVlwup0OpWSkiKbzWZuS09Pl9PpNMvT0tJ8jmmoPCUlRVarVU6n09zn2PL09HQVFhaqrKyszngee+wxxcXFma8OHTr450IBAAAAAPUKyoT18OHDiomJ8dkWExOjvLy8JpVbrVY5HA7l5eXp8OHD5v7HHus9ri4zZsxQQUGB+dqzZ89JXiEAAAAA4HjCAh1AXRITE1VUVOSzrbCwUElJSQ2W9+7du85yj8ejoqIiJSUlKTExUZJUVFSk+Ph481jvcXWx2+1m92MAAAAAQMsIyhbWtLQ0OZ1Oud1uc1t2drbZjTctLU05OTk+xzRU7nQ6ZRiG0tLSzH2OLc/OzlZ8fLwiIyOb7ZqaIju/TPM+/E7OwvJAhwIAAAAALS4oE9aBAwfK5XJp48aNkqrXTV2xYoWysrIkSUOHDtXatWvlcrkkSXv37tX27dt9ypcvX26eb8WKFUpJSVHv3r2VkJCgfv361Sr3HhtMFny6W8+v2aW5728NdCgAAAAA0OIC2iU4Ly9PFRUVKi4uVmVlpfbv3y+bzaa2bdtqwoQJmj59up577jm9/fbbOnDggK6++mpJ0qWXXqrExETdfvvt+vWvf637779fAwcO1JlnnilJuvnmm3XOOedo/vz5OvvsszVz5kxNmTJF4eHhkqRp06bpvvvu04ABA5SXl6dXXnlFS5YsCdh9qE9OQXXL6kdbncopKFO7uKgARwQAAAAALSegCeu4ceO0evVq8327du3UqVMn7d69Wy+88IImT56srKwsde3aVR999JGSk5MlSREREVq+fLluuukmDR48WAMHDtQ777xjnqdPnz5atGiRHnjgATmdTl199dWaM2eOWT558mQ5nU5dd911ioqK0jPPPKOLL764xa67sQ4VV7cguz2GFm78WXdd0jPAEQEAAABAy7EYhmEEOohQ01KLno/4vzX63lk9eVSyI0Lrfpcle5jtOEcBAFpKS9UHpxLuGQBAanx9EJRjWFHtUEl1C2uY1aKDxRXasCsvwBEBAAAAQMshYQ1Sbo+hvJIKSVLPtOp1Yp0FzBYMAAAAoPUgYQ1S+aUV8hzprN09xSFJOnikxRUAAAAAWgMS1iDlbV2NbxOu1Ljq9WEPFVcEMiQAAAAAaFEkrEHq4JHkNCk6QknREZKOzhoMAAAAAK0BCWuQ8k64lOSwKynafmQbLawAAAAAWg8S1iDl7f6b7IhQkqO6hfUgXYIBAAAAtCIkrEHK2/03KdquZEd1C2seky4BAAAAaEVIWIPUwSPdf5OOaWE9VFwhwzACGRYAAAAAtBgS1iBltrA67Eo8MulSlcdQYVlVIMMCAAAAgBZDwhqkzDGs0RGyh9kUExkmibVYAQAAALQeJKxB6pDZJbh6/Kp3HCtrsQIAAABoLcICHQDqdtDsElzdHTgpOkI/HixhLVYAAE6A0+lUQUFBk46Ni4tTamqqnyMCAJwIEtYg5Kpyq6i8eqxq8pE1WM2lbViLFQCARnE6nbr2ul+psqJpf+wNj7DrtVdfIWkFgAAiYQ1CeUeS0jCrRbFR1Y8o8UjimkeXYAAAGqWgoECVFS6VdRkiT2Scud1alq+oH9eo7LTB8kTF13mstbxA2rVaBQUFJKwAEEAkrEHIO041yREhi8UiSUr2Lm3DpEsAAJwQT2ScPNHJtbdHxde5HQAQPJh0KQiZ41ePtKpWf390LVYAAAAAaA1IWIOQd/yqtzuwdHS24INMugQAAACglSBhDUJlFW5JUpuIYxNWb5dgWlgBAAAAtA4krEGorLI6YY0Kt5nbjq7DSgsrAAAAgNaBhDUImQlrxNGE1TuG9XBpparcnoDEBQAAAAAtiYQ1CJVW1G5hjW8ToSMTButwaWUgwgIAAACAFkXCGoTK62hhtVktiosKlyTllzKOFQAAAMCpj4Q1CJXV0cIqSQltjnYLBgAAAIBTHQlrEDK7BEf4JqzxbapbWA/TwgoAAACgFSBhDULldcwSLB1tYaVLMAAAAIDWgIQ1CJVWVEmqo4U1ytvCWrtL8Adf5+jplTtlGEbzBwgAAAAALSAs0AGgtrrWYZWqZwqWpPw6EtYZ73ytwvIqjTgjVd1SYpo/SAAAAABoZrSwBqGyyup1Vmt3Ca57luCi8koVlle3yu7JK2uBCAEAAACg+ZGwBqGyI12C29TsEhztnSXYN2F1FrrM77MLSFgBAAAAnBpIWIOQt0twZETdLaw1x7A6C8vN73PyywUAAAAApwIS1iBUVlFfl+C6ZwneX3A0SaWFFQAAAMCpgoQ1CNXbJbieFtb9x7SwZueTsAIAAAA4NZCwBhnDMOqdJfjYFtZjl6/x6RJcQJdgAAAAAKcGlrUJsOLiYpWWlprvK6o88hzJRYsL8qTyo48oPNwuSap0GyqpcMthry6rmbAahiGLxdIC0QMAAABA86GFNYCKi4vVqfNpSk1NNV+dunY3y7t27uhT1qt7N0XYqhPRwyVHx7HuP2aW4Ioqjw6V+I5xBQAAAIBQRAtrAJWWlirv0EHd+/wSRcclSpJKKjx689t8WS3S3H+sNfctKcjT76eMUpfIMB0oqVR+aaU6VB8iZ41uwNn5ZUp22FvsOgAAAACgOZCwBoHouETFJCRJkqpKKyTlK8xmNbcdKy6qOmH1rsXq9hg6UFzdwpoeF6nsgnJl55erb0aLhQ8AAAAAzYIuwUGm0l29pE24re4xqHGR1X9jyC+rnin4YLFLbo8hq0U6MyNOkpTD0jYAAAAATgEkrEGmyl0941KYte5HExd1JGE90sLqnXCpbYxdGQltJDFTMAAAAIBTAwlrkGlsC+vhkuoW1v1HktO02Ei1i4uUxFqsAAAAAE4NJKxBpurImjbhtoZbWA/XaGFNiY1UenyUJBJWAAAAAKcGEtYg421hDbMeZwzrkYR1f+HRFlZvwkqXYAAAAACnAhLWIGOOYT1uC6u3S3D1DMFpcZGKjwqXJBWVVzV3mAAAAADQ7EhYg8zxxrDGRtokHW1h9Xb/TYuNlD28+nG6qtzNHSYAAAAANDsS1iDjHcNa3yzBiW2qW1GdhdUtqz8eLJEkndY2WpFh1clspduQ+8h5AAAAACBUkbAGGW+X4PpaWDslVM8EvL+wXLlF5eYY1tOSos0WVolWVgAAAAChL6gT1pKSEk2dOlXJyclKT0/Xvffeq6qqKrNs4sSJSkpKUmZmpjZs2OBz7Pbt2zV48GAlJCRo1KhRys3N9SlfvHix+vbtq9TUVN1xxx3meQPNnHSpnjGsMZFhahtjlyR98l31NcW3CVdCdITsR1pYJam80tPMkQIAAABA8wrqhPXBBx/Upk2b9Mknn+i1117Tyy+/rGeffVaSNGnSJO3cuVPLly/XiBEjNGLECDMpdblcGjZsmHr27KnVq1fLYrFo7Nix5nm/+uorXXnllZo2bZref/99vffee5o1a1ZArrGmSs+RMaz1zBIsSd1THJKkj//rlCSdlhwtSbJZLWbLLC2sAAAAAEJdUCesy5cv1/33369+/fopKytL119/vZYvX679+/fr7bff1pNPPqn+/fvroYceUmpqqhYuXChJWrJkiQoKCvTXv/5Vffv21fz587VhwwZt2bJFkvTiiy9q6NChmjp1qjIzM/Xoo4/qxRdfVEVFRQCvttrRLsH1P5puRxLW/+w8KOlowirJHMdKCysAAACAUBfUCWuvXr20Y8cO831UVJR69uypdevWKSoqSpmZmZIki8WirKwsrVy5UpK0atUqDRo0SHZ7ddfZ9u3bq2fPnj7lw4cPN8+blZWlgwcPauvWrS11afU62iW47hbWAwcOKDWqOqmtqKreNyVKys3NVW5ursKP9ArOcR5QcXFx8wcMAAAAAM0kqBPWe+65R7///e81b948/fTTT3rrrbd04403yul0KiUlRTbb0TGb6enpcjqru8g6nU6lpaX5nKuh8pSUFFmtVrO8JpfLpcLCQp9XczFnCa7RwuoqL5UsFvXp00d3T77Wp+zhe3+t1NRUpaamKjdnnyRp6MUj1KnzaSStAAAAAEJWUCesXbp0UZcuXfT666+rS5cuGjhwoE4//XQdPnxYMTExPvvGxMQoLy9Pkk643Gq1yuFwmOU1PfbYY4qLizNfHTp08Odl+jDXYa0xhrXSVS4Zhm7/8yLdMe8Zn7Kb75unuW9+qrlvfqqklHaSpGt+9yflHTqo0tLSZosVAAAAAJpT0CasVVVVysrK0t13361vvvlGb7/9tt5//33Nnj1biYmJKioq8tm/sLBQSUlJknTC5R6PR0VFRWZ5TTNmzFBBQYH52rNnjz8v1Yd3DGt9swRHxyYopW2y7GFHy9NT2yomIUkxCUmKCK9epzW8jaPZYgQAAACAlhAW6ADqs3r1ah08eFDjx4+XxWLR5ZdfLqvVqvHjx+vVV1+V0+mU2+02uwVnZ2eb3XzT0tJ8xr7WVZ6Tk2OWOZ1OGYZRqxuxl91uN8fDNrfjrcMqVY/ZTYyOUE5BuRz2MEUck7zajrTMuplzCQAAAECIC9oW1pKSEkVERMgwDHNbu3btVFlZqaysLLlcLm3cuFGSZBiGVqxYoaysLEnS0KFDtXbtWrlcLknS3r17tX37dp/y5cuXm+ddsWKFUlJS1Lt375a6vHp5l7Wpr4XVK6FNhKTqNViP5Z2sye0xah0DAAAAAKEkaBPWQYMGqaysTLfeequ+++47ffHFF5o+fbqysrLUtm1bTZgwQdOnT9eWLVv04IMP6sCBA7r66qslSZdeeqkSExN1++236+uvv9bUqVM1cOBAnXnmmZKkm2++WatXr9b8+fP12WefaebMmZoyZYrCw8MbCqlFmC2sDazDKknt4iIlSamxkT7bzRZW8lUAAAAAIS5oE9aEhAQtX75ce/fu1YABAzRq1Ch1795db7zxhiTphRdeUNeuXZWVlaWlS5fqo48+UnJysiQpIiJCy5cv1/fff6/BgwdLkt555x3z3H369NGiRYv09NNPa9SoURozZozmzJnT4tdYl6PL2jT8aHq3i9X4szN0/mmJPtvDjiSsVbSwAgAAAAhxQTuGVZLOOOMMffDBB3WWRUdHa+HChfUe2717d61Zs6be8jFjxmjMmDEnHaM/GYZhJpoNjWGVJKvVovYJUbW2h1mrE13GsAIAAAAIdUHbwtoaHdsq6k08T5Q5htWghRUAAABAaCNhDSJVxww8DTtOC2t9js4STMIKAAAAILSRsAaRqiMzBNssFlktTUtYj45h9VtYAAAAABAQJKxBxNsl2NbE1lXpmDGsdAkGAAAAEOJIWIOIt0tw2HGWtGmIzVyH1S8hAQAAAEDAkLAGEe+4U9tJJKxhjGEFAAAAcIogYQ0i3jGsJ9PCao5hpUswAAAAgBBHwhpEvK2iTV3SRjp2lmC/hAQAAAAAAUPCGkSq/NIl+MikS3QJBgAAABDiSFiDiDnp0snMEuyddIl8FQAAAECII2ENIv6cdKmKFlYAAAAAIY6ENYj4Y9IlG7MEAwAAADhFkLAGkSo/TLpkjmElXwUAAAAQ4khYg4g/ugTTwgoAAADgVEHCGkTMFlY/TLpUxbI2AIBTWHl5ubZv367y8vJAh3LCQjl2AGhpJKxBxO2dJdgPky65DVpYAQCnrp9//llTpkzRzz//HOhQTlgoxw4ALY2ENYh4J13yxzqsHkOShccLAAAAIHSR0QQRf0y6dGyya7GFn3RMAAAAABAoJKxBxO3xX5dgSbKERZx0TAAAAAAQKCSsQaTKD7MEW60WeQ8nYQUAAAAQykhYg0iVu3oM68nMEiwd7VJMwgoAAAAglJGwBhF/rMN67PEkrAAAAABCGQlrEPHHpEvS0RZaElYAAAAAoYyENYj4Y9IliRZWAAAAAKcGEtYg4o9Jl6SjCS8JKwAAAIBQRsIaRMwWVr9NusQ6rAAAAABCFwlrEDFnCT7ZMaxmC6v9pGMCAAAAgEAhYQ0i/uoSbDMnXaKFFQAAAEDoImENIv6adIkWVgAAAACnAhLWIGEYhv9aWK20sAIAAAAIfSSsQcJtGOb3/pt0iRZWAAAAAKGLhDVIeLsDS/5c1oYWVgAAAAChi4Q1SFS5j0lYLSeZsNoYwwoAAAAg9JGwBoljJ1yynGTCao5htdHCCgAAACB0kbAGCX9NuCQdM4Y1nBZWAAAAAKGLhDVIVHk8kk5+wiXpmDGstLACAAAACGEkrEHiaJfgk38kNu8YVlpYAQAAAIQwEtYg4Z10KcwvXYK9ky5FnPS5AAAAACBQSFiDhD/HsDLpEgAAAIBTAQlrkHA3x6RLrMMKAAAAIISRsAYJf066dLSFlS7BAAAAAEIXCWuQqPLjpEuMYQUAAABwKiBhDRJudzOMYaVLMAAAAIAQRsIaJI62sDJLMAAAAABIJKxBw+3HhJVZggEAAACcCkhYg4R30iW/zBJs884STAsrAAAAgNBFwhokzC7BtpN/JDa6BAMAAAA4BZCwBgl/TrpkjmG12sxEGAAAAABCTdAnrBs2bNCFF16o2NhYnXfeeVq5cqUkqaSkRBMnTlRSUpIyMzO1YcMGn+O2b9+uwYMHKyEhQaNGjVJubq5P+eLFi9W3b1+lpqbqjjvuUFVVVYtdU138OenSsUlvRZXnpM8HAAAAAIEQ1Anr5s2blZWVpTFjxuizzz7TlClTtHr1aknSpEmTtHPnTi1fvlwjRozQiBEjzKTU5XJp2LBh6tmzp1avXi2LxaKxY8ea5/3qq6905ZVXatq0aXr//ff13nvvadasWQG5Ri/vGFa/J6xuElYAAAAAoSks0AE0ZN68ebrmmmt03333SZJ69eolSdq/f7/efvttrV27Vv3799dZZ52lN998UwsXLtSdd96pJUuWqKCgQH/9619lt9s1f/58ZWRkaMuWLTrrrLP04osvaujQoZo6daok6dFHH9Udd9yhuXPnKiIiMOM+vbME+6NLsNVikdUieQzJVUWXYAAAAAChKWhbWN1utxYvXqxrrrmmVtm6desUFRWlzMxMSZLFYlFWVpbZXXjVqlUaNGiQ7Ha7JKl9+/bq2bOnT/nw4cPN82VlZengwYPaunVrnbG4XC4VFhb6vPztaJdg/zwS25G8ly7BAAAAAEJV0CasOTk5qqqqksVi0ejRo9W+fXtNmDBBTqdTTqdTKSkpstls5v7p6elyOp2SJKfTqbS0NJ/zNVSekpIiq9Vqltf02GOPKS4uznx16NDB35drTroUZjv5FlbpaEstXYIBAAAAhKqgTVj37t0rSbrjjjt03XXX6c0339T27dt1yy236PDhw4qJifHZPyYmRnl5eZJ0wuVWq1UOh8Msr2nGjBkqKCgwX3v27PHbdXpV+bFLsCTZLNXnoUswAAAAgFAVtGNYvQnl008/rUGDBkmSHn/8cY0ePVrDhg1TUVGRz/6FhYVKSkqSJCUmJtZZ3rt37zrLPR6PioqKzONrstvtZvfi5uL24yzBkuRdzpUWVgAAAAChKmhbWL3dbiMjI81tnTt3ltvtVkpKipxOp9xut1mWnZ1tdvNNS0tTTk6Oz/kaKnc6nTIMo1Y34pZ0dJZgf41hPdIlmDGsAAAAAEJU0CassbGxOvfcc81lbKTqtVUdDoeysrLkcrm0ceNGSZJhGFqxYoWysrIkSUOHDtXatWvlcrkkVXcv3r59u0/58uXLzfOuWLFCKSkpZgtsIPi9S/CRJ+ty0yUYAAAAQGgK2i7BknTfffdpypQp6tWrl9q1a6f7779ft9xyi9q2basJEyZo+vTpeu655/T222/rwIEDuvrqqyVJl156qRITE3X77bfr17/+te6//34NHDhQZ555piTp5ptv1jnnnKP58+fr7LPP1syZMzVlyhSFh4cH7FqrmmnSpUpaWAEAAACEqKBOWMePH6/CwkLdc889ys3N1dVXX61HHnlEkvTCCy9o8uTJysrKUteuXfXRRx8pOTlZkhQREaHly5frpptu0uDBgzVw4EC988475nn79OmjRYsW6YEHHpDT6dTVV1+tOXPmBOISTX4fw+pd1oYWVgAAAAAhyu8J6/79+/06FnTSpEmaNGlSre3R0dFauHBhvcd1795da9asqbd8zJgxGjNmjF9i9Ae/j2G1emcJpoUVAAAAQGhqUnZks9mUm5tba/t///tfc0ZfNJ7HMHSkgdV/XYItrMMKAAAAILQ1KWE1DEMWS+3E6vPPP9fBgwdPOqjW5tic0u/L2rAOKwAAAIAQdUJdgtu2bSuLxSKLxaLTTz9d1mO6r5aXl6ukpETTpk3ze5CnOu8MwZIfZwk+8gcFFy2sAAAAAELUCSWsS5culWEYyszM1IMPPqi4uLijJwoLU7du3XTeeef5PchTnfuYJW3qarluijCzhZWEFQAAAEBoOqGE9ZxzzpEkzZ49WzfffLPatGnTLEG1Nt5eu/7qDiwdM4aVLsEAAAAAQlSTZgmePXu2v+No1fy9pI10dAwrXYIBAAAAhKomJay7du3S/fffr02bNik/P79WeV0zCKN+3jGsYTb/LGkjHR0LS5dgAAAAAKGqSQnrNddco+LiYk2YMEHdunXzmXwJJ87bCOrfLsHVXyvcdAkGAAAAEJqalLBu375d//nPf9S7d29/x9MqVRlHJ13yF++5XLSwAgAAAAhRTWoaveiii7Rz505/x9JqmS2stmaYdIkxrAAAAABCVJNaWP/4xz9qzJgxSktLk8PhqFVOy+uJOTrpkj/HsFZ/ZZZgAAAAAKGqSQnr6aefroqKCp1//vnmNovFIsMwZLFY5Ha7/RZga+DtEtwcy9owSzAAAACAUNWkhPX777/3dxytWrN0CTZbWElYAQAIRlOmTAl0CM0iKipKLpdLHs/x/w0SHh4uj8ej6OholZWVKSIiQuXl5bLb7YqJidHIkSP15ptvqry8XJJks9lksVjk8XhkGIbZWGK1WpWQkKCDBw+a505NTVXbtm01ZMgQffDBByooKFBxcbHsdruioqLkcDhUUlKiyspKORwO2Ww29e7dWxEREeratas2bNigsrIyVVRUqFOnTsrPz1dycrIyMjI0atQobdu2TdnZ2frggw+Uk5Mju92u0aNHa9y4cdq2bZsOHjyo/Px8xcbGqrCw0PzqcDj0/fffy+PxyGq16vTTT1diYqJ27dqlffv26dChQ0pMTJTNZtPpp5+ulJQUnXHGGdq6davy8vKUmJhY7/vc3Fx99913kqR27dqpS5cuys/PV35+vuLj45WcnKxevXppyZIlys7OVnp6usaOHauIiIg6n4/b7dbXX39d77W0adNGn376qVwulzIyMnTLLbcoKiqqwWfuPac39r59+8pmszX2xyvgmhJ/zWNqPr+TuQctfT8D8fyalLB26tTJ33G0alXN0SXYO4aVLsEAAASVUzVR9SorK2v0vpWVlZKkwsJCn/elpaUqLS3Vyy+/7LN/Xb34DMOQ2+32SVYlyel0yul06ttvv631mcXFxTpw4IC57fDhw5Kk3bt31xlnzXM8/fTTde73wgsv6IUXXqizrD6LFy8+bpnNZvO59uO9PxHz58/XhAkTNHXqVJ/ta9as0TPPPKP9+/c36jxffPGFFi9erAsvvFCPPvponfvUdc60tDRNmzZNgwcPblL8Lakp8dd1TM3n1dR70NL3M1DPr0kZ0iuvvNLgCyfm6BjWZmhhpUswAABB46KLLgp0CDgJdS3lGBYWpoyMjFrb4+PjJUlt2rQx9zv2HA21RHr3PfarxWLRFVdcIYvFotjYWEnSFVdc4bNfdHS0Bg4cWOt8v/jFL9SuXTtJMlvD7rnnHt19992KjY3VG2+8ofnz55v7r1mzRrNnz1ZcXJwsFou6d+9unr+u+3DaaaeZ5163bp0eeOCBWjF4z9mlSxc9/fTT+vDDD/X000+rS5cumj17ttasWVPv/QgGTYm/5jHe+xIbGyuLxaIHHnigyfegpe9nIJ9fkxLWu+66q9Zr2rRpuuuuu/Tqq6/6O8ZTnjentPm1SzDL2gAAEExGjRoV6BDQBDabTcnJyWb35ZrJ2nvvvae///3vstvtPl0j8/Pzdd5558nhcCg+Pl5VVVUKDw+XVJ3MepPOY4WHh8tut+v9999XQkKCqqqqFBYWpqqqKmVmZmrx4sU6//zztWjRIg0YMECLFy/Weeedp8rKSoWHh8vhcGjHjh06//zzZbfbZbfbFR8fry+//FJOp1Ph4eFKTk7WgAED9Nprr+mXv/ylFi1apISEBC1atEgVFRVyu9165plndP7556ugoEDnn3++ioqKNGDAAEVHRys2NlYej0cWi0Vt27bVgAEDVF5ergEDBigpKUnh4eFat26dT0u795wDBgzQI488ojPOOENt2rTRGWecoUceeUQDBgzQs88+G7Tz4DQl/prH9OrVSy+99JIuuOAC8/n97W9/U69evU74HrT0/Qz082tSwnrgwIFar59++km9evXSQw895O8YT3nNM+lS9dcKN12CAQCB43K5VFhY6PPyp59++knbt2+v8/XTTz816/lP9FVcXOyHK0Zz8yad3pbRfv366eDBg+rTp48kKTIy0mf/Dz74QN9++61cLletf7Db7Xbl5ubqrLPOkiT16dNHHo9Hl1xyiZxOZ63PrqyslMvl0rZt23TxxRdLks4880y53W7Z7Xa53W5lZmYqLCxMmZmZ5naPx6Px48eb3aDPO+88uVwuuVwuXXLJJfJ4PD77ZGZmKicnR19//bXCwsI0adIkud1uvfvuu/r666+1f/9+ZWZm1vqam5trtqaec845OnDggHkub/mgQYMkSc8995x5Xd5zTpw4sVbCb7VaNXHiRDOeYNSU+Gsec+z7sLAwn2NO9B609P0M9PNr0hjWuiQlJenBBx/Uvffeq7Vr1/rrtK2COemSX5e18Y5hpYUVABA4jz32mObOndts569vrFyonB/+dTJjOb3Cw8PldrtlHGlQSEpK8vlaVVXls392drYSExPrPJf3jxTeJDc5OVmSlJ6e3mAMeXl5Zhde7+cWFRX5nMtut/t8xqWXXqp//OMfPmWSzPMcu4+3PC8vT5I0YMCAWtfi3cf71fu53vvSv39/ffHFF7X269mzp1asWKG9e/f6XI90tOtwTd7t3v2CTVPir3lMzff1bW/MPWjp+xno5+e3hFWSysvL9c033/jzlK2COemSP7sEm8va0MIKAAicGTNm6Le//a35vrCwUB06dPDb+R944IF6J4P86aefTjrhbOj8J+pUn2wpGPijS6J34ifLkX9LHTp0yOdrWFiYKioqzP3T09PrTVgdDockmbMceyeGys7ObjCGxMREc1UO7+fGxMT4nMvlcvl8xocffmge7y2TpJycHPN77z7ecm/c69evr3Ut3n28X72f670vmzdvrnM/b9zHjuv1nvPHH3/UGWecUet6f/zxR5/9gk1T4q95TM33NY85kXvQ0vcz0M+vSQnrlVde6fPe4/Hohx9+0NatW2uV4fiac9Ilt8dQldujMJv/Wm8BAGgs7zi65tKpUyf16NEjJM7vcDjoFhwCvElvaWmpJOmrr75ScnKyOVOwN3Hzuuyyy8yxp1VVVT5Js8vlUkpKirZs2SKperZhq9WqZcuWKTU1tVa34PDwcFmtVp9hdt98841sNptcLpdsNps+++wzjR49Wp999pm53Wq16q233lJqaqokaePGjebv3bJly8xunN59PvvsM7Vr1059+/ZVVVWV/va3v8lms2ns2LGy2WxKS0vTZ599VutrSkqKmZx8+eWXatu2rXkub7m3p+Utt9xiXlffvn2Vlpam119/XY888ohPt1KPx6PXX3/djCcYNSX+mscc+/6hhx7yOeZE70FL389AP78mZTHR0dE+r5iYGA0aNEjPPfecXnrpJX/HeMrzrjzTHMvaSMwUDABAMFiyZEmgQ0ATeJfMqayslNVqrbW+7JgxY3TDDTfUGsMaHx+vjRs3qri4WPn5+QoLCzNbb/Pz8+scz+0dwzp69GgdPnzYnHApLCxMn332mS6//HJt2LBBEyZM0Pr163X55Zdr48aNCg8PN5fr6d69uzZs2GCOYc3Pz9fZZ5+t1NRUVVZW6uDBg1q/fr0mTpyoDz/8UBMmTNDhw4c1YcIERUREyGazadq0adqwYYPi4uK0YcMGxcTEaP369SopKVFhYaGsVqsMw9CBAwe0fv16RUZGav369Tp06JAqKyt14YUX+syC7D3n+vXrNXPmTG3dulWlpaXaunWrZs6cqfXr1+vWW28N2vVYmxJ/zWO2bdumm266SZ9++qn5/CZNmqRt27ad8D1o6fsZ6OdnMbwd0dFohYWFiouLU0FBQZ0zvDVWbm6uUlNTdd7DS7W/uEq/7JOmHqkxde6bs3uH/jBllO77+3Kltj9+V6qCvINasLl6TbHNsy5WQnTdC0IDAJrOX/VBa+Kve7Z9+3ZNmTJFzz//fL0toN59SnqPkSc62dxuLTmo6P++V2v7sbz7NHT+pmJpG5yo5lyH1Waz+WUdVq8TXYe1Xbt2uvXWW0N2Hdbjxd+YdVibeg9a+n76+/MaWx+c1BjWjRs3asuWLfJ4PDr77LN13nnnnczpWq3m6BJstVhkuKtksYWxtA0AAEHk+eefP6XHs0ZFRcnlctVqiayLd6mY6OholZWVKSIiQuXl5bLb7YqJidHIkSP15ptvmt1wbTabLBaLPB6PDMOQYRiyWCyyWq1KSEgwx4hKUmpqqtq2bashQ4bogw8+UEFBgYqLi2W32xUVFSWHw6GSkhJVVlbK4XDIZrOpd+/eioiIUNeuXbVhwwaVlZWpoqJCnTp1Un5+vpKTk5WRkaFRo0Zp27Ztys7O1gcffKCcnBzZ7XaNHj1a48aN07Zt23Tw4EHl5+crNjZWhYWF5leHw6Hvv//eXCLn9NNPV2Jionbt2qV9+/bp0KFDSkxMlM1m0+mnn66UlBSdccYZ2rp1q/Ly8pSYmFjv+9zcXH333XeSqhOJLl26KD8/X/n5+YqPj1dycrJ69eqlJUuWKDs7W+np6Ro7dqwiImo3bAwePFgXXnihvv7663qvpU2bNvr000/lcrmUkZGhW265pcH1ZY89pzf2vn37Bm3Lak1Nib+uY2o+v6beg5a+n4F6fk1KWEtKSjRhwgR99NFH6ty5syRp9+7dGjFihBYtWmQuKozG8XYJtvkxYZUko6riSMIanGtaAQDQmjVH6+2p6MYbbzzpc0yYMOGEjxk9enSD5f3791f//v112WWX1VnWkF/+8pe1tv3iF7847uc15v2IESMaPI/U+Pths9mOey11Xf/JnjOYNSX+uo7x1z1o6fsZiOfXpEGT9913nw4dOqSdO3fqhx9+0A8//KCdO3cqLy9P9913n79jPOWZLax+nhjJcFePk6CFFQAAAEAoalKGtHjxYj311FM+a/Gcdtpp+vOf/6x//etffguutTi6Dqv/W1glyVVJwgoAAAAg9DQpYfWOF6h1Mj/OctuaVDXDGFZJMqq8Lax0CQYAAAAQepqUYY4dO1a/+c1v9PPPP5vbfv75Z915550aO3as34JrLdxGc3UJPtLCSpdgAAAAACGoSRnS73//e8XGxqpr167q3r27unfvrq5duyo6Olq///3v/R3jKa+qubsE08IKAAAAIAQ1aZZgh8Ohjz76SOvWrdNXX30lwzB01llnqVevXnI4HP6O8dRmO/oImq1LMGNYAQAAAISgRrewbtmyRcOHD/dZU+vCCy/UtGnTdNttt6lr167q0aOHvvrqq2YJ9FRlCbOb39tsfk5Y6RIMAAAAIIQ1OmGdNWuWLrroononVkpLS9P06dP1wAMP+C241sAadnShZlsdE1mdDCZdAgAAABDKGp2wfvrppxo3blyD+1x++eXauHHjSQfVmliOJKxhVkudMy+fjKNjWGlhBQAAABB6Gp2wpqamav/+/Q3uc+DAASUkJJx0UK2JJby6S3CYn7sDS6zDCgAAACC0NTphHT58uB555BG53XV3L62qqtLjjz+uoUOH+i241sBiC5ckhTXDGrZGlUuSVFZJl2AAAAAAoafRWdK8efOUnZ2tc889VwsXLtS2bduUl5en7777Tq+//royMzO1a9culrU5Qd5Jl/w9Q7AkGRXlkqQSV5Xfzw0AAAAAza3Ry9o4HA5t3LhR8+bN09SpU1VcXCyLxSLDMNSmTRvdcsstuv/++xUXF9ec8Z5yLOFHxrA2Q5dgT0WZJKmYhBUAAABACDqhdVjj4uL0v//7v/rf//1fZWdna+/evWrfvr3S09P9PmFQa3F00iX/dwn2JqylFXQJBgAAABB6TihhPVZ6errS09P9GUur1LxdgmlhBQAAABC6/N+shxPibWG1NWOXYMawAgAAAAhFJKwBduw6rP7mbWEtoUswAAAAgBBEwhpg5jqszTGGtZIWVgAAAAChi4Q1wMwW1mboEmzQJRgAAABACCNhDTBrM3YJZgwrAAAAgFBGwhpgR2cJ9v+jMCrKJVWPYTUMw+/nBwAAAIDmRMIaYM07S3CpJMntMeSq8vj9/AAAAADQnEhYA8wS3oyzBFe6zO/pFgwAAAAg1IREwlpRUaGePXuqc+fO5jan06nLLrtM8fHxGjJkiHbs2OFzzKeffqrMzEwlJSXp2muvVUlJiU/5888/r+7du6tDhw565JFHAtZl1tslONzWDI/C8CgyrPq8JS6WtgEAAAAQWkIiYX366aeVnZ1tvjcMQ2PGjJHNZtOaNWvUvXt3DR8+XBUVFZKk/fv3a+TIkRo5cqSWL1+u7du36+abbzaP//DDD/XrX/9ajz/+uF5++WX98Y9/1PPPP9/i1yVJlvBISc3TwipJbSKqH3ExLawAAAAAQkzQJ6wHDhzQ3Llzdfvtt5vbNm3apM8++0zz589X37599fTTTysvL08ffvihJOn1119Xenq65s6dq/79++vJJ5/UW2+9pdzcXEnSs88+qxtuuEFXXHGFsrKydM899+jZZ58NyPU1awurpOgImySptIKEFQAAAEBoCfqE9cEHH1T//v11ySWXmNtWrVql3r17Kz09XZJkt9t14YUXauXKlWb5sGHDZLFUt1pmZmYqIiJC69atM8uHDx9uni8rK0tfffWVDh8+3FKXZbJGHJkluBkmXZKkqPDqhJUWVgAAAAChJizQATTkm2++0YIFC7Rp0yY5nU5zu9PpVFpams++6enp5j5Op1OZmZlmWVhYmFJTU+V0OlVSUqLi4mKf472Jr9PpVEJCQq04XC6XXK6jExgVFhb65wLV/C2s3i7BjGEFAAAAEGqCtoXVMAzdeeeduuuuu3T66af7lB0+fFgxMTE+22JiYpSXl3fc8vz8fPP9sWWSzONreuyxxxQXF2e+OnTocFLXdqzmHsPq7RJcQpdgAAAAACEmaBPWd999Vz/++KPuv//+WmWJiYkqKiry2VZYWKikpKTjlicmJkqST7m3xdR7fE0zZsxQQUGB+dqzZ0/TL6wGS3jztrBGhXtbWElYAQAAAISWoO0S7J0ZuGPHjpKkyspKFRUVKTk5WdOnT1dOTo7P/tnZ2erdu7ckKS0tzae8qqpKubm5SktLU1RUlGJjY33KvTMQp6am1hmL3W6X3W736/V5Wb0trM00hrWNt4WVhBUAAABAiAnaFtaFCxdq586d2rJli7Zs2aLZs2crPT1dW7Zs0fDhw/Xdd99p3759kqTy8nKtW7dOWVlZkqShQ4dq+fLl5tqqGzduVGVlpQYOHOhT7rVixQqdffbZio+Pb9FrNAyj2VtYzYS1gjGsAAAAAEJL0Casbdu2VUZGhvlKTExUWFiYMjIydN5552nAgAGaOnWqvv76a91+++1q27atRo4cKUm65ppr5HQ6NXv2bG3ZskXTp0/XVVddZXb5vfXWW/Xyyy/rnXfe0YoVK/TEE09o2rRpLX6NlW5DFmt1QhneXGNYw2lhBQAAABCagjZhPZ7FixfL7XZr8ODB2rFjhz7++GOFh4dLklJSUrR06VL9+9//VlZWlnr06KHnnnvOPHbEiBH661//qnvvvVfXX3+97r77bk2aNKnFr6G8ymN+H9ZcY1iPzBLMsjYAAAAAQk3QjmGt6YYbbtANN9xgvk9JSdGHH35Y7/4DBgzQ559/Xm/55MmTNXnyZH+GeMLKKqsTVoskWzO1sDKGFQAAAECoCtkW1lOBt4U1vJkmXJKk6COzBJcyhhUAAABAiCFhDaDyyuokspl6A0uSoo60sNIlGAAAAECoIWENoPIjXYKba8IliS7BAAAAAEIXCWsAecewNtf4Velol+ASF12CAQAAAIQWEtYAMsewNmPCGmWuw0oLKwAAAIDQQsIaQOVmC2vzfUZ0hLeFlYQVAAAAQGghYQ2gFhnDGl7dwlrpNlRxzLqvAAAAABDsSFgDqKzKO0tw83cJlmhlBQAAABBaSFgDqCVaWMOsFtnDqh8zS9sAAAAACCUkrAHknSU4rJmfgsMeJkkqrWCmYAAAAAChg4Q1gLyzBIc1YwurJLWxV3cLLnZVNuvnAAAAAIA/kbAGUHllyySsidF2SdKh4opm/RwAAAAA8CcS1gAqr6zuotvcXYJTYqoTVmeRq3k/CAAAAAD8iIQ1gMpaqIXVm7AeKCxv1s8BAAAAAH8iYQ2glhrDmhobKUnKpYUVAAAAQAghYQ2glhrD6m1hJWEFAAAAEEpIWAPoaAtr835OSqw3YaVLMAAAAIDQQcIaQC03hrW6S7CzkBZWAAAAAKGDhDWAzFmCbc2csMZ6l7Vxye0xmvWzAAAAAMBfSFgDyBzDamnehDUp2i6rRfIY1UkrAAAAAIQCEtYAMsew2pr3c2xWi5IdTLwEADh1dOzYUc8//7w6duwY6FBOWCjHDgAtjYQ1QNweQxXu6u65zT2GVWLiJQDAqSUyMlI9evRQZGRkoEM5YaEcOwC0NBLWACk7Mn5VapmENZWJlwAAAACEGBLWACmtqJIkGYZHzTznkqRjWlhJWAEAAACECBLWACmvqB6/alS6ZGnmSZckqe2RFla6BAMAAAAIFSSsAVJaeaSFtbJlWjxTYph0CQAAAEBoIWENkLKK6jGsRmXLtHiSsAIAAAAINSSsAeJNWD1VLZNApsYe6RJcSJdgAAAAAKGBhDVAvLMEGxUt1CX4yKRLB4pccnuMFvlMAAAAADgZJKwBUurtEtxSLawxkYoMt6rKY+jnvNIW+UwAAAAAOBkkrAHibWH1tNAYVqvVou4pMZKk7c6iFvlMAAAAADgZJKwBcnTSpZabBKl7qkOStH0/CSsAAACA4EfCGiATzs3Q4pv66vAnL7TYZ/ZMrW5h/Z4WVgAAAAAhgIQ1QNpEhCk9zi538aEW+8weRxLWHc7iFvtMAAAAAGgqEtZWpEdadcL6w4FiVVR5AhwNAAAAADSMhLUVSY+LlMMepiqPod2HSgIdDgAAAAA0KCzQAaB5HThwwOd950S7vs2p0ufb9ynekuhT1qZNGzkcjpYMDwAAAADqRcJ6inKVl0oWi/r06eOzPXHkrxXTb4Ruu/8RFfzndd+ypGT9tPtHklYAAAAAQYGE9RRV6SqXDEO3/3mR2qa1N7dvzS3Xxr2l6nfprzTs17eZ20sK8vT7KaNUWlpKwgoAAAAgKJCwnuKiYxMUk5Bkvm9vlEp7S5XnMny2AwAAAECwYdKlViY1NlIWi1RUXqWi8spAhwMAAAAA9SJhbWUiwqxq67BLknIKygMcDQAAAADUj4S1FWoXFylJys4vC3AkAAAAAFA/EtZWqF1clCRaWAEAAAAENxLWVig9vrqF9UCxSxVVngBHAwAAAAB1I2FthWIiw+Wwh8kwJGchrawAAAAAghMJayvlbWVlHCsAAACAYEXC2kq1j68ex/pzXmmAIwEAAACAugV1wrpy5UqNGDFCcXFx6tevn/7973+bZSUlJZo4caKSkpKUmZmpDRs2+By7fft2DR48WAkJCRo1apRyc3N9yhcvXqy+ffsqNTVVd9xxh6qqqlrkmoJF56RoSVJOYblcle4ARwMAAAAAtQVtwvrVV19p/PjxGj9+vDZu3KhRo0bp8ssv1w8//CBJmjRpknbu3Knly5drxIgRGjFihJmUulwuDRs2TD179tTq1atlsVg0duxYn3NfeeWVmjZtmt5//3299957mjVrVkCuM1Bio8KV0CZchiH9fJhWVgAAAADBJ2gT1r59++qLL77Q5MmT1atXLz3yyCNKS0vTkiVLtH//fr399tt68skn1b9/fz300ENKTU3VwoULJUlLlixRQUGB/vrXv6pv376aP3++NmzYoC1btkiSXnzxRQ0dOlRTp05VZmamHn30Ub344ouqqKgI4BW3vE5HWll/OkTCCgAAACD4BG3CarFYdNppp/m8T0hIUGFhodatW6eoqChlZmaaZVlZWVq5cqUkadWqVRo0aJDsdrskqX379urZs6dP+fDhw81zZ2Vl6eDBg9q6dWudsbhcLhUWFvq8TgWdk9pIqk5YDcMIcDQAAAAA4CtoE9aaysrK9N133+nMM8+U0+lUSkqKbDabWZ6eni6n0ylJcjqdSktL8zm+ofKUlBRZrVazvKbHHntMcXFx5qtDhw7+vryAaB8fJZvVomJXlfLLGccKAAAAILiETML67LPPKikpSSNHjtThw4cVExPjUx4TE6O8vDxJOuFyq9Uqh8Nhltc0Y8YMFRQUmK89e/b489ICJsxmVYeE6tmCd+dXBjgaAAAAAPAVFugAGmPfvn2aN2+eHnnkEUVGRioxMVFFRUU++xQWFiopKUmS6i3v3bt3neUej0dFRUXm8TXZ7Xaze/GppntqjHYfKtWuPFegQwEAAAAAH0HfwlpRUaEJEyZowIABuuWWWyRJaWlpcjqdcruPdmPNzs42u/mmpaUpJyfH5zwNlTudThmGUasbcWvQtW20bFaLClwehbftHOhwAAAAAMAU1Amr2+3WpEmTlJ+fr5dfflkWi0WSNHDgQLlcLm3cuFGSZBiGVqxYoaysLEnS0KFDtXbtWrlc1a2Ge/fu1fbt233Kly9fbn7OihUrlJKSYrbAtib2MJs5+VL06UMCHA0AAAAAHBW0Cas3WV29erXeeustVVRUaP/+/dq/f7/atm2rCRMmaPr06dqyZYsefPBBHThwQFdffbUk6dJLL1ViYqJuv/12ff3115o6daoGDhyoM888U5J08803a/Xq1Zo/f74+++wzzZw5U1OmTFF4eHggLzlgeqRWj+eNPn0QswUDAAAACBpBm7D+85//1CuvvKK9e/fqjDPOULt27cyXJL3wwgvq2rWrsrKytHTpUn300UdKTk6WJEVERGj58uX6/vvvNXjwYEnSO++8Y567T58+WrRokZ5++mmNGjVKY8aM0Zw5c1r8GoPFacnRCrNKYfFp+vznU2PJHgAAAAChL2gnXbr66qvNFtO6REdHa+HChfWWd+/eXWvWrKm3fMyYMRozZsxJxXiqCLdZ1T3Jru8OuPTm5lyN+kX3QIcEAAAAAMHbwoqW1bttpCTpP7vy9dOhkgBHAwAAAAAkrDgiLtKmsl1fyJD08qc/BTocAAAAACBhxVGFX74vSXrz8591uKQiwNEAAAAAaO1IWGEq3/WlerRto5IKt178z65AhwMAAACglSNhhY/JA9IlSQvW7aaVFQAAAEBAkbDCx+Cu8erdLpZWVgAAAAABR8IKHxaLRXcOr17WhlZWAAAAAIFEwopaLu6dSisrAAAAgIAjYUUttLICAAAACAYkrKgTrawAAAAAAi0s0AEguBw4cMD8/sZfpOie9wr19//8qLG9YhUf5fvj0qZNGzkcjpYOEQAAAEArQcIKSZKrvFSyWNSnTx+f7e1u+LOU2lXnXfc75a991acsMSlZP+3+kaQVAAAAQLMgYYUkqdJVLhmGbv/zIrVNa29u/ym/Qp/sKlbSwKt069RbFBlW3Yu8pCBPv58ySqWlpSSsAAAAAJoFCSt8RMcmKCYhyXx/Rryhr3P36ECxSzsKLbqga1IDRwMAAACA/zDpEhpksVh0XpdESdKWPfkqq3QHOCIAAAAArQUJK46rS3K02jrsqnQb2vzz4UCHAwAAAKCVIGHFcdVsZS2tqApwRAAAAABaAxJWNEqX5GilxFS3sn7xE62sAAAAAJofCSsaxWKxaMCRCZe+3lugkgpPgCMCAAAAcKojYUWjdUpso/S4SLk9hr7aXxbocAAAAACc4ljWBo1msVh0QddkvbVpr74/6FJYXGqgQwIA4Lis5QW+78vyfb425hgAQGCQsOKEtE+IUqfENvopr1RxF14T6HAAAKhXXFycwiPs0q7VdZZH/bimwePDI+yKi4trjtAAAI1EwooTNqBrkn7KK1X0GRdp16EypaQEOiIAAGpLTU3Va6++ooKCprWWxsXFKTWV3kQAEEgkrDhhqbGR6hQXrp8KpD+t/Flv9uooi8US6LAAAKglNTWVpBMAQhiTLqFJftG+jYyqCn32c6E++CYn0OEAAAAAOAWRsKJJYiNtKlj/T0nSQ+//V/mlFQGOCAAAAMCphoQVTVaw8W11iLcrt8ilya98ofJKd6BDAgAAAHAKIWFF07kr9fsx3RQTGabPdx/Wb/6xmaQVAAAAgN+QsOKkxBol+v3orgq3WbTsv05d8fRabd21V7m5uT6v4uLiQIcKAAAAIMQwSzCaxFVeKlks6tOnjyQpsmNfJV/+O23dL4144mM5//mgqg7tNfdPTErWT7t/lMPhCFTIAAAAAEIMCSuapNJVLhmGbv/zIrVNay9JKnS59fHOIhXEpui0W57TiG4xahsdppKCPP1+yiiVlpaSsAIAAABoNLoE46RExyYoJiFJMQlJap+WoqvO66y02EhVuA0t31WiKnuMouMSAx0mAAAAgBBEwgq/igq36X/6t1dKjF1llW4t3rxPZZWeQIcFAAAAIASRsMLvIsKsGtMvXXFR4Sosr9KyH4pkCY8MdFgAAAAAQgwJK5pFtD1MY89KV1S4TYdK3Wp7+Qy5qmhpBQAAANB4JKxoNgltIjSmX7rCrFJUl3N057+2q9hVFeiwAAAAAIQIElY0q7S4SF3cNUYeV6m+3FOk8c9+qu9yCgMdFgAAAIAQwLI2aHbtYsLlfON+9ZryF23bX6Qxf/2Pbshsp+vObafI8Lr/ZtKmTRuWwAEAAABaORJWNDtXeakqnD/o2yeuUdLIX6tN9/P0wvpsPbt0sw6veEml2z+tdUxiUrJ+2v0jSSsAAADQipGwotlVusolw9C0x15Qcmq6fjxcoc/3lakkLlVt/+d+tYsJ04UdoxVrt0mSSgry9Pspo1RaWkrCCgAAALRiJKxoMdGxCYpNTFa/RKl3Z4++2H1YX/58WDlFVVr8XaEu6JqkszrEBzpMAAAAAEGCSZcQEOE2qwZ0TdJ153dSRkKUqjyG1uw4qEVf7lVBuTvQ4QEAAAAIAiSsCKi4qHCN699eQ3u2VbjNopyCci3+rkCxmePk9hiBDg8AAABAAJGwIuAsFov6ZsTr2vM7qWNiG7kNKWHoJN38xnfa4SwKdHgAAAAAAoSEFUEjNjJcl5+VroEdo+UpL9bW/SW69C9rNee9rTpY7Ap0eAAAAABaGJMuIahYLBb1SLbrjftu07jH/qnP9pZpwae7tXDjT7q4Z6Kyuifq9LRoJbUJk8ViMY9j3VYAAADg1EPCiqDjKi+VuyRPi349TJGd+il+8PVSeg998N9D+uC/hyRJhrtKnrIiucsK5S4+JFvxAT163+3q0zFZ3VMcim8TEeCrAAAAAHCyWm3CahiGHn74Yb3wwguKiorSvffeq5tvvjnQYUFH1229/c+L1DatvQzD0IFSt7YfdCm3pEr55W5ZbGGyORJkcyRIbTtJkuZ+uEPSDklSsiNC3VIc6p4Sc+SrQ91SHEqMjlCYjZ7wAAAAQChotQnr/Pnz9eSTT+rtt9/WoUOHNHHiRGVkZGjkyJGBDg1HRMcmKCYhSZIUmyh1zajeXuXxqLzCo7JKt8oq3co9dFj/fut1Db38Gu0rcmt/UYUOFlfoYHGeNuzKq3XeCJtFbSJsiraHyx5uVXK0XV1TotW1rcN8tU+Iks1qqXUsAAAAgJbTKhNWwzD0zDPP6J577tHQoUMlScuWLdP8+fNJWENAmNUqR6RVjsjqH982rkM6vOpvemflS5IkS3ikwpMyFJ7UQeFJHRWe3EHhSR0UFp8mi9WmCrehirIq5ZdVSZJ2HSjRZ7trJ7Y2i2S1WmTVka8WyWqxKMxqUUxkmGIjbYqLDFNsZNiRrzbFRoapbVwbJcU65DEMeQxDbo9U6faoosojl9ujyiqPWeYxpMoqj1xVHlW4Pap0exQRZlVsZLhSYuxKjY1UWlykou1hirBZFRFmlT3MqgibVVYSagAAAJziWmXCmpeXp2+//VbDhw83t2VlZemWW24JYFRoqppdiOvj9hiq9Bg65Nyvv8/7rSxhdlnC7bI5EqsT3MQjSW5ie1nCIuQ2JLf7yFqwbt81YQ8fSXYDKcxqUYStOoEOO/LVZvV9f/Rlrb3tyHufc4aFqY09QhFhVvMV7u1CbRgyqr/IkCHDkFwVFaqsrDyyTZK37Mj5vAm+9/Oi7BGKjoo0Y/Vh8X1fMx231Nz9mD0sFslqqZ60y2axyGqt/my3x5Dbc/QPB26Pp3qbIXk8hqrMsuqX1WJRZHj1HwXs4TZFhh9z/ccwaiwRXNeKwUbNnepR125GjTPWuU+dxzUujlpbGhGDl0UWHflPVovlyL23+Dwf74Roxz4yb7n3udV8nkfPXz/vz9+x8Zk/e/K9Vu/PqY49psa+YTaL/qd/RgOfCAAAAq1VJqxOp1OSlJaWZm5LT09XYWGhysrKFBUV5bO/y+WSy3V0WZWCggJJUmFh4UnFUVRUvcZo3v69Ki8taXDf/NxsSdLh3H2y1vMPyWDdv6ViqSgvPe59lCSjNE+VB3/WtQ/8RYltU2uVe4xKVborZUg6dGCf/vXMoxp3+0OKTUyWYUgeSZWe6pfLLVUc+b7CLZWWl+vnH3fKEhYlGR4ZcksejwxPlYyqKhnuSsldJcPwHPnXs6d6v0pXdZnHI0tYuCwRbWSLSZQtOlG26ARZwyNlCQv3ibPiyAtA00SGWzWsa+xJncNbDzT2DxQ4eq9Otg4FAIS2xtahFqMV1rLr1q3TwIEDdfjwYcXHx0uSNm/erLPPPlv79u1Tenq6z/5z5szR3LlzAxApACAU7NmzRxkZtNY2xt69e9WhQ4dAhwEACBLHq0NbZcL63XffqXfv3vr555/NSnP16tW66KKLVFZWpsjISJ/9a7awejwe5eXlKSkpyWct0BNVWFioDh06aM+ePYqNPbm/8rc0Yg+cUI6f2AMnlOMP5tgNw1BRUZHS09NltTIDeWN4PB5lZ2crJiaGOpTYW1wox0/sgRPK8Qdz7I2tQ1tll2BvV+CcnBwzYc3OzlZ8fHytZFWS7Ha77Ha7zzZvy6w/xMbGBt0PUGMRe+CEcvzEHjihHH+wxh4XFxfoEEKK1Wr1a2t0sP5cNAaxB04ox0/sgRPK8Qdr7I2pQ1vln4MTEhLUr18/LV++3Ny2YsUKZWVlBTAqAAAAAMCxWmULqyRNmzZN9913nwYMGKC8vDy98sorWrJkSaDDAgAAAAAc0WoT1smTJ8vpdOq6665TVFSUnnnmGV188cUtGoPdbtfs2bNrdTcOBcQeOKEcP7EHTijHH8qxo/mE8s8FsQdOKMdP7IETyvGHcuxerXLSJQAAAABA8GuVY1gBAAAAAMGPhBUAAAAAEJRIWAEAAAAAQYmENUAMw9BDDz2kDh06qEePHnrxxRcDHVK9Vq5cqREjRiguLk79+vXTv//9b7Osc+fOslgsPq9Vq1YFLtgabrjhhlrxzZkzR5JUUlKiiRMnKikpSZmZmdqwYUNgg61hwYIFtWK3WCwKC6ueK62ust27dwcs3gMHDmjmzJnq2LGjzj33XJ+y493r7du3a/DgwUpISNCoUaOUm5vbkqHXG7thGJo/f74yMzMVGxurSy65RNu3bzfLd+/eXedzCIbYpeP/fgb6vkv1x1/X767FYtHw4cMlSatWrapV1rlz5xaPH4FBHdoyqENbDnUodWhTtJY6lIQ1QObPn68nn3xSr7zyiubNm6fbbrtNS5cuDXRYtXz11VcaP368xo8fr40bN2rUqFG6/PLL9cMPP5j7/PnPf1ZOTo75uuCCCwIYcW1XXXWVT3x33323JGnSpEnauXOnli9frhEjRmjEiBEB+Z9NfWrGnZOTo5EjR2r69OnmPu+8845PeYcOHQIW7549e7Rz5846F6Vu6F67XC4NGzZMPXv21OrVq2WxWDR27NigiP3ZZ5/Vn//8Zz344INat26dwsLCNHbsWFVWVvrs9/nnn/s8h5bU0H2X6v/9DIb7LtUff824d+3apdTUVN15553mPhaLRfv27TP3+fzzz1s4egQKdWjLoQ5tGdSh1KFN0WrqUAMtzuPxGH369DHmzZtnbps8ebIxduzYwAVVD4/HY+zatcvnfceOHY0nn3zSMAzD6NSpk7FkyZJAhXdc119/vXH33XfX2p6Tk2PYbDbj008/NQyj+rq6d+9u/N///V8LR9h4a9asMTp27GgUFRUZhmEYkoxvv/02wFHVNnv2bOOcc84x3x/vXr/11ltGTEyMUV5ebhiGYezdu9eQZGzevLmlQ68Ve1FRkeF0Os33P/zwgyHJ2LJli2EYhvHjjz8akozi4uIWj7WmmrEbRsO/n8F03w2j7viP9fDDDxvjxo0z369cudJITk5uidAQZKhDWw51aMujDg0M6tDgRgtrAOTl5enbb781m+UlKSsrSytXrgxgVHWzWCw67bTTfN4nJCSosLDQ3JacnByI0BqtrvjWrVunqKgoZWZmSqq+rmB9Bl4zZszQzJkz5XA4zG3Bfu+l49/rVatWadCgQeb6YO3bt1fPnj2D4lk4HA6lpKSY7xMTEyXJ5+c/MjJSbdq0afHYGqu+n5Fgvu815efn6/HHH9djjz3msz0Ufv7hf9ShLYs6NLCoQwOLOjQ4kLAGgNPplCSlpaWZ29LT01VYWKiysrJAhdUoZWVl+u6773TmmWea2+bNm6dOnTrprLPO0sKFCwMYXd2WLFmiPn36qHv37po1a5YqKirkdDqVkpIim81m7peenm4+m2Dz/fffa9OmTbryyit9tv/mN79RRkaGBgwYoGXLlgUouoYd7147nU6f34Wa5cFk06ZNkqQ+ffqY26xWq6644gq1a9dOw4YN0xdffBGo8OpU3+9nKN33N998U/369VOPHj18tufn5+uXv/yl0tPTNXbsWO3YsSNAEaIlUYe2LOrQwKIODSzq0OBAwhoAhw8fliTFxMSY27zfe8uC1bPPPqukpCSNHDlSknTddddp4sSJ+te//qVLL71UEydODKq/Ll188cUaP368Xn31Vf3ud7/Tk08+qXnz5unw4cM+91+qfgZ5eXkBirRhr7/+un75y18qLi7O3Hb77bfrV7/6lRYvXqzevXtr9OjR+v777wMYZd2Od69D6Vk8+eSTGjdunBISEiRV/3Xy+uuv1+23365//etfatOmjUaOHKmDBw8GONJqDf1+htJ9f/311/X//t//89nWvXt3jR8/XjNnztTChQuVm5urUaNGyeVyBShKtBTq0JZDHRp41KGBQx0aPMICHUBr5O0SUVRUpPj4eElHu0d4y4LRvn37NG/ePD3yyCOKjIyUJD388MNm+dlnn61169bpH//4h4YOHRqoMH1MnDjR/L5///76+eeftXDhQk2fPl1FRUU++xYWFiopKamlQ2yU9evX66KLLvLZ9tRTT5nfP//881q5cqXefvtt3X///S0cXcMSExMbvNf1lffu3bvFYmyMjz76SEuXLvX566/D4dAzzzxjvl+4cKHS0tK0bNkyXXPNNYEI00dDv5+hct+rqqr02Wef1erK1L59e5/fgYULF6pLly768ssvg27SGvgXdWjLoQ4NPOrQwKEODR60sAaAtwvBsTOhZWdnKz4+3qzEgk1FRYUmTJigAQMG6JZbbql3v969e2vfvn0tGNmJ8caXlpYmp9Mpt9ttlmVnZ9fq3hEMDMPQ5s2bG/yfoM1mU8+ePYPy3h/vXqelpdWaFTDYnsXu3bt1zTXX6LHHHlPfvn3r3S8mJkYdO3YMyucg+f5+hsJ9l6Rt27bJ5XLp9NNPb3C/zp07KzIyMmjvPfyHOjRwqENbHnVo8KAODRwS1gBISEhQv379tHz5cnPbihUrlJWVFcCo6ud2uzVp0iTl5+fr5ZdfNtfIKiwslGEY5n6GYWjLli3q1atXoEL1UVVVpZKSEp9t3vgGDhwol8uljRs3SqqOPVifQVFRkQ4dOqT09HRzW0FBgc8+lZWV2rp1a9Dc+2Md714PHTpUa9euNbuh7N27V9u3bw+aZ3Hw4EGNHj1al1xyic9yCFLt55Cfn6/du3cHxXM43u9nsN93r127dikyMrJWy1nNe799+3aVl5cHxb1H86IObRnUocGBOjQwqEODTCCmJoZhPPfcc0Z8fLyxYsUK46233jIiIiKMZcuWBTqsWqqqqoxf/epXRkZGhrF161YjJyfHfN14443G2LFjjaVLlxrbtm0z7r33XqNNmzbGzp07Ax22YRiG8fLLLxtnnHGG8cYbbxjff/+98be//c2Iiooy3njjDcMwDOPqq682MjMzjc2bNxszZ8404uLijAMHDgQ46tr27dtnSDK++uorc9vQoUONm266yVi5cqWxdetW41e/+pWRlpZmHDp0KGBxHjp0yMjJyTHuuusuo2/fvkZOTo6Rm5trGEbD99rlchkdO3Y0br75ZuOrr74yLrvsMmPgwIFBEfvBgweNfv36GcOGDfP52T906JBRWVlp9OzZ07jnnnuM9evXG5s2bTJGjBhhnHnmmYbL5Qp47Mf7/QyG+95Q/F6vv/66kZCQ4HNMfn6+kZaWZjz66KPGl19+aXz66afGOeecY4wcOdLweDwtfQkIAOrQ5kcd2rKoQ6lD/Rm/16lSh5KwBojH4zEeeugho3379ka3bt2MF198MdAh1WnhwoWGpDpfxcXFxj333GP079/fcDgcxoUXXmisX78+0CGbPB6P8cwzzxhDhgwxYmJijF69ehkLFiwwfxmLi4uNq6++2khISDDOPfdcY8OGDQGOuG7ff/+9IcnYvn27uS03N9eYOnWq0bt3byMmJsYYMWKE8d133wUwSsMYMmRIrZ+RTp06GYZx/Hu9fft2Y9CgQUZcXJxx2WWX+fzPNpCxT5kypc6f/SFDhhiGYRi7du0yrrvuOqNr165GfHy8MWHCBGPv3r1BEXtjfj8Dfd8bit/rueeeM9LT02sdt3nzZmP8+PFGRkaG0bZtW2Py5MnG4cOHWy5wBBR1aPOjDm1Z1KHUof6M3+tUqUMthnFMezcAAAAAAEGCMawAAAAAgKBEwgoAAAAACEokrAAAAACAoETCCgAAAAAISiSsAAAAAICgRMIKAAAAAAhKJKwAAAAAgKBEwgoAAAAACEokrABO2qpVq2SxWFRVVeXX8xYVFemyyy5TdHS0hg0b5tdzAwAQDKhDgYaFBToAAKjPq6++qk8++UQrVqxQWlpaoMMBACBkUIfiVEHCCiBo5ebmqnfv3rrgggsCHQoAACGFOhSnCroEAyHooosu0iuvvKI5c+aoS5cuSk9P10svvSRJWrBggZKTk332Hz9+vG644QbzfefOnbVs2TJNmTJFbdu21YUXXqitW7dqw4YNGjJkiOLj43XzzTefcPekb775RiNGjFBcXJwuvvhi5ebm+nzm6tWrdcMNNyghIUFbt26VJH388cc655xzFBMToyFDhujbb7+VJM2ZM0dz587V5s2bZbFYtGDBApWWlur6669XYmKiOnbsqLvuukslJSVNuYUAgFaKOpQ6FKGFhBUIUb/5zW/k8Xi0ZMkSTZw4Ubfddpvy8vIaffy1116r/v37a82aNXK73Ro7dqzuvfdezZs3T6+99ppeeuklvf/++ycU0y233KK7775bn3zyib755hs98cQTPuWTJ09Wly5dtH79evXs2VMbNmzQNddcoxkzZmjz5s0aOXKkRo8eLZfLpbvvvlt33XWX+vbtq5ycHF111VV68skntWzZMr3zzjt65513ZLFYTig+AAAk6lDqUIQSugQDIeqqq67SQw89JKm6EnviiSf03//+t9HHT58+XbfeeqskacKECbr77ru1efNmxcTESJK6deumL7/8Uv/zP//T6HO+++67ateunSQpKytLmzZt8ik//fTTNWvWLLOSnD17th566CGNHz9ekvS73/1OCxYs0MaNGzV48GA5HA6Fh4ebY28KCgrUtWtXDR48WFarVeeee26jYwMAwIs6lDoUoYMWViBEeStFqbpilKT8/PwmHd+9e/c6t53I+eqKqebxF198sc9fdDdt2qTf/va3cjgccjgciomJ0Y4dO7Rv3746z3/nnXfKMAz16dNHf/vb31RZWXlC8QEAIFGHUocilNDCCpwCrFbfvz15PJ6TOr6+bSd7zpoMw9DDDz+syy+/3Gd7fbMZtmvXTv/5z3/08ccf66GHHtLjjz+uNWvWMPshAKDJqEOpQxHcaGEFTjExMTE6fPiwORbH7XYrOzs7wFHVrV+/ftq2bZu6devm83I4HPUeY7FYdMkll2j16tXyeDz65z//2YIRAwBOZdShQPChhRU4xfTr10+S9NRTT+mSSy7RX/7yF+3Zs0c9evQIcGS1zZ07VxdddJG6dOmiK664QgcOHNDatWs1Y8aMWvsahqFx48YpKytLw4YN09atW7V792517NgxAJEDAE5F1KFA8KGFFTjFdOvWTY8//rieeuop3XjjjRo6dKimTJkS6LDqNHDgQC1dulTvvvuuzjnnHN10000yDKPO7lgWi0X33nuv3n//fV1wwQW68847NWvWLI0dOzYAkQMATkXUoUDwsRiGYQQ6CAAAAAAAaqKFFUCD8vPzzRkI63qd6DpzAAC0FtShwMmjhRVAg9xut3788cd6y9PS0hqc4AEAgNaKOhQ4eSSsAAAAAICgRJdgAAAAAEBQImEFAAAAAAQlElYAAAAAQFAiYQUAAAAABCUSVgAAAABAUCJhBQAAAAAEJRJWAAAAAEBQImEFAAAAAASl/w9jLz381Lwl8AAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAGHCAYAAACnGJ9sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAfVtJREFUeJzt3Xd4VFX6B/DvnZlkUmbSe4MQQgkBATGAIkpAQWURCyqC5Yc0USwruouigIoNd2UVFgW7gLsirAor4iJNqVJDFUJPm/SeaZnz+yPMJUMmkDLJTfl+nmceMvfce+e9J2HOvHPOPUcSQggQERERERERNTOV0gEQERERERFR+8SElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiOiirl27IiUlxWHbTz/9hI4dOyoTUBPw8PDA2bNnlQ6DiIjauQEDBuDzzz9XOgxqAZiQUpPr2LEjFi5cWOf958yZg379+jXqNSVJwtq1axt1jqayevVqdOrUCRUVFUqH0uw+//xzBAUFNeocu3btQmRkJC5cuOCiqC45d+4czGZzvY977LHHMGXKFJfHU189e/ZEUFCQ/LjuuusAACNHjmSjT9QGsX11xPa1ZbWvXbp0wa+//io///XXX9GlS5d6naMu7WvPnj0RGRkJSZIQFhaGqKgoREVFQavVws/PD1FRUdiwYYPDMbfffru83+UPX19fjB49ul5xUuMwISVqQnv27KnRQAQFBaFbt25wc3NTKCogNzcXkiShtLRUsRgaytfXF926dYOXlxeAxl3LiRMn4OfnJz9MJhNuvvlm+fmKFSvqdJ6TJ0/i1KlTV9xn4cKF0Gg0Dq93+SMvL6/Gtep0OqcPNzc3TJw40WH/Q4cOITc3F7m5ufj2229RUFBQvwohImol2L66nivbVwAoLy9HZWWl/LyyshLl5eX1Okdd2tdDhw7J++zcuRNpaWlIS0vDwIED8dZbbyEtLQ3Dhg1zOObHH3+U97v88fLLL9crRmo8jdIBELU3gwcPxuDBg5UOo9Xq1q0bfvnlF5ecKz4+Hrm5ubWWq1Qq/Pzzz1c8R3p6Ovbs2QO1Wo2srCyEhYXVuu/NN99c41vaKykqKqq17IUXXmDCSURUDdvXxnFl+2o3f/58LFu2DACQmZlZr2Pr077W1+DBg3Hy5El4e3vXKLNarRg6dKjLXouujj2k1GiZmZkYM2YMfH19IUmS/PjrX//qdP+SkhJMmzYNERERiIiIwBNPPFHj2zchBJYsWYIePXogODgYkyZNchiC8/vvv2PMmDGIjo5GSEgIZsyY4fAtXH1kZ2dj9OjR8PHxQXx8PObNmweLxQIAsFgseOmllxAdHY3w8HA89dRTchxnz56FVqvF6dOncdddd8Hf3x/9+/dHamoqgKqhUddddx3y8vLkOgGAtWvXyj8DwKOPPop58+Zh/vz5iI2NRffu3bFy5UpkZWXhvvvug7+/P4YMGeLwRi6EwHvvvYfOnTsjMDAQ48ePR35+vlwuSRJSUlLw8MMPIygoCD169MDu3bsBVA3rCQ4OBgDo9XpIklSnewrnzJmD8ePHY+HChUhISEBISAimTJni8Hsxm82YNWsWYmNjERwcjHHjxiE7O7vWc+bn5+PFF19E7969odfrMWTIEJw+fdqhbt566y28++67CA8Px5IlS3D48GE5ZmfXcubMGfn6q0tISMDHH3/ssE2SJGg0Ghw+fBgjR45EVFQUBgwYgFWrVkGj0UCluvJbpNFoxJgxY/DEE09g8uTJGDNmDEwmU637b968udbe0ZtvvvmKr3U5k8kErVbrsO1Pf/oTevfujd69e9foPZ0yZQp0Ot0V4yOiloXtK9tXu9bWvtp17doV/fr1Q79+/dC1a9ca5fa2yV6HdvVpXwcMGIC4uDj5Z/vQ2x07duCvf/2r/HN1ZrMZ8+fPR2pqao3H2bNn8cknn9Rat9QEBFEj3XrrrWLgwIFi69at4vvvvxdRUVHiq6++EsXFxUIIITp06CA++OADIYQQNptN3HrrrSIpKUns2LFD7NixQ1x33XVixIgRwmazCSGEmD17tgAgHn74YbFv3z7xv//9T0RHR4unn35afs0ZM2aIBQsWiEOHDom1a9cKDw8P8a9//UsuByDWrFlTp/gnTZokevToIXbs2CG2bt0q/vKXvwir1SqEEOLxxx8Xw4YNE3v27BH79+8XgwcPFi+++KIQQogzZ84IACIhIUGsXr1aHDp0SCQkJIgxY8YIIYQoKSkRCxYsEP7+/iIzM1NkZmYKIYRYs2aNqP5f75FHHhF6vV5Mnz5dHD58WEybNk3odDpx7bXXim+//Vbs379fREdHi6eeeko+5u233xa9e/cWW7duFUePHhX33nuvePDBBx2uPzo6WnzxxRfi2LFj4uabbxbXXXedEEKI8vJysWrVKgFApKamiszMTPl6r2T27NlCkiTx8MMPi71798q/l2eeeUbeZ/LkyaJz587il19+Efv27RO33367SExMFGazWQghxGeffSYCAwPl/Tdt2iQee+wxsWXLFnH48GExaNAgMWLECIe66dy5s7jnnnvEwYMHRX5+vjh06JAAIM6cOVPrtdxyyy3i+eefl89z9OhRodFoRG5ubo3rysrKEoGBgWLJkiWiqKhIbNmyRYSGhoqffvpJCCHEunXrRIcOHWoct3fvXpGYmCjuvPNOUVFRIcrLy8Udd9whevXqJfbt21dj/w8++EAMHTr0qvVcV1OmTBHPPvusw7a4uDjx3XffiZycHJGZmSlOnTol8vPzxR133CE+++wzIYQQWq1WnDlzxmVxEFHTYfvK9tWuNbavkZGRYtOmTQ4xRUZGys/79+8vt03V1bd9bYj+/fsLHx8fERoa6vQRHx/vktehumFCSo3m6+srVqxYIT9/5plnxJNPPik/r95g/vbbb0KlUonTp0/L5adOnRKSJInt27cLIaremBMTEx1eY+nSpcLHx0duVC83ZMgQMXXqVPl5fRrM++67T4wdO7bGuS9cuCA8PDxEQUGBvG379u0iNjZWCHGpwbTHLYQQM2fOFHFxcfLzyxsIIZw3mLfeeqv8fM+ePQKA+OGHH+RtEydOFDfccIMQQgiz2Sy8vb3FkSNH5PKMjAzh5uYmLBaLfP3VfycfffSR0Gg0orKyUghR1SgAECUlJXWqIyGu/ns5f/68kCRJbN26VS4vLCwUPj4+cizO6qO6L774Qnh4eMi/i0ceeURERkaKoqIieZ/qDWZt1/Lvf/9bREZGyh8EXnvtNXH77bc7fc3PP/+8RqL4xhtviIcfflgIUTMhLS8vlxuyd999V65zIap+N2+//bbQ6/Wif//+oqysTC5zdUJ6xx13iPnz5ztsi4uLE35+fsLX11f4+vqKxMRE8eabbzIhJWql2L6yfW3N7WtkZKTw9vaW2yRvb+8rJqQNaV9DQkLk81/tsXz5cqdx2v/eqr8eNS8O2aVGu+222/D1118jPT0dhw8fxrp165CQkOB03wMHDqBjx46IjY2Vt3Xq1AkdO3bEgQMH5G1qtdrhuJ49e6K4uFie9MVisWDlypW45557kJiYiL179yInJ6dB8c+ePRspKSkYMGAAVq9eDZvNBgA4ePAgjEYjoqKi5Ilkhg4divT0dIfj9Xq9/HPnzp1RWFhY7xiqnyM+Pt7pNvt5T5w4gbKyMiQlJclxde7cGRaLxaEOLo/LarXWezKBy13p95KSkgKNRoPrr79eLvf19cW1117r8Lu9XEpKCp566ikkJSVh1qxZMBqNKCsrk8sHDBgAHx+fesV55513wmQyYcuWLQCAVatW4f7773e6b2VlJTQax9vp3dzcYLVane7v6emJv//978jMzMRzzz3ncKybmxteeOEFZGZm4u9//7s8MQRQVXc7duyQZ8DV6/Xw9/d3mBX3SvV0uVOnTtVYjub48ePIy8tDYWEhCgsLcejQoRpD+1577TX4+/vX+XWISDlsX9m+tub2NS0tDaWlpXKbVFpairS0NLn83nvvRbdu3eTnDWlfDQaDfP6rPR588EFs27bNod0NCgpCnz59AAChoaE1yqoPc6amw0mNqNFmzZqFAQMGoFOnTjCbzRg/fnyN+9fshBC1nsfeUDljf6P38vKCzWbD6NGjUVJSgjfeeAMDBgzAxIkTGzwLXEJCAlJSUvDtt99izpw5ePfdd/G///0PQghoNBrs27fP4V7CK91XeLV7DuvC2Tmqb7PX4bp16xAeHu6wX0hISJPF5Uz130tDfrdff/01nnjiCbz77rt47bXXsH//fgwZMqTRcWm1Wjz88MNYtmwZ4uLicOzYMdx5551O9x0+fDhmzJiBr7/+Gvfccw8OHTqEBQsWYNGiRbWev/qHAme8vb1r7PP444/j8ccfl5/369cPzzzzDMaPH1+PK6sihEBGRkaNhPTyxNpu+fLl8v2mzz//fL1fj4iUwfYVdSqrK7avzdu+Xs2MGTNqbGtI+/ryyy/j66+/rvUYf39//P777wCAG264ocZkhiUlJVi/fj3uuuuuGl8MUPNgQkqNNnfuXLz99tt46KGHIEkSdDpdrfv27t0bZ8+exdmzZ+UP02fOnMHZs2flb6ic2bx5M+Lj4+Hl5YWjR4/ixx9/xOnTp+Vvgq/U2NaFSqXCfffdh5EjRyIoKAi//PILevfuDavViuzsbAwaNKhB59VoNDAajbDZbC5rtOLj4+Hp6Ylz587hxhtvbHBcQFWDd6Xf19VU/7306tULFosFO3bskOuruLgYe/furfUD1HvvvYfp06djwoQJABr2e6ztWiZOnIjrr78effv2xW233QZfX1+nx0dGRmLt2rWYMWMGJk+ejPDwcMyZM6fBDWxzkCSp1p6Cs2fPIjY2Fj4+Pg6Te9gVFRXhzJkzNZJZImp52L7Wju1ry29fAeDGG2/EoUOHnJaVlJTgk08+waOPPlrv2Kp77bXX8Nprrzkt27NnD0aMGHHF49evX48xY8Zg586d6N+/f6NioYbhkF1qtPz8fOzatQtZWVkoLS1FUVGRw7d5er0e6enpsNlsuOGGG5CcnIyxY8di9+7d2LVrFx544AHccsstGDhwoHzM8ePHMX/+fPzxxx/46quv8O677+LFF18EAHmYxhdffIGjR4/izTffxOrVqx1i0ul0dVrY2Wg0YujQofjiiy9w/PhxfP7556ioqEB0dDRiYmIwadIkPPTQQ/jvf/+L1NRULFu2DOvWratz3XTu3BllZWX48ssvceTIkSt+y1lXWq0Ws2bNwjPPPIN//etfSE1NxXfffYfly5fX+RyxsbGQJAlLly7FH3/8UedFxI8fP4533nnH6e8lJiYGEydOxIQJE7Bp0yYcPHgQDz74IKKjozFmzBgAVX8L5eXl8tAwnU6H9evXIyUlBWvWrMFTTz1Vz9qo/Vq6d++OHj164NVXX611OJHd9ddfj+3bt6OkpAQnTpyotYEHqhbprm1t0Msft912W72vBwB279591XPr9Xro9XrodDqcO3euxjmqD92t/iCi1oPta+3YvraO9vXXX3+tdQjtdddd57BvQ9vXmTNnIiIiAt26davxeOCBB2rt3QaqvrR5/vnnccstt+Dxxx9v8PB0aiRF7lylNmX58uXCw8NDaDQaAUAAEDExMWLbtm1CCCFef/114enpKc6dOyeEEKKoqEhMmTJFhIWFibCwMDF16lR5xkAhqm7uHz9+vJgxY4YIDg4WnTp1EosWLXKYFOGdd94R/v7+Ii4uTsydO1e89dZb4p577pHLp0yZIvR6vTz73JWsWbNGDBo0SOh0OhEbGys+/PBDucxkMolZs2aJTp06Cb1eL2666SZ5UgH7TfCHDh2S9798UgGbzSamTZsmfHx8RKdOnUR6errTSReqx15SUiIAOMxMN3/+fNGjRw/5eWVlpViwYIHo1q2b8Pb2Ftddd534z3/+I5fjskknnE1MMG/ePBEQECAiIyPF3r17r1pPs2fPFt27dxfPPPOMCAoKcvp7MRqNYubMmSImJkYEBASIBx54QGRlZcnl2dnZIjo6WkyePFkIIcS+fftEjx49hJ+fn7j77rvlCSfscV5eN0LUnHThSteyePFi4eXlJUpLS696fbWpbZbd+oiPjxfe3t51esybN69Rr3W1yRkurzsiarnYvrJ9tWuL7Wtts+zW19NPP+0wU/TVlJWViZ9//llMnz5d+Pn5iffee0/YbDbx0ksviYCAAPHXv/5VbN68WRiNxkbHRnUjCeGCr5So3SoqKkJCQgJ27NiBmJgYCCGQlZWF2267Df369at1XSpqfebMmYO1a9diz549SodSZ1OnToXZbMann37a4HP89NNPmDp1ap3WkmsJ7EN2vby8nA7ZLSsr45BdolaA7Wv70V7b1wEDBmDq1KmNHrL7zDPP4NNPP0VAQECt+3zyyScYOnQoACAjIwPTp0/Hn/70J3kouV1mZiZ++OEHrF+/Hl988YXDBFbUdHgPKTWKwWBAVlYW1qxZg0GDBsFqtWLbtm04c+YMXn75ZaXDQ0pKyhVvkN++fTt69erVjBG1TGvWrMHYsWNrLa8+K15rkJGRgd27d2P58uU4cuRIo841YsSIVpOMAkDHjh1dMnSNiJTF9rVtYPtau507d7okpgULFmDBggV13j8iIgKrVq1yWhYeHo4pU6ZgypQpLomN6oYJKTVKly5dsGTJErz33nt47rnnoNPpkJiYiK+++gqjRo1SOjx07dr1ilOiR0dHN18wLdiQIUOuWE+t7RvCW265BaWlpVi6dCliYmKUDoeIqN7YvrYNbF+Jro5DdomIiIiIiEgRnGWXiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwUmNnLDZbMjIyIBer3e6bAIREbUPQgiUlJQgIiICKhW/w60LtqFERATUvQ1lQupERkYGZ4cjIiLZhQsXEBUVpXQYrQLbUCIiqu5qbSgTUifsU3BfuHABPj4+CkdDRERKKS4uRnR0dKtbmkFJbEOJiAioexvKhNQJ+xAjHx8fNqZERMShp/XANpSIiKq7WhvKG2KIiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiaiGjccNWLL1FGw2oXQoRERERNSGaZQOgIhanr+sOoScEhM6Bnrj1h5hSodDRERERG0Ue0iJyIHRUomcEhMA4Js9FxSOhoiIiIjaMiakROTAUGyUf954PBtZRcYr7E1ERERE1HBMSInIQWa1BNQmgFX70hSMhoiIiIjaMiakROTA3kOqVkkAgG/3MiElIiIioqbBhJSIHNh7SAd2CgQAnM0rQyVn2yUiIiKiJsCElIgc2O8Z7R6uBwAIARRVWJQMiYiIiIjaKCakROTAnpBG+XtB71G1MlR+mVnJkIiIiIiojWJCSkQOMi/eQxrm6wF/L3cAQGE5E1IiIiIicj0mpETkwHCxhzTMxwP+3lUJaUE5h+wSERERkesxISUimbXShuySqoQ03NcD/l5uAIACDtklIiIioibAhJSIZLmlZtgEoFFJCNRpEeBl7yFlQkpERERErqdROgAiaj6lpaUoLy+vtfxoZikAINDbDXm5OfCu6iBFPhNSIiIiImoCTEiJ2onS0lJ06BiL/LzcWvfx7DIQIXe9hHPHDyI09FaEDf0/aPvdg8Iy3kNKRERERK7HhJSonSgvL0d+Xi5eWLIW3r4BTvc5km3ErrRydOtxDR5Zshb//OciaMEeUiIiIiJqGkxIidoZb98A6P0DnZZZc3MBlMNP7wVvXx1sFcUAuOwLERERETUNJqREJCs1WQEAOq0GgA2VFxNSLvtCRETtgcFgQFFRkdJhyHx9fREaGqp0GERNigkpEcmM1koAgKebGoANtooSAFz2hYiI2j6DwYDxDz0Mi9mkdCgyN3ctln31JZNSatMUTUgfffRRfPHFFw7bZs+ejTlz5qCsrAyTJ0/GTz/9hLi4OLz//vsYMGCAvN+JEycwceJEHDp0CDfccAM+/fRThISEyOXfffcdXnnlFRgMBjzwwAP429/+Bo2G+TfRlZitNgCAu6ZqRSh5yG6FBTabgEolKRYbERFRUyoqKoLFbEJFp5tg8/Bt1LlUFYXwPLMVFbGDYfP0a9g5jEXA6S0oKipiQkptmuIZ2v33348FCxbIz3U6HQBgwoQJOHv2LDZs2IDVq1dj+PDhOHnyJEJCQmAymTB06FCMGDECCxcuxEsvvYQ777wTO3bsAAAcPHgQ9913H95//3307dsX999/P7y8vPDmm28qcYlErYbpYkKqvZiQ2ofsVtoESoxW+Hq5KRYbERFRc7B5+MLmHeSac3n6uexcRG2VSukAoqOjERYWJj90Oh2ysrKwatUqLFiwAH369MGrr76K0NBQrFixAgCwdu1aFBUVYeHChejVqxc+/PBD7Ny5EwcOHAAAfPzxxxgyZAimTp2KpKQkzJs3Dx9//DHMZg47JLqSy3tIUWmFl1vVzwWc2IiIiIiIXEzxhDQoqOa3Rtu2bYOnpyeSkpIAAJIkITk5GZs2bQIAbN68GTfeeCO0Wi0AIDIyEl27dnUoHzZsmHy+5ORk5Obm4siRI019OUStmuniPaT2HlIA8PWsGkjBpV+IiIiIyNUUT0jXrl2LxMRExMfH4+WXX4bZbIbBYEBISAjUarW8X0REBAwGA4Cqm87DwsIcznOl8pCQEKhUKrn8ciaTCcXFxQ4PovbGJgQslQJAtR5SXEpIufQLEREREbmaoveQ3nLLLejTpw8GDx6Mffv24ZlnnoFarYabmxv0er3Dvnq9Hvn5+QCAgoICREREXLG8+vEqlQo6nU4uv9ybb76JuXPnuvLSiFod+3BdANBq1Ci/+LOvR9XbREEZl34hIiIiItdSNCEdN26c/HOfPn1w/vx5rFixAs8++yxKSkoc9i0uLkZgYCAAICAgwGl5QkKC03KbzYaSkhL5+MvNnDkTf/7znx3OFR0d3biLI2pl7AmpWiVBXW02Xb+LPaS8h5SIiIiIXE3xWXarS0hIQHp6OsLCwmAwGFBZWSkP283IyJCH4YaFheHkyZMOx15enpmZKZcZDAYIIWoM87XTarXy/ahE7dXlM+zayT2kTEiJiIiIyMUUu4fUarWirKzMYduBAwfQrVs3DBo0CCaTCbt27QIACCGwceNGJCcnAwCGDBmCX3/9FSZT1cLFaWlpOHHihEP5hg0b5PNu3LgRISEhcg8qEdVUY4bdi+w9pPkcsktERERELqZYQrpixQr0798f//73v3HixAl89tln+Mc//oEXXngBwcHBGDNmDJ599lkcOHAAr7zyCnJycjB27FgAwO23346AgAA8+eSTSElJwdSpUzFo0CD07NkTADBx4kRs2bIFH374IXbv3o1Zs2Zh8uTJcHPjGopEtTFV1pxhF+CkRkRERETUdBQbsvvQQw+hrKwMixcvxr59+xAZGYnFixfjvvvuAwAsXboUkyZNQnJyMuLi4rB+/Xp5iRh3d3ds2LABjz32GAYPHoxBgwZh9erV8rkTExOxcuVKvPTSSzAYDBg7dizmzJmjxGUStRq19ZD6eNgTUvaQEhEREZFrKZaQSpKExx9/HI8//rjTcm9vb6xYsaLW4+Pj47F169Zay0eNGoVRo0Y1Ok6i9kK+h7TacksAoHOvel5qsjZ7TERERETUtim+DikRtQy19ZB6a5mQEhEREVHTYEJKRAAu9ZDWSEgv9pCWGJmQEhEREZFrMSElIgCXekgvn9TIWx6yy3tIiYiIiMi1mJASEQDAZK2aZbe2HlKjxQZLpa3Z4yIiIiKitosJKREBuEIPqfbSJEelHLZLRERERC7EhJSIANR+D6lGJcHTjRMbEREREZHrMSElIgDVe0jVNcp0F9ci5cRGRERERORKTEiJCEDtPaQAoNdWJaTsISUiIiIiV2JCSkQAar+HFKjeQ8qZdomIiIjIdZiQEhGEEDBfnEHXXe2kh9SDPaRERERE5HpMSIlITkaBWnpItbyHlIiIiIhcjwkpEcnDddWSBI2THlKd1g0Ae0iJiIiIyLWYkBLRFSc0AqoN2WUPKRERERG5EBNSIpJ7SGtLSHWcZZeIiIiImgATUiKSe0id3T8KXOohLeYsu0RERETkQkxIiejqPaQcsktERERETYAJKRHBZK0EUHsPKYfsEhEREVFTYEJKRFdcgxTgOqRERERE1DSYkBJRHSY1urjsC4fsEhEREZELMSElIlisAsDVl30pZkJKRERERC7EhJSIYKqsuoe0tiG7l+4h5Sy7REREROQ6TEiJ6KpDdu09pEaLDZaL95sSERERETUWE1IigqXy4pDdWnpIvS/2kAJAGSc2IiIiIiIXYUJKRFftIXVTq+DhVlVWwvtIiYiIiMhFmJAS0VUTUgDQe1TNtMuElIiIiIhchQkpEcnrkLrVMmQXAPRarkVKRERERK7FhJSI6tRDqvPgTLtERERE5FpMSInaOSGE3ENa26RGwKWlXzhkl4iIiIhchQkpUTtnn2EXuEoPKYfsEhEREZGLMSElaufsvaOSBGhUUq372YfssoeUiIiIiFyFCSlROyffP6pWQZJqT0h9Ls6yW8qElIiIiIhchAkpUTsn3z96heG6AIfsEhEREZHrMSElaueq95BeCYfsEhEREZGrMSElaufqsuQLUL2HlMu+EBEREZFrMCElaucsdVjyBQD07CElIiIiIhdjQkrUztW1h9SekPIeUiIiIiJyFSakRO2cqc6TGnGWXSIiIiJyLSakRO2c5WIPqdvVJjW6eA9pCXtIiYiIiMhFmJAStXP1HrLLHlIiIiIichEmpETtnH0dUm0de0grLJXyREhERERERI3BhJSonbP3kLpd7R7Siz2kAFDGYbtERERE5AJMSInaOXMdl31xU6vg4Va1D5d+ISIiIiJXaBEJqdlsRteuXdGxY0d5m8FgwB133AE/Pz/cdNNNOHnypMMx27dvR1JSEgIDAzF+/HiUlZU5lC9ZsgTx8fGIjo7G66+/DiFEc1wKUatT13tIgWoz7bKHlIiIiIhcoEUkpIsWLUJGRob8XAiBUaNGQa1WY+vWrYiPj8ewYcNgNpsBAFlZWRgxYgRGjBiBDRs24MSJE5g4caJ8/I8//ojp06fjrbfewhdffIG//e1vWLJkSbNfF1FrUNceUoBrkRIRERGRaymekObk5GDu3Ll48skn5W379u3D7t278eGHH6JXr15YtGgR8vPz8eOPPwIAli9fjoiICMydOxd9+vTBggUL8O233yI7OxsAsHjxYjz66KO45557kJycjOeffx6LFy9W5PqIWrr69ZBypl0iIiIich3FE9JXXnkFffr0wa233ipv27x5MxISEhAREQEA0Gq1uOGGG7Bp0ya5fOjQoZAkCQCQlJQEd3d3bNu2TS4fNmyYfL7k5GQcPHgQBQUFzXVZRK1GfRJSew9psdHSpDERERERUfugufouTefQoUP4/PPPsW/fPhgMBnm7wWBAWFiYw74RERHyPgaDAUlJSXKZRqNBaGgoDAYDysrKUFpa6nC8PbE1GAzw9/evEYfJZILJZJKfFxcXu+YCiVo4mxCw2qrur67LkF25h5RDdomIiIjIBRTrIRVC4JlnnsFzzz2H7t27O5QVFBRAr9c7bNPr9cjPz79qeWFhofy8ehkA+fjLvfnmm/D19ZUf0dHRjbo2otbCYr20nqibRrrq/valXzhkl4iofTIajThx4gSMRqPSoVArxr8jqk6xhPT777/HmTNn8OKLL9YoCwgIQElJicO24uJiBAYGXrU8ICAAABzK7T2e9uMvN3PmTBQVFcmPCxcuNPzCiFoR08UJjdQqCRpVHYbssoeUiKhdO3/+PCZPnozz588rHQq1Yvw7ouoUG7Jrn1k3JiYGAGCxWFBSUoKgoCA8++yzyMzMdNg/IyMDCQkJAICwsDCHcqvViuzsbISFhcHT0xM+Pj4O5fYZfENDQ53GotVqodVqXXp9RK2BvYe0LsN1gUs9pFyHlIiIiIhcQbEe0hUrViA1NRUHDhzAgQMHMHv2bERERODAgQMYNmwYjh07hvT0dABV3frbtm1DcnIyAGDIkCHYsGGDvLborl27YLFYMGjQIIdyu40bN6Jv377w8/Nr3oskauFM9ZjQCAD0HlXrkDIhJSIiIiJXUCwhDQ4ORlRUlPwICAiARqNBVFQU+vfvj4EDB2Lq1KlISUnBk08+ieDgYIwYMQIA8OCDD8JgMGD27Nk4cOAAnn32Wdx///3ykNzHH38cX3zxBVavXo2NGzfi3XffxbRp05S6VKIWy56QauuYkF6a1Iiz7BIRERFR4ym+7EttvvvuO1RWVmLw4ME4efIk/ve//8HNrap3JiQkBD/99BPWrVuH5ORkdOnSBR999JF87PDhw7Fw4UK88MILeOSRRzBjxgxMmDBBqUsharFM1koAgNatrj2kvIeUiIiIiFxH0WVfqnv00Ufx6KOPys9DQkLw448/1rr/wIED8fvvv9daPmnSJEyaNMmVIRK1OSaLvYdUXaf95R5SDtklIiIiIhdosT2kRNT0jBd7SD3qOWS3hD2kREREROQCTEiJ2rFL95DWrYeUkxoRERERkSsxISVqx+Qhu/W9h5QJKRERERG5ABNSonZMntSonkN2KyyVsFbamiwuIiIiImofmJAStWP1ndTIW3tpHrQyU2WTxERERERE7QcTUqJ2TJ7UqI5Ddt01Krk3tYRrkRIRERFRIzEhJWrH6jupEcCJjYiIiIjIdZiQErVj9Z3UCLg0sRETUiIiIiJqLCakRO2UTQiYK+09pPVPSIsrOGSXiIiIiBqHCSlRO2W2Xpoltz5Ddv283AEABeVml8dERERERO0LE1KidspoqZrQyE0tQa2S6nycv1fVPaSF5ewhJSIiIqLGYUJK1E41ZEIjAPBnDykRERERuQgTUqJ26lJCWr+3Ab+LPaQF7CElIiIiokZiQkrUTpkuDtmtzwy7wKUe0kL2kBIRERFRIzEhJWqnjA0csuvHe0iJiIiIyEWYkBK1UyZrVQ+pRz2H7PIeUiIiIiJyFSakRO2UyXKxh9StYZMasYeUiIiIiBqLCSlRO2W82EPa8EmN2ENKRERERI3DhJSonTJbGjbLrr93VQ+pyWpDhbnS5XERERERUfvBhJSonbJPauRRzyG73u5quKklAOwlJSIiIqLGYUJK1E6ZGjhkV5Ik+HFiIyIiIiJyASakRO2UPKlRPZd9AQB/Lv1CRERERC7AhJSonTLZ1yF1q//bgJ8ne0iJiIiIqPGYkBK1Q0KIBg/ZBarPtMseUiIiIiJqOCakRO2Q0WKDTVT97OWuqffx8lqkZewhJSIiIqKGY0JK1A6Vma0AAA83FdQqqd7H+3mzh5SIiIiIGo8JKVE7VGaqSki9G9A7ClTrIeU9pERERETUCExIidqhcnPV/aNe2vrPsAtcmmWXkxoRERERUWMwISVqh+xDdhvaQ3ppHVIO2SUiIiKihmNCStQOlZmqekg5ZJeIiIiIlMSElKgdKr/YQ9rYIbuFFewhJSIiIqKGY0JK1A41tofUPmS3qMKCSvv6MURERERE9cSElKgdsveQejeih1QlAUIAeaUmV4ZGRERERO0IE1KidsjeQ+rVwB5SjVqFUB8PAEBGkdFlcRERERFR+8KElKidsdoEzJU2AA3vIQWAMN+qhDSrqMIlcRERERFR+8OElKidKbdUJaMalQR3dcPfAiJ8PQEAGYXsISUiIiKihmFCStTOVFxMSL3c1ZAkqcHnkXtIi5mQEhEREVHDMCElamcqLFWz4nprG3b/qF34xYQ0o5BDdomIiIioYVyekGZlZbn6lETkQuXWSz2kjRHhVzVkN5OTGhERERFRAzUoIVWr1cjOzq6x/ejRo7jxxhsbHRQRNR37kN3G9pBemtSICSkRERERNUyDElIhhNN7z37//Xfk5uY2OigiajrykN0GLvliZ5/UKKvYiEqbaHRcRERERNT+1OsTaXBwMCRJgiRJ6N69O1SqS/ms0WhEWVkZpk2b5vIgich17LPsejViyRcACNZroVZJqLQJ5Jaa5HVJiYiIiIjqql49pD/99BN+/PFHCCHwyiuvYP78+fJj8eLF2L59Oz744IM6n+9///sfbr75Zuj1enTv3h3Lly+XywwGA+644w74+fnhpptuwsmTJx2O3b59O5KSkhAYGIjx48ejrKzMoXzJkiWIj49HdHQ0Xn/9dQjBHhwiACg1VwIA9I0csqtWSQjVawFwYiMiIiIiaph6fSK99tprAQCzZ8/GxIkT4eXl1eAXLigowKOPPoqXXnoJH330EdavX4+HHnoInTt3RlJSEkaNGoXQ0FBs3boV77//PoYNG4aTJ0/C3d0dWVlZGDFiBJ555hncddddmDJlCiZOnIivv/4aAPDjjz9i+vTpWLFiBfz9/XHPPfcgODgYU6ZMaXC8RG2CpEKxqaqH1N/bvdGnC/fzREaREZlFRvRp9NmIiIiIqL1pUBfJ7NmzG/3C/v7+SE1Nhadn1X1oXbt2xdKlS/G///0PGo0Gu3fvRnp6OiIiIrBo0SIEBQXhxx9/xOjRo7F8+XJERERg7ty5kCQJCxYswE033YR//OMfCAkJweLFi/Hoo4/innvuAQA8//zzWLx4MRNSavc0fmGwCUCjkurcQ5qTk1NrmX9VBylOpucgO1QNLy8v6HQ6V4RKRERERO1AgyY1On36NB544AF06dIFISEhNR51ZU9GAcBms6G0tBQ6nQ6bN29GQkICIiIiAABarRY33HADNm3aBADYvHkzhg4dKk+slJSUBHd3d2zbtk0uHzZsmHzu5ORkHDx4EAUFBQ25XKI2wy0gCgDg7+XudGKy6kzGckCSkJiYiNDQUKePVcs+AQDMfed9hIaGokPHWJSWljb5dRARERFR29CgHtIHH3wQpaWlGDNmDDp37uwwuVF9CSGQlZWFt99+GxUVFXjwwQfx7rvvIiwszGG/iIgIGAwGAFX3lyYlJV26CI0GoaGhMBgMKCsrQ2lpqcPx9sTWYDDA39+/Rgwmkwkmk0l+Xlxc3ODrIWrJ3AIvJqTeblfd12IyAkLgyX+sRHBYpNN9jmQbsSutHNfcej+Sxt6FdyaPRHl5OXtJiYiIiKhOGpSQnjhxAr/99hsSEhIaHcBzzz2H9957DzqdDj/++CNCQkJQUFAAvV7vsJ9er0daWhoA1Fqen5+PwsJC+Xn1MgDIz893GsObb76JuXPnNvpaiFo6TUBVYunvVff7R719/KH3D3RaFmgpAdLKUWFTwds3wCUxEhEREVH70aCuzZtvvhmpqakuCeD555/H5s2bMX36dNx+++345ZdfEBAQgJKSEof9iouLERhY9aH4SuUBAVUfiquX23s87cdfbubMmSgqKpIfFy5ccMm1EbU0cg9pPRLSK/HzrDpPfrmZM1kTERERUb01qIf0b3/7G0aNGoWwsDCnQ/Pq03MaHh6O8PBw3HTTTSgpKcGrr76K0aNHIzMz02G/jIwM+bxhYWEO5VarFdnZ2QgLC4Onpyd8fHwcyjMyMgAAoaGhTmPQarXQarV1jpmotbLfQxrgghl27edRSYDZakPZxfVNiYiIiIjqqkE9pN27d8eRI0cwYMAAJCYmIjExET179pT/rQuLxYLy8nKHbX5+figvL8eQIUNw7NgxpKenAwCMRiO2bduG5ORkAMCQIUOwYcMGuUdm165dsFgsGDRokEO53caNG9G3b1/4+fk15HKJ2oTCCgvUXr4AAD+vq99DWhdqlSQvH5NfUemScxIRERFR+9GgHtI//vij0S+8bNky/P3vf8fLL7+MPn36ICUlBQsXLsTMmTPRu3dvDBw4EFOnTsW8efPw/vvvIzg4GCNGjABQNanS7NmzMXv2bNx999149tlncf/998tDch9//HGMGjUKI0aMgJ+fH9599128++67jY6ZqDU7l28EAHi7q+CmbvhEZJcL8tYir9SMAiakROQilZWVOHDgAPbt24fs7GyEhISgb9++6N27N9RqtcM+Bw4cAAD06tULAHDw4EEYDAaEhoaiT58+6NmzJw4dOoQDBw7AZrPB29sbqamp8siqW265BSqVCikpKQCA3r17y69jf429e/fi+PHjMBqNsFgsCA4OxjXXXIO77roLAPD9998jLS0Nubm5CAwMRGRkJDp16oTCwkJ5fosTJ06goqICRUVF8hfqVqsVNpsNUVFRiIqKgkajQUhICDIzM5Geno78/HxoNBpIkoSoqCio1Wr4+fnh8OHDSE1NRWVl1fuuErdMzJkzB4sXL4avr2+zvza1fvZ5YSZPntxsr6lSqeT3D5utalSXm5ub/P/I/nP1iU4lSYJWq4XVaoXVaoUkSdDr9fDy8kJ5eTnMZjO0Wi18fX3h5uYGs9mM/Px8uLu7o1u3bnj++efxyy+/4Ny5czh8+DBsNhtMJhPCwsJgsVhgs9lQUFAAIQSCgoIQFxeHhIQEFBcXo6SkRF6FRKVSITw8HB07dsThw4cBVL1XJSQkYO3atXInXteuXVFYWIjU1FQYDAaEhYVh+PDh6Nu3r3ztZrMZ33//PTIyMhAWFia/VxUWFsLPz0++/bGwsBABAQHo1auXfGxTkYRCN34JIbBw4UKsXLkS+/fvR0hICCZNmoQXXngBKpUK2dnZePTRR7F9+3Zcc801+OSTT9C5c2f5+B07duCpp57CqVOncPvtt2PJkiXw8vKSy5cuXYq3334bJpMJU6dOxYsvvnjVZS7siouL4evri6KiIvj4+Lj82omU8PHGI3j957OI1Gtwb1LsVffPPHsS8yePxF8+24DQyOha99tzNh/bTuUh1t8dm/96KwwGQ72WfyJqydge1F9j62zr1q34+9//Lk9SWJ2fnx/+/Oc/A0Ct+1xOkqR6J2x+fn4YMWIEfvrpp6u+RkPO35ZERkZi+fLlSofhEidOnMDkyZNRljAKNu+gRp1LVZYL76M/NOpc9nMsWbIEXbp0aVQ8LcnNN9+sdAjtjpeXF/7617/i6NGjWLlypZyE10VYWBimTZuGwYMH1/t169oeNKiH9Msvv7xi+cMPP3zVc0iShOnTp2P69OlOy0NCQvDjjz/WevzAgQPx+++/11o+adIkTJo06apxELUXqTkVAABfD9d+yxWkq7r/mj2kRNRYW7duxSuvvCI/79GjB4YOHYoNGzbg6NGjKCwsdCjv2bMnrr32Wnz++ecO57n99tuRkpKCtLS0eiWLjz76KPbu3YtDhw7hX//6V41yZ8mn/bmvry86dOgg97S2F+np6Rg3blybSUqpaTEZrT9vb2+UlZUBqOrltdlsiI6OdpiE1b78pZ19hEdISAiys7NRXl4uv3f6+/vjxhtvxA8//OBwXFxcHE6dOgUAiImJwfnz5zFp0iQcOXIEs2fPxty5cxuUlNZFg8btPffcczUe06ZNw3PPPYevvvrK1TESkQscyKiaeTrEu0HfQ9UqUFd1D2mRsRJQu/bcRNR+VFZWYtGiRXB3d4dWq8XAgQPxwQcf4O6778bChQsxcOBAuLlduv99wIAB+Pvf/45169ZBq9XCzc0N/v7+0Gq12LdvH0wmEyRJclgrXZIk9O/fH/3795e3+fr6yvusW7cO8+fPd3gd++gqf39/rF+/HgMHDoRGU/O9btmyZcjOzkb//v3rPCKrLpy9VkuTnp6OoqIipcOgFs5VK3S0ViqVyuH9qC6SkpJQUVEhHx8SEgJfX19cuHABvr6+cHd3l0eWAlXvV/bhtQMGDIBKpXJ4T5IkCcuXL8fu3bsxcOBASJKEAQMGwN/fH6dOnUL//v0xcOBAmM1mDBw4EGvXrsXcuXMxcOBALF68uF49q/XRoHe5nJycGtvy8vIwevRovPrqq40Oiohcq9RkxYnsqknEQnWumdDITqfVQKtRwWS1wS2w9qG9RNQ2mUwmh3uu7Eut1VdKSorDN/zjx4+XP7ypVCqMHz8eO3bskMv79++Pw4cPOxwzYcIE/O1vf0NWVpa8rXqPphACDz/8ME6cOIFdu3YBAIYPH45vvvkGAGAwGLBmzRpYLJYaxz/22GNwd3evEYfdJ598gqysLNx///3yuV3BarUiKCgIubm5LjtnU/jzn/+Mv/zlL0qH0Sjnzp1TOgSnWmpc9TVlyhSlQ1CUTqer9/ujh4eHfL+rzWZDVlYWrrnmGhw8eBCxsbHyPfR2Qgjcf//9WLFiBSIjI7Fz506H9yQhBP773//K71U7duzAyy+/jC1btuCbb75BVFQUkpOT8cQTT8jlhw8fxrhx4/DEE08gJSUFffr0cUl9VOeyr90CAwPxyiuv4IUXXsCvv/7qqtMSkQvsP18AmwCshVnwdg9w6bklSUKQTov0wgq4B1/93lQialvefPNNzJ07t9Hnyc/Pd3geGxt7xedarbbGMQMHDrzq68TGxjp8wI+IiHAoty8Vdzn7uS+Pw84+SYuHh8dVY6gvd3fXLNXVlE6dOtWsE9S0J/PmzVM6BHKBhvQulpSU1Nhm/5LMnqhe7vbbb8eKFSvkLwovX9rS/h5n3x4bG4sTJ04AqPqC0f4eZy/Pz8+X3/8uf891FZeOAzEajTh06JArT0lELvD7mao3EGPaUQB1Xye4roJ07lUJaWgnl5+biFq2mTNnyhMNAVU9pNHR9R8tYZ/Z0e7MmTPo0aOHw/PqTCYTwsPDHbY567m83JkzZxx6dC9PQC9PUKufe+TIkTXisIuKisKePXtgNBqvGkN9mc1ml5/T1eLi4tpED2lLTP5eeukldOjQQekwGm3KlCntegKwhsxUq9fra2yzD7+tbfivfQ4ee0JZ/f0OuPQeZ99+5swZZGZmysfY3+Ps5QEBAfK2y9+nXaVBCel9993n8Nxms+HUqVM4cuRIjTIiUt7us1UJqSntCIB7XX7+cF9PHEwrgkdML5efm4haNq1WW+Mb+Ibo1asXQkNDUVBQAEmSsGzZMsybN0+exGPZsmVwc3OTh9Pu2rULI0eORGhoKAoLC2Gz2fDpp59Cq9XC398flZWVyM3NhSRJck+CJEk1JmZcv369/BqhoaH405/+hCVLlsivY5/I6JNPPsGtt96KZcuWQaPRwGq1Opznsccew86dO7Fz506Xzryr0Wha/HBdoGrWYy4B0zQ6dOjQJmbZXbp0KSZOnKh0GIqxL99SW8+mM0ajUT7Gfg/p2bNnAVQlku7u7rBarRBCQAgBSZLw73//G2q1Gunp6QgLC3N4T5IkCXfccQdWrVqF3bt3IywsDF999ZW8pGdaWhqWLVuGsLAw7N69G+Hh4UhMTMTs2bMRHh4uL6/lag2a1Mjb29vhodfrceONN+Kjjz7CJ5984uoYiagRzFYb9p8vBAAY0440yWtEB3gCANxDOyGvzHKVvYmIalKr1XjiiSdgNpthMpmwY8cOPPnkk1i1ahWefPJJ7Nixw+Hezp07d+LPf/4zRowYAZPJBIvFgoKCAphMJvTp0wdarRZCCIcPf0II7Nq1y+Eez6KiInmf4cOH4/nnn3d6D2lBQQGGDx+OHTt21EhGgap7XkNCQrBr1y6X9gI5e62WJjIykskoXVX15RvbI5vNVq9kFAB2794NT09P+fjs7GwUFRUhKioKRUVFMJvNsNls8nJ7Qgh5aPDOnTtRWVnp8J4khMC4ceNw3XXXYceOHRBCYOfOnSgoKEBcXBx27dqFHTt2wN3dHTt27MAdd9yB2bNnY8eOHXj88cebbD3SBvWQfvbZZ66Og4iayKH0IpisNvh6aHAuL61JXsPLXYNATzXyKirx+/lidI+NbJLXIaK2bfDgwXj11VflNUaPHj2Ko0ePyuX+/v549tlnAVT1yB06dMjprULr1q2Tf65Pb6W999Tf3x/Dhw+vsQ6ps/PYz19UVNTulnwB2tY6pNT0Nm/ezKVf6sm+5Atw6b5R+z3rdtUndwMu3a9qn4j28nVI16xZU+M4+5IvAHD+/HkAwMcff4zw8PAmXfIFaOQ9pLt27cKBAwdgs9nQt29fh2nUiahl2PxH1VTg10br0ZQflSJ83JBXUYmd54pw9ZWIiYicGzx4MG644QYcOHAA+/btQ3Z2NkJCQtC3b1/07t1b/obevo99lkn7ULKDBw/CYDAgNDQUffr0Qc+ePXHo0CH584q3tzdSU1ORnZ2NsLAw3HLLLVCpVHIy2bt3b/l1Jk2ahAMHDmDv3r04fvw4jEYjLBYLgoODcc011+Cuu+4CAHz//fdIS0tDbm4uAgMDERkZiU6dOqGwsBD5+fkoLCzEiRMnUFFRgaKiIjmxtVqtsNlsiIqKQlRUFDQaDUJCQpCZmYn09HTk5+dDo9FAkiRERUVBrVbDz88Phw8fRmpqqvyhU4n78iIiIrB48WL2jFK9bd68GRs3bmz2lTlUKpX8/mFP7Nzc3OT/R/afq99zKUkStFotrFYrrFYrJEmCXq+Hl5cXysvLYTabodVq4evrCzc3N5jNZuTn58Pd3R3dunXD888/j19++QXnzp3D4cOHYbPZYDKZEBYWBovFApvNhoKCAgghEBQUhLi4OCQkJKC4uBglJSWw2WzyUN/w8HB07NgRhw8fBlD1XpWQkIC1a9ciPT0dANC1a1cUFhYiNTUVBoMBYWFhGD58OPr27Qu1Wo3BgwdjwoQJ+P7775GRkYGwsDD5vaqwsBB+fn7yfaKFhYUICAhAr169mqxn1K5BCWlZWRnGjBmD9evXo2PHjgCAs2fPYvjw4Vi5ciW8vb1dGSMRNcKGY1UJ6Y1xfmjKsQ2RPm44ZDBi97li+T4FIqKGUKvVuPbaa3HttdfWe5/rrruuxr5XO1dtx9UlDgAYM2bMFcvbkhMnTmDy5MmYM2cOk1FqsKioKADAkiVL2sT9sVfi6veHyzsA63t+d3f3Fvee1aB7SP/yl78gLy8PqampOHXqFE6dOoXU1FTk5+e3+hnWiNqS9MIKHMsshkoCboht2g8Ood4a2CxG5JZZ8Ieh5jTlRERERESXa1BC+t133+GDDz5wWIsrNjYW//jHP/Cf//zHZcERUeP8cqzq3oBrO/jDz9OtSV9LrZJgPF91L9f6w4ar7E1ERERE1MCEtLbheLWth0NEyrAP1x3aPbRZXq/82FYAwHcH0tv1WmNEREREVDcNyiDvvPNOPPXUU/IMTEDVbEzPPPMM7rzzTpcFR0QNV2qyYuepPADAsO4hzfKa5Sd2wEOjwpncMhxMK2qW1yQiIiKi1qtBCek777wDHx8fxMXFIT4+HvHx8YiLi4O3tzfeeecdV8dIRA2w8Xg2zJU2xAZ5Iy5Y1yyvKSxG3NTZDwDw3f70ZnlNIiIiImq9GjTLrk6nw/r167Ft2zYcPHgQQgj07t0b3bp1g07XPB98iejKfjqcCQAYkRjWrDPe3tY9EOuP52PNwQy8dEd3uKk5lJ+IiIiInKvzJ8UDBw5g2LBh8ro9QNUaYNOmTcMTTzyBuLg4dOnSBQcPHmySQImo7oyWSmw6XrUY8m2JYc362kkdfBGkc0demRmb/8hp1tcmIiIiotalzgnpyy+/jJtvvrnWiYvCwsLw7LPP4qWXXnJZcETUMFtO5KDCUolIP0/0jGzedeI0Kgl39YkEAKzcc6FZX5uIiIiIWpc6J6Tbt2/H3XfffcV9Ro8ejV27djU6KCJqnJ8OZwFo/uG6dmP6RQOouo81p8TU7K9PRERERK1DnRPS0NBQZGVlXXGfnJwc+Pv7NzooImo4S6UNGy6uPzqimYfr2nUJ1aN3tB+sNsHJjYiIiIioVnVOSIcNG4bXX38dlZWVTsutViveeustDBkyxGXBEVH97TydhxKjFUE6d/SNUe4Lovsu9pJ+s+cC1yQlIiIiIqfqnJC+8cYbyMjIQL9+/bBixQocP34c+fn5OHbsGJYvX46kpCScPn2ay74QNbPS0lJkZ2fLj+/3nAUADIr1RV5ujrw9J6d5JxgaeU04PNxUOJldigMXCpv1tYmIiIiodahzQqrT6bBr1y7ceuutmDp1KhISEhAcHIwePXpgypQpGDJkCHbu3Alf3+adQIWoPSstLUWHjrEIDQ29+AjDv347BgD46JUnqm0PRWJiIgDAYjE3S2w+Hm64LTEcAPDNnrRmeU0iIiIial3qtQ6pr68v3n77bbz99tvIyMhAWloaIiMjERERocjEKUTtXXl5OfLzcvHCkrXw9g1ATpkVa/4ohkYFvDDvH1CrLv2/zL5wGotmjIfFYm3SmKr3xN4Sp8N/9gM/HEjD4/2D4OGmdtjXy8uLaxcTERERtWP1Skiri4iIQEREhCtjIaIG8vYNgN4/ECl5uQCA2CAd/AKDHPYpLcpv0hhMxnJAkuSe2CoSIqYsRZlfGBKGj0PZkU0OxwQEBuHc2TNMSomIiIjaqQYnpETU8pzKKQUAxAU3f4JnMRkBIfDkP1YiOCxS3r4/swL7MyvQa+xfMCJ+nry9rCgf70weifLyciakRERERO0UE1KiNiK/zIyCcgtUEtAxyEuxOLx9/KH3D5Sf99SasT/zHDJLrdDo/OB52bBdIiIiImq/6jypERG1bPbe0Wh/L2g1LSfp8/dyR5DOHUIApy/GSEREREQEMCElajNO55QBUGa47tV0DqmKKTWbCSkRERERXcKElKgNKDfbkFVsBAB0CvZWOJqa4kP0AIDz+eUwWSoVjoaIiIiIWgompERtwPniqrVFw3w84K1tebeGB3i7I8DbHTYBnMktUzocIiIiImohmJAStQEZxRYAQMdA5SYzupq4iz23Z/KYkBIRERFRFSakRK2dpEJGiRUAENOCE9IOgVUJ6fm8ctiEUDgaIiIiImoJmJAStXLuYZ1hrhRw16gQqvdQOpxahft4wF2jgtFqg+Hi/a5ERERE1L4xISVq5Tw79gEARPt7QqWSFI6mdiqVhJiAqh7cs3nlCkdDRERERC0BE1KiVs4jtiohtSd7LZn9HtdzvI+UiIiIiMCElKhVKzNXQhvRDcClezRbMnuMhmITKiw2haMhIiIiIqUxISVqxfZdKIGk1kDvroKvp5vS4VyVTqtBkM4dAJBRYlE4GiIiIiJSGhNSolZs17kiAECkT8tPRu3svaRpRUxIiYiIiNo7JqRErdiuc8UAgAh960lI7feRppdYALTcSZiIiIiIqOkxISVqpTIKK3CuwAhhq0S4XqN0OHUW7usJd7UKRquAe1hnpcMhIiIiIgUxISVqpX47mQsAMGeehFbTev4rq1USogM8AQCena5VOBoiIiIiUlLr+RRLRA62nswBAFSc3adwJPXX8eJ9pJ6d+ikcCREREREpSdGEdNOmTRg+fDh8fX1xzTXXYN26dXJZWVkZxo0bh8DAQCQlJWHnzp0Ox544cQKDBw+Gv78/Ro4ciezsbIfy7777Dr169UJoaCiefvppWK3WZrkmouZgswlsS63qITWeOaBsMA3Q4eJ9pO7h8Sis4P9NIiIiovZKsYT04MGDuPfee3Hvvfdi165dGDlyJEaPHo1Tp04BACZMmIDU1FRs2LABw4cPx/Dhw+Wk02QyYejQoejatSu2bNkCSZJw5513Opz7vvvuw7Rp07BmzRr88MMPePnllxW5TqKmcCSjGAXlFni7q2DK/EPpcOpN7+EGPw81JJUav58vUjocIiIiIlKIYglpr169sGfPHkyaNAndunXD66+/jrCwMKxduxZZWVlYtWoVFixYgD59+uDVV19FaGgoVqxYAQBYu3YtioqKsHDhQvTq1Qsffvghdu7ciQMHDgAAPv74YwwZMgRTp05FUlIS5s2bh48//hhms1mpyyVyqV9Tq4brXhvtA9gqFY6mYaIuLlWz82yxwpEQERERkVIUS0glSUJsbKzDc39/fxQXF2Pbtm3w9PREUlKSXJacnIxNmzYBADZv3owbb7wRWq0WABAZGYmuXbs6lA8bNkw+d3JyMnJzc3HkyJHmujyiJvXriarhuv07+CgcScPZ107dcbYIQgiFoyEiIiIiJbSYSY0qKipw7Ngx9OzZEwaDASEhIVCr1XJ5REQEDAYDAMBgMCAsLMzh+CuVh4SEQKVSyeWXM5lMKC4udngQtVTlZiv2nisAACR18FU4moYL1WlgMxuRW2bB8awSpcMhIiIiIgW0mIR08eLFCAwMxIgRI1BQUAC9Xu9QrtfrkZ+fDwD1LlepVNDpdHL55d588034+vrKj+joaFdeGpFL7TqTD3OlDZF+nojx0yodToNpVBKM51MAAFtO5CgcDREREREpoUUkpOnp6XjjjTfwyiuvwMPDAwEBASgpcewxKS4uRmBgIADUu9xms6GkpEQuv9zMmTNRVFQkPy5cuODKyyNyKfv6ozfGB0GSJIWjaRzjmaola7b8wYSUiIiIqD3SKB2A2WzGmDFjMHDgQEyZMgUAEBYWBoPBgMrKSnnYbkZGhjwMNywsDCdPnnQ4z+XlmZmZcpnBYIAQosYwXzutVivfj0rU0v16cf3RG+ODFY6k8SpO7wUA7DmXj1KTFTqt4m9JRERERNSMFO0hraysxIQJE1BYWIgvvvhC7u0ZNGgQTCYTdu3aBQAQQmDjxo1ITk4GAAwZMgS//vorTCYTACAtLQ0nTpxwKN+wYYP8Ohs3bkRISAgSEhKa8/KIXM5QbMQJQykkCbg+znmPf2tiLcxElK8WlkqBHafylA6HiIiIiJqZYgmpPRndsmULvv32W5jNZmRlZSErKwvBwcEYM2YMnn32WRw4cACvvPIKcnJyMHbsWADA7bffjoCAADz55JNISUnB1KlTMWjQIPTs2RMAMHHiRGzZsgUffvghdu/ejVmzZmHy5Mlwc3NT6nKJXOLXi8N1e0X6wt/bXeFoXGNgx6qJmbacyFY4EiIiIiJqboolpN988w2+/PJLpKWloUePHggPD5cfALB06VLExcUhOTkZP/30E9avX4+goCAAgLu7OzZs2IA//vgDgwcPBgCsXr1aPndiYiJWrlyJRYsWYeTIkRg1ahTmzJnT7NdI5Gq/XRyuOyg+SOFIXGdArD0hzeHyL0RERETtjGI3bI0dO1bu8XTG29sbK1asqLU8Pj4eW7durbV81KhRGDVqVKNiJGpJbDaB31LtExq1/vtH7a6N0sNdrcKF/AqczStHbJC30iERERERUTNpEbPsEtHVHc8qQW6pGV7uavSN8Vc6HJfxclfjutiq69n8B4ftEhEREbUnTEiJWomtF4fr9o8NgLumbf3XHdI1BACw4ZhB4UiIiIiIqDm1rU+1RG3YxmNVvYdDuoUoHInr3ZIQCgDYeTofReUWhaMhIiIioubChJSoFSgsN2PPuXwAl3oT25IOgd7oGqpHpU1gE4ftEhEREbUbTEiJmpnRUonNf2TDaKms8zFbTuTAJoCuoXpEB3g1YXTKsfeS/nw0S+FIiIiIiKi5MCElamazvjuMRz/7HXe8/ysOpRXV6ZhfLg7XHdq97fWO2t3aoyoh3fJHTr2SdSIiIiJqvRRb9oWovSgtLUV5eTkAILfMgu/2pwMATuWU4Z7F2/DV+B6IDfSU9/fy8oJOp5OfWytt8uyzbTEhzcmpmqwp1E0gROeG7FIL1u09hRs6+Tnsd3m9EBEREVHrx4SUqAmVlpaiQ8dY5OdVrR/qe/0D8LtxPEyZJyGsJiA6Ebf/+W/I/+kD+Rj/gEDs3fM7vL2r1uPcfb4YxUYrfD00iNBakJ196R5LezLXGpmM5YAkITExUd4WcMtU6PuOxMQ5HyB//UKH/QMCg3Du7BkmpURERERtCBNSoiZUXl6O/LxcvLBkLTx9/PHvw4WosAjcOrA3dO4q/PdECfx6D8fk8ffD002FAkM6Fjx9Hzp16iSfI/D2Z6DrOQxpO9YgYu4Ip69jsZib65JcxmIyAkLgyX+sRHBYJAAgvdiC9aklCOl3G576v7GQJAkAUFaUj3cmj0R5eTkTUiIiIqI2hAkpUTPw9g1AtlWLCouAl7saPWPDoZKAkEwzsktMOFumRlJsAEqL8h2SNEulwNeHCmC1AQ/cfx9CHxvncN7sC6exaMZ4WCxWha6s8bx9/KH3DwQAxPsKbDpThgqrDWVqb4T7el7laCIiIiJqzTipEVEzySoyAgA6BXtDrZIgSRL6xPgBAA6mFaLSJuR97UlatsUdVhvg6+mGuKhQ6P0DHR5ePn4KXEnTUaskdAyqmkX4VE6ZwtEQEdHlYmJisGTJEsTExCgdCrVi/Dui6piQEjWTnFITACBYp5W3xYfo4a1Vo9xciZOGkhrHHMuq2tY9TC8PX23r4oKrhuSeyilVOBIiIrqch4cHunTpAg8PD6VDoVaMf0dUHRNSomaSV1p1n2dQtYRUrZLQK8oPALD/QiHEpU5SGIqNOJ9fNTtvt3CfZotTaR0CvaCWJBSWW5B7MYknIiIioraJCSlRMzBabSg1Vd3nWT0hBYCeEb5QqyRkl5iQWzWqF0II/JZaNTNvtzA9fD3dmjVeJWk1anQIrBq2+0dWzV5jIiIiImo7mJASNYOCikoAVfeCumsc/9t5uqvRPUwPAEjJByQ3LTLKbEgrqIBaJWFgp8Bmj1dpXS/WxwlDCUT1bmMiIiIialOYkBI1g/yLCWmQzt1ped8O/nBTS8gzAuGPvo/f0iwAgN5RfvBpR72jdrFB3nBTSyg2WpFVbFQ6HCIiIiJqIkxIiZpBfrnz4bp2/l7uuKtPJNxUgFtAJASqkrKk2IBmjLLlcFOr0Oni5EYctktERETUdjEhJWoGl3pInSekABDu64khEUDJwfW4KcoNo66JqDG8tz3pGlo1bPdkdilsHLZLRERE1Ca130+7RM1FUqHQeOUhu3Z+WiD/pw8QrlM3R2QtWkyAFzw0KpSbK5FZYlU6HCIiIiJqAkxIiZqYxj8ClQJwU0vtarbcxlKrJHQOqRq2e7qAy78QERERtUVMSImamJt/OADAz9MdkiQpHE3rYp9t91yhBVBrFI6GiIiIiFyNCSlRE9P4VSWk7B2tv0g/T+i0GpgrBTw79VM6HCIiIiJyMSakRE1M4xcGAPD1YkJaX5IkIT60atiurkeywtEQERERkasxISVqYhp/9pA2RkK4DwDAs3MS8sosCkdDRERERK7EhJSoibnZe0iZkDZIkE6LYG81JLUGa4/kKh0OEREREbkQE1KiJlRpE9D4ViWkfkxIG6xroAcA4IfDORBck5SIiIiozWBCStSEckrNkDRukADotJwltqFi/d1hM5XjQqEJO0/nKx0OEREREbkIE1KiJpRWWLV+pl6rgkrFJV8ayk0toezoFgDAv34/r3A0REREROQqTEiJmlBakT0hVSscSetXmrIeALDucBYKy80KR0NERERErsCElKgJpRcaAQB6d/5XayxzViq6BHvBbLVh9b50pcMhIiIiIhfgp2SiJmTvIfXR8r+aK4zuGQygatguJzciIiIiav34KZmoCaUXcsiuK43oHgAPNxVOGEqx73yB0uEQERERUSMxISVqIkII9pC6mE6rwcheEQCA5bs4uRERERFRa8dPyURNpLDcglJTJQD2kLrSg/1jAAD/TclEUblF4WiIiIiIqDGYkBI1kXP55QAAa0keNFzyxWX6RPuhe7gPTFYbVu1LUzocIiIiImoEJqRETeRcXhkAwFqYpXAkbYskSXIv6YrdnNyIiIiIqDVjQkrURC7Ye0gLMxWOpO0Z3TsCXu5qpGaX4veznNyIiIiIqLViQkrURM7l2RNS9pC6mt7DDaOusU9udE7haIiIiIiooTRKB0DUVtnvIbUUsIfUVXJycuSfR8Tr8a/fgR8PZeKJgSHw83Rz2NfLyws6na65QyQiIiKiemBCStREzudxyK6rmIzlgCQhMTHRYXvYIwuAsM7oN2Y6Sn7/j0NZQGAQzp09w6SUiIiIqAVjQkrUBIyWSmQVGwFwyK4rWExGQAg8+Y+VCA6LlLcfzzVi+/lyxN42CXc/NwOSVDWbcVlRPt6ZPBLl5eVMSImIiIhaMCakRE3APqGRt7satopihaNpO7x9/KH3D5Sf99JXYnfaGRSZbChTeyPc11PB6IiIiIiovjipEVETsE9oFOmrVTiStk2rUSM+tKoH9EgGE38iIiKi1kbRhDQnJwezZs1CTEwM+vXr51BWVlaGcePGITAwEElJSdi5c6dD+YkTJzB48GD4+/tj5MiRyM7Odij/7rvv0KtXL4SGhuLpp5+G1Wpt8ushsrNPaBTlx4S0qfUI9wUAnDCUwGy1KRwNEREREdWHognphQsXkJqaCh8fnxplEyZMQGpqKjZs2IDhw4dj+PDhctJpMpkwdOhQdO3aFVu2bIEkSbjzzjvlYw8ePIj77rsP06ZNw5o1a/DDDz/g5ZdfbrbrIjqfVwaAPaTNIcLPA36ebrBUCpzMLlE6HCIiIiKqB0UT0r59++Jf//oX7r33XoftWVlZWLVqFRYsWIA+ffrg1VdfRWhoKFasWAEAWLt2LYqKirBw4UL06tULH374IXbu3IkDBw4AAD7++GMMGTIEU6dORVJSEubNm4ePP/4YZrO5uS+R2qmzeewhbS6SJCEhoupLraMctktERETUqrTIe0i3bdsGT09PJCUlAaj6wJmcnIxNmzYBADZv3owbb7wRWm3Vh/3IyEh07drVoXzYsGHy+ZKTk5Gbm4sjR444fT2TyYTi4mKHB1FjpGaXAgBiAznJTnPoHu4DCUBGkREFZfziiYiIiKi1aJEJqcFgQEhICNRqtbwtIiICBoNBLg8LC3M45krlISEhUKlUcvnl3nzzTfj6+sqP6OhoV18StSNlJivSCysAAB0DmJA2B51Wgw6BXgCAI5n8QomIiIiotWiRCWlBQQH0er3DNr1ej/z8/AaVq1Qq6HQ6ufxyM2fORFFRkfy4cOGCKy+H2plTOVW9o0E6d/h5cmWl5tIjompyo2OZxbAJoXA0RERERFQXLfLTckBAAEpKHCcnKS4uRmBg4BXLExISnJbbbDaUlJTIx19Oq9XKw3+JGuukoSoh7RyiUziS9iU2yBuebmqUmyuRVmRROhwiIiIiqoMW2UMaFhYGg8GAyspKeVtGRoY8DDcsLAyZmZkOx1yp3GAwQAhRY5gvUVM4efH+0fgQ/VX2JFdSqyR0D6+q8xN5JoWjISIiIqK6aJEJ6aBBg2AymbBr1y4AgBACGzduRHJyMgBgyJAh+PXXX2EyVX3oTEtLw4kTJxzKN2zYIJ9v48aNCAkJkXtQiZpS6sWlR+JD2UPa3BLCq2bbvVBkgcrLT9lgiIiIiOiqFE1I8/PzkZWVhdLSUlgsFmRlZSEnJwfBwcEYM2YMnn32WRw4cACvvPIKcnJyMHbsWADA7bffjoCAADz55JNISUnB1KlTMWjQIPTs2RMAMHHiRGzZsgUffvghdu/ejVmzZmHy5Mlwc3NT8nKpnbDPsMshu80vUKdFmI8HBABd4hClwyEiIiKiq1A0Ib377rsRHh6Ov/3tb0hJSUF4eDiuu+46AMDSpUsRFxeH5ORk/PTTT1i/fj2CgoIAAO7u7tiwYQP++OMPDB48GACwevVq+byJiYlYuXIlFi1ahJEjR2LUqFGYM2dOs18ftT9GSyXO51etQcohu8rocXFNUl2vWyE4uRERERFRi6bopEabN2+utczb2xsrVqyotTw+Ph5bt26ttXzUqFEYNWpUY8IjqrfTOWWwCcDPyw1BOnfkVCgdUfsTH6rDlhPZQGA09lwowR2hoUqHRERERES1aJH3kBK1Vicv3j/aOVgHSZIUjqZ90mrU6BxQNWv2N/udrz1MRERERC0DE1IiFzqWaZ/QiMN1lZQQ7AEA+PV0IS5cHEJNRERERC0PE1IiF9p3vgAA0CfaT9lA2jk/TzUqzuyDTQDLdp5TOhwiIiIiqgUTUiIXsVbakJJWCADo28FP0VgIKNm7BgCwfNd5FJVbFI6GiIiIiJxhQkrkIsezSmC02KD30KBTEJd8UVrFqT3oHOSJUpMVn28/q3Q4REREROQEE1IiF9l/cbhu72g/qFSc0Eh5Av/XPxwA8Om2MygxspeUiIiIqKVhQkrkIvvPFwIA+sb4KxsIyZLjA9Ap2BtFFRZ8vu2s0uEQERER0WWYkBK5iDyhUYyfsoGQTK2S8PTQeADAh1tOIbvEqHBERERERFQdE1IiF8gvM+NsXtXyIn2i2UPakvypVwSuifJFmbkS7/3vpNLhEBEREVE1TEiJXGDHqTwAQOcQHXy93BSOhqpTqSTMGpkAAPj37+dxJKNI4YiIiIiIyE6jdABEbcGGYwYAwJCuwQpHQtXl5OQAADp4AcO6+GPDiQI89699+PTBBGgum3jKy8sLOh1nRyYiIiJqTkxIiRrJUmnDxuPZAIBbEsIUjoYAwGQsByQJiYmJ8jaVtx8iJn6I49lA9zunoXj3aodjAgKDcO7sGSalRERERM2ICSlRI+05W4CiCgv8vdxwbQfeP9oSWExGQAg8+Y+VCA6LlLefyDXht/NlCEqegElPPAsfDzUAoKwoH+9MHony8nImpERERETNiAkpUT2VlpaivLxcfv793vMAgOs7+iAvN8dhX/uQUVKGt48/9P6B8vO+fgLnStJxoaACOzPNuLtPJCSJa8YSERERKYWTGhHVQ2lpKTp0jEVoaKj8+GrjQQDAZ6//2WF7aGioPGTUYjErGTZdJEkShnYPhUYlIa2gAkcyipUOiYiIiKhdYw8pUT2Ul5cjPy8XLyxZC2/fAGSWWLDuZAk0KuC5ue/CTe3Y25Z94TQWzRgPi8WqUMR0OV9PNwzsFIhfU3Px68lcdAj0UjokIiJqQVTGxs/GrqoodPhXqTiIWgMmpEQN4O0bAL1/IH5NywQAdAv3QUBQUI39Sovymzs0qoPeMX44mV2KrGIjfjmWjeQOWqVDIiIihfn6+sLNXQuc3uKyc3qe2dqo493ctfD19XVRNEQtExNSogYqNVlxKqcUANAr0k/ZYKheVJKEWxJCsWL3eZzLL8dJHe8jJSJq70JDQ7Hsqy9RVNRyeiZ9fX0RGhqqdBhETYoJKVEDHU4vgk0AEb4eCNazh621CfB2x8BOgfgtNRe70sqh1tfs4SYiovbFPgcEETUfTmpE1ADmShsOphUCAHpF+SkaCzVcnxg/hPl4wGITCBwxHUIIpUMiIiIialeYkBI1wGGDEUaLDf5ebogP4bqVrZV96K5aAjw7XYvvD+cqHRIRERFRu8KElKieVF5+OJxtBABcHxcElYr3H7ZmAd7u6BvhCQD4x5bzSC+sUDgiIiIiovaDCSlRPfnf9AisNiDUR4u4YG+lwyEX6BHiAWP6MZSZbfjrqhQO3SUiIiJqJkxIieph+5lC6HrdAgAYHB8MSWLvaFugkiTk/bgAWrWEX0/m4tNtZ5UOiYiIiKhdYEJKVEdF5Ra88b+zAIAeIVpE+HkqGxC5lDU/HU/fFAMAeGvdMRxKaznT/hMRERG1VUxIierAUmnDEyv2IbvUAkt+Bq6N8FI6JGoC91wTjOE9QmGpFJi2Yi8KysxKh0RERETUpjEhJboKIQRe+f4IfkvNhaebCjnfvwkNJzJqkyRJwjv3XIPoAE9cyK/AEyv2wVJpUzosIiIiojaLCSnRFVTaBF78zyF8vfs8JAl4/fY4WLLPKB0WNZGcnByYSgvw9h2d4OmmwvZTeXjhX3tgMBiQnZ0tP0pLS5UOlYiIiKhN0CgdAFFLVWy0YMY3B/HzUQNUEvDm3T1xYwcPpcOiJmAylgOShMTERHmbZ/xABN81E/85lIPPlyxC4dYv5bKAwCCcO3sGOh3XoCUiIiJqDCakRE4cTi/Ckyv24WxeOdzVKix4oDdu7xmO7OxspUOjJmAxGQEh8OQ/ViI4LFLefjzHiO0XyuE78D4Mvuth9A33RHlxAd6ZPBLl5eVMSImIiIgaiQkpEYDS0lKUl5ej0ibw1Z5MLNmeAatNIEzvjjdHxqFHqBrZ2dnIyclROlRqQt4+/tD7B8rPr/MHVNoC/Jaai4NZRlSq3dEvxF/BCImIiIjaFiak1O6VlpaiQ8dYFFdqEHjHn+ERlQAAKP9jO37/6QMkzyqpcYzFwtlX24trO/jDTS1h0x85OJxejOxCDdT6wKsfSERERERXxYSU2r2ysjKYo65Fh9umwyoANxUwINobnfvcAWnsSId9sy+cxqIZ42GxWBWKlpTQK8oP3loNfj5iQHaZFeETFmF1SjYmJwdDxRmXiYiIiBqMCSm1axfyy/GX/5xE4IiqZDTSzxO3JoTCx9PN6f6lRfnNHCG1FHHBOoxNcsfag2nIgw5vbTiHH44U4PnhXXFz12BIEhNTIiIiovrisi/ULlkqbVi8+RRueW8Ltp8tgrBacF2kJ+7pG1lrMkrk5+WOP3X1Qf6Gj+DtrsbRzGL83+e/494Pd2Dn6TylwyMiIiJqdZiQUrtiswmsP5KFO97/FW//dBxGiw19o/TI+Hw6eoZ6speLrkolSSjZuwb/eawXpgzuBK1Ghb3nCvDAkp146JNdOHihUOkQiYiIiFoNDtmldsFsteG7A+n4aMspnMopAwAEeLvjxdu748YoN/xneprCEVJrYyktwGP9gjGqmx6f7crEfw7l4NeTufj1ZC5u7uyHKddHIS7IEwDg5eXFJWKIiIiInGBCSm2SfRmXMnMlvjuUg6/3ZiG71AIA0GnVuPeaEDx4bRj8PDVcyoXqxWQsByQJiYmJDts1vqHwvWEsvHsMwebUQmw6mY+yI5tR9Nty+Ggqce7sGSalRERERJdhQkptTmlpKTp26wVrpxug73MH1J56AIC1JA8le77H+QPrMNdcgbmXHcelXKguLCYjIASe/MdKBIdF1igvqKjE/sxynC20QJeYDJ/EZBT+/h3OGArRkwkpERERkQMmpNSmnM4pxeINx+B1/3yo3DwAAD5aFXqGeqBzgD/UN80AMMPhGC7lQg3h7eMPvX/N9Uj1/kBMBGAoNmL7qTyczy+Hz3WjMfrjgxiWkIWh3UPRN8YfUf6e8HBTKxA5ERERUcvBhJRaPZtNYPOJbHyx/Ry2nKgafqty80CQlxpJccGIC9ZBdYXJiriUCzWFUB8P3NUnEsfPZeH7X7bBo0MvrD9iwPojBgCABCBY54ZIXw+E+bhXPfTu6BDgiWtjgxDs76vsBRARERE1Ayak1CqVmazYc64AW/7IwY+HMpFVbAQASBJwQ6wvVr3xJP7v7X/CJ0CvcKTU3gW5W2D490twC+oAr67XwzO2L9wCY6DSeiG71FJ1b3O64zHCZkWPSD8kxQbhhs5BGBgXCJ2Wb9dERETU9rTpTzhCCLz22mtYunQpPD098cILL2DixIlKh0VXUFRuwYWCcuSVmVFYbkZhuQWF5RbkFJchr8SIYqMV5wuMSC8ywSYuHafXqvGnxCDce00ItJYSLD93kEu4UItgv+d0yqz58j2nQggYrQIlZhtKTJUoM9tQarahzGxDbpkFFdDgaGYpjmaW4vPtZ+GmlnBtB3/c1CUEg7sEoXuYD1Qq/n0TERFR69emE9IPP/wQCxYswKpVq5CXl4dx48YhKioKI0aMUDq0di87vwgHz+XiuKEcRw1lOJ1XgfQiE0pNlXU+h7UwC8YLh1B+YgfOndmPw5UWvFmtnJMUUUty+T2nPgBCnOxXnJ+L1yaNxvsr1uB0iQo7zhYhvciEnafzsfN0Pt7+CfB0UyE2wBMhejfotRrYv3sRwvFckgT4aDXw9dTA10MDfy8NogL1iAn1R7BOC3cNl6ImIiIiZbXZhFQIgX/+8594/vnnMWTIEADAzz//jA8//JAJaSPYbALmShtMFhtM1kqYrFX/mq2XPgnbPxzbhEBxhRWF5Wbkl5uRXlCBE4ZS/JFVhPP55ZAk5x+GK0sLYC3Ng81YCpuxBLaKqn9vuO0e+Pn4wNtdBX9PNbzcAgAkALjf4XhOUkStmdlUgcrSXDzxpwHyNo1fODxi+8Kz07XwiOmJCnjiqKEMRw2Ney1fDw0Cvd0Q5O2GwIsPXw8NvN3V8HJXwdtdjQC9F/x9dXBTqaBRS9CoJKhVEtzUKqhV0sVtl8q0GjXU7L0lIiKiOmqzCWl+fj4OHz6MYcOGyduSk5MxZcqU5nn9MjM+334WZpMJZotFHl4qhIBNAAIABGCDgLj4vKpMAJAubrtY5lB+8TwAbDYbBKo++NnEpfNU2gQqbVX72oSA9eLPlTbbxW1ViaU9hRTyOYV87kqbgNlqg8l6MQG9+K+l8rIumAaSJBU81FWTugR5aRDgqYaPVgW9Vg2NKgBAnLyvPcFM/L+HERoZcdVzc5Iias2utqyMTQgUG20oNFaiwmpDfl4edq77BgNHPghvvY/DvkIApkpx8YsjgdIKE3Ly8qD29oOkdkOR0YoioxWn8ypceg3uagkebip4uqnhoVHB000FrZsKGpUElSRBQtUXV/a0Va2SIEmQy1SS/Xn1nyWopKp95Z8lCSpVVZn9Z7Xq4s8S4KF1x7jr4xDq4+HS6yMiIiLXabMJqcFQ1XUQFhYmb4uIiEBxcTEqKirg6ekpbzeZTDCZTPLzoqIiAEBxcXGDX/98bikW/Hiwwce3BkLYICxmiEozYLOhKpV17BmxmUpRaSyBrbwEtvJCWPIuwJKXBnNeGh5/7Z/wDQwBYKt6CMBqBC7v1zQbywEABdnpUOHqCXFhdkaT7d+U527q/VtSLPXdvz3GYjaWw1he5nQfDwBhWgBaQF9YgJ93rkSHu0Yi0Ke2dU4lABJy07OwdPFkjHvxfegCQ2GqBCoqAVMlYKwEjFbAYgOsArDaAKPFgtxsA1Qad0ClBlRqSCoVJEkNqDSQVGpApar6txrjxUfhVa+26fUK0cCzc3iDj7e3A+Ly8dBUK3tdNaYNJSKi1q+ubagk2mgru23bNgwaNAgFBQXw8/MDAOzfvx99+/ZFeno6IiIu9bTNmTMHc+fOVShSIiJq6S5cuICoqCilw2gV0tLSEB0drXQYRETUQlytDW2zCemxY8eQkJCA8+fPyw3jli1bcPPNN6OiogIeHpeGcF3eQ2qz2ZCfn4/AwMCrztRaXFyM6OhoXLhwAT4+Plfctz1hvTjHeqkd68Y51otzzVUvQgiUlJQgIiICKhUngaoLm82GjIwM6PV6tqENxHpxjvVSO9aNc6wX51paG9pmh+zah+pmZmbKCWlGRgb8/PwcklEA0Gq10Gq1Dtvsvap15ePjwz90J1gvzrFease6cY714lxz1Iuvr2+Tnr+tUalU9e5N5t+3c6wX51gvtWPdOMd6ca6ltKFt9utef39/XHPNNdiwYYO8bePGjUhOTlYwKiIiIiIiIrJrsz2kADBt2jT85S9/wcCBA5Gfn48vv/wSa9euVTosIiIiIiIiQhtPSCdNmgSDwYCHHnoInp6e+Oc//4lbbrnFpa+h1Woxe/bsGkN+2zvWi3Osl9qxbpxjvTjHemkb+Ht0jvXiHOuldqwb51gvzrW0emmzkxoRERERERFRy9Zm7yElIiIiIiKilo0JKRERERERESmCCSkREREREREpgglpPZSVlWHcuHEIDAxEUlISdu7cWafjXn31VUiShM2bNzdtgAqpT70UFBRg0qRJiI6ORmRkJF544QWYzeZmjLZ5CCHw6quvIjo6Gl26dMHHH39c674GgwF33HEH/Pz8cNNNN+HkyZPNGGnzqmu9VFZW4vXXX0evXr3g7++Pe++9F5mZmc0cbfOpz9+L3datWyFJEubMmdP0ASqkPvVis9nwxhtvID4+HsHBwRg3bhzy8vKaMVq6GrahzrENrYltqHNsQ51jG+pcq2pDBdXZfffdJ5KSksS+ffvErFmzhI+PjzAYDFc85sKFC0Kn0wlPT0+xadOm5gm0mdW1Xmw2m0hKShITJkwQe/fuFWvWrBGBgYHi9ddfVyDqpvXPf/5T+Pv7i40bN4qVK1cKd3d3sW7duhr72evkT3/6kzh48KB47LHHRExMjDCZTApE3fTqWi/PP/+8uP7668Uvv/wifv/9d9GvXz8xbNgwBSJuHnWtFzur1Sp69+4tdDqdmD17dvMF2szqUy/PP/+86NChg1i/fr04dOiQePzxx8WuXbuaOWK6ErahzrENrYltqHNsQ51jG+pca2pDmZDWUWZmplCr1WL79u1CiKo3wfj4ePHee+9d8bhx48aJBx54QHTo0KFNNqb1rZfTp08Lm80mP3/55ZdF7969myPUZmOz2URiYqJ444035G2TJk0Sd955Z4199+zZIwCI9PR0IYQQRqNR6HQ68Z///KeZom0+9amXrKwsUVpaKj//5ZdfBABRUFDQDJE2r/rUi93SpUtFTEyMGDt2bJttTOtTL9nZ2cLT01P8+uuvzRgh1QfbUOfYhtbENtQ5tqHOsQ11rrW1oRyyW0fbtm2Dp6cnkpKSAACSJCE5ORmbNm2q9ZidO3fi22+/xdy5c5srzGZX33qJjY2FJEny84CAABQXFzdLrM0lPz8fhw8fxrBhw+RttdXJ5s2bkZCQgIiICABV60LdcMMNV/y7aq3qUy+hoaHw9vaWnwcEBAAASkpKmj7QZlafegGA4uJivPTSS3j11Vfh7u7eXGE2u/rUy7p16xAUFIQbbrihOUOkemAb6hzb0JrYhjrHNtQ5tqHOtbY2lAlpHRkMBoSEhECtVsvbIiIiYDAYnO5vs9nw9NNP44UXXkCXLl2aK8xmV996udy+ffvQs2fPpgpPEfZrDwsLk7dFRESguLgYFRUVNfatvp9937rWX2tSn3q53L59++Dn54eoqKgmjVEJ9a2X119/Hd26dcPDDz/cbDEqoT71cv78eXTo0AHffPMNrrnmGnTp0gXz58+H4DLbLQbbUOfYhtbENtQ5tqHOsQ11rrW1oZpme6VWrqCgAHq93mGbXq9Hfn6+0/2XL1+OvLw8zJw5sznCU0x966W69PR0rFy5Et98801ThaeIgoICAHCoF/vPBQUF8PT0dNjXWf2lpaU1Q6TNqz71Up3NZsP777+PCRMmOPQMtBX1qZfU1FT885//xK5du9pkXVRXn3pJS0vD8ePHsWLFCixatAh//PEHHn/8ccTHx2P06NHNGjc5xzbUObahNbENdY5tqHNsQ51rbW0oe0hrsWzZMuh0OvlhtVprDHUoLi5GYGBgjWNLS0vx17/+FYsWLar1DaK1aky9VCeEwPPPP49rrrkGI0eObMqQm52zoTH2IVX2sur7NqT+WqP61Et1H3/8Mc6ePYsZM2Y0bYAKqU+9zJgxA9OnT0ePHj2aL0CF1Kde9Ho9goKCsHLlSgwaNAiPPfYY7rrrLnz//ffNFzA5YBvqHNvQq2Mb6hzbUOfYhjrX2tpQ9pDWYtSoURgwYID8fP/+/TAYDKisrJSH1mRkZNQYKgIA33//PTIyMjBu3Dh5W35+Pu6880489NBDWLhwYdNfQBNpTL1Ut2jRIvz3v//F/v3729y3VPZrz8zMRHR0NICqOvHz84OHh0eNfS+fij0jIwMJCQnNE2wzqk+92O3ZswdPPfUUli9fjvDw8GaLtTnVtV7S09Px/fffY/PmzVi6dCmAqoZGrVbj+++/x/79+5s/+CZUn7+X6OhoqFQqh/uBOnbsiJSUlOYLmBywDXWObejVsQ11jm2oc2xDnWt1bahi0ym1MtnZ2UKj0Yht27YJIapmr+rcubN4//33a+xbWloqLly44PAAIL755huRl5fX3KE3qfrUi92aNWuEh4eH+OGHH5orzGZ3zTXXiHnz5snPJ06cKO6+++4a++3fv18AEGlpaUIIISoqKoS3t3ebrZu61osQQqSmporIyEjxl7/8pbnCU0xd6sVisdR4XxkwYIB49tlnRUZGRnOH3Czq+vdy6NAhodFoRGZmprxt9OjR4oknnmiWOOnq2IY6xzbUObahzrENdY5tqHOtqQ1lQloPY8eOFUlJSWL//v1i1qxZwtfXV+Tk5AghhFi/fr2IiIgQhw4dcnosgDY5Zb0Q9auX//73v0Kr1YoPP/xQGAwGkZmZKTIzM0V5ebmSl+ByH330kfDz8xMbN24U3377rXB3dxc///yzyM7OFjExMeKzzz6T9x04cKAYOXKkvIZax44dhdlsVi74JlTXejl9+rSIjo4WDz30kMjJyZH/TgoLC5W9gCZSn7+X6m666aY2O2W9EPWrl5EjR4rhw4eLgwcPis8++0y4ubmJAwcOKBc81cA21Dm2oTWxDXWObahzbEOda01tKBPSeigtLRVjx44V/v7+ol+/fmLnzp1y2bp160R4eLg4ePCg02PbcmNan3pxd3cXAGo8anuzaK1sNpt49dVXRWRkpOjcubP4+OOPhRBVa4NFR0eLTz75RN7XYDCI2267Tfj6+orBgweLkydPKhV2k6trvdx6661O/04eeeQRBaNvOvX5e6murTem9amXkpIS8fDDD4vAwEDRpUsXsXr1aqXCplqwDXWObWhNbEOdYxvqHNtQ51pTGyoJwXnxiYiIiIiIqPlxll0iIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUgQTUmoXOnbsiIULF9Z5/zlz5qBfv36Nek1JkrB27dpGnaOprF69Gp06dUJFRYXSoTS7zz//HEFBQY06x65duxAZGYkLFy64KCoiopaLbagjtqFsQ8m1mJAStXF79uyp0XgEBQWhW7ducHNzUygqIDc3F5IkobS0VLEYGsrX1xfdunWDl5cXgNZ9LUREVDu2oa7HNpQup1E6ACJqfoMHD8bgwYOVDqPV6tatG3755RelwyAiIgWwDW0ctqF0OfaQUpuRmZmJMWPGwNfXF5IkyY+//vWvNfYtKSnBtGnTEBERgYiICDzxxBM1vpkTQmDJkiXo0aMHgoODMWnSJIfhOb///jvGjBmD6OhohISEYMaMGaisrGxQ7NnZ2Rg9ejR8fHwQHx+PefPmwWKxAAAsFgteeuklREdHIzw8HE899ZQcx9mzZ6HVanH69Gncdddd8Pf3R//+/ZGamgqgatjUddddh7y8PLk+AGDt2rXyzwDw6KOPYt68eZg/fz5iY2PRvXt3rFy5EllZWbjvvvvg7++PIUOGIDMz06F+3nvvPXTu3BmBgYEYP3488vPz5XJJkpCSkoKHH34YQUFB6NGjB3bv3g2gashPcHAwAECv10OSJJw9e/aq9TRnzhyMHz8eCxcuREJCAkJCQjBlyhSH34vZbMasWbMQGxuL4OBgjBs3DtnZ2bWeMz8/Hy+++CJ69+4NvV6PIUOG4PTp0w5189Zbb+Hdd99FeHg4lixZgsOHD8sxO7uWM2fOyNdfXUJCAj7++OOrXicRUXNjG8o2FGAbSspgQkptxqOPPor09HSsXbsW33//PaKiovDVV1/hpZdecthPCIF7770Xe/fuxerVq7F69Wq5YRRCyPvt27cP27Ztw7Jly/D1119j/fr1mDlzplz+zTffYNCgQVi3bh0+++wzLFq0CN9++22DYp81axZSU1Px888/49NPP0VJSQlUqqr/nk8//TR2796N7777DuvWrcPBgwfx+uuvy8eazWb86U9/wsMPP4xff/0VpaWlePHFFwEAM2bMwIIFC+Dv74/MzEyHxvByb7/9Ni5cuIC1a9ciOTkZEyZMwMiRI3H//fdj06ZNOHXqFN566y15//nz5+PLL7/EZ599ht9++w0mkwnTp093OOfIkSMxbNgw/PbbbwgJCcGTTz4JALj//vuxatUqAEBqaioyMzMRHR1dp7pasWIFfv/9dyxbtgwrVqzAunXr5OsFgOnTp+Pf//43PvnkE/z8888oLCzE0KFD5Q8nl0tJSUF2djbef/997Ny5E1arFU888YTDPp988gl27tyJ9evXY8yYMQ5lzq4lJiYGt9xyC5YtWybvd+zYMZw8eRJ33XVXna6TiKg5sQ1lGwqwDSWFCKI2wtfXV6xYsUJ+/swzz4gnn3xSCCFEhw4dxAcffCCEEOK3334TKpVKnD59Wt731KlTQpIksX37diGEELNnzxaJiYkO51+6dKnw8fERNpvN6esPGTJETJ06VX4OQKxZs6ZOsd93331i7NixNc594cIF4eHhIQoKCuRt27dvF7GxsUIIIc6cOSMAyHELIcTMmTNFXFyc/Pyzzz4TgYGBDudds2aNqP7f/5FHHhG33nqr/HzPnj0CgPjhhx/kbRMnThQ33HCDEEIIs9ksvL29xZEjR+TyjIwM4ebmJiwWi3z91X8fH330kdBoNKKyslIIIcSmTZsEAFFSUlKnOhLi6r+X8+fPC0mSxNatW+XywsJC4ePjI8firD6q++KLL4SHh4f8u3jkkUdEZGSkKCoqkvc5dOiQACDOnDlT67X8+9//FpGRkcJqtQohhHjttdfE7bffXudrJSJqTmxDq7ANZRtKzY89pNRm3Hbbbfj666+Rnp6Ow4cPY926dUhISKix34EDB9CxY0fExsbK2zp16oSOHTviwIED8ja1Wu1wXM+ePVFcXIy8vDwAVcOAVq5ciXvuuQeJiYnYu3cvcnJyGhT77NmzkZKSggEDBmD16tWw2WwAgIMHD8JoNCIqKgo6nQ46nQ5Dhw5Fenq6w/F6vV7+uXPnzigsLKx3DNXPER8f73Sb/bwnTpxAWVkZkpKS5Lg6d+4Mi8XiUAeXx2W1WlFeXl7v2Kq70u8lJSUFGo0G119/vVzu6+uLa6+91uF3e7mUlBQ89dRTSEpKwqxZs2A0GlFWViaXDxgwAD4+PvWK884774TJZMKWLVsAAKtWrcL9999fr3MQETUXtqFV2IayDaXmx0mNqM2YNWsWBgwYgE6dOsFsNmP8+PGYOHFijf1EtSFFl7M3Ys7YGwEvLy/YbDaMHj0aJSUleOONNzBgwABMnDixwTPEJSQkICUlBd9++y3mzJmDd999F//73/8ghIBGo8G+ffvk4UcAHH6+3JXK6srZOapvs9fhunXrEB4e7rBfSEhIk8XlTPXfS0N+t19//TWeeOIJvPvuu3jttdewf/9+DBkypNFxabVaPPzww1i2bBni4uJw7Ngx3HnnnY0+LxFRU2AbevWyumIbyjaU6ocJKbUZc+fOxdtvv42HHnoIkiRBp9M53a937944e/Yszp49i44dOwIAzpw5g7Nnz6JPnz61nn/z5s2Ij4+Hl5cXjh49ih9//BGnT5+WvyW+UkNcFyqVCvfddx9GjhyJoKAg/PLLL+jduzesViuys7MxaNCgBp1Xo9HAaDTCZrO5rEGLj4+Hp6cnzp07hxtvvLHBcQFVjWFtv6u6qP576dWrFywWC3bs2CHXV3FxMfbu3ev0gxUAvPfee5g+fTomTJgAoGG/x9quZeLEibj++uvRt29f3HbbbfD19a33uYmImgPbUOfYhrINpabHIbvUZuTn52PXrl3IyspCaWkpioqK5G/79Ho90tPTYbPZcMMNNyA5ORljx47F7t27sWvXLjzwwAO45ZZbMHDgQPl8x48fx/z58/HHH3/gq6++wrvvvivf+G9fO+uLL77A0aNH8eabb2L16tUO8eh0ujot+mw0GjF06FB88cUXOH78OD7//HNUVFQgOjoaMTExmDRpEh566CH897//RWpqKpYtW4Z169bVuV46d+6MsrIyfPnllzhy5MgVvwGtK61Wi1mzZuGZZ57Bv/71L6SmpuK7777D8uXL63yO2NhYSJKEpUuX4o8//qjzAuPHjx/HO++84/T3EhMTg4kTJ2LChAnYtGkTDh48iAcffBDR0dHyRAp6vR7l5eXysDGdTof169cjJSUFa9aswVNPPVXP2qj9Wrp3744ePXrg1Vdf5VAjImrR2IY6xzaUbSg1A2VuXSVyveXLlwsPDw+h0WgEAAFAxMTEiG3btonXX39deHp6inPnzgkhhCgqKhJTpkwRYWFhIiwsTEydOlUUFxfL55o9e7YYP368mDFjhggODhadOnUSixYtcpgw4Z133hH+/v4iLi5OzJ07V7z11lvinnvukcunTJki9Hq9MJvNV419zZo1YtCgQUKn04nY2Fjx4YcfymUmk0nMmjVLdOrUSej1enHTTTfJEw7YJ2Q4dOiQvP/lEw7YbDYxbdo04ePjIzp16iTS09OdTshQPfaSkhIBQGzatEneNn/+fNGjRw/5eWVlpViwYIHo1q2b8Pb2Ftddd534z3/+I5fjsgkpnE1aMG/ePBEQECAiIyPF3r17r1pPs2fPFt27dxfPPPOMCAoKcvp7MRqNYubMmSImJkYEBASIBx54QGRlZcnl2dnZIjo6WkyePFkIIcS+fftEjx49hJ+fn7j77rvlySjscV5eN0LUnJDhSteyePFi4eXlJUpLS696fURESmEbWoVtKNtQan6SEC74qodIYUVFRUhISMCOHTsQExMDIQSysrJw2223oV+/fly3qo2YM2cO1q5diz179igdSp1NnToVZrMZn376qdKhEBE5xTa0fWAbSi0V7yGlNsFgMCArKwtr1qzBoEGDYLVasW3bNpw5cwYvv/yyorGlpKQ4zFh3ue3bt6NXr17NGFHLtGbNGowdO7bW8rS0tGaMpvEyMjKwe/duLF++HEeOHFE6HCKiWrENbf3YhlJrxoSU2oQuXbpgyZIleO+99/Dcc89Bp9MhMTERX331FUaNGqVobF27dr3idOl1Xcy6rRsyZMgV66n69PetwS233ILS0lIsXboUMTExSodDRFQrtqGtH9tQas04ZJeIiIiIiIgUwVl2iYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgRTEiJiIiIiIhIEUxIiYiIiIiISBFMSImIiIiIiEgR/w9RRCjdDPEvAAAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAGHCAYAAACnGJ9sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgplJREFUeJzs3XlcVNX/P/DXnQGGbRj2VVBTXHDJLXJfkI+amUuJWlYfP2ampm0ufS1zadHqW79v9TE1tfWT1kfT7JOf0lREzS0V0TQF3FIQhp1hWIaBOb8/kJsjg4LCXMDX8/G4D5l7zr33fa/I8c059xxJCCFAREREREREZGcqpQMgIiIiIiKiuxMTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSImoSBg8ejJ9++slq39mzZyFJkkIR1b127dohLi5O6TCIiOguNmHCBCxevFjpMKgJYUJK1Ah89NFHiIyMVDqMenPq1ClIkoRLly7d9jlSU1NRVFRU6+PeeOMNDB069LavW1cefPBB+Pr6yltAQAAAYObMmWz4iYjqAdtW2/72t7/h66+/lj//+eefcHd3R1lZWY3PUZO2dfjw4WjWrBlUKhX8/PzQrFkzNGvWDK6urtBqtWjWrBk+//xzq2OmTp0q17tx8/b2RpcuXWp1r9QwMCGlRi0rKwuSJMFoNCodSp357rvv0KNHD6t9QUFBaNeunUIRNUwlJSXw9PSUt6SkJDz55JPy57fffrtG50lOTsb58+dvWmfr1q1QqVRW17txO378uNUxnTt3hru7u81No9EgOjraqv5///tfZGVlISsrC0ePHkV2dnbtHggRUR1h23p3Ky4utko+hRAoLCys1Tlq0rb+9NNPSElJgb+/PzZu3IiUlBSkpKRg3LhxePbZZ5GSkoJ//OMfVsesXr1arnfjtnr16lrFSA2Hg9IBENGtxcTEICYmRukwGhSNRoOsrKxqy1UqFZKSkm56joKCAvzyyy/Izc3FiRMncO+991Zb95577sG5c+dqHN/JkyerLVuxYgW+//77Gp+LiIjqHtvW6n322Wf49ddfAVS0lbVRm7a1tp544gls374dHh4eVcrKy8sRHh5eZ9ci+2EPKTUYAwcOxDfffIM5c+bA19cX27ZtQ1FREd555x1ERkZCq9WiR48eiI+PBwB88cUX8PPzAwBotVqrYSkXLlzAAw88AA8PD3Ts2LFW//kfOHAgvvrqKyxevBj33HMPgoOD8emnn1rVudn5zWYz5s6di6CgIEiSJG89e/YEUDG0dNasWYiIiICHhwdGjRolJ1aTJk1CTEwMjh07BkmS0KJFCwDA8uXL5a8nTpyICRMmWMXzzTffoFmzZrBYLACAHTt2oHv37tBqtRgwYABOnTpVo3sfNGgQ5s6dK39es2YNJElCWlqavK9r16745JNPAAClpaVYsGABWrZsCT8/P0ycOBEZGRly3S+++ALDhg3Dtm3bEBERgccffxwAkJOTg8ceewze3t7o0KEDNm/ebBVHRkYGRo8eDQ8PD4SHh+Ott96C2Wy2qiNJEhwcHHD58mVMmDABYWFh6NatG1auXAm1Wg2V6uY/3srLyzF58mT069cP7733HsaNG4fc3Nxq61+4cKHa3tF77rnnFk/Wmslkgkajsdr3zDPPoEuXLujSpQuGDx9uVbZ06VK4u7sjMTGxVtchImLbyra1Uk3a1kotW7ZEjx490KNHD3Tu3LlKeWW79N1331ntr03bGhMTg2bNmiEjI0P+ulmzZtiwYQM+/vhj+evrmc1mPPfcczh37lyV7eLFi/jll19sXosaOEHUQAwYMECEh4eL6dOni9OnTwuj0SgSExNFTEyM2LFjhzh79qwYO3asaN++vbBYLKKoqEhs2rRJABDnzp0TaWlpoqysTOTl5YmgoCDx9ttvi6SkJLF582ah0+nEH3/8UeM4dDqdeO2118Tp06fFnDlzhEajEdnZ2UIIccvzL126VPj7+4tNmzaJ/fv3i4EDB4rp06eLrKwsIYQQX3/9tZg9e7Y4dOiQiI+PF23bthXTpk2Tzz179mzRuXNnkZaWJjIyMoQQQvzzn/8UzZs3F0II8cMPPwitVitKSkrkmGNiYsTzzz8vhBDi4MGDwtfXV2zcuFEkJyeLpUuXihYtWljVr84bb7whIiMj5c8TJkwQYWFhYt26dUIIIXJzc4UkSSIxMVEIIcTUqVNF69atxa5du0R8fLwYPny46NixoygtLRVCCPH555+LwMBA0bNnT7Fv3z6RmpoqLBaL6Nevn+jWrZs4ePCgOHLkiHjooYcEAHHx4kUhhBBPP/206NChgzh48KDYu3evePnll0VZWVmVeIuKikTr1q3F4sWLRW5urjh+/Lho27at+OSTT4QQQpw5c0bY+jF37tw50a9fP9GzZ0+Rk5MjysrKxNSpU0Xz5s3Frl27hMVisar/448/ilatWt3y+dXUsmXLxJgxY6z2DR48WHz00UciMzNTpKeni3PnzonMzEzx7LPPikWLFgkhhGjbtq3YvXt3ncVBRE0f21a2rbVtW/v06SM+//xz+fPFixcFAGE2m4UQQowfP15ul65X27b1dowfP164ubmJgICAare8vLw7vg7ZFxNSajAGDBggunbtKv/As2XPnj0CgEhPTxdCCLF7924BQBQUFMh1li5dKsaNG2d13NSpU8WSJUtqHMfUqVPlz4mJiQKA2LdvX43OP2rUKKvjt2zZIjp27Fjt9ZYsWSLatWsnf160aJHo3r27VZ3rG82SkhKh0+nEzz//LIQQori4WLi7u4v9+/cLIYQYMmSIWLFihXysxWIRbdq0EXv27LnlvR84cECo1WphMBiExWIRISEh4t133xVPPfWUEEKIrVu3ipCQEGGxWMTly5eFJEli79698vF5eXnCw8NDrF+/XghR0WhKkiSOHDki19mzZ4+QJEkkJyfL+44fP27VaI4bN048+uijt2y84uLiRKtWrazqrV+/XvTv318IUTUhtVgs4qGHHhIuLi5i/vz5orCw0Kps7dq1ws/PT7Rr106kpKTIZXWdkD777LPi2Weftdo3ePBg4eHhITw9PYVWqxXt2rUTzz33HBNSIrojbFsrsG2tedvap08f4eLiInQ6ndDpdEKr1d40Ib2dtrVHjx7y+W+1LVu2rNpYAVjdMzVOfIeUGpSoqCg4OFh/W54/fx5r167Fnj17oNfrAQCZmZnyLKQ3io+Pxw8//AB3d3d5n8lkwuTJk2sch1arlb9u3bo1ACAvL69G53/ggQfwzjvv4MSJE/D398fXX3+NiIgIq/Pv378fn3/+OeLj45GWllbtkBlbNBoNRo8ejS1btmDYsGHYuXMndDqdPGwpPj4ee/futRoeVFRUhNTU1Fue+7777oOrqysOHjyI0NBQ+Pr6YsiQIVi5ciUAYN++fRg0aBAkScLJkyfh4OCA3r17y8frdDp0794dCQkJePTRRwEA3t7eVhNJnDx5Ei1atJCfqy2LFi3CuHHj0LNnT7z88ssYPXq0zSG45eXlcHBwsFraxdHRsdqZACVJwsKFC/HVV1/B09OzStlTTz2FJ554AgcPHkRISIhcplKpkJqaCl9fXwAVw6lUKpXV9+pnn32GkSNHVntP1zt//jwGDx5stW/79u2QJKnKfc6cOVP++qWXXqr18GAiIratt8a29S+V745WZ8iQIQgMDJQ/307beuTIkZte40aXL19Gt27dbJbdd999UKvVVvs2bdqEAQMG1OoapBwmpNSgxcXFYfTo0Xjttdfwww8/oLCwEC1btrzpMUIIjBo1CsuWLbPar9PpbiuGG39Y3+r8kyZNwrJlyzBgwADk5+cjMjLS6h2Ld999Fx999BE++OADfPTRR9iwYQPmzJlTq5jGjx+PyZMnY8WKFdiyZQtiYmLkOIUQeOONNzB69GirY65vPKrj4OCAAQMGYO/evQgJCUH//v3RqVMn5OXl4dKlS9i3bx+mTJkiX6c6le/b2GI2m6v8x+hGEREROHnyJL777jssXrwY7733Hnbs2AE3NzerepGRkSguLsb777+PZ599FpcvX8brr78ux2jLjbMs3sjJyalKIzZ8+HAUFxfLn8eOHYsuXbpgwYIFNz1Xda5evSq/t1Tpxsa00ttvvy3/3U6dOvW2rkdEdD22rbaxba0ZW7+EuJ22ddWqVXjvvfduetyJEyfg5uaGsLCwKhMZWiwWbN68GUOGDLE5yRE1HkxIqUFbsWIFRo0ahdmzZwOoOtNb5Q/foqIi+beq9957L9atW4cWLVrc8ofz7bjV+T/99FNERkZi3bp1yM/Pl3vVKr333nt45513MHbsWABVGxgHB4dbrqcZHR2N0tJSxMfH47///S82bdpkFd/Zs2dv+lvSmxk8eDC2bduGS5cuYcyYMVCpVOjfvz9iY2MRHx+PQYMGAahY1sRsNuPgwYPo27cvAMBgMODYsWM3TQjDw8Nx8eJF6PV6+Tfx5eXlVeqpVCqMGzcOI0aMgK+vL3bt2lWlB9Ld3R0///wzXnjhBbz++uvw8vLC1KlTrXoVG6KEhIRq/9MhSRLc3d1tJqgGgwGxsbEYOHBgPUdIRE0Z21bb2Lb+ZeLEifjvf/9r81qFhYV49dVX73iN7GnTpmHatGk2y7KysuDn53fTBD0+Ph4xMTH49ttvMX78+DuKhZTFWXapQXN3d8e+fftw7NgxxMbG4sknn7Qqb9myJSRJwpo1a5CYmIji4mLMnDkTOTk5ePLJJ5GQkIATJ05g4cKFtZ62vDq3On9OTg6Sk5Nx6tQplJWVITs726phdHd3x5YtW3D69GmsX78eS5YssTp/69atkZiYiB07duDMmTM2Y3B0dMTDDz+MN954Aw4ODvKQIgBYsmQJvvjiCyxduhSJiYn49ddfq/zG+WYGDx6MY8eO4eDBg+jfvz+AitkRV65ciaCgILlnLywsDFOmTMHkyZOxe/dunDhxAo899hhCQ0NvOo3+0KFDERoaikmTJuHkyZOIjY21amRLSkowePBgfPnllzh79iy++OILFBcXIzQ01Ob5IiIi8MsvvyA/Px+XLl3CK6+8Uu0Mu6+//nq1a4PeuN04FKym9Hr9Lc+t1Wrh4eEhf3/f6Pjx48jLy6uyhYWF3VZMRETXY9vKtvVWbeu6detstkN5eXl45JFHrOrebtu6cuVKBAQEoF27dlW23r17IyAgwOqVnOtlZ2dj2rRpiI6Oxssvv3zLNU+pgVPq5VWiGw0YMEDMnj3bat/FixdFz549hVarFUOGDBHHjx8XWq1W/P7773Kdt956S3h7e4uQkBBx7NgxIUTFTG8jRowQXl5eIiQkREyZMkWeie924gAgfvzxR/nzzc5/8eJF4ezsLFxcXAQAAUB4eHiIjz/+WAghxC+//CJatGghfH19xaRJk8S2bduEj4+PfO6SkhIxduxY4ebmJs+qd/3EC5V27NghAIgXXnihyj3s2LFDREZGCjc3N9GmTRvx1ltvifLy8hrdv8ViEf7+/qJDhw7yvsqJESZPnmxVt6SkRMyfP1+EhYUJb29vMWHCBHlSDCEqJl64/t4qJSYmikGDBgmtVit69+4t9u7dKyRJkide+PHHH0Xfvn2Fu7u7aNmypVi1alWNYr9RdbPs1kZ0dLRwc3Or0Xb9hBu3CzeZoKF58+ac1IiIaoVtawW2rXXXtlY3y25t/d///Z8YNWpUjeubTCaxZ88e8corrwhvb28xd+5cUV5eLpYvXy68vLzEzJkzxfbt260m46LGQRLiJn3hRFRr0dHRmDp1KsaNGwcAyM/Px7x587Br1y6cO3dO4ejuLmfPnkX79u1vOuSnoZEkCS4uLjZ7eQsLC7F7924O2SWiuw7b1oZjwoQJaNeu3R0P2f3ggw/wyiuvwN/fv9o6S5Yswd///ncAFUPIn3zySURFRWHkyJFo1qyZXC87Oxv//e9/8Z///Afvv/8+mjdvfkexkX0xIaW7yrx587BixQqbZZ06dcLBgwfv+BrBwcEYM2YMJk6cCG9vbxw7dgxvvfUWoqOj8dFHH93x+e9Er1698Pvvv9ssmzFjBt599107R0RERI0d21a2rUR3ggkp3VUyMzORn59vs8zJyalO3tHbv38/5s+fj4SEBFgsFrRu3RpPPvkknnvuuXqZCKI2Ll++jNLSUptlOp0Ofn5+do6IiIgaO7atbFuJ7gQTUiIiIiIiIlIEZ9klIiIiIiIiRTAhJSIiIiIiIkUwISUiIiIiIiJFKPsWeANlsVhw9epVaLXaahfkJSKipk8IgYKCAgQHB9tcioeqYhtKRERAzdtQJqQ2XL16FaGhoUqHQUREDcSVK1es1ryj6rENJSKi692qDWVCaoNWqwVQ8fA8PDwUjoaIiJRiMBgQGhoqtwt0a2xDiYgIqHkbyoTUhsohRh4eHmxMiYiIQ09rgW0oERFd71ZtKF+IISIiIiIiIkUwISUiIiIiIiJFMCElIiIiIiIiRTAhJSIiIiIiIkUwISUiIiIiIiJFMCElIiIiIiIiRTAhJSIiIiIiIkUwISUiIiIiIiJFMCElIiIiIiIiRTAhJSIiIiIiIkU0iIS0tLQUbdu2RYsWLeR9er0eDz74IDw9PTFgwAAkJydbHXPgwAFERkbCx8cHjz/+OAoLC63KV69ejfDwcISGhuLNN9+EEMIet0INhNFoREZGRo03o9GodMhERERERHedBpGQfvzxx7h69ar8WQiBkSNHQq1WY+/evQgPD0d0dDRKS0sBAOnp6Rg2bBiGDRuGnTt3IikpCVOmTJGP/+mnnzBr1iy8/fbb+PLLL/H+++9j9erVdr8vUobRaETzFi0REBBQ4615i5ZMSomIiIiI7EwSCncdZmZmIjw8HNOnT8c333yDS5cu4dixY+jRowdSU1MRHBwMk8kEX19f/Otf/8Lo0aPx/vvvY82aNThz5gwkScKBAwcwYMAApKamwt/fHw899BCCg4PxySefAACWLl2KDRs2ICEhoUYxGQwG6HQ65Ofnw8PDox7vnupDRkYGAgICMG/1VrjpvG9ZvzA/B+9OHQG9Xg9/f387REhEjQXbg9rjMyMiIqDm7YHiPaQLFy5E165dMWTIEHlfXFwcIiIiEBwcDADQaDTo06cPdu/eLZcPHjwYkiQBACIjI+Hk5IT9+/fL5dHR0fL5oqKicOLECeTm5tqMwWQywWAwWG3U+LnpvKH18rnlVpOklYiIiIiI6p6iCenvv/+OL774AitWrJCTS6Di/dHAwECrusHBwdDr9TbLHRwcEBAQAL1ej8LCQhiNRqvyysS28vgbLVu2DDqdTt5CQ0Pr7B6JiIiIiIjINsUSUiEEXnjhBcyePRvt27e3KsvNzYVWq7Xap9VqkZOTc8vyvLw8+fP1ZQDk4280f/585Ofny9uVK1fu6N6IiIiIiIjo1hyUuvAPP/yAixcv4scff6xS5u3tjQsXLljtMxgM8PHxkcsLCgpslnt7Vwy/vL68cghu5fE30mg00Gg0t38zREREREREVGuK9ZBWzqwbFhYGX19fjBo1CpcvX4avry88PDyQlpZmVf/q1avyMNzAwECr8rKyMmRkZCAwMBAuLi5Vjq+cwTcgIMAOd0ZEREREREQ1oVhCun79epw7dw4JCQlISEjAokWLEBwcjISEBERHR+PMmTNITU0FAJSUlGD//v2IiooCAAwaNAg7d+6U1xY9fPgwzGYz+vbta1VeKTY2Ft26dYOnp6d9b5KIiIiIiIiqpVhC6ufnh2bNmsmbt7c3HBwc0KxZM9x///3o1asXpk2bhpMnT2LmzJnw8/PDsGHDAACPPfYY9Ho9Fi1ahISEBLz44osYP368PCR3+vTp+PLLL7F582bExsbivffew4wZM5S6VSIiIiIiIrJB8WVfqrNlyxaUl5ejf//+SE5Oxo4dO+Do6AgA8Pf3x7Zt2/Dzzz8jKioKbdq0kdccBYChQ4di+fLlmDdvHv7+979jzpw5mDx5slK3QkRERERERDZIonLcK8m4qHfjlpGRgYCAACz59wFovWxPZHW9gtxsLBrfG3q9Hv7+/naIkIgaC7YHtcdnRkREQM3bgwbbQ0pERERERERNGxNSIiIiIiIiUgQTUiIiIiIiIlIEE1IiIiIiIiJSBBNSIiIiIiIiUoSD0gEQERERUc3p9Xrk5+crHYbd6HQ6BAQEKB0GEdUTJqREREREjYRer8fjTzwJc6lJ6VDsxtFJg6//9RWTUqImigkpERERUSORn58Pc6kJxfcMgMVZZ9drq4rz4HJxL4pb9ofFxdM+1yzJBy7sQX5+PhNSoiaKCSkRERFRI2Nx1sHi5qvMtV08Fbs2ETU9nNSIiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUwYSUiIiIiIiIFMGElIiIiIiIiBTBhJSIiIiIiIgUoWhCumPHDgwcOBBarRbt27fHunXr5DJJkqpsly5dkssPHDiAyMhI+Pj44PHHH0dhYaHVuVevXo3w8HCEhobizTffhBDCXrdFRERERERENaBYQpqbm4tJkyZh3LhxOHr0KKZPn44nnngChw8fluts3rwZaWlp8hYaGgoASE9Px7BhwzBs2DDs3LkTSUlJmDJlinzcTz/9hFmzZuHtt9/Gl19+iffffx+rV6+2+z0SERERERFR9RyUurCXlxfOnTsHFxcXAEDbtm2xZs0a7NixA/fffz8AoE2bNggMDKxy7Lp16xAcHIwlS5ZAkiR88MEHGDBgAD788EP4+/tj5cqVmDRpEh555BEAwNy5c7Fy5Uo888wz9rtBIiIiIiIiuilFh+xWJqMAYLFYYDQa4e7uLu/z9fW1eVxcXBwGDx4MSZIAAJGRkXBycsL+/fvl8ujoaLl+VFQUTpw4gdzc3Pq4DSIiIiIiIroNik9qJIRAWloaXnrpJRQXF+Oxxx6Ty5577jk0a9YMvXr1wi+//CLv1+v1Vj2nDg4OCAgIgF6vR2FhIYxGo1V5cHCwfJwtJpMJBoPBaiMiIiIiIqL6pXhCOnv2bAQHB+PTTz/Fxo0b4e/vDwCYOXMmnnzySWzZsgURERF46KGHkJiYCKDi/VOtVmt1Hq1Wi5ycHOTl5cmfry8DgJycHJsxLFu2DDqdTt4q31UlIiIiIiKi+qN4Qjp37lzExcVh1qxZGD58OHbt2gUA+Oc//4kHH3wQPXr0wOrVqxESEoJNmzYBALy9vVFQUGB1HoPBAB8fH3h7ewOAVXllj6ePj4/NGObPn4/8/Hx5u3LlSp3fJxEREREREVlTbFKjSkFBQQgKCsKAAQNQUFCA119/HYMHD7aqo1ar0bZtW6SmpgIAAgMDkZaWJpeXlZUhIyMDgYGBcHFxgYeHh1X51atXAQABAQE2Y9BoNNBoNHV9a0RERERERHQTivWQms1mFBUVWe3z9PREUVER8vPzq9Q9ffo02rVrBwAYNGgQdu7cKa8tevjwYZjNZvTt29eqvFJsbCy6desGT0/PerwjIiIiIiIiqg3FEtKvv/4a999/PzZs2IDk5GRs2rQJy5cvR0xMDMaMGYMpU6YgLi4Of/zxB6ZMmQKz2YyJEycCAB577DHo9XosWrQICQkJePHFFzF+/Hh5SO706dPx5ZdfYvPmzYiNjcV7772HGTNmKHWrREREREREZINiQ3YnTZoEo9GI5cuX4/jx4/D398fLL7+MOXPm4B//+AcWLlyIZ599FleuXEHv3r2xe/du+f1Qf39/bNu2Dc899xyWL1+O4cOH45NPPpHPPXToUCxfvhzz5s2DyWTCnDlzMHnyZKVulYiIiIiIiGxQLCGVJAmzZs3CrFmzqpT5+flh5cqVNz2+V69eOHLkSLXlTz/9NJ5++uk7jpOIiIiIiIjqh+Kz7BIREREREdHdiQkpERERERERKYIJKRERERERESmCCSkREREREREpggkp3VUu5xTBUGJWOgwiIiIiIgITUrqL/JldiO+Pp+K7Yykwl1uUDoeIiIiI6K7HhJTuGscu5wIACkrKcPRSrsLREBERERERE1K6K2QZTbiSUyx/PnY5F3lFpQpGRERERERETEjprnD8ch4AoLW/O0K9XVBuETh+JU/RmIiIiIiI7nYOSgdAVN/KLBYk6gsAAF1DPWE0leFKTjEyDCaFIyMiIiIiursxIaUmL7fQjHKLgMZBhSCdM3KLKmbZzTKaYBFC4eiIiIiIiO5eHLJLTV6WsaIn1MfdCZIkwdPVEQ4qCWUWgfwiLgFDRERERKQUJqTU5GUXVkxe5OumAQCoJAk+7k4AgEwjh+0SERERESmFCSk1edf3kFbyc9dYlRERERERkf0xIaUmL9t4rYf0WhJ6/deZBUxIiYiIiIiUwoSUmjSTuRxGUxkAwMftrx5SX21lDynXIiUiIiIiUgoTUmrSsq69P+qucYDGUS3v9702fNdoKoOpzKJIbEREREREdzsmpNSkZV97R9T3uvdHAUDjoIaHc8WqRznF5XaPi4iIiIiImJBSE1c5JNfnuvdHK1W+R5pXwoSUiIiIiEgJTEipScuRl3xxqlLm4ewIADCWcsguEREREZESmJBSk5ZfbAYAeLpWTUi1LhVDdpmQEhEREREpgwkpNVnlFiHPsKu99r7o9bSain2FTEiJiIiIiBTBhJSarEJzRaKpVklwdVJXKde6VA7Z5TukRERERERKYEJKTVblUFwPZwdIklSlvHKW3SKzAFRVe1CJiIiIiKh+KZqQ7tixAwMHDoRWq0X79u2xbt06uUyv1+PBBx+Ep6cnBgwYgOTkZKtjDxw4gMjISPj4+ODxxx9HYWGhVfnq1asRHh6O0NBQvPnmmxBC2OWeqOGoTEi11yYvupGLoxoOqopE1cHD125xERERERFRBcUS0tzcXEyaNAnjxo3D0aNHMX36dDzxxBM4fPgwhBAYOXIk1Go19u7di/DwcERHR6O0tGLG1PT0dAwbNgzDhg3Dzp07kZSUhClTpsjn/umnnzBr1iy8/fbb+PLLL/H+++9j9erVSt0qKcRoqhiK62Hj/VEAkCRJfrfUwcPfbnEREREREVEFxcYpenl54dy5c3BxcQEAtG3bFmvWrMGOHTvg4OCA3377DampqQgODsbHH38MX19f/PTTTxg9ejTWrVuH4OBgLFmyBJIk4YMPPsCAAQPw4Ycfwt/fHytXrsSkSZPwyCOPAADmzp2LlStX4plnnlHqdkkBcg+pi+0eUqCi9zS3yAy1jgkpEREREZG9KTpktzIZBQCLxQKj0Qh3d3fExcUhIiICwcHBAACNRoM+ffpg9+7dAIC4uDgMHjxYfi8wMjISTk5O2L9/v1weHR0tnzsqKgonTpxAbm6uvW6NGoDr3yGtjgd7SImIiIiIFKP4pEZCCKSlpeGll15CcXExHnvsMej1egQGBlrVCw4Ohl6vB4Aq5Q4ODggICIBer0dhYSGMRqNVeWViW3n8jUwmEwwGg9VGjd9fCenNe0gBwEHnZ5eYiIiIiIjoL4onpLNnz0ZwcDA+/fRTbNy4Ef7+/sjNzYVWq7Wqp9VqkZOTAwA3Lc/Ly5M/X18GQD7+RsuWLYNOp5O30NDQuro9UoqkktcXvVlCWtlDqtayh5SIiIiIyN4UT0jnzp2LuLg4zJo1C8OHD8euXbvg7e2NgoICq3oGgwE+Pj4AcNNyb29vALAqr+zxrDz+RvPnz0d+fr68Xblypc7uj5Sh1vpAAFBJgJum6hqkldhDSkTUMJWUlCApKQklJSVKh0LUpPHfGilN8YQ0KCgIAwYMwNKlSzFp0iS8/vrrCAwMRFpamlW9q1evysNwbywvKytDRkYGAgMD4eLiAg8PD6vyq1evAgACAgJsxqDRaODh4WG1UeNW+U6o1tnR5hqkleRZdrV+sHBpICKiBuPy5cuYOnUqLl++rHQoRE0a/62R0hRLSM1mM4qKiqz2eXp6oqioCIMGDcKZM2eQmpoKoOI3N/v370dUVBQAYNCgQdi5c6e8tujhw4dhNpvRt29fq/JKsbGx6NatGzw9Pe1wZ9QQOOgqfvmgvcmERgDgrnGABEBycER2odkOkRERERERUSXFEtKvv/4a999/PzZs2IDk5GRs2rQJy5cvR0xMDLp06YJevXph2rRpOHnyJGbOnAk/Pz8MGzYMAOSJjxYtWoSEhAS8+OKLGD9+vDwkd/r06fjyyy+xefNmxMbG4r333sOMGTOUulVSgMO1ZVxu9v4oAKhUElwdK/4ZZBSU1ntcRERERET0F8XWIZ00aRKMRiOWL1+O48ePw9/fHy+//DLmzJkDANiyZQsmTZqE/v37495778WOHTvg6FiRXPj7+2Pbtm147rnnsHz5cgwfPhyffPKJfO6hQ4di+fLlmDdvHkwmE+bMmYPJkycrcp+kDLVHxTuht+ohBQBXRwmFZiCTPaRERERERHalWEIqSRJmzZqFWbNm2Sz39/fHTz/9VO3xvXr1wpEjR6otf/rpp/H000/fcZzUODloK3rLa5aQqgCUI8vIhJSIiIiIyJ4Un9SIqD6otb4AKt4RvRVXp4p/BpmFHLJLRERERGRPTEipSapVQnrtHdJM9pASEREREdkVE1JqcopKy6F2dgcAuNd4yC6QaWQPKRERERGRPTEhpSanMrF0VAEaB/Ut61cmpHyHlIiIiIjIvpiQUpOjv5ZYujnV7Nvb1VECwHdIiYiIiIjsjQkpNTmV64lW9nzeSmU9Q0k5Sszl9RYXERERERFZY0JKTU7GtSG7Ne0hdVJLsJhLKo41mOotLiIiIiIissaElJqcyh5Stxr2kEqShHJjDgBAX1BSb3EREREREZE1JqTU5FT2kLrWsIcUwF8JqYEJKRERERGRvTAhpSYno+DapEY17CEF/kpI0/OZkBIRERER2QsTUmpy5HdIa5OQFmRXHFvAd0iJiIiIiOyFCSk1KSXmcuQVlwGo+aRGAFDGIbtERERERHbHhJSalMqE0mIugZNaqvFx5cZsq+OJiIiIiKj+MSGlJiXt2jug5QXZkKTaJKSVPaQcsktEREREZC9MSKlJSZcT0qxaHVf5DqneUAIhRJ3HRUREREREVTEhpSalsoe07FqCWVPlhRU9pEWl5TCayuo8LiIiIiIiqooJKTUp6fnFAGrfQyrMJrhr1AD4HikRERERkb0wIaUm5a8e0tolpADg5+YIgO+REhERERHZCxNSalLSDbf3DikA+Lk7AWAPKRERERGRvTAhpSbl+ll2a8vXnT2kRERERET2xISUmozSMguyjBXJZJkhs9bH/zVklz2kRERERET2wISUmoyMghIIATiqJViKDbU+3pdDdomIiIiI7IoJKTUZlWuQ+l9LLGvL3509pERERERE9sSElJqMtDtMSH3dKntI+Q4pEREREZE9KJqQ7t69G0OHDoVOp8O9996Ln3/+WS5r0aIFJEmy2uLi4uTypKQk9O/fH15eXhgxYgQyMjKszr1lyxZ07twZAQEBeP7551FWVmav2yKFyD2kWsfbOt7vWg9pRkEJLBZRZ3EREREREZFtiiWkJ06cwNixYzF27FgcPnwYI0aMwOjRo3H+/Hm5zocffoi0tDR56927NwDAZDJh8ODBaNu2Lfbs2QNJkjBq1Circ48bNw4zZszAjz/+iP/85z947bXX7H6PZF932kPqc21SI3O5QG5RaZ3FRUREREREtimWkHbu3BlHjx7F008/jXbt2uHNN99EYGAgtm7dKtdp1aoVAgMD5c3JqSLR2Lp1K/Lz87F8+XJ07twZq1atwqFDh5CQkAAAWLt2LQYNGoRp06YhMjISb731FtauXYvSUiYZTVm6oRgA4K+9vYTUUa26bmIjDtslIiIiIqpviiWkkiShZcuWVp+9vLxgMPw1O6qvr6/NY+Pi4tCvXz9oNBoAQEhICNq2bYvdu3fL5dHR0XL9qKgoZGVl4fTp0/VxK9RAXM27sx5SAPDXOgMA9AWc2IiIiIiIqL41mEmNiouLcebMGXTq1Enet3TpUjRv3hxdunTB+vXr5f16vR6BgYFWxwcHB0Ov19ss9/f3h0qlkstvZDKZYDAYrDZqfCrfIQ24zXdIASDAo+KXHPp8JqRERERERPWtwSSkK1euhI+PD4YNGwYAeOKJJzBx4kR8//33GD58OCZOnCj3gObm5kKr1Vodr9VqkZOTY7NcpVLB3d1dLr/RsmXLoNPp5C00NLQ+bpHqUVm5BRkFd95DGuBxrYeUQ3aJiIiIiOqdg9IBAEBqaiqWLl2KN998E87OFQnBG2+8IZd369YN+/fvxzfffINBgwbB29sbBQUFVucwGAyIiIgAgCrlFosFBQUF8PHxsXn9+fPn46WXXrI6F5PSxiWjwASLABxUErxc76SHlEN2iYiIiIjsRfGEtLS0FDExMejVqxeeeeaZautFRETg8uXLAIDAwEAkJydblV+9elUephsYGIi0tDS5TK/XQwhRZZhvJY1GI7+PSo1Tal7FhEZBns5Qq6TbPk9lQpphYEJKRERERFTfFB2yW15ejsmTJyMvLw9ffvklJKkikTAYDBDir3UghRBISEhAu3btAACDBg3Cvn37YDJVDKtMSUlBUlISoqKi5PKdO3fKx8fGxsLf31/uQaWmJyW3CADQzNP1js5T+Q5pOhNSIiIiIqJ6p1gPaWUyumfPHmzfvh2lpaVIT08HALzyyivIycnB9OnT0aJFC3z22Wc4efIkvv76awDA8OHD4e3tjZkzZ2LWrFl45ZVX0LdvX3lCpClTpqB79+5YtWoVunXrhgULFmDq1KlwdLz9oZzUsKXmVvSQhni53NF5+A4pEREREZH9KJaQbtiwAV999RUAoEOHDlZlRqMRS5Yswfz585GcnIx7770Xu3btQqtWrQAATk5O2LlzJ5566in0798fffv2xebNm+XjO3bsiI0bN+LVV1+FXq/Ho48+isWLF9vt3sj+Uq4lpM3qKCHNMppQVm6Bg7rBzPtFRERERNTkKJaQPvroo3j00UerLX/33Xdvenx4eDj27t1bbfnIkSMxcuTI246PGpe/EtI7G7Lr4+YEtUpCuUUgy1iKQJ1zXYRHREREREQ2sPuHmoTKSY3utIdUpZLgr+V7pERERERE9sCElBo9i0XI75DeaUIKXP8eKRNSIiIiIqL6xISUGr1Mowml5RaoVRICPe58iG3lTLtc+oWIiIiIqH7VeUJaOVMukb1ULvkS6OFcJ5MQVfaQcsguEREREVH9uq3/vavVamRkZFTZ/8cff6Bfv353HBRRbdTVDLuVuPQLEREREZF93FZCKoSAJElV9h85cgRZWVl3HBRRbdTVDLuV+A4pEREREZF91GrZFz8/P0iSBEmS0L59e6hUf+WzJSUlKCwsxIwZM+o8SKKbqUxIQ+qsh7TyHVL2kBIRERER1adaJaTbtm2DEAKRkZFYuHAhdDrdXydycEDr1q1x//3313mQRDdTV0u+VOI7pERERERE9lGrhLR79+4AgEWLFmHKlClwda2bIZJEdyIlp2JSo2aedZSQaisS0vxiM0rM5XB2VNfJeYmIiIiIyFqtEtJKixYtqus4iG5LWbkFV67Nstvc1+2OzpWZmQmg4h1pjYMKpjILzlxMRYin9VIyrq6ucHd3v6NrERERERHRbSakFy5cwCuvvIL4+Hjk5eVVKbc1Ay9RfbiSWwxzuYCzowpBt7kGqamkCJAkdOzYUd4XPHU1HL2C0SvqAZhS/7Cq7+3jiz8vXWRSSkRERER0h24rIX3sscdgNBoRExOD1q1bW01uRGRPFzKNAICWvu5QqarO/FwTZlMJIARmfrgRfoEhAID/JhmgN5ZhwoKPcY+3Rq5bmJ+Dd6eOQFFRERNSIiIiIqI7dFsJaVJSEn799VdERETUdTxEtXIhsxAAcI/fnQ3XBQA3Dy9ovXwAADrXUuiNRpQ7ukDr5XXH5yYiIiIioqpuq2tz4MCBOHfuXF3HQlRrF7KuJaR3+P7ojbQujgCAguKyOj0vERERERH95bZ6SN9//32MHDkSgYGBNoctsueU7KVyyG5d9JBez8O54p9Gfom5Ts9LRERERER/ua2EtH379igtLUXPnj3lfZIkQQgBSZJQXl5eZwES3cxfPaR1+z6nx7UeUkMxE1IiIiIiovpyWwlpYmJiXcdBVGsFJWZkFpgAAC3ruIdU53wtIS0xy79oISIiImpqdu7cCQCYOnVqrY5Tq9Vwc3ODwWCw2ufj44Pc3FyYzTf/pX5ISAgkSYJarcaff/4p73d2dobFYkF5eTlUKhVcXCrWmff390f79u1RUlKCHTt2yPX79esHT09PnD9/HllZWSgqKoKjoyPCwsKQmJgIk8kEjUaDPn364MqVK/Dw8EBMTAx0Oh2mTZsmn+f5559HTk4OysvLYTQakZ2dDVdXVwwZMgRBQUGYNm0aiouL4eDggAceeABarRadO3eGSqVCVlYW4uPjsWfPHpSWlsLNzQ1jxoxBaGgoPD09ceHCBaSnpyMwMBD33HMPcnJycObMGfk5jBgxAmfPnkVOTg68vb3RuXNnqNVqlJeX4+TJk8jKykJeXh48PT2tzhccHIxRo0ZBrVbj5MmT8vEdOnTA6dOnrc4HwKpO5TVulJSUZPW9sHr1arRp06Y23xq1dlsJafPmzes6DqJaq5zQyNddA49rCWRd0V4bsmsuFygxW+DiVPUfLBEREVFjNnDgwNs+try83CoZrdxX0+UfU1NTbe4vKSmxOl9lYmswGGzOYbNv3z6b58nNzbU6565du+TPR48erVL/ww8/tHme65Pfypi2bNlis26lgoICfPXVVzetc72PP/7Y6nNgYCAGDhyIuLg4pKen3/TYlStXwsXFBYWFhfK+ymS2kqenJwBYLdcZGBiIGTNmoH///vI+W98PlclpXFxcDe+m9m4rIb3VA37yySdvKxii2riYVXcz7N7IQa2Cm5MahaXlyC8xMyElIiKiJuVOktGmztnZ2Soxvp6DgwPKymxPeln5CuONdDod8vPz4erqiqKiIgBAu3btYDabcf78eTmBfOSRRxAVFYWPPvoI3377LYKDgyFJEiIjI2GxWHDkyBEAQEBAADIyMtC9e3ccPXoUhYWFiI6Oxv3334+33noLHh4eyMvLwyuvvIKMjAysWbMGAPD0009jzJgxuHjxItatW4dFixZhyZIl6N+/v9X3g0qlwrhx47BhwwZYLBYAkBPk+nBbCens2bOr7CsuLoaLiwu6dOnChJTsonJCo1b1kJACFe+RFpaWw1BsRqCHc71cg4iIiMjetm7dqnQI9cLR0bHaocKfffYZJk+ebLMsMjISv/32m/z5//2//4c1a9bg+PHjVvU0Gg1++OEHPPHEE8jMzLQqc3Jygk6nQ1lZGZycnKDX6+Wy/Px8OSn19PSEJElITk6Gt7c3vLy84OTkhNLSUmzZsgVTpkyR6+n1evTs2ROLFi3Cgw8+CC8vL7Rt2xaXLl3C/fffjyNHjqBnz544e/YsYmNj8fvvv6N37954/fXXsXDhQnz22WewWCzo1asXgIq/9wkTJqBDhw548803sWDBAqxcuRL+/v5yrF999RXCwsIAANOmTcPly5fl3C4pKalehu/eVkJ6418AAGRnZ2P06NF4/fXX7zgooppIzrg2w24dT2hUycPFEWn5JTBwpl0iomqZTCaYTCb5841D+O7U9e+W0d37PO7W+64v7733ntIh1IvKZNRWYvrpp59afY6KikJsbCyAih7R682YMaPKPqDi592PP/6IzMxMOYmsVFpaiszMTMyePRvvv/9+lWNbtGiBEydOYMiQIQgNDcX7779vVb+yR/KTTz5Benq6/DkyMhI//vgjysvL8dRTT6Fly5Z49tln0adPHxw6dAj3338/+vTpg/fffx96vR4LFy6Eg4MDJk6ciGeffRYAsHDhQgDAs88+i5MnT6Jr165QqVRyncp3aVUqlZyMVgoLC4NKpYLFYsHUqVPrpZf0thJSW3x8fLBw4ULMmzev2rHcRHXp1NV8AEBEsEe9nF+e2IhrkRIRVWvZsmVYsmRJvZ3/rbfeqrdzU+PB7wOqjb59+2L37t1W+65evWr1uV27dnJCajQaq5zj+mTT1nnatGmDU6dOVSmv7I28UeVQ3uDgYKs6lV8HBQUBAFJSUuR6QEWynJSUJNetnOSp8heBzs7OuO++++TztWzZ0urPG7/OycmxuR8Axo0bZzP2MWPGYNOmTTbL6kKdJaRAxQvDv//+e12eksim/CIzruQUAwA6Buvq5Rpal4p/Hlz6hYioevPnz8dLL70kfzYYDAgNDa2z87/66qucTPE6f/75512ZnPH7oG7VdkbdxubXX3+tsi84OBgXL16UP589e1b+2t296mg7Jycnm++RViaKlUnijQ4ePGhzf+WKDVevXrWqU/l1WloaAKBZs2Y4evSonPiWlJTI1zx48KCcRGo0Grn8+vNdvHgRHTp0sLrX67/29va2uR8ANmzYYDXzcKXvv//e5j3VldtKSG/Mni0WC86fP4/Tp09Xm1kT1aXK3tFQbxfoXOt2ht1KlT2k+RyyS0RULY1GI//HqD40b9683pccoIaP3wd1a86cOU1y2G7lUF1b75E+9dRT2L9/v/y5sncUQJXEc8WKFdW+Q/rQQw9hw4YN1b5D+tlnnyEgIMDqHVIAuHTpEgDgl19+kZe78fb2luvv2LEDarUazzzzDA4dOoRffvkFarUav/32GxYtWoRVq1bh008/Rdu2bREYGIjU1FSo1WocPnwYZ8+ehUqlgp+fH9atW4fXX38d69atQ1BQECwWC77++msAFb2wlUvAWCwWuc6iRYswbdo0WCwWXL582WrY7uXLl+WJjVavXl2jv4fauq2E1M2t6iQy/fr1w3PPPYfHHnvsjoMiupVTqRUJaaeQ+ukdBSreIQWAgpIyrkVKRERETcaIESOaZEJ6s7VPq5vQCIDVhEYA8NJLL9nsHTWZTHjwwQetllSpVFpaiqysrFvOslu59Ep1s+xeuHABOp1OXmf00KFDWLRoEbp27YqjR4/i0KFDCAgIwOHDh9G9e3ccOnQIAKxm2Y2JiZFn2dXr9Vi7di2Aill2TSaTPMvuwYMHsWTJErRr106O9cknn4RKpcKYMWPw/fffy8kogHr7pdBtJaSff/55nVx89+7dePvtt3Ho0CG0aNECb7/9Nh544AEAQGFhIaZOnYpt27ahVatW+Oijj9CzZ0/52KSkJEyZMgW///47+vTpg88++8xqhqgtW7Zg4cKF0Ov1mDBhAt5//304ONTpCGVS0O/XEtIO9TRcFwC0GgdIElBuESgsLYe7ht8/RERE1DTExcVx6ZdqVLfkCwCbyWglW8koUDHLLgB5yRfAeshw5Tk3bdqETZs2ISgoCBMmTEBcXByEEDh8+LDV+Sp7X48ePQqVSgUXFxfs3LkTO3fuBFDx6oQQQh7e7+XlBSEE1qxZIy8BExQUJC/5Alh/P1gslirvjDa4dUgrHT58GAkJCbBYLOjWrRvuv//+Gh974sQJjB07Fm+//TY+/PBD/Otf/8Lo0aPxxx9/oFWrVpg8eTIuXbqEnTt3YvPmzRg6dCiSk5Ph7+8Pk8mEwYMHY9iwYVi+fDleffVVjBo1Sh4/feLECYwbNw4fffQRunXrhvHjx8PV1RXLli27k9ulBuT01YpZHOuzh1SlkqDVOMBQUob8IjMTUiIiImpS4uLisGLFCmzYsKHWx6rVari5uVnNrK1Wq+Hj44Pc3Nyb9lYCQEhIiDx09fpZlJ2dnWGxWFBeXi4nWwDg7++P9u3bo6SkBDt27JDr9+vXD56enjh//jyysrJQVFQER0dHhIWFITExESaTCRqNBn369MGVK1fg4eGBmJgY6HQ6q/cln3/+eeTk5KC8vBxGoxHZ2dlwdXXFkCFDEBQUhGnTpqG4uBgODg544IEHoNVq0blzZ6hUKmRlZSE+Ph579uxBaWkp3NzcMGbMGISGhsLT0xMXLlxAeno6AgMDcc899yAnJwdnzpyRn8OIESNw9uxZ5OTkwNvbG507d4ZarcbTTz+NkydPIisrC3l5efD09LQ6X3BwMEaNGgW1Wo2TJ0/Kx3fo0AGnT5+2Oh8AqzqV17jx+yEpKcnqHePVq1fX+3D52/ofdmFhIWJiYrB9+3a0aNECQMW46KFDh2Ljxo02h/TeqHPnzjh69Kj8Yu6bb76Jr7/+Glu3bsX48eOxadMm7Nu3D127dkWXLl3w73//G+vXr8cLL7yArVu3Ij8/H8uXL4dGo8GqVavQrFkzJCQkoEuXLli7di0GDRokf5O99dZbeP7557FkyRI4OTndzi1TA1JQYsbFrEIAQMd6TEgBwMvVCYaSMuQWlSLEy6Ver0VERERkb9HR0diwYYNdEo+68uqrr9bJeWrT63ertVuHDh2K+fPn2yy7fhbc6+tfr2vXrlXqqNVqm/ttne/GeraOs7XvRm3atKnX3lBbVLdz0Msvv4zs7GycO3cO58+fx/nz53Hu3Dnk5OTg5ZdfrtE5JEmymmpYkiR4eXnBYDBg//79cHFxQWRkpFwWFRUlT98cFxeHfv36yZMohISEoG3btlbl0dHR8rmjoqKQlZWF06dP387tUj0wGo3IyMio8Xb9dNyVvaMhni7wdqvfXzB4XTt/TqHtqb+JiIiIiOj23VYP6ZYtW7B58+Yq69t8+OGHePjhh7F8+fJan7O4uBhnzpxBp06dcPXqVfj7+1t1IwcHByMhIQFAxbjpwMBAq+ODg4Pl8dQ3lvv7+0OlUlWZ7apSfS/qTdaMRiOat2iJnOysGh/j7eOLPy9dhLu7O45fzgMAdKin9UetrsuElIiIiIio3txWQlrdjKMq1W11uAIAVq5cCR8fHwwbNgzvv/8+tFqtVblWq5UXcs3NzZXX46mu/PrjVSoV3N3drRaCvV59L+pN1oqKipCTnYV5q7fCTed9y/qF+Tl4d+oIFBUVwd3dHfvPVSSyvVr51HeofyWkRUxIiYiIiIjq2m1lkKNGjcJzzz2Hy5cvy/suX76MF154AaNGjar1+VJTU7F06VIsXLgQzs7O8Pb2RkFBgVUdg8EAH5+KBKS25RaLBQUFBXL5jebPn4/8/Hx5u3LlSq3vgWrPTecNrZfPLbfrk9YSczl+u1Txi4V+4b71HmNlQlpQUobSMsstahMRERERUW3cVkL67rvvwsPDA61atUJ4eDjCw8PRqlUruLm54d13363VuUpLSxETE4NevXrhmWeeAQAEBgZCr9dbTat89epVeRhuYGAg0tLSrM5zs3K9Xg8hRJVhvpU0Gg08PDysNmqYjl7KRWmZBQEeGrTyc6/367k4quHiWDF0PJe9pEREREREdeq2ElJ3d3ds374dcXFxePHFF/HCCy8gLi4O33zzDdzda54klJeXY/LkycjLy8OXX34pDwPu27cvTCaTvOaOEAKxsbGIiooCAAwaNAj79u2T3/tMSUlBUlKSVXnlOjwAEBsbC39/f0RERNzO7VID8uu14bp9W/vZHDZeH/geKRERERFR/ahxQpqQkIDo6GhYLH8NW+zTpw9mzJiBZ599Fq1atUKbNm1w4sSJGp2vMhnds2cPvvvuO5SWliI9PR3p6enw8/NDTEwMXnzxRSQkJGDhwoXIzMzEo48+CgAYPnw4vL29MXPmTJw8eRLTpk1D37590alTJwDAlClTsGfPHqxatQq//fYbFixYgKlTp8LR0bE2z4YaoF/PZQIA+obX//ujlZiQEhERERHVjxonpK+99hoGDhxY7cRFgYGBePHFF2u8LtCGDRvw1VdfISUlBR06dEBQUJC8AcCaNWvQqlUrREVFYdu2bdi+fTt8fSveGXRycsLOnTuRmJiI/v37AwA2b94sn7tjx47YuHEjPv74Y4wYMQIjR47E4sWLa3qr1EDlFZvlJV/6tK7/90crVSakHLJLRERERFS3ajzL7oEDB/DOO+/ctM7o0aPx4Ycf1uh8jz76qNzjaYubmxvWr19fbXl4eDj27t1bbfnIkSMxcuTIGsVCjcPOpFwIAUQEecBf62y363q5VvSsZxeWAtDY7bpERERERE1djXtIAwICkJ6eftM6mZmZ8PLyuuOgiGz56Y+K90cf7hZi1+v6uFUkofnFZpRZhF2vTURERETUlNU4IY2Ojsabb75pNfPt9crKyvD2229j0KBBdRYcUSUH7xCcSiuEWiVhZJfgWx9Qh9w0arg6qSEEkFNUZtdrExERERE1ZTVOSJcuXYqrV6+iR48eWL9+Pc6ePYucnBycOXMG69atQ2RkJC5cuFDrZV+IasK9Q8UvOvqH+9p1uC4ASJKEAI+Ka2YW2f6FDBERERER1V6N3yF1d3fH4cOHsXTpUkybNg1GoxGSJEEIAVdXVzzzzDN45ZVXoNPp6jNeugtZhIBbx4olfR7u1kyRGAI8NLiYVYjMQvaQEhERERHVlRonpACg0+nwzjvv4J133sHVq1eRkpKCkJAQBAcH221NSLr7nMsuhYOHP7xdHfC3iABFYgi81kOaxSG7RERERER1plYJ6fWCg4MRHGzfd/no7lNuEUhILwYAPHFfEJwd1YrEUTlk12CyQOXsrkgMRERERERNTY3fISVSwtl0A4ylFpQX5uKRzn6KxeHsqIbOpWL5F6fAcMXiICIiIiJqSpiQUoNVYi7HgfPZAID8w5sV6x2tFOBRsfyLUxATUiIiIiKiusCElBqs/eeyUFRaDp1GhYL4rUqHI79Hqglqq3AkRERERERNAxNSapCu5BTh1FUDAKBPczeg3KxwRECQzgUA4BzWEWUWoXA0RERERESNHxNSanAKTWXYdjodANApRIdAd0eFI6rg76GBRi1BpXHDH+lGpcMhIiIiImr0mJBSg2IRAttPp6OotBzebk7oF+6rdEgylSQhWFuRHB+6ZFA4GiIiIiKixu+2l30hqg/7z2XhSm4xHFQSHuwUBEe1CiXXyjIzM2t0jprWux0hHo64mFeKw3/m19s1iIiIiIjuFkxIqcE4fTUf8ZfzAAB/iwiAt5sTAMBUUgRIEjp27Fir85nNpXUdIoI9Kv7JnE4vRH6xWV4KhoiIiIiIao8JKTUIqXnFiD2bAQCIbOmNNgFaucxsKgGEwMwPN8IvMOSW58q4cgEfz3kcZnNZncfp7qSGOfsKHH1CceBcFh7oFFTn1yAiIiIiulswISXFGYrN+O/JNFgE0NrPHT1betus5+bhBa2Xzy3PZ8zPqesQrRRfPA5Hn1DsOKNnQkpEREREdAc4qREpylxuwY8nr6LYXA4/rQZDOgRAkiSlw7qposT9AIBfTutRYi5XOBoiIiIiosaLCSkpRgiBXWczkGUshYujGg91rpjEqKEzpfyBAK0TjKYy7L42zJiIiIiIiGqv4f/vn5qsM5kmJKYXQJKA4Z0CoXVuLBMECfytbcWw4v+cuKpwLEREREREjRcTUlKEplkHHE4pAgD0a+2LZl6uCkdUO0PbVSSku85moKDErHA0RERERESNExNSsrssYyn8Rv0PBIA2Ae7oEuqpdEi11sbPFa383FBaZsGPJ9KUDoeIiIiIqFFiQkp2ZbEILNl+EWp3L3g5qxHdvuFPYmSLJEl4NDIMAPDlgUsQQigcERERERFR48OElOzqq4OXcPhPAyxmEwa1dG8UkxhVJ6ZHKFwc1UjUF+DghWylwyEiIiIianQabzZAjU6SvgBLfz4LAMjd/Rk8XdQKR3RndC6OeKR7CICKXlIiIiIiIqodRRPSzMxMLFiwAGFhYejRo4dVWYsWLSBJktUWFxcnlyclJaF///7w8vLCiBEjkJFhvfzGli1b0LlzZwQEBOD5559HWVmZPW6JqmEqK8fz3yagtMyC3i10MB7/r9Ih1Ym/92oBANjxhx4XMo3KBkNERERE1MgompBeuXIF586dg4eHh83yDz/8EGlpafLWu3dvAIDJZMLgwYPRtm1b7NmzB5IkYdSoUfJxJ06cwLhx4zBjxgz8+OOP+M9//oPXXnvNLvdEtv2/X5JwJs0AbzcnLBjaUulw6kx4gBaD2/nDIoB/xp5TOhwiIiIiokZF0YS0W7du+PbbbzF27Fib5a1atUJgYKC8OTk5AQC2bt2K/Px8LF++HJ07d8aqVatw6NAhJCQkAADWrl2LQYMGYdq0aYiMjMRbb72FtWvXorS01F63Rtc5fjkXa/ZdAAC8/XAn+Lo1lvVGa+bFv7UBAPyQkIpzGQUKR0NERERE1Hg06HdIfX19be6Pi4tDv379oNFoAAAhISFo27Ytdu/eLZdHR0fL9aOiopCVlYXTp0/Xf9BkpbTMgv/Z9DssAhjTNQRDOgQqHVKd6xiiw9AOAbAI4P92JisdDhERERFRo9GgE9KlS5eiefPm6NKlC9avXy/v1+v1CAy0TmyCg4Oh1+ttlvv7+0OlUsnlNzKZTDAYDFYb1Y2VceeRqC+At5sTXhsRoXQ49eaF6DaQJOC/J9Pw28UcpcMhIiIiImoUGmxC+sQTT2DixIn4/vvvMXz4cEycOFHuAc3NzYVWq7Wqr9VqkZOTY7NcpVLB3d1dLr/RsmXLoNPp5C00NLSe7urukqwvwPLdFT2Gix6KgLebk8IR1Z/2QR7yuqSL/nMa5RauS0pEREREdCsNNiF94403MG7cOHTr1g1Lly5F//798c033wAAvL29UVBg/a6ewWCAj4+PzXKLxYKCggK5/Ebz589Hfn6+vF25cqWe7uruUW4ReHnTSZjLBaLa+WPkvcFKh1Tv5g5pC52LI86kGfD1oT+VDoeIiIiIqMFrsAnpjSIiIpCamgoACAwMRFpamlX51atX5WG6N5br9XoIIaoM862k0Wjg4eFhtdGdWXf4T8RfzoO7xgFvju4ISZKUDqneebk5Yc7QtgCAd7adxZWcIoUjIiIiIiJq2BpkQmowGCDEX0MehRBISEhAu3btAACDBg3Cvn37YDKZAAApKSlISkpCVFSUXL5z5075+NjYWPj7+yMioum+w9iQZBaY8L/bEwEAc4e2RbCni8IR2c/EyDBEtvRGUWk55n53AhYO3SUiIiIiqpaiCWlOTg7S09NhNBphNpuRnp6OzMxMvPDCCxgzZgy2b9+OxMRE/M///A9OnjyJGTNmAACGDx8Ob29vzJw5EydPnsS0adPQt29fdOrUCQAwZcoU7NmzB6tWrcJvv/2GBQsWYOrUqXB0bFrLjTQ0RqMRGRkZWPT9cRSUlKGdvyuG3OOCjIwMqy0zM1PpUOuNSiXhvbH3wtVJjUMXcvDZ/otKh0RERERE1GA5KHnxhx9+GHv27JE/BwUFoXnz5jh9+jSWLFmC+fPnIzk5Gffeey927dqFVq1aAQCcnJywc+dOPPXUU+jfvz/69u2LzZs3y+fp2LEjNm7ciFdffRV6vR6PPvooFi9ebO/bu6sYjUY0b9EShS4BCJz4DoSwYPd70xA8O6naY8zmprkubJiPK14Z3h4LtpzCO9vOouc9PugYolM6LCIiIiKiBkfRhDQuLq7asnffffemx4aHh2Pv3r3Vlo8cORIjR4683dColoqKipCTm4tO07+AwQy083PBUx98YbNuxpUL+HjO4zCby+wbpB1NvD8M+5Izsf20HrO+OY6ts/rCTaPoPzciIiIioganQb5DSo2TtvtIGMyAs6MKAyJCoPXysbm5engqHWq9kyQJ7zzSGcE6Z1zMKsTCH04rHRIRERERUYPDhJTqRJaxFJ59HgUA9GntCxdHtcIRKc/T1QkfTOgKlQRsik/BluOpSodERERERNSgMCGlOrFyfypUGlf4uqrRIYjL5lSKbOmN5waHAwAWbDmFP7MLFY6IiIiIiKjhYEJKd+xUaj62ns4CAPRs5npXrDlaGzMHtUZkC28YTWV47pvjKC2zKB0SEREREVGDwISU7ogQAq//+AcEgMI/4uDvzqV1buSgVuGDCV2gc3HEiZR8vL8jUemQiIiIiIgaBE77Wc+MRiOKiopqXN/V1RXu7u71GFHd+vlUOn67lAONgwopcV8Ajz+sdEgNUrCnC955pDOmfX0Mn+y5gD6tfNG/jZ/SYRERERERKYoJaT2qXJszJzurxsd4+/jiz0sXG0VSWmIux9KfzgAAnugRiNcKan6fd6NhHQPxeM8wfH3oMl7acAI/P98PflqN0mERERERESmGCWk9KioqQk52Fuat3go3nfct6xfm5+DdqSNQVFTUKBLSLw5cQkpuMQI9nPHEfYF4TemAGoEFD0bgyMVcJOoLMGfjCXw+6T6oVHznloiIiIjuTkxI7cBN5w2tl4/SYdSp/GIzVsadBwDMGdqWy7zUkLOjGv98rCse+uev2JOUiU9/vYin+9+jdFhERERERIrgpEZ0W1bvPY/8YjPaBLhjTNcQpcNpVNoEaLHwoQgAwLvbz+KPqwaFIyIiIiIiUgZ7SKnWMgpK8NmvlwAAs4e0hZpDTmvtscgw7EnMxC9/6DFv0wlsmdEHDuqK3w819YmwiIhqIiwsDKtXr0ZYWJjSoRA1afy3RkpjQkq1tjz2HIrN5egS6okhEQFKh9MoSZKEN8d0xOGLOTiVasDqfRcwY2DrJj8RFhFRTTk7O6NNmzZKh0HU5PHfGimNCSnVypWcInzz22UAwLxhbSFJ7B29Xf5aZ7w2IgJzNp7ABzuTMbRDILSiaU+ERURERER0PSakVCv/tyMJ5nKBfuG+6N3KV+lwGr1HuoXgxxNXsScpEy9/dxLLH24FoGlOhEVEREREdCNOakQ1lphegO8TUgEAc4e2VTiapkGSJCx9uBPcnNQ4+mcuNiZkKB0SEREREZHdMCGlGnvvl0QIATzQMRCdm3kqHU6TEeLpgv95oB0AYMWvKVB7+CscERERERGRfTAhpRo59mcudvyhh0oCZg/hi+91beL9zRHZ0hvFZgt8HpgFIYTSIRERERER1TsmpHRLQgj87/azAIBHujVDa3+twhE1PSqVhHce6QyNWoJLi65Izi5VOiQiIiIionrHhJRuaV9yFg5dyIGTWoUX/sbe0frS0tcNU3uHAAB+Sy2C0VSmcERERERERPWLCSndVEXvaCIA4PGezRHi6aJwRE3bo90DYUpLQmm5wO6zGRy6S0RERERNGpd9oZv6+VQ6fk/Nh5uTGs8OaqV0OI2S0WhEUVFRjermZmch+6cPEfLUx7iQVYjkDCPaBHCINBERERE1TUxIqVpl5Ra890tF7+hT/e6Bj7tG4YgajszMzBrVKywsRPce9yE3J7tW5+/k74iTGWbEJWYi1MsVLk7q2wmTiIiIiKhBY0JK1doUn4ILmYXwcnXE0/1aKh1Og2AqKQIkCR07dqzVcS9+/D08fQNuWS/jygV8POdxtPVSIbXQCdmFpdiTlIlhHQNvN2QiIiIiogaLCSnZVGIuxwc7kwEAzw5qDa2zo8IRNQxmUwkgBGZ+uBF+gSG3rF+ZYDq5aqH18rllfWN+DgBALUmIbu+PDUevIFFfgBY+rmgX5HHH8RMRERERNSSKTmqUmZmJBQsWICwsDD169LAqKywsxMSJE+Hj44PIyEgcOnTIqjwpKQn9+/eHl5cXRowYgYyMDKvyLVu2oHPnzggICMDzzz+PsjLOWFobXx/6E2n5JQjSOePxns2VDqfBcfPwgtbL55abq4fnbV8jUOeM+1p6AwB2nc1ATiGXgiEiIiKipkXRhPTKlSs4d+4cPDyq9vxMnjwZ586dw86dOzF06FAMHTpUTjpNJhMGDx6Mtm3bYs+ePZAkCaNGjZKPPXHiBMaNG4cZM2bgxx9/xH/+8x+89tprdruvxs5oKsOKuPMAgOcHh8PZke8vKuX+lt5o5uWCMovAT7+nwVxuUTokIiIiIqI6o2hC2q1bN3z77bcYO3as1f709HRs2rQJH3zwAbp27YrXX38dAQEBWL9+PQBg69atyM/Px/Lly9G5c2esWrUKhw4dQkJCAgBg7dq1GDRoEKZNm4bIyEi89dZbWLt2LUpL2cNUE2v3XUBOYSnu8XXD2O7NlA7nrqaSJAzrEAhXJzWyC0u5FAwRERERNSkNch3S/fv3w8XFBZGRkQAASZIQFRWF3bt3AwDi4uLQr18/aDQVs76GhISgbdu2VuXR0dHy+aKiopCVlYXTp0/b+U4an2yjCWv3XQQAvDSkDRzUDfJb5K7ipnHAAx0DIQE4k16A01cNSodERERERFQnGuSkRnq9Hv7+/lCr/xoqGhwcLPeA6vV6BAZazzoaHBwMvV5vs9zf3x8qlUouv5HJZILJZJI/GwxN8z/8NVkP8//iLsNoKkM7f1f0b8n1LxuKZl6u6NXKBwfOZ2N3Yga83ZwQ7OmidFhERERERHekQXZ/5ebmQqu1Toa0Wi1ycnJuq1ylUsHd3V0uv9GyZcug0+nkLTQ0tC5vp0EwGo1o3qIlAgICqt2CW3fA+t+uAAD2LJ+Dli3vgdFoVDhyqtSjuRda+7nDIoCtJ9NQUGJWOiQiIiIiojvSIHtIvb29UVBQYLXPYDDAx8fnpuURERE2yy0WCwoKCuTjbzR//ny89NJLVudqaklpUVERcrKzMG/1VrjpvG3W+fXPQiRlmxDo7oBx//M6/veZEfjzzz/h5+d3y/NnZmbWdch0A0mSMKRDAPKOliLLWIqtJ9MwrJWr0mEREREREd22BpmQBgYGQq/Xo7y8XB62e/XqVXkYbmBgIJKTk62OubE8LS1NLtPr9RBCVBnmW0mj0cjvozZ1bjpvm+th5haWIjm7oge5f7tAOBZlAZKEjh071ur8ZjMnjqpPjmoVHuocjG+PXEFGgQn7/uQER0RERETUeDXIhLRv374wmUw4fPgwevfuDSEEYmNj8dxzzwEABg0ahM8++wwmkwkajQYpKSlISkpCVFSUXL5z507MmzcPABAbGwt/f3+5B5WqOnghGwJAS183BOlckJZbAgiBmR9uhF9gyC2Pz7hyAR/PeRxmM9d7rW8eLo54sFMQNh9PwcXcUnj0jFE6JCIiIiKi26JoQpqTk4PS0lIYjUaYzWakp6dDrVbDz88PMTExePHFF/HJJ59g06ZNyMzMxKOPPgoAGD58OLy9vTFz5kzMmjULr7zyCvr27YtOnToBAKZMmYLu3btj1apV6NatGxYsWICpU6fC0dFRydttsPSGEiRnVLwr2ruVde+pm4eXzR7VGxnzbb+fS/UjxMsFA9v6I/ZsBrwG/B17z+dirL+/0mEREREREdWKopMaPfzwwwgKCsL777+PkydPIigoCPfddx8AYM2aNWjVqhWioqKwbds2bN++Hb6+vgAAJycn7Ny5E4mJiejfvz8AYPPmzfJ5O3bsiI0bN+Ljjz/GiBEjMHLkSCxevNju99dYHDifDQBoF6iFr/vdMXS5KegUokN7v4q/r4U/XUCSvuAWRxARERERNSyK9pDGxcVVW+bm5ob169dXWx4eHo69e/dWWz5y5EiMHDnyTsK7K6TkFuFyThFUEtDznlv3hFLDcn8zVxw/+hvQ/F5M+fIofni2D7zcnJQOi4iIiIioRhrksi9Nhb6gFJAa7iMWQmD/uYre0Y7BOuhcOKS5sVFJEjJ/eAfBOg0u5xTh2fXxMJdblA6LiIiIiKhGGm621MhlG02Y+u8z8B+7EKUNNEG4mFWIdEMJHFQSIlvaXgqGGj5LsQHvj2oNNyc1DpzPxlv/PaN0SERERERENdIgZ9ltCs6mFyCnqAwu9/TAj2cNGNPds0H1QFqEkN8d7RLqCTcNvxUas1a+rvi/8V0w9V/H8MWBS4gI9sC4Hne+lq7RaERRUVGN67u6usLd3f2Or0tEREREdwf2kNaTPq19sWZ8O5QZMpFvsmDXGb3SIVlJSi9AdmEpNA4qdG/upXQ4VAeGdAjES39rAwBY+MOpO57kyGg0onmLlggICKjx1rxFSxiNxrq4HSIiIiK6C7BbrB61C3CD/ptXEPLMGlzJLUZBiRlaZ+V7ScstAgcvVPSOdm/uBWdHtcIRUV2ZOag1jlzKwb7kLMxYF4//zOwDV6fb+2deVFSEnOwszFu9FW66Ww/pLszPwbtTR6CoqIi9pERERERUI+whrWdleWkIdK9ICM6mN4xlOU6l5sNQUgZXJzW6hHoqHQ7VIZVKwv+N7wJ/rQbnMoxY+MPpOz6nm84bWi+fW241SVqJiIiIiK7HhNQOWntXrBV5Nr0AQghFYzGXC/x2KQcAENnSG45qfgs0BZmZmcjIyEBGRgYsRfl4fVgLqCTgu2Mp+Gz3H3IZh9MSERERUUPCbMQOWng5Qq2SkFNYiowCk6Kx/JFZgqLScng4O6BjsE7RWOjOmUqKAElCx44drd7lfKBHOHL2/AsAsHjrGTTr1JPveBIRERFRg8N3SO3ASa3CPb5uSM4w4nymEQEezorEIWnc8Lu+BADQq5UP1CpJkTio7phNJYAQmPnhRvgFhliVWYTAjvNGpBqA8Gc+RnSQBR/N4DueRERERNRwMCG1k+Y+rkjOMCIlt1ixGDx6jEJpuYCPmxPaBGgVi4PqnpuHF7RePlX2j9B64d9HriCv2IwDWWpITi4KREdEREREZBuH7NpJiGdFIqA3lKCs3GL36xtKyuBx3ygAwP0tvaGS2Dt6N3B2VOOhe4OhcVAhs7Ac/o8sRLG5XOmwiIiIiIgAMCG1G52LI9w0algEkJZfYvfrf3NMD5XGDV7OarT253DNu4m3mxPGdA2Bo0qCc1gnPPXNGVzI5HukRERERKQ8JqR2IkmS3EuammffYbt5RaX49ng6AKBrkAsk9o7edQI8nDE0XIvywlycyyrGiH/+imU/nbH79yIRERER0fX4DqkdhXi6IElvtHsSsHbfRRSWWlCacRHNu3az67Wp4fB3c0Da589h5NtbEJ9SgE/2XsAney+gmZcL2gd5IMzbFR1DPNCjuTdCvV2VDpeIiIiI7gJMSO2omVfFf/LT8ktQZrHAQVX/HdQ5haX4fP9FAEDer+shDete79ekhqu8MBcrYtridA7wyd4LOHopBym5xVUm2/pbRAD+3t1XoSiJiIiI6G7BhNSOvFwd4eKoRrG5HBkGE4I963/G089+vYjC0nK08XPFn8kH6/161PCpJAmD2/tjcPsAFJSYcTIlHxcyjbiQVYiEK3k4cSUPO/7QI/aMHm4dopQOl4iIiIiaMCakdiRJEoJ0zriQVYj0/JJ6T0iNpjJ8dfASAOCpnkHYUa9Xo8ZI6+yIPq190af1X72h5zKMePvns9h5Rg/fES/hdEYJenopGCQRERERNVmc1MjOAnXOAIB0Q/3PtPvN4cswlJThHl839G/FjIJqprW/O1Y/0R2PdQ8AABxOKcKf2YUKR0VERERETRETUjsLupaQ1vfSL6VlFnz6a8W7o88MuAdqFWfWpZpTqSQ83z8UBcd/BgBsP62H0VSmcFRERERE1NQwIbUzf60zJFQMp63P/+BvSUhFuqEEAR4ajO4aUm/XoaZLkiTk7FoNL5eK9553ntFDCKF0WERERETUhDAhtTMnBxV83J0AAOn11EtqsQh8suc8AGByn5bQOKjr5Tp0Fyg3Y1BLd6glCX9mF+FCFofuEhEREVHdYUKqgECP+n2PdOcZPc5nFkLr7IDH7g+rl2vQ3cPTWY2uYZ4AgL1JmSgrtygbEBERERE1GUxIFSBPbFQPPaRCCKy81jv6eM/m0Do71vk16O4T2dIb7hoHGErKEH85T+lwiIiIiKiJYEKqgMoeUr2hBBZL3b6T99vFHBy/nAcnBxX+0adFnZ6b7l6OahX6Xlsa5tifuSgq5QRHRERERHTnmJAqwNvNCU5qFcosAtmFpXV67lXXekfHdm8Gf61znZ6b7m5tAtzhr9WgtNyCI5dylQ6HiIiIiJqABp2QTpo0CZIkWW2LFy8GABQWFmLixInw8fFBZGQkDh06ZHVsUlIS+vfvDy8vL4wYMQIZGRkK3IFtkiTJw3bT8ovr7Lxn0w3YnZgJlQRM7XdPnZ2XCKj4vu1zrZf0ZEoe8ovNCkdERERERI1dg05IAWD8+PFIS0uTtzlz5gAAJk+ejHPnzmHnzp0YOnQohg4dKiedJpMJgwcPRtu2bbFnzx5IkoRRo0YpeRtV1MfERp/suQAAeKBjEFr4utXZeYkqhXm7IszbFRYBHDyfrXQ4RERERNTIOSgdwK2EhoYiMDDQal96ejo2bdqEffv2oWvXrujSpQv+/e9/Y/369XjhhRewdetW5OfnY/ny5dBoNFi1ahWaNWuGhIQEdOnSRZkbuUFdTGxkNBpRVFQEAEgzmPCfhFQAwLjOXlV6hDMzM2/7OkTX69PaB5d/K0KivgDdmntyaDgRERER3bYG30Pq6+tbZd/+/fvh4uKCyMhIABVDCaOiorB7924AQFxcHPr16weNRgMACAkJQdu2beXyG5lMJhgMBqutvlX2kOYWmVFiLq/18UajEc1btERAQAACAgIwaNqbKBdA8aUEDOx8j7y/cuvYsSMAwGyu23dW6e7jr3VG2wAtAGD/uWwIUbcTcxERERHR3aPB95Bu3boV//rXv2AymTBhwgS89tpr0Ov18Pf3h1qtlusFBwcjISEBAKDX66v0qgYHB0Ov19u8xrJly7BkyZJ6uwdbXJzU0Lk4Ir/YDL2hBM19ajfEtqioCDnZWZi3eivUbp7YcCoPZRZgdHQ/hDx8oEr9jCsX8PGcx2E2c3ZUunO9WvkgOaMAl3OKcDGrEPf4uSsdEhERERE1Qg06If3b3/6Grl27on///oiPj8cLL7wAtVoNR0dHaLVaq7parRY5OTkAgNzcXAQHB1dbfqP58+fjpZdekj8bDAaEhobW8d1UFahzRn6xGen5tU9IK7npvHE6FyizAH5aDdqGBUCSpCr1jPm2753oduhcHNE1zAvH/szFnqRMhHm7Kh0SERERETVCDTohnThxovx1165dcfnyZaxfvx4vvvgiCgoKrOoaDAb4+PgAALy9vW2WR0RE2LyORqORh/faU5CHMxLTC5B2BxMbmcsFEq7kAwB6NPeymYwS1YfIFt5ITC+AoaQMR//MRQcvpSMiIiIiosamwb9Der2IiAikpqYiMDAQer0e5eV/vXt59epVeZhuYGAg0tLSrI69vryhCPZ0AQBczStGueX23sM7m1UCU5kFXq6OaO3PYZNkP04OKvQLr3jH+8ilHGQVcTg4EREREdVOg01Iy8rKUFhYaLUvISEB7dq1Q9++fWEymXD48GEAgBACsbGxiIqKAgAMGjQI+/btg8lkAgCkpKQgKSlJLm8ofN2d4OKohrlcQH8bvaSSgxNO6SuO69HcGyr2jpKdhfu7o7WfOywC2HPRCMnB/iMNiIiIiKjxarBDdtevX493330Xr732Grp27Yr9+/fjww8/xOeffw4/Pz/ExMTgxRdfxCeffIJNmzYhMzMTjz76KABg+PDh8Pb2xsyZMzFr1iy88sor6Nu3Lzp16qTwXVmTJAmhXi5IyjDick5RrYc8unX6G4rLBLTODmgbqL31AUSo+RJANaknSRKi2vsjzVCMfFM5vIfN5Ky7RER2oCrJt/81i/Os/rTLNRW4TyKyrwabkD7xxBMoLCzEypUrER8fj5CQEKxcuRLjxo0DAKxZswZPP/00oqKi0KpVK2zfvl1eIsbJyQk7d+7EU089hf79+6Nv377YvHmzkrdTrVBvVyRlGHElpwgdvGo+MUxZuQW6+x8BAHQP84Jaxd5RujlTSREgSfISQDV1q6WCXBzVGBoRiO+Pp8K9wyCs+DUFSx4JuJNQiYioGjqdDo5OGuDCHsVicLm4167Xc3TSQKfT2fWaRGQ/DTYhlSQJ06dPx/Tp022Wu7m5Yf369dUeHx4ejr177fsD83ZUzk6abiiBudylxsdtO5sDB50/XBwkdAj2qK/wqAkxm0oAITDzw43wCwy5Zf3aLBUU6u2Kvs3dsO/PQnx5JB3u7mcxZ0hbTrJFRFTHAgIC8PW/vkJ+/t3Tc6jT6RAQwF90EjVVDTYhvVt4uDjK65GmG801OqbcIvDFbxWTNnUMcIaDusG+CkwNkJuHF7RePresV9ulgsJ9NPjPl8vhNfAf+Hj3eaTkFuOtMZ3gruGPGSKiuhQQEMAEjYiaDGYyDUCod0XP6JX8miWkm+JTcDm3BOXFBWjn61yfoRHViuHwJrz6txZQqyT8kHAVD3y4F/vPZSkdFhERERE1UExIG4DWfhXLtVzILQXUN+9NKjGX4//9kgQAMBzaAEc1h0RSwzKqkx/WTbkfIZ4uuJJTjIlrD+PxtYdx/HKu0qERERERUQPDhLQBCPV2hbvGAaXlAq6tIm9a97P9F5FuKEGg1gmGY1vtFCFR7fS8xwfbXuiHSb1bwFEt4ddzWRiz4gCmfHkEv6fcPe89EREREdHNMSFtAFSSJC/b4tZxcLX1UvOKsWL3eQDAtD4hQHnNhvgSKUHr7IjFIzsgdvZAjO3eDCoJ2HkmAw8t/xV//+w3HLlUu3dUiYiIiKjp4WwjDUREkAeO/ZkLl1Y9kF1ohv8N5UIIvPr97zCaytC9uReGtb/1pDRESrhx/VINgHkDgjCukyc+P5yG7WezsScpE3uSMtEjTIcX/tYOfVr7cEZeIiIiorsQE9IGwtvNCX6uamQWAav2p+DDltbLcmw8loK4xEw4OajwziOdoUKRQpES2VbTdU4dPAPhcf9YuHcajKOX8/H4p4cR2cIbCx+KQMcQrjNHREREdDdhQtqA3Bfiip+SC/DDqSyMO5eFPq19AQC7EzPw6ve/AwCeHxyO1v7uyMhgQkoNS23XOc3MzsG6Tf+B3/2j8NulHDy0/FeM7dYMc4e2hb8HZ48mIiIiuhswIW1AArWOKIjfCm23EXjh3wl4ITocGQYTPtl7HuZygYfuDca0Aa2UDpPopmq6zikA5O5ajbhVC/Dp0Sz8kHAVG4+l4L+/p2He0LZ4olfF8jFERERE1HRxUqMGJnfPlwjzckZmgQmvfn8KH+5KRonZgqh2/vh/4+7lf9CpyQn00ODDCV2xeUZvdAn1RFFpORb/+AdiVh1Asr5A6fCIiIiIqB6xh7SBEaXF+HJiBHZdLMaGo1fg5eqEx+4Pw4OdguCg5u8PqOmpnASpmTOwamxrbD6ZiY/3XUH85Tw88OE+PNUzGH+/LxAeWne4u7srHC0RERER1SUmpA2Qm5MaU/rdgyn97lE6FKJ6c7NJkNRaX3gPmQHX1pH45EAqPtq8B2UHvsDF+H1MSomIiIiaECakRKSIW02CJITAhdxSHLpSBAS2htOoRfhn7DnMebAzHDlagIiIiKhJYEJKRIq62SRIXbyB8GZl+OX3FFzOBz45kIo953KxcGhLtPZzvel5XV1d2ZtKRERE1MAxIW2AKt+pu9M6RE2Bm8YBfYNV+PDr9+AdPRVnM4BHvziBgvityNv/DYSp0OZx3j6++PPSRSalRERERA0YE9IG5Gbv1FXHbC6tx4iIGoayUhOK/ojDY1OeRZLJEX/mAR73jYZ/rzHoFuSCNr4aqKS/ZqAuzM/Bu1NHoKioiAkpERERUQPGhLQBudU7ddfLuHIBH895HGZzmZ2iI1Kej5cXRoeE4s/sQuxNykJOUSkOXCnCH1lmdG/uhbaBWr5fSkRERNSIMCFtgG72Tl0lY36OnaIhania+7jhsftd8XtqPg5fyEZesRm7zmZgX3IWwgPcEeRigeSoUTpMIiIiIroFJqRE1CipVRK6hHoiIsgDp67m48SVPBhKynD6qgGnAYS+sAET/3UK7YK90MzLBc28XBDs6QJ/rQb+Wmf4uDlBpfprmK/RaERRUVGNrs0Jk4iIiIjqBhNSImrUnBxU6Bbmha6hnkjJLca5DCPOZxagsBRIzixGcmaxzePUKgm+7k4I8HDGPT4u+HbF/yL3QgLMGRchym7+bjYnTCIiIiKqG0xIiahJkCQJod6uCPV2RY8ANV6f8hDWb9uP/DJHpOQWITWvGKl5JcgsKEF2YSnKLQJ6gwl6gwknU/Lh3OcJBPV5AioJ8HdzQKC7A4K1jvBzc4BaxQmTiIiIiOoDE1IiapLKC7IxsLUX/P39q5SZyy3INpYio6AEV/OKcSQ5Hf9c/wN82kaiuEwg3ViGdGMZEtJL4KCSEOTpjGZergj1coGrh1DgboiIiIiaJiakRNRk3Wy9XhWAQCcg0F+NUMkJC79bgme/3Q+Lsweu5BYjJbcIV3KKUWwux5WcYlzJKcZBAA4qwG/sInx9NA0926rQys8d/loNpOuWnSEiIiKimmFCSkRNzu2s6QsAZWVmeLs6wdPVCZ1CdBBCIKewFCm5xbiSW4TU3GKUlFng2uo+fLQ3BR/tTQEAuDmpEOrpDD93J/i6O8LPzREezg5wdVLDzUkFL60rfHRaeDg7wMPFEVpnB7g4qpnEEhER0V2vSSekQgi88cYbWLNmDVxcXDBv3jxMmTJF6bCIqJ7VZk1foPp1fSVJgo+7Bj7uGtwb6gkhBJIvXca6tSvg3KwDHH2bw8EzAIWlwNmMIpzNqNksvUDFpEpajRoezg4I8nBCsE6DEJ0GwToNWgXo0C7EFzpXx1rfOxEREVFj0qQT0lWrVuGDDz7Apk2bkJ2djYkTJ6JZs2YYNmyY0qERkR3UZE1foObr+kqSBK1UioIjW/D3xyfCLzAE5RYBg8mCAlM5isosKCq1oMgsUFpugdkCFJeUID3lMlROLpA0rlBp3CCp1Ci3COQVlyGvuAyXc0tsXs/D2QGh3q4Iu7YFe7rA280JPu5O8HXXwNvNCV6uTlaTLhERERE1Jk02IRVCYMWKFZg7dy4GDRoEAPjll1+watUqJqREdMeuT3Y9b1Iv7VIy/veNWXJvrRACZRagtLwiaS0pEzCWViS0BSYL8opKkZGdC7W711/rql413DQWF0fVteHBarg6qeDmqIbrta+d1Co4OUhwVKvgpJbg6qyB1tX52v6KTXPtzyr71GpoHK33V9bTOKga7JDjsnILSsosKDGXQ+fiCEe1SumQiIiIqBpNNiHNycnBqVOnEB0dLe+LiorCM888o2BURHS3qmlvbVbaFSx940lIDk5w0AXAQRcIB8+KTa31gdrFA2o3T6hcPKB21QEAis0WFJstyC401/dtWHFQSdBcl+w6qlVwVEsVCaxagpPDX58r/qyoU5nHCgE4ODjA0dEB4rrJiyu/tN4nUFRSiiKTGaayioTTVGaByXzd12UCpjILyix/Hfjf5/qiQ7Cu/h8GERER3ZYmm5Dq9XoAQGBgoLwvODgYBoMBxcXFcHFxkfebTCaYTCb5c35+PgDAYLh5r8StFBQUAABy0lNQUlR4y/p5GVcBALkZqVDh5ktL1KZuQ6vfkGKpbf2GFEtt6zekWGpb/26KJSv1IiAEJs77X3j7Bdy0rkUUQp+egs2r3sFD0xfD1dMHZQIoswBmS8WfFgGUi4okr7i4EGeO7AfUDoDaEVLl5uBQ8afKEZKDI6C+9tnBEZLaoWL/tc/XK722NWQpV9MQ6n77PbmV7YAQt/67owqVz+pO21AiImrcatqGSqKJtrL79+9H3759kZubC09PTwDA8ePH0a1bN6SmpiI4OFiuu3jxYixZskShSImIqKG7cuUKmjVrpnQYjUJKSgpCQ0OVDoOIiBqIW7WhTTYhPXPmDCIiInD58mW5YdyzZw8GDhyI4uJiODs7y3Vv7CG1WCzIycmBj4/PHb0jZTAYEBoaiitXrsDDw+P2b6aJ4XOpHp+NbXwu1eOzsa2unosQAgUFBQgODoZKxXdRa8JiseDq1avQarVsQ+sBn0v1+Gxs43OpHp+NbfZuQ5vskN3KobppaWlyQnr16lV4enpaJaMAoNFooNForPZV9qrWBQ8PD36T28DnUj0+G9v4XKrHZ2NbXTwXnY7voNaGSqWq095kfm/bxudSPT4b2/hcqsdnY5u92tAm++teLy8v3Hvvvdi5c6e8LzY2FlFRUQpGRURERERERJWabA8pAMyYMQMvv/wyevXqhZycHHz11VfYunWr0mERERERERERmnhC+vTTT0Ov1+OJJ56Ai4sLVqxYgb/97W92u75Go8GiRYuqDAe+2/G5VI/PxjY+l+rx2djG59L48e/QNj6X6vHZ2MbnUj0+G9vs/Vya7KRGRERERERE1LA12XdIiYiIiIiIqGFjQkpERERERESKYEJKREREREREimBCeoeEEHj99dcRGhqKNm3aYO3atdXW1ev1ePDBB+Hp6YkBAwYgOTnZjpHaV02fS3l5Od5880107twZXl5eGDt2LNLS0uwcrX3V5num0t69eyFJEhYvXlz/ASqkNs/FYrFg6dKlCA8Ph5+fHyZOnIjs7Gw7RmtftXk28fHx6Nu3L9zc3HDvvfdi+/btdozUvjIzM7FgwQKEhYWhR48eN617N/38bUzYhtrGNrR6bENtYxtaPbahtjWoNlTQHVmxYoXw8vISsbGxYuPGjcLJyUn8/PPPVepZLBYRGRkpHnroIXHixAnx1FNPibCwMGEymRSIuv7V9LnMnTtX9O7dW+zatUscOXJE9OjRQ0RHRysQsf3U9NlUKisrE126dBHu7u5i0aJF9gvUzmrzXObOnSuaN28utm/fLn7//Xcxffp0cfjwYTtHbD81fTZFRUUiODhYLFiwQCQlJYk33nhDuLq6irS0NAWirn/Hjh0T48ePFx06dBDdu3evtt7d9vO3MWEbahvb0OqxDbWNbWj12Iba1pDaUCakd8BisYiOHTuKpUuXyvuefvppMWrUqCp1jx49KgCI1NRUIYQQJSUlwt3dXXz//fd2itZ+avNc0tPThdFolD/v2rVLABC5ubl2iNT+avNsKq1Zs0aEhYWJRx99tMk2prV5LhkZGcLFxUXs27fPjhEqpzbPJj4+Xuh0OmGxWIQQFf8RCwgIEJs3b7ZXuIpYtGjRTRvTu+nnb2PCNtQ2tqHVYxtqG9vQ6rENvbWG0IZyyO4dyMnJwalTpxAdHS3vi4qKwu7du6vUjYuLQ0REBIKDgwFUrO/Tp08fm3Ubu9o8l4CAALi5ucmfvb29AQAFBQX1H6gCavNsAMBgMODVV1/F66+/DicnJ3uFaXe1eS4///wzfH190adPH3uGqJjaPJtWrVqhpKQEer0eAKBWq6HRaNC2bVu7xdsQ3U0/fxsTtqG2sQ2tHttQ29iGVo9t6J2zx89fJqR3oPIbNjAwUN4XHBwMg8GA4uLiKnWvr1dZt/IcTUltnsuN4uPj4enpiWbNmtVrjEqp7bN588030a5dOzz55JN2i1EJtXkuly9fRvPmzbFhwwbce++9aNOmDf73f/8XookuqVybZ+Ph4YEXX3wRUVFR2L59O/7973+jTZs2aN++vV1jbmjupp+/jQnbUNvYhlaPbahtbEOrxzb0ztnj569DnZ3pLpSbmwsA0Gq18r7Kr3Nzc+Hi4mJV9/p6lXVTUlLsEKl91ea5XM9iseCjjz7C5MmTIUlS/QeqgNo8m3PnzmHFihU4fPhwk30elWrzXFJSUnD27FmsX78eH3/8MRITEzF9+nSEh4dj9OjRdo3bHmr77yk6OhqbNm3CuHHjYDQasW/fvib//XMrd9PP38aEbahtbEOrxzbUNrah1WMbeufs8fOXPaR3wNbQGIPBYFV2fd0bh9AYDAb4+PjUc5T2V5vncr21a9fi0qVLmDNnTv0GqKDaPJs5c+Zg1qxZ6NChg/0CVEhtnotWq4Wvry82btyIvn374qmnnsKYMWPwww8/2C9gO6rNszlw4ABmzJiBX3/9FZcuXcLcuXPxwAMPICEhwW7xNkR308/fxoRtqG1sQ6vHNtQ2tqHVYxt65+zx85cJ6R2o7L6+for1q1evwtPTE87OzlXq3jgV+9WrV6t0gTcFtXkulY4ePYrnnnsOn376KYKCguwSpxJq+mxSU1Pxww8/YOXKlfD19YWvry+++eYbvPvuu+jatavd465vtfmeCQ0NhUqlsnofqEWLFkhPT7dPsHZWm2ezcuVKxMTEwN/fH15eXnj77bcxePBgfPDBB/YMucG5m37+NiZsQ21jG1o9tqG2sQ2tHtvQO2ePn79MSO+Al5cX7r33XuzcuVPeFxsbi6ioqCp1Bw0ahDNnziA1NRUAUFJSgv3799us29jV5rkAwPnz5zF69Gi88MILeOSRR+wVpiJq+mwCAgJw5coVnDp1CgkJCUhISEC3bt0wbdo0/PTTT/YOu97V5nsmKioKSUlJVo1nUlISWrVqZZdY7a02z6awsBCOjo5W+4KCgpCfn1/vcTZkd9PP38aEbahtbEOrxzbUNrah1WMbeufs8vO3zubrvUt98sknwtPTU8TGxorvvvtOODk5iV9++UVkZGSIsLAw8fnnn8t1e/XqJUaMGCGv4dOiRQtRWlqqXPD1qKbP5cKFCyI0NFQ88cQTIjMzU6SlpYm0tDSRl5en7A3Uo9p8z1xvwIABTXbKeiFq91xGjBghhg4dKk6cOCE+//xz4ejoKBISEpQLvp7V9Nl8++23QqvViq+//lqcP39efPvtt8LV1VV89dVXyt5APcnOzhZpaWli9uzZonPnziItLU1kZGTc9T9/GxO2obaxDa0e21Db2IZWj22obQ2pDWVCeocsFot4/fXXRUhIiGjdurVYu3atEKJibbDQ0FDx6aefynX1er144IEHhE6nE/379xfJyclKhV3vavpchgwZIgBU2f7+978rGH39qs33zPWaemNam+dSUFAgnnzySeHj4yPatGnT5NcIq82z+eyzz0SHDh2Ei4uLaNu2rVixYoW8plpTM2DAgCo/O5o3b37X//xtTNiG2sY2tHpsQ21jG1o9tqG2NaQ2VBKiic7zTERERERERA0a3yElIiIiIiIiRTAhJSIiIiIiIkUwISUiIiIiIiJFMCElIiIiIiIiRTAhJSIiIiIiIkUwISUiIiIiIiJFMCElIiIiIiIiRTAhJSIiIiIiIkUwISWiW/roo48QGRmpdBj15tSpU5AkCZcuXVI6FCIiamLYhhLdHBNSojuQlZUFSZJgNBqVDqXOfPfdd+jRo4fVvqCgILRr106hiIiIqCliG0pEAOCgdABE1PDFxMQgJiZG6TCIiIgaHbahRDfHHlKi6wwcOBDffPMN5syZA19fX/z000945513EBkZCa1Wix49eiA+Ph4A8MUXX8DPzw8AoNVqrYarXLhwAQ888AA8PDzQsWNHfP/997WK4auvvsLixYtxzz33IDg4GJ9++qlVnZud32w2Y+7cuQgKCoIkSfLWs2dPAEBqaipmzZqFiIgIeHh4YNSoUcjKygIATJo0CTExMTh27BgkSUKLFi0AAMuXL5e/njhxIiZMmGAVzzfffINmzZrBYrEAAHbs2IHu3btDq9ViwIABOHXqVI3ufdCgQZg7d678ec2aNZAkCWlpafK+rl274pNPPgEAlJaWYsGCBWjZsiX8/PwwceJEZGRkyHW/+OILDBs2DNu2bUNERAQef/xxAEBOTg4ee+wxeHt7o0OHDti8ebNVHBkZGRg9ejQ8PDwQHh6Ot956C2azuUb3QER0t2IbyjYUYBtKt0EQkWzAgAEiPDxcTJ8+XZw+fVokJCSImJgYsWPHDnH27FkxduxY0b59e2GxWERRUZHYtGmTACDOnTsn0tLSRFlZmcjLyxNBQUHi7bffFklJSWLz5s1Cp9OJP/74o8Yx6HQ68dprr4nTp0+LOXPmCI1GI7Kzs4UQ4pbnX7p0qfD39xebNm0S+/fvFwMHDhTTp08XWVlZQgghvv76azF79mxx6NAhER8fL9q2bSumTZsmn3v27Nmic+fOIi0tTWRkZAghhPjnP/8pmjdvLoQQ4ocffhBarVaUlJTIMcfExIjnn39eCCHEwYMHha+vr9i4caNITk4WS5cuFS1atLCqX5033nhDREZGyp8nTJggwsLCxLp164QQQuTm5gpJkkRiYqIQQoipU6eK1q1bi127don4+HgxfPhw0bFjR1FaWiqEEOLzzz8XgYGBomfPnmLfvn0iNTVVWCwW0a9fP9GtWzdx8OBBceTIEfHQQw8JAOLixYtCCCGefvpp0aFDB3Hw4EGxd+9e8fLLL4uysrIa/f0REd2t2IayDRWCbSjVHhNSousMGDBAdO3aVZjNZpvle/bsEQBEenq6EEKI3bt3CwCioKBArrN06VIxbtw4q+OmTp0qlixZUuMYpk6dKn9OTEwUAMS+fftqdP5Ro0ZZHb9lyxbRsWPHaq+3ZMkS0a5dO/nzokWLRPfu3a3qXN+YlpSUCJ1OJ37++WchhBDFxcXC3d1d7N+/XwghxJAhQ8SKFSvkYy0Wi2jTpo3Ys2fPLe/9wIEDQq1WC4PBICwWiwgJCRHvvvuueOqpp4QQQmzdulWEhIQIi8UiLl++LCRJEnv37pWPz8vLEx4eHmL9+vVCiIrGVJIkceTIEbnOnj17hCRJIjk5Wd53/Phxq8Z03Lhx4tFHHxUWi+WWMRMRUQW2oWxDhWAbSrXHIbtEN4iKioKDw1+vV58/fx7z589H79698Y9//AMAkJmZWe3x8fHx+P777+Hu7i5vn332GVJTU2scg1arlb9u3bo1ACAvL69G53/ggQewY8cOnDhxAmlpafj6668RERFhdf79+/djypQp6NatG1auXHnT+7mRRqPB6NGjsWXLFgDAzp07odPp5OFM8fHxeOmll+TYtFotkpOTa3T/9913H1xdXXHw4EGcPXsWvr6+GDJkCGJjYwEA+/btw6BBgyBJEk6ePAkHBwf07t1bPl6n06F79+5ISEiQ93l7e1tNMHHy5Em0aNFCfq62LFq0CCdPnkTPnj2xefNmeRgVERHdHNvQm2MbSlQVE1Kim4iLi0P37t3h6+uLH374Abt27brlMUIIjBo1CgkJCfJ25swZvPnmm7cVg0pl/c/0VuefNGkSLBYLBgwYgODgYFy+fBnvvfeefPy7776L8ePHY9iwYfj111+xbNmyWsc0fvx4/PDDD7BYLNiyZQtiYmLkOIUQeOONN6ziS0pKwkMPPXTL8zo4OGDAgAHYu3cv4uLi0L9/f3Tq1Al5eXm4dOkS9u3bh6ioKPk61blZ42c2m63+s2RLREQETp48idmzZ2Px4sXo27cvCgsLbxk/ERH9hW2obWxDiaxxll2im1ixYgVGjRqF2bNnAwAKCgqsyit/KBcVFcHd3R0AcO+992LdunVo0aLFLX9o345bnf/TTz9FZGQk1q1bh/z8fPj6+lqVv/fee3jnnXcwduxYAFUbHgcHBxQVFd00hujoaJSWliI+Ph7//e9/sWnTJqv4zp49e9Pfnt7M4MGDsW3bNly6dAljxoyBSqVC//79ERsbi/j4eAwaNAgA0LlzZ5jNZhw8eBB9+/YFABgMBhw7dgxTpkyp9vzh4eG4ePEi9Ho9AgICAADl5eVV6qlUKowbNw4jRoyAr68vdu3ahZEjR97WPRER3Y3YhtrGNpTIGntIiW7C3d0d+/btw7FjxxAbG4snn3zSqrxly5aQJAlr1qxBYmIiiouLMXPmTOTk5ODJJ59EQkICTpw4gYULF1ZpiG/Xrc6fk5OD5ORknDp1CmVlZcjOzrZqMN3d3bFlyxacPn0a69evx5IlS6zO37p1ayQmJmLHjh04c+aMzRgcHR3x8MMP44033oCDg4M81AgAlixZgi+++AJLly5FYmJirX+DPHjwYBw7dgwHDx5E//79AVTMmrhy5UoEBQXJMxWGhYVhypQpmDx5Mnbv3o0TJ07gscceQ2ho6E2n1x86dChCQ0MxadIknDx5ErGxsVaNb0lJCQYPHowvv/wSZ8+exRdffIHi4mKEhobW+B6IiIhtKNtQtqFUQ8q9vkrU8AwYMEDMnj1b/nzx4kXRs2dPodVqxZAhQ8Tx48eFVqsVv//+u1znrbfeEt7e3iIkJEQcO3ZMCCHEuXPnxIgRI4SXl5cICQkRU6ZMkWfoq20MQggBQPz444/y55ud/+LFi8LZ2Vm4uLgIAAKA8PDwEB9//LEQQohffvlFtGjRQvj6+opJkyaJbdu2CR8fH/ncJSUlYuzYscLNzU2ebe/6CRkq7dixQwAQL7zwQpV72LFjh4iMjBRubm6iTZs24q233hLl5eU1un+LxSL8/f1Fhw4d5H2VEyZMnjzZqm5JSYmYP3++CAsLE97e3mLChAnyZBlCVEzIcP29VUpMTBSDBg0SWq1W9O7dW+zdu1dIkiRPyPDjjz+Kvn37Cnd3d9GyZUuxatWqGsVORHQ3YxvKNlQItqFUe5IQNxlETkSNTnR0NKZOnYpx48YBAPLz8zFv3jzs2rUL586dUzg6IiKihottKJH9ccgukR3NmzfPama/67devXrVyTX++OMP7NmzBwcOHMDZs2exdetW7Nu3D8OHD6+T89+JXr16VXv/8+bNUzo8IiJqwNiGsg2lpok9pER29P/buUMcCGEggKKjaquwVZyrR8BzQDxnwKKwCMIFMJvN7mQ379kRrZv8pOm+73Ecx+OslBKttbfPWJYl5nmOdV3juq4YxzF67zFN00c+iHjFtm1xnufjrNYawzB8+UYA/Ao71A7lPwlSAAAAUniyCwAAQApBCgAAQApBCgAAQApBCgAAQApBCgAAQApBCgAAQApBCgAAQApBCgAAQIobmEiEl9Vbjv4AAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAGHCAYAAACnGJ9sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAez5JREFUeJzt3Xd4VGX6N/DvtEwyyaSThEAIndCLEER6RCmygCCgoiurgMiiKxpZFRRUUBcsqAgo+HNBVFaERWURX5GONIEAEiCEmt7bZJLJZOZ5/5jMIUMmkH5Svp/rmovMeU65zyGZJ3eephBCCBARERERERHVMaXcARAREREREVHTxISUiIiIiIiIZMGElIiIiIiIiGTBhJSIiIiIiIhkwYSUiIiIiIiIZMGElIiIiIiIiGTBhJSIiIiIiIhkwYSUiIiIiIiIZMGElIiIiIiIiGTBhJSIGoWffvoJ999/f5ntDz/8MBYvXlz3AdWCTZs2YdiwYXKHQURETdyFCxegUCjkDoMaCSakRLVk69ataNu2LQoKCqq1T30ybNgwREZGVusckZGReOihh2ooopvy8/ORmJhYpWNVKhUOHz5cwxFVzr59++Dv7+/wev/995Gens5Kn4ioFNavztVk/bpnzx506dLFYdvTTz+NN998s1LnuVP9umvXLrRs2RLe3t7QarVo2bIlWrZsiaCgICgUCrRo0QLdu3d3OObChQvSfs5earUa27Ztq1ScJC8mpES1xN/fH2FhYdBoNACAP/74A/7+/rfdpykICQlB+/btpffvvfdelSvQxYsXw9vbG97e3njyyScRHR0tvff29q7QOa5cuQKr1YrLly/fdr++fftCp9M5nL/0a+rUqQ77b9q0CR4eHuW+FAoFDh48KO0/dOhQpKenS6+xY8c2mF+kiIjqEutX52qyfrVYLDAajQ7bTCYTioqKKnyOitSvI0aMQHx8PN59910MGDAA8fHxiI+Px5EjRwAAly9fxtmzZx2OCQsLk/Zz9goLC6vEnVJ9oJY7AKLGasiQIRgyZEi192ls/vGPf9TYuV577TUsXLiwWufYunUrXFxcsGXLFkybNu22rZGff/45HnvssQqd9+GHH8bDDz9cbnlAQABbPomIqoD1q3M1Wb8CQHp6OmbMmCG9P3ToUJk/vt5OZerXyrh48SJ69uyJli1bOi03mUxwdXWtkWtR3WALKcnu4sWLmD59Otq1awdfX19Mnz5d+qvcwIED8fLLLzvs/84772DgwIEAgLy8PMyYMQP+/v5QKBTS63aJQGkKhQK//vorJk6cCB8fH/Tq1Qt79uxx2OfEiRMYNmwYPD090aNHD2zatMmhfMOGDWjfvj08PT0xcuRInDlzBgCwfft26cN38eLF6NevHzIyMqQYb91nwYIFuPvuux3OffjwYbi6uiI3NxcAcPLkSQwePBh6vR59+/bFgQMHKnSfANC6dWt8++23mD59Ovz9/dG5c+cy93L58mWMHTsW3t7eCAsLw8cffwyr1VruOY8fP47JkycjJCQEAQEBiIyMhMVicXi+58+fx/jx4+Ht7Y2cnBxERkZK4yCHDRuGl156CVu2bIFCocCwYcPw5ptvok+fPg7XOXbsGNzc3KTnYKdSqaBSqfD555/jrrvuQqtWrTBlyhRcv34davWd/9527NgxLF68GL/99htOnDiB1atX33b/WbNmldtC+vnnn9/xeqWZTCZotVrp/alTp9CrVy/p9eOPPzrs7+HhgSeeeKJS1yCipo31K+vXqtavAKDVatG3b1/pdWsrNGCrm249p/28FalfDx8+jJYtW+Lll1+Wvm7ZsqX0/9WuXbsy/3dCCBQXFyM2NtbpKy4uDqNGjSr32VI9JIhktnz5cvHWW2+JkydPiv3794uAgADx7rvvCiGE+Oijj0SnTp0c9u/Xr5/48MMPhRBCzJo1S3Tq1Ens3LlT7Nq1S3Tv3l28/fbbIisrq0LXBiCCg4PFxo0bRXR0tJg3b57QarUiKSlJCCHE5cuXhaurq3j11VdFdHS0+PLLL4VOpxObNm0SQghx48YNoVQqxdtvvy2io6PFhx9+KH777TchhBA//fSTsP+I5eXliRUrVggfHx+RlJQknb/0PqdPnxYAREJCghTfSy+9JMaPHy+EEOLKlSvCx8dHrFu3Tly6dEl88cUXwtvbW6SlpVXoXkNDQ4WXl5dYtWqVOH/+vPjXv/4lAIhTp04JIYTIzMwUAQEB4sknnxRnz54VW7duFc2aNZP+L4QQYujQoeLFF1+U3kdGRooVK1aIs2fPiu3btwtXV1fp2difb6dOncSaNWvExYsXhRBCvPjii2Lo0KFCCCEyMjLElClTxAMPPCCSkpJERkaGuHHjhlAoFOLcuXPSeebPny8mTpzo9L7Wrl0rOnToIE6cOCGys7PFW2+9Jdq2bSvy8/OFEEJMnTpVLFq0yOEYs9ksVq5cKfR6vdiwYYMQQoiTJ0+KZs2aiTlz5oicnJwy17nrrrvEV199VaFnfSdWq1VotVpx+vRpaduBAwdEy5YtRVpamkhNTRVxcXHi6tWrIj4+Xvoe+fbbb6VnR0R0J6xfWb9WtX799ddfRWhoqMO2J554QixYsEAIIcT58+eFszSiKvVrZdmvHRgYWO5r4cKF1b4O1R0mpFTv/O1vfxOjRo0SQgiRkJAgFAqFOH/+vBBCiLi4OKFQKERcXJwQQoiePXuKt99+Wzp2xYoVYuzYsRW+FgDx/fffS+8tFosICQkRH3zwgRBCiBkzZoiIiAiHYxYtWiQ6duwohBAiOjpaAJAqg9JKV4ZCCPHll18KPz+/cvexWq0iLCxMrF69Wnrfvn178fXXXwshbL8czJ8/3+H4+++/X6xfv75C9xoaGiree+89h20DBw4Uzz33nBBCiCVLloj27duL4uJih5jd3d1FUVGREKJshXmr4cOHi9mzZ0vvAUjntytdYQphq+AmTZrksM/o0aPFK6+8IoSwPYd27dqJ7777zuk1hw0b5pAoWq1W0bFjR7F7924hRNmEdOfOnaJZs2aid+/e4tChQw7nunbtmhg/frxwc3MTb731lkNZTSakaWlpAoDDLzsHDhwQSqVS+Pv7C3d3dxEUFCTuvvtu8csvvzAhJaIawfqV9WtF69dff/1VKBQK4eXlJb00Gs1tE9LK1q/ff/+9w/nv9CrPokWLxLRp08otp/qPXXZJdlarFTt37sS0adPQo0cP/Pjjj0hLSwMABAcHY8iQIdJsadu2bcOAAQOkcQOjR4/Gtm3bEBsbi6tXr2LLli1lZoW7E5VKJX2tVCrRtWtXaQB+VFRUmWU2IiIiEBMTg/z8fISFheHVV1/FPffcg3nz5iEhIaGKT8HW/Wbq1KnSvZ47dw5xcXEYO3YsAFt3ohUrVjhMjPPbb79V6pql7xUAunfv7nCvQ4YMcdgnIiIC+fn5iI2NdXo+s9mMzZs3Y9KkSejWrRtOnDgh/d/Z3XfffRWOz27mzJn4+uuvYbVacebMGSQnJ+OBBx5wuq/FYnHonqtQKKDRaFBcXOx0/wEDBuB///sfTpw4gXvuucehLDQ0FNu2bcO5c+fKdEtzcXHB3//+d/j7+8PX1xceHh4OM+IOGjSowvd3+fJluLu7w8/PT9o2cOBAmEwmpKamwmAwICkpCYcPH3boCtWjRw/MmjWrwtchoqaN9asN69ebKlq/jhgxAlarFdnZ2dKrqKgIS5YsAQB4e3vj73//u8Mxla1fJ02a5HD+O70AYO7cuWVmpF+2bBk2b95cZnt5vzdQ/cOElGQ3d+5cREZG4vHHH8fx48cxd+5ch/LSlci2bdswZcoUqeyFF15AbGwsevbsibZt20Kn0+HVV1+tVjxGoxEeHh4AbOMUyiOEgEKhwNKlS3Hq1CkUFxejc+fO+Pbbb6t87alTp2L37t3Izc3Ftm3bMHr0aHh6ekrXmzNnDqKioqTXhQsX8Mwzz1T5ehW9V2fjXKxWKyZMmIBPPvkE8+bNQ1RUFB588MEqx1La2LFjYTKZcPDgQfzwww8YN24cdDqd032nTJmCJUuW4OLFiygsLMSKFSuQk5ODAQMGON3f09MT/fr1u+3kCm3atHGYqRAAfv/9d+Tk5CA9PR379+9HYWGhw6y4pWfMvZO4uDi0bdvWIQaFQgG1Wl0mLl9fX+mXkC5duuDRRx+t8HWIqGlj/XoT61ebytSvtxMUFISVK1c6bKtq/dqlSxe0b9++3Nc777wj7bty5UqHujc9PR3Hjh3Dvn37ymz/3//+V+n7InkwISVZ5ebm4rPPPsMnn3yCUaNGQavVlvlwnjRpEk6ePInLly/jwIEDmDRpklS2fPlyPPXUU0hPT0dWVhZ27twJLy+vKseTk5ODU6dOoUePHgCAXr16Yd++fQ777NmzBx06dJAqGsA21fonn3yCF198EcuXL3d6brVajcLCwttOYtC5c2eEhYXh119/xfbt2zF58mSprGfPnjh79myZD+qKLm9yq+LiYhw8eNDhXg8cOOAwacKePXug0+nQsWPHMsdfuHABO3bswPr16zFo0CCo1erb3lt51Gp1manlNRoNpk+fjv/85z/YuXPnbWf1mzNnDh577DGMGjUKAQEB2L59O3bu3Onw/1PfPPTQQ4iKinJatnjxYmg0GmmyJF9fX+n/WafTlWlRICJyhvWrI9avNpWpX8+ePVvuRH56vb7GZs2Njo4ud4KiUaNGISMj47bHv/zyy/jb3/5WI7GQPJiQkqy0Wi00Gg02bdqE8+fPY/Xq1Vi1apXDPgEBARg6dCheeOEF9OvXz2Ga78zMTJw+fRpXr15FYWEhsrKybvuXSGeWL1+Offv24cyZM3jssccQGBgordv18ssv49ChQ3jttddw4cIFbNiwAcuXL8cbb7wBAPjyyy8xffp0/P777zh+/Dh27tyJVq1aOb1O+/btkZ+fjw0bNuDcuXPlxjl16lR8+eWXOHv2LP7yl79I21955RUcOnQI8+bNQ3R0NI4fP47XX3/doYK7k7Vr12LHjh2Ijo7G7NmzkZWVhdmzZwOwJXbZ2dmYPXs2oqOj8eOPP2L+/PlYuHChtI6bXq9HcnIyzGaz9BfV9evXIzo6Gu+88w62bt1a4VhKP5ejR4/iyJEjiImJkbY/+eST2LRpEy5evIiRI0eWe7xSqcTLL7+Mq1evIjc3F7t27ULXrl2d7ltcXHzbtUFvfW3cuLHS9wPYpt6/07k9PT3h4eGB0aNHlzl+6tSpTrsr3fqzQURUHtavZbF+talo/dq9e/dyu88eP37cYd/q1K+hoaFo164dwsLCyrx+/PFH6PX6cmP8v//7P5w6dQpqtRqLFi2q9Pco1ROyjFwlKuWrr74SQUFBokWLFuL5558X69evF3fddZfDPmvXrhUAxIoVKxy2Hzp0SGg0GuHi4iIACACiWbNmYuvWrRW6NgAxf/580a9fP+Hp6SnGjBkjbty44bDP8ePHxeDBg4WHh4fo2rWrNAmCELZZ7ObMmSPatGkj9Hq9eOCBB6Tjb510wWq1ijlz5ghPT0/Rtm1bkZCQUGYfIYS4dOmSACAmTJhQJt4TJ06IYcOGCb1eL1q3bi0iIyOF0Wis0L2GhoaKp59+WgwfPlzo9XoxZMgQcfbsWYd9YmJixOjRo4Wnp6fo0KGD+OCDD4TFYpHK169fL3Q6nThw4IAQQohly5YJHx8f0a5dO/HGG2+Id99912ECBQDip59+crjGrZMupKeni3vvvVfodDpx3333Oezbv39/8cQTT1To/srjbJbdyti9e7dwd3ev8OvatWvVivd2kzN8+eWXnNSIiCqM9Svr19qqX8ubZbcqvLy8pBmJK+L69eviq6++EsOHDxcdO3YUp06dEjdu3BD33HOP6NWrl/jss8/EhQsXaiQ2qhsKIfinBGqYrFYrwsLCsHHjRoSHhwOwLeL8xBNPwGQyYdeuXXc8h0KhwE8//SRNbNCYtW7dGpGRkWXGENVXeXl5aNu2LbZs2VKtxc0ffvhhhIWFYfHixTUXXC1avHgxlixZ4nRRb7PZjAEDBmDv3r11HxgRNRmsXyunKdavFy5cQOfOnWukRdI+JMXFxcVpuY+PD06dOiW9/+KLL3D69GlMmDABgwcPllqZrVYr/vjjD/zwww9wcXHBokWLqh0b1Y07rxxPVE8ZjUZcu3YNP//8MxQKBVxdXXHs2DFERUXhxRdfxIABA3D27Fmnx86ZMwfLli2r44hrz8MPP4zt27c7LRs7dmyZBbrrM5PJhISEBCxduhT9+/evVjIKoEHdO2BLSBtK8kxEjRPr15tYvzoXFhZWY91j7TPoVtRTTz3ldLtSqUR4eLj0RxRqOJiQUoPl4eGB77//Hm+99RaWLVsGjUaDTp06YcmSJZg+fToeeughFBUVOT22OhMz1EcffvihNBX7rdzd3es4muo5fvw4Ro4cifDwcPz73/+WOxwioiaH9etNrF+Jah+77BIREREREZEsOMsuERERERERyYIJKREREREREcmCCSkRERERERHJgpMaOWG1WpGYmAi9Xg+FQiF3OEREJBMhBPLy8hAcHAylkn/DrQjWoUREBFS8DmVC6kRiYiJCQkLkDoOIiOqJuLg4tGzZUu4wGgTWoUREVNqd6lAmpE7o9XoAtofn6ekpczRERCSX3NxchISESPUC3RnrUCIiAipehzIhdcLexcjT05OVKRERsetpJbAOJSKi0u5Uh3JADBEREREREcmCCSkRERERERHJggkpERERERERyYIJKREREREREcmCCSkRERERERHJggkpERERERERyYIJKREREREREcmCCSkRERERERHJggkpERERERERyYIJKREREREREclCLXcARA2NwWCA0Wis8P46nQ4eHh61GBERERERUcPEhJSoEgwGA0Jbt0FmRnqFj/H188f1a1eZlBIRERER3YIJKVElGI1GZGakY/7n2+Hu5etQlmeyIN1ogcliRWtvF7iqlcjPycSyWWNhNBqZkBIRERER3YIJKVEVuHv5Qu/jJ73PKTDj+1PXIITtfb5Vg2Gd/Mo5moiIiIiIAE5qRFQj0vJMUjIKAJn5RfIFQ0RERETUQDAhJaoBBlMxAMBNowIA5JW8JyIiIiKi8jEhJaoBhkJbAhrk5QoAyCsshijdZEpERERERGUwISWqAXkmM4CbCanFKlBgtsgZEhERERFRvceElKgG2LvsertpoHOxddu1t5oSEREREZFzTEiJaoA9+fTQqqF3tU1ezXGkRERERES3x4SUqJqEEMg32brnemjV0Gs1AGzjSImIiIiIqHxMSImqqcBsgaVkAiP30i2khWY5wyIiIiIiqveYkBJVk727rs5FBZVSUSohZQspEREREdHtMCElqib7hEb2RNSDCSkRERERUYUwISWqJvvkRR5aWyKqd7WNITVwUiMiIiIiottiQkpUTaVn2AUAfcm/BlMxrCVjS4mIiIiIqCwmpETVZG8JtXfVtY8lBYD8IqtscRERERER1XdquQMgaugMt3TZVSgU8NCqkVNgRr6ZCSkRETUtKSkpyMnJkTUGLy8vBAYGyhoDEVUME1KiarJ32bWvPwrYJjjKKTCzhZSIiJqUlJQUPPb4X2EuMskah8ZFi41fbWBSStQAMCElqgYhRJkuu4Ct2y4AFBZzDCkRETUdOTk5MBeZUNB2KKyuXpU6VlmQDber+1HQZgisbt5VjkFZmANc2YecnBwmpEQNABNSomowWwSKrbak056EAoCr2va1qZgtpERE1PRYXb1gdfev2rFu3lU+logaHk5qRFQNpmILAECpANQlExkBgKumJCG1sIWUiIiIiKg8TEiJqqGwZNIirVoFhaJ0Qmr70WKXXSIiIiKi8jEhJaoGewupPQG1s7eQFrHLLhERERFRuZiQElWDfYyoVq1y2K4tSVDZZZeIiIiIqHxMSImqwWTvsntLC6mbfQwpu+wSEREREZVL1oR0+vTpUCgUDq/FixcDAPLz8zFt2jT4+fkhPDwcR44ccTg2JiYGQ4YMgY+PD8aOHYvU1FSH8m3btqFHjx4IDAzEP/7xDxQXF9fVbVETUljSZVervqXLrpqTGhERERER3YnsLaRTp05FUlKS9IqMjAQAPPnkk4iNjcWuXbswcuRIjBw5Uko6TSYT7r33XnTq1An79u2DQqHA+PHjpXOePn0aU6ZMwZw5c/DTTz/hxx9/xGuvvSbL/VHjZm8hdS2ny26RRQAK2X/MiIiIiIjqJdl/Uw4JCUFQUJD08vDwQHJyMrZs2YIVK1agd+/eePPNNxEYGIhvvvkGALB9+3bk5ORg5cqV6NGjB9asWYMjR44gKioKALBu3ToMHz4cs2fPRnh4OJYuXYp169ahqKhIxjulxsg+qdGtXXZLJ6hKV486jYmIiIiIqKGQPSH19y+78PGhQ4fg5uaG8PBwAIBCoUBERAT27NkDANi7dy8GDx4MrVYLAGjRogU6derkUD5ixAjpfBEREUhPT8e5c+dq+3aoiSksdt5CqlQq4FLSjZcJKRERERGRc7InpNu3b0e3bt3QoUMHvPbaaygqKkJKSgoCAgKgUt38JT84OBgpKSkAgJSUFAQFBTmc53blAQEBUCqVUvmtTCYTcnNzHV5EFWEyO28hBQBXe0Lq5lmnMRERERERNRRqOS9+3333oXfv3hgyZAhOnjyJ559/HiqVChqNBnq93mFfvV6PzMxMAEBWVhaCg4NvW176eKVSCQ8PD6n8Vu+88w7eeOONmrw1aiLKW/YFsK1FmltYDBVbSImIiIiInJI1IZ02bZr0de/evXHjxg188803mDdvHvLy8hz2zc3NhZ+fHwDA19fXaXmXLl2cllutVuTl5UnH3+qVV17BCy+84HCukJCQ6t0cNQmFJS2krs5aSEuWflG66cuUERERERGRzAnprbp06YKEhAQEBQUhJSUFFotF6rabmJgodcMNCgrCpUuXHI69tTwpKUkqS0lJgRCiTDdfO61WK41HJaqM27aQSmNImZASERERETkj2xjS4uJi5OfnO2yLiopCWFgYBg0aBJPJhKNHjwIAhBDYvXs3IiIiAADDhw/HgQMHYDKZAADx8fGIiYlxKN+1a5d03t27dyMgIEBqQSWqCUIIadkXp2NIpRZSdtklIiIiInJGtoT0m2++Qf/+/fGf//wHMTEx+PLLL/HRRx9h/vz5aNasGSZPnox58+YhKioKr7/+OtLS0vDII48AAMaMGQNfX1/MnTsXZ86cwezZszFo0CB0794dADBjxgzs27cPa9aswbFjx7Bw4ULMmjULGo1GrtulRqjYKmARAkDZWXaBUgmpKyc1IiIiIiJyRrYuu48//jjy8/OxevVqnDx5Ei1atMDq1asxZcoUAMDatWsxc+ZMREREoF27dvjll1+kJWJcXFywa9cuPPXUUxgyZAgGDRqErVu3Sufu1q0bNm/ejAULFiAlJQWPPPIIFi9eLMdtUiNmbx1VKACNSlGm3N5qqmILKRERERGRU7IlpAqFAs888wyeeeYZp+Xu7u745ptvyj2+Q4cO2L9/f7nl48aNw7hx46odJ1F5CotLJjRSq6BQlE1Ib7aQcgwpEREREZEzsq9DStRQSeNH1c5/jOwz7zIhJSIiIiJyjgkpURWZSlpInU1oBNwcV8plX4iIiIiInGNCSlRFhSVLvjib0AjgOqRERERERHfChJSoikzmkhbSO3TZVbl6wGIVdRYXEREREVFDwYSUqIrsLaRaTTktpKVaTvNMljqJiYiIiIioIWFCSlRFRXeY1EipVECjtM2+m1NYXGdxERERERE1FExIiapIWvalnBZSANCqbQlpHhNSIiIiIqIymJASVZGp+PYtpADgorInpOyyS0RERER0KyakRFVUaL79si8AoC1JSHNNbCElIiIiIroVE1KiKiqSWkjL77LromYLKRERERFReZiQElVRRbrsalW2MraQEhERERGVxYSUqIqKKjWGlAkpEREREdGtmJASVYFVCBRZbAmpy+1aSEu67Oayyy4RERERURlMSImqwGwR0te3HUNqbyFll10iIiIiojKYkBJVQVFJQqpWKqBSKsrdj8u+EBERERGVjwkpURXYE9LbddcFuOwLEREREdHtMCElqgJ7Qnq7CY2AmwkrW0iJiIiIiMpiQkpUBWYpIS1//Chws4WUY0iJiIiIiMpiQkpUBaYKdtm1jyHNL7KiuGRWXiIiIiIismFCSlQF5gp22bUv+wIAuVyLlIiIiIjIARNSoiqo6BhSpUIBq8kIAMgpMNd6XEREREREDQkTUqIqqOgsuwBgNRkAMCElIiIiIroVE1KiKiiq4KRGAGAtzAfAhJSIiIiI6FZMSImqoKJddgHAWsgWUiIiIiIiZ5iQElVBUcmMuRXqsluSkOYyISUiIiIicsCElKgK2EJKRERERFR9TEiJqsBcqTGkbCElIiIiInKGCSlRFVRqll22kBIREREROcWElKgKKtNl18KElIiIiIjIKSakRJWlUqMkH+UYUiIiIiKiamBCSlRJSq279LWmIgmpiQkpEREREZEzTEiJKsmekLqolFAqFHfcny2kRERERETOMSElqiSlVgegYhMaAaUSUiMTUiIiIiKi0piQElWSvYW0IuNHAcBqzAUA5JmKYbZYay0uIiIiIqKGhgkpUSVJXXYrmpCa8qEs6dmbZSyqrbCIiIiIiBocJqRElaSoZAsphBWermoAQFY+u+0SEREREdkxISWqJKWrPSFVVfgYbzdbQpqZzxZSIiIiIiI7JqRElVTZSY0AwKskIWWXXSIiIiKim5iQElWSUusBoBJddgF4u7KFlIiIiIjoVkxIiSpJ6WpLSF01le+ym8WElIiIiIhIwoSUqJLsCalWU4kWUvsYUnbZJSKichQWFiImJgaFhYVyh0Iy4vcBNTX1IiEtKipCp06d0Lp1a2lbSkoKHnjgAXh7e2Po0KG4dOmSwzG///47wsPD4efnh8ceewz5+fkO5Z9//jk6dOiAkJAQLFmyBEKIurgVagKkFtJKTWqkAcAWUiIiKt+NGzcwa9Ys3LhxQ+5QSEb8PqCmpl4kpJ9++ikSExOl90IIjBs3DiqVCvv370eHDh0wYsQIFBXZfplPTk7GqFGjMGrUKOzatQsxMTGYMWOGdPyOHTvw7LPP4t1338X69evx/vvv4/PPP6/z+6LGSeWmBwC4VqKF1EtqIeWyL0REREREdrInpGlpaXjjjTcwd+5cadvJkydx7NgxrFmzBj169MCnn36KzMxM7NixAwDw9ddfIzg4GG+88QZ69+6NFStW4Pvvv0dqaioAYPXq1Zg+fTomTZqEiIgIvPTSS1i9erUs90eNz81JjTiGlIiIiIioOmRPSF9//XX07t0b999/v7Rt79696NKlC4KDgwEAWq0WAwcOxJ49e6Tye++9FwqFAgAQHh4OFxcXHDp0SCofMWKEdL6IiAicPn0aWVlZdXVb1EgJIaB0s09qVIUxpExIiYiIiIgkajkvfvbsWfz73//GyZMnkZKSIm1PSUlBUFCQw77BwcHSPikpKQgPD5fK1Go1AgMDkZKSgvz8fBgMBofj7YltSkoKfHx8ysRhMplgMpmk97m5uTVzg9ToFBZboVDZxoNWqYWUkxoREREREUlkayEVQuD555/Hiy++iM6dOzuUZWVlQa/XO2zT6/XIzMy8Y3l2drb0vnQZAOn4W73zzjvw8vKSXiEhIdW6N2q8cguLAQBKBaBRKSp8nD0hNRZZUGi21EpsREREREQNjWwJ6Q8//ICrV6/i1VdfLVPm6+uLvLw8h225ubnw8/O7Y7mvry8AOJTbWzztx9/qlVdeQU5OjvSKi4ur+o1Ro5ZbaEsmXVQKqct4Rbi7qKBW2vZnKykRERERkY1sXXbtM+u2atUKAGA2m5GXlwd/f3/MmzcPSUlJDvsnJiaiS5cuAICgoCCH8uLiYqSmpiIoKAhubm7w9PR0KLfP4BsYGOg0Fq1WC61WW6P3R42TvYVUW4nWUQBQKBTwcXdBWp4JmflFaO7lVhvhERERERE1KLK1kH7zzTeIjY1FVFQUoqKisGjRIgQHByMqKgojRozA+fPnkZCQAMC2QPChQ4cQEREBABg+fDh27dolrS169OhRmM1mDBo0yKHcbvfu3ejTpw+8vb3r9iap0bG3kGrVlf/R8dW5AACy8rn0CxERERERIGNC2qxZM7Rs2VJ6+fr6Qq1Wo2XLlujfvz8GDBiA2bNn48yZM5g7dy6aNWuGUaNGAQAeffRRpKSkYNGiRYiKisK8efMwdepUqUvuM888g/Xr12Pr1q3YvXs33nvvPcyZM0euW6VGxN5C6lLJFlIA8HG3TYaUyS67REREREQA6sGyL+XZtm0bLBYLhgwZgkuXLuHXX3+FRmP7hT4gIAA7d+7Ezz//jIiICHTs2BGfffaZdOzIkSOxcuVKzJ8/H0888QQiIyPx5JNPynUr1IjkmUq67Korn5D6uttbSJmQEhEREREBMi/7Utr06dMxffp06X1AQAB27NhR7v4DBgzA8ePHyy2fOXMmZs6cWZMhEt3ssluVFtKSLrtci5SIiIiIyKbetpAS1UfV6bIrtZCyyy4REREREQAmpESVkmeq+qRG9hbSDLaQEhEREREBYEJKVClVXfYFAPz1tqWF0vJMNRoTEREREVFDxYSUqBKkLrtVmNQoyNMVAJCSW1ijMRERERERNVRMSIkqoTqTGjX3siWkSTmF0hq6RERERERNGRNSokrIk7rsVv5HJ8DT1mW3qNiKbKO5RuMiIiIiImqImJASVZDVKqRJjarSZVerVsGvZKbdpBx22yUiIiIiYkJKVEF5hcWwd7StSpddAAjkOFIiIiIiIgkTUqIKyi6wLddiLSqESlm1hLT0OFIiIiIioqaOCSlRBeUU2MZ9WgvzqnyOwJKENJktpERERERETEiJKso+EZG10FDlczQv6bKbnFNQIzERERERETVkTEiJKijLWNJlt6AmWkhNNRITEREREVFDxoSUqILSDbaE1GLMrvI57GNI2UJKRERERMSElKjC0vJsrZqW/OwqnyNI6rLLMaRERERERExIiSoo3WBPSLOqfI6gkhbS3MJiGIuKayQuIiIiIqKGigkpUQXZE1JrNbrs6l01cHdRAWArKRERERERE1KiCrrZQppdrfMEebHbLhERERERwISUqMLS80omNaqphJRrkRIRERFRE8eElKgChBDIyK+hFlJPNwBAQhZn2iUiIiKipo0JKVEF5BSYYbYIAIDFWPVJjQCgbTN3AMDlNEO14yIiIiIiasiYkBJVgH3JF71WBViqNztuu2YeAIDLafnVjouIiIiIqCFjQkpUAWklExr56jTVPlf7AHtCaoDVKqp9PiIiIiKihooJKVEFpBtsExrVREIa6qeDWqmAsciCJE5sRERERERNGBNSogpIL+my6+uurva5NColWvvbxpHGpnIcKRERERE1XUxIiSogvQa77AJA+5JxpExIiYiIiKgpY0JKVAE1npAGMCElIiIiImJCSlQB9jGkfjWckF5mQkpERERETRgTUqIKsC/74udewy2kXIuUiIiIiJowJqREFXCzy271JzUCgLbNbJMaZeYXITO/qEbOSURERETU0DAhJboDIQQy7Mu+1FALqc5FjRbebgCAi8l5NXJOIiIiIqKGhgkp0R3kFhSjyGIFAPi41UxCCgA9Q7wAAKfismrsnEREREREDQkTUqI7iM82AgB83V3gqqm5H5k+rXwAACeuMSElIiIioqapZgbElZKcnIygoKCaPi2RbOIyCwAAIT5uVT5HWlpamW3tPG3/Hr+WgZSUFCgUCgCATqeDh4dHla9FRERERNRQVCkhValUSEpKQkBAgMP26OhojB8/HpcuXaqR4Ijqg/gsWwtpS19dpY81FRoBhQLdunUrW6hUI+T5/yAXWrTscheKMxMAAL5+/rh+7SqTUiIiIiJq9KqUkAohpNac0o4fP4709PRqB0VUn8Rl2hLSEJ/KJ6RmUyEgBOZ+tBnNglqUKf9fTC5SDMWYumQjOvppkZ+TiWWzxsJoNDIhJSIiIqJGr1IJabNmzaBQKKBQKNC5c2colTfH0xUWFiI/Px9z5syp8SCJ5BSXVdJl17fqXXbdPX2g9/Ersz3ETyDFkIUss8ppORERERFRY1aphHTnzp0QQiA8PByvv/46vLy8bp5IrUb79u3Rv3//Gg+SSE6OLaSiRs/d3MsVAJCUXVij5yUiIiIiaggqlZDeddddAIBFixZhxowZ0Okq34WRqCERQiBeaiHVAdb8Gj1/cy9bq2umsQj5puIaPTcRERERUX1XpTUsFi1axGSUmoR0QxEKzBYoFECwt2uNn9/NRYVmei2Amy2xRERERERNRZUS0itXruDhhx9Gx44dERAQUOZF1FjElcywG+TpCq1aVSvXCC2Zvfc6E1IiIiIiamKqNMvuo48+CoPBgMmTJ6N9+/YOkxsRNSbVmWG3okL9dPjjehauZxgxoLlnrV2HiIiIiKi+qVImGRMTg++++w5Lly7F3/72NzzxxBMOr4r69ddfMWzYMOj1enTu3Blff/21VJaSkoIHHngA3t7eGDp0aJm1TX///XeEh4fDz88Pjz32GPLzHcf2ff755+jQoQNCQkKwZMkSCFGzk9FQ02AfP9qyGjPs3klzLzdoVAoUmC3ILLDU2nWIiIiIiOqbKiWkw4YNQ2xsbLUunJWVhenTp2PKlCn4448/8Mwzz+Dxxx/H0aNHIYTAuHHjoFKpsH//fnTo0AEjRoxAUVERACA5ORmjRo3CqFGjsGvXLsTExGDGjBnSuXfs2IFnn30W7777LtavX4/3338fn3/+ebXipaapLlpIVUoFWpacPyHXXGvXISIiIiKqb6rUZff999/HuHHjEBQUBA8PjzLlXbp0ueM5fHx8EBsbCzc3W8tTp06dsHbtWvz6669Qq9U4duwYEhISEBwcjE8//RT+/v7YsWMHJkyYgK+//hrBwcF44403oFAosGLFCgwdOhQfffQRAgICsHr1akyfPh2TJk0CALz00ktYvXo1nn766arcLjVh9jGkIb61O4lXqK8OV9PzmZASERERUZNSpRbSzp0749y5c7j77rvRrVs3dOvWDd27d5f+rSh7MgoAVqsVBoMBHh4e2Lt3L7p06YLg4GAAgFarxcCBA7Fnzx4AwN69e3HvvfdCoVAAAMLDw+Hi4oJDhw5J5SNGjJDOHRERgdOnTyMrK6sqt0tN2PUMewtp7XXZBYBWfraENyW/GApNzc/mS0RERERUH1WphfTixYs1FoAQAsnJyfjXv/6FgoICPProo3jvvfcQFBTksF9wcDBSUlIA2MaXhoeHS2VqtRqBgYFISUlBfn4+DAaDw/H2xDYlJQU+Pj5lYjCZTDCZTNL73NzcGrs/arjyCs3SGNJOQfpavZa3mwaermrkFhbDtVXF/6hDRNQYDRs2rMy2vXv3lrt/Tk4OFixYgJSUFAQGBmLp0qXw8vJCUVERfvjhByQmJiI4OBijR4/Gzz//jMTERAQFBSEkJAR79uxBQUEBwsLCkJycjJiYGHh6emLixIlQKBRYt24dLl++LF2rb9++8Pb2hp+fH65cuYI///wTBQUFUrlSqYTVaq3W/c+aNQvr1q1D+/btq3UeangMBgPeeustALbvA7kolUoEBgYiOzvb4fvbTqVSQa1WS79jp6WlwWKxwGKxzYXh4uICb29vFBYWAgDat28PV1dXh5/jBx98EGlpadBqtXB3d4cQAhkZGQAAd3d3RERE4Pr16zhz5gzS0tKg0Wjg6uqKVq1aYd++fSgoKICbmxsee+wxWK1WeHh4SDlK8+bN0bZtW+Tm5sLX1xc9evSASmVbLcFiseDMmTO4ceMGvvnmG+Tn58Pf3x8ffPABfH19nT4P+zGZmZnw9fVF165dce7cOaSnpyM7Oxve3t7w9/eXrmPfv7zy+qy8z9PaVKWENDQ0tMYCePHFF/Hhhx/Cw8MDO3bsQEBAALKysqDXOyYAer0e8fHxAFBueWZmJrKzs6X3pcsAIDMz02kM77zzDt54442auiVqJC4m5wEAmnu5wlvnUqvXUigUaOWnw58JuXBt06dWr0VEVJ85S0bt250lpdOmTUNCQoL0Pi0tDePHj4eHhwcKCgqkX5AB4NNPPy33ugcPHnR4/8cffzjdr7ztdtVNRu3sc2PcLhGnxmX27Nm4cOGC3GEAsH0fJyUllVtuTz6vXr3qtLyoqAgGg0F6n56eXmaf//73v7eN4ddff3W6/eTJk9LXhYWF+OSTT257HgAICgrCnDlzAACrVq1CcnKyQ7nBYMDEiRPh6+uLrVu3OpTt37+/zDH2pNPZdeyfVbdeo3QcQ4YMuWPMcijv87RFixYOk8/WtCp12d2wYcNtX5Xx0ksvYe/evXj22WcxZswY/Pbbb/D19UVeXp7Dfrm5ufDz8wOA25bb/7JRutze4mk//lavvPIKcnJypFdcXFyl7oEap/MlCWlYLbeO2oX6ugMA3JiQElETVV4yWl556V+ewsPDsXLlSqkHlcFggBACkZGRGD9+PABIQ30akjs9E2oc6lMyKreaakF0dbUNgRoxYgTatm2LRYsW4fXXX5daYQGgdevWDnPfZGZmYuLEidL7/fv3Y9GiRWjbti0+/fRTLFiwAAqFQjp3hw4dEBkZif79+wOwtSxv2rRJWhKzf//+UrlCoYCXlxcWLVqE/fv318g91qTbfZ4mJCRg2rRptXbtKrWQvvjii2W22ZvNe/Xqhb/+9a8VPlfz5s3RvHlzDB06FHl5eXjzzTcxYcKEMn+VSUxMlL5hgoKCHMqLi4uRmpqKoKAguLm5wdPT06E8MTERABAYGOg0Bq1WC61WW+GYqWk4n2T7Q0ZYHa0NGuLrBgUAjW8LJOSYEBBQJ5clIqoXbk28SrcMli6ztz7k5ORIvzzt2LEDOp1tLP6SJUswcuRICCFgtVrRv39/fPjhh/Dx8cHGjRvxl7/8BVarFV5eXvj3v/+NBx98EIDtFzCFQoGjR486jU+j0UCr1Tq0+tSV2NhYdt9txAwGQ5NIRku3Krq7u5dZslGtVqO4uNhpy2NFaTQaKJVK9O7dG9euXYPJZMKePXvw448/YuLEiRBCSKt2bN++HR4eHrBarVi4cCEuX76MlJQUZGZmIjMzE15eXli1ahUGDBggLSH51ltv4e6778bVq1fh4uKCvLw8jB49GmPGjMGCBQtw7Ngx+Pj4IDk5GQMGDMDSpUuhVCoxZswYLFy4EFevXsXdd9+N1atXY+DAgfWm+255n6fLli2D0WjEmDFjkJCQgJycnFrpvlulhDQtLa3MtoyMDEyYMAFvvvlmhc5hNpthNpulGwYAb29vGI1GDB8+HC+88AISEhLQokULFBYW4tChQ5g7dy4AYPjw4Vi1ahWEEFLlYTabMWjQIKl8165dmDJlCgBg9+7d6NOnD7y9vatyu9REXShJSDvXUUKqVasQ4K5GSn4xjl7PQe8OIXVyXSKi6qiNeRhu7aa6d+/eMgnrggULANgSydK/S/zwww8QQiA0NBTXr1/H3LlzYbFY8NRTTyEmJkbqUtu7d2+sX79eOi4kJAQtW7YsNyE1m83o0qULTp8+Xe37q6yZM2fis88+q/PrVsX169flDkFSn2K5ndt1JW9MfHx8pK67Xl5eZRLSyZMn49tvv73tOfR6fZlekqW3m8221Qr69++PI0eOYMqUKfjuu++wdu1ah8+pLl26SCuFKJVKTJs2DX//+9/RqlUr3LhxAy+88AL+8Y9/IDk5Ga+99hqUSiVOnTqF5ORkTJ06FYcPH0ZkZCTee+89nDlzBr1790Z4eDgOHz6Mnj17Yu/evQgPD5daSktfY8qUKTh8+LB0XH1Q3ucpAOh0OvTr1w/Hjx/HggULsHLlyhq/fpUSUmf8/Pzw+uuvY/78+Thw4MAd99+4cSM++OADvPbaa+jduzfOnDmDlStX4pVXXkGvXr0wYMAAzJ49G0uXLsXHH3+MZs2aYdSoUQCARx99FIsWLcKiRYswceJEzJs3D1OnTpW65D7zzDMYN24cRo0aBW9vb7z33nt47733aupWqQmwWoU0hrRzHXXZBYAWnhqk5BfjyLUczK6zqxIRVZ1c8zDYJzq8tVeWvVfUjBkz8NprryEnJwcAMGDAAERFRUn7ubm5SXNTALYxb3fqLSWEqInQK00IIesENw3V0qVL5Q6BSind8mmf7Ki0MWPG3DEhHTZsGH766acy2wcPHowdO3ZI7+0/y82bNwcAh591AHjqqacc3rdp0wYAMHDgQNy4cQMZGRnS3DP2Mvt7+7kHDBjgdLu9O++tnyf289i3lze3jRzK+zy1e/zxx3H8+HFpv5pWYwkpYPvmOnv2bIX2nT59OgwGA1auXIlTp04hICAA//znPxEZGQkA2LZtG6ZPn44hQ4agZ8+e+PXXX6HRaAAAAQEB2LlzJ5577jmsXLkSY8aMcfjL4ciRI7Fy5UrMnz8fJpMJkZGRePLJJ2vyVqmRi8syIr/IAheVEm383evsui08NTiZVIDjN/JgtlihUVVpmDcRUZ155ZVX8MILL0jvc3NzERJS+z08AgMDkZaWhg0bNmDZsmXSdvusn+vWrQNga4kpLCzE4cOH0aJFC2m/goICtGzZUpqkyMXFxaEFxRm5xqAqFIoG1UJaXxLBBQsW1OhEnLXl008/laXlva6V7p5qT9pKK51Qlqe8Sb5ubQyz/yzbh/CV/lkHgC+++AJ33XWX9N4+OZN9CcnS89JcvXoVXbt2ld7bz3348GEAKLPdnmzf+nliv4Z9e3kz+sqhvM9Tu6+++krarzZUKSG1d4W1s1qtuHz5Ms6dO1emrDwKhQLPPvssnn32WaflAQEBt/3GHDBgAI4fP15u+cyZMzFz5swKxUJ0q/NJttbRDoEeUNdhUuinU8FSkIt8eCIqLhv9WtefDysiImdqYx6GW2fUdTaxz9KlSzF+/HgcO3YMRqNR6mY2fvx4rFq1SuquuXLlSjz88MP44osvsHHjRmlZllOnTuHf//43tm3bBgCIi4sr04pSmkajcVj+pS6tXbuWY0irIDQ0FB07dpQ7jDtaunQpxo4dK3cYtS4rK0v62t5zobTNmzff8RzOuuuW3m4fQ3r06FEEBQXh119/hUqlwsyZM/Hzzz9LY0ijo6NhMBikMaRff/01AgMDcePGDQDABx98AC8vLwQFBeHrr7/GkiVL0KNHDwQFBeHYsWMICgrCF198gaCgIPTo0QNWqxXHjh2DSqXC6dOnoVQqcezYMYwfP176zPn666/RvHlzHDt2DM2bN0ePHj2q8hhrRXmfpwBgNBqlnKu2/thUpYTU3b1si9HgwYPx3HPP4dFHH612UER1zWAwwGg0Su9PXLb9Ra2NjwtSU1Ol7c7GT9ckpUKBwmtRcO88BAdi0piQElGTces40fJml7Unql5eXmjRogUSEhIwZswY9OvXD48//ji++uorqWutUqnEkSNH8MADD+DHH3/E2LFjpbKcnBxpQiMAOHbs2G3js899IQcmo42bh4cHwsLCGv3ERqW77N46fhSwTVIKlL+kSkXYf0aPHDkCV1dXFBYWYsSIEVi6dCmKiooghIBGo4HZbMbYsWMRGhoKnU6H8+fPS+fw9fWVWi/nzJmDRYsWYeHChZg2bRqeeuopvP3229DpdMjPz0eHDh2wY8cOHDx4EEePHkVwcLC07vHhw4fxyiuvYNCgQTh48CCOHTuGjh074siRI3jjjTfqzYRGwO0/T+3JaIsWLWptPVKFkGtARD2Wm5sLLy8v5OTkwNOzbia0IfkYDAaEtm6DzIyba2Q1m/gadB36I/O3z5H3x49ljlm4cS98A5rf8dxJ1y5h+ayx+OeXuxDY4s5d2PKyMrBsyevwH/M8eoZ444e/D6zczRBRjWJ9UHnVfWa3W+akIuuQ2jlbh7QhamjrkMbExGDWrFnI7zIOVnf/Sh2rzE+He/SPVTrW2Xk+//zzBtFCaselX2pP8+bN8cwzzwBwvg6pXXXXIbWvHFLeOqT2OBrKOqR2VV2HtKL1QbXGkB49ehRRUVGwWq3o06ePtAYPUUNiNBqRmZGO+Z9vh7uXL4QQ+OZMNkwWgb/OiUSA+8vSvqlxV/Bp5GMwm4trLZ7Ca6cAAGfis5GVXwQfd5dauxYRUX3jbEZd+3Znvv76a+Tk5GDBggVISUlBYGAgli5dCi8vLxQVFeGHH36QWixGjx6Nn3/+GYmJiQgKCkJISAj27NmDgoIChIWFITk5GTExMfD09MTEiROhUCiwbt06h666ffv2hbe3N/z8/HDlyhX8+eefKCgokMrt3fOqa926dWwZbWLWrFkDg8GAZ555BnFxcbLGolQqERgYiOzsbIfvbzuVSgW1Wi2N2U5LS4PFYpGSNBcXF3h7e0vjKdu3bw9XV1eHn+MHH3wQaWlp0Gq1cHd3hxBCWiPU3d0dERERuH79Os6cOYO0tDRoNBq4urqiVatW2Ldvn7Tk5GOPPQar1QoPDw9cvHgRgC3xa9u2LXJzc+Hr64sePXpILZIDBw7EmTNncOPGDXzzzTfIz8+Hv78/PvjgA6fjOocMGSIdk5mZCV9fX3Tt2hXnzp1Deno6srOz4e3tDX9/f+k6M2fOxJkzZ8otr69u93lam6qUkObn52Py5Mn45Zdf0Lp1awDAtWvXMHLkSGzevNlpl16i+s7dyxd6Hz9kGYtgsmRBpVSgdXAAVMqbk1gYcmp/RjRLXgba+rnhSkYBfr+cgQd63LklloioMalsq6CXl5fTpQhcXFwwefJkh223vr/nnntue277TJp1wd66+PnnnzMZbaI8PDzw2muvSd8HDamFtzYMGDAADz/8cJntzz//vNP9R48efcdzqlQq9O7dG71798b48eMrFIf9mNJut2SLs/0bivI+T2tTlWZr+ec//4mMjAzExsbi8uXLuHz5MmJjY5GZmYl//vOfNR0jUZ1Kyrb9NS9Qr3VIRuvSXSG2pWaOXc2Q5fpERERERHWhSgnptm3b8Mknn0jr6QC2tXU++ugj/Pe//62x4IjkkJRj65rS3NtNthj6tLQlpEev1p81qoiIiIiIalqVElIhhNO1uJRKrplIDV9ijq2FtLlX2TWy6kqvFraE9GJKHrKNRbLFQURERERUm6qUQY4fPx7PPfectFYPANy4cQPPP/98hftiE9VHhWYLMvNtCaCcCamfuwZtm7lDCOD4taw7H0BERERE1ABVKSFdtmwZPD090a5dO3To0AEdOnRAu3bt4O7ujmXLltV0jER1JrmkddTbTQOdS7Umoa62/m38AHAcKRERERE1XlX6jdvDwwO//PILDh06hNOnT0MIgV69eiEsLAweHh41HSNRnUnIto8fla911K5/G198e+wGjnEcKRERERE1UhVuIY2KisKIESMc1tYaOHAg5syZg7///e9o164dOnbsiNOnT9dKoER1wZ6QtpBxQiO78Da2tbD+TMyFwVR7654SEREREcmlwgnpa6+9hmHDhpU7cVFQUBDmzZuHBQsW1FhwRHXJbBFIybV12W3po5M5GiDY2w0tfdxgsQqcuM5xpERERETU+FQ4If39998xceLE2+4zYcIEHD16tNpBEckhNb8YVgF4aNXwdJV3/Kgdx5ESERERUWNW4d+6AwMDkZycjC5dupS7T1paGnx8fGokMKK6lmwwAwBa+Lg5XdaoLqWlpQEAOvvbfkQPxqTgid6+TvfV6XQcu01EREREDVKFE9IRI0ZgyZIlGDp0KFQqVZny4uJivPvuuxg+fHiNBkhUV5INtnGaLWUcP2oqNAIKBbp16wYAUHs3R4un1yLqehaCWoRAFJddk9TXzx/Xr11lUkpEREREDU6FE9K3334bffv2Rd++ffHSSy+hT58+CAgIQEpKCk6ePIn3338feXl5+O6772ozXqJaoVC7IC3flpC28JEvITWbCgEhMPejzWgW1AJCCPznz2wYocHTa35Fc73GYf/8nEwsmzUWRqORCSkRERERNTgVTkg9PDxw9OhRvP3225g9ezYMBgMUCgWEENDpdHj66afx6quvwsvLqzbjJaoVLs07wSoAdxcVvN00dz6glrl7+kDvYxs/2tLXjJgUA7KKNehYso2IiIiIqDGo1MwtXl5e+Ne//oV//etfSExMRHx8PFq0aIHg4GDZx9wRVYdrK1sX2fowfvRWLbzdEJNiQHx2AfrLHQwRERERUQ2q8lSiwcHBCA4OrslYiGTjGlKSkNaD9UdvFVwSU3JOIaxWAaWyfiXMRERERERVVeFlX4gaq6JiK1yCwwDUj/VHb+Xn7gIXtRLFVoF0g0nucIiIiIiIagwTUmryolPyodRo4apWwEcn//jRWykUCjT3cgUAJOUUyhwNEREREVHNYUJKTd6JuDwAQJCHut6NH7WzJ6SJOQUyR0JEREREVHOYkFKTdyrenpDWv9ZRu+ZetnGkbCElIiIiosaECSk1aWaLFWeTDACAIH2V5/iqdUGerlAAyCsshqGwWO5wiIiIiIhqBBNSatLOJ+WiwGyFpdAAH1eV3OGUy0WthL+HFgCQxG67RERERNRIMCGlJu34tSwAgCk+ut6OH7XjxEZERERE1NgwIaUm7Y9rmQBsCWl919ybCSkRERERNS5MSKnJEkLcbCFNOC9zNHdmn9goNa8QxRarzNEQEREREVUfE1Jqsm5kGpFuMEGjUsCUFCN3OHfk6aqGzkUFqwBS8kxyh0NEREREVG1MSKnJsreOdg50ByxmmaO5M4VCUWocKSc2IiIiIqKGjwkpNVn28aM9gz1kjqTigu3rkWZzHCkRERERNXxMSKnJOl6SkPZqoZc5koorPbGREELmaIiIiIiIqocJKTVJmflFuJyWDwDo3oBaSJvptVApFSgwW5BTUP+7GRMRERER3Q4TUmqSTly3jR9tH+ABbze1zNFUnFqpRIBeC4DLvxARERFRw8eElJok+/jRfq19ZI6k8uzjSBM5sRERERERNXBMSKlJso8f7RvqK3MklVd6HCkRERERUUPGhJSanEKzBWcTcgAAfRtgC2mQpy0hzTAUochilTkaIiIiIqKqY0JKTc6Z+ByYLQLN9Fq08tXJHU6luWvV8HLTAADS8i0yR0NEREREVHVMSKnJudld1wcKhULmaKqmuZetlTTFwJl2iYiIiKjhYkJKTc6xq7aENLxNwxs/amdPSFPzi2WOhIiIiIio6mRNSPfs2YORI0fCy8sLPXv2xM8//yyV5efnY9q0afDz80N4eDiOHDnicGxMTAyGDBkCHx8fjB07FqmpqQ7l27ZtQ48ePRAYGIh//OMfKC7mL+4EWKxCWvKlX+uGnJDaZtpNyy8GFPy7EhERERE1TLL9Jnv69Gk89NBDeOihh3D06FGMHTsWEyZMwOXLlwEATz75JGJjY7Fr1y6MHDkSI0eOlJJOk8mEe++9F506dcK+ffugUCgwfvx4h3NPmTIFc+bMwU8//YQff/wRr732miz3SfXL+aRcGEzF0GvV6NzcU+5wqszPwwUuKiXMVkDjHyp3OEREREREVSJbQtqjRw/88ccfmDlzJsLCwrBkyRIEBQVh+/btSE5OxpYtW7BixQr07t0bb775JgIDA/HNN98AALZv346cnBysXLkSPXr0wJo1a3DkyBFERUUBANatW4fhw4dj9uzZCA8Px9KlS7Fu3ToUFRXJdbtUT9i7697V2gcqZcMcPwoASoUCgV5aAIA2uJPM0RARERERVY1sCalCoUCbNm0c3vv4+CA3NxeHDh2Cm5sbwsPDpbKIiAjs2bMHALB3714MHjwYWq3tF/IWLVqgU6dODuUjRoyQzh0REYH09HScO3eurm6P6il7QtqQu+vaNfe0ddvVtgiTORIiIiIioqpRyx2AXUFBAc6fP4/u3bsjMTERAQEBUKlUUnlwcLDUApqSkoKgoCCH44ODg5GSkuK0PCAgAEqlUiq/lclkgslkkt7n5ubW1G1RPSKEkGbY7d+AJzSyCyqZ2EgbzISUiIiIiBqmejMbyurVq+Hn54dRo0YhKysLer3eoVyv1yMz05ZMVLZcqVTCw8NDKr/VO++8Ay8vL+kVEhJSk7dG9cTltHxk5BfBRa1E95ZecodTbfaEVOPXEtkFnLSLiIiIiBqeepGQJiQk4O2338brr78OV1dX+Pr6Ii8vz2Gf3Nxc+Pn5AUCly61WK/Ly8qTyW73yyivIycmRXnFxcTV5e1RP2FtHe4d4Q6tW3WHv+s9No4Kn1vYjfC7JIHM0RERERESVJ3tCWlRUhMmTJ2PAgAF4+umnAQBBQUFISUmBxWKR9ktMTJS64QYFBSEpKcnhPLcrT0lJgRCiTDdfO61WC09PT4cXNT6NYf3RWwW423rdn2VCSkREREQNkKwJqcViwZNPPons7GysX78eCoVt1tNBgwbBZDLh6NGjAGxj/3bv3o2IiAgAwPDhw3HgwAFp3Gd8fDxiYmIcynft2iVdZ/fu3QgICECXLl3q8vaonmnMCemfSfkyR0JEREREVHmyJaT2ZHTfvn34/vvvUVRUhOTkZCQnJ6NZs2aYPHky5s2bh6ioKLz++utIS0vDI488AgAYM2YMfH19MXfuXJw5cwazZ8/GoEGD0L17dwDAjBkzsG/fPqxZswbHjh3DwoULMWvWLGg0Grlul2SWkF2AhOwCqJQK9GnlI3c4NaZZSUJ6LtkAi1XIHA0RERERUeXINsvud999hw0bNgAAunbt6lAmhMDatWsxc+ZMREREoF27dvjll1/g7+8PAHBxccGuXbvw1FNPYciQIRg0aBC2bt0qHd+tWzds3rwZCxYsQEpKCh555BEsXry4zu6N5GcwGGA0GqX3v53PAAB0aqZDfk4mSrcnpqWl1XF0NcfHTQWryYh86BCbakCnIP2dDyIiIiIiqidkS0gfeeQRqcXTGXd3d3zzzTfllnfo0AH79+8vt3zcuHEYN25ctWKkhslgMCC0dRtkZqRL23xH/h36XqNx5KeNCHzhC6fHmc1FdRVijVEqFDAlXYJb6544eSOLCSkRERERNSj1Zh1SoppiNBqRmZGO+Z9vh7uXbbzo1uhsZBda8eDDjyN09lMO+6fGXcGnkY/BbG6YS6cUJV6wJaTXs/BIeCu5wyEiIiIiqjAmpNRouXv5Qu/jh3xTMbILbRMatWsRADcXxyVfDDnO16dtKEyJFwAAp+Ky5Q2EiIiIiKiSZF/2hai2xWcVAAD8PVzKJKONgSnxIgAgNtWAHKNZ5miIiIiIiCqOCSk1enFZtsmNWvnqZI6kdlgLchHirQUAnIrLkjkaIiIiIqKKY0JKjZoQAjcybQlpiE/jTEgBoFtzDwDAqRvZ8gZCRERERFQJTEipUcspMCOvsBhKBRDs7SZ3OLWme0lCevIGW0iJiIiIqOFgQkqNWlzJ+NEgL1e4qBvvt3v3YHcAQFRcNqxWIXM0REREREQV03h/QycCENcEuusCQDt/Hdw0KuQVFuNymkHucIiIqApatWqFzz//HK1acQmvpozfB9TUMCGlRssqhJSQNtYJjezUSgV6tPQCwG67REQNlaurKzp27AhXV1e5QyEZ8fuAmhompNRoZRgtKCy2wkWtRJBn4/9Q7xPqAwA4eT1b3kCIiIiIiCqICSk1WvG5tjU5W/nooFQqZI6m9vVpZUtIufQLERERETUUTEip0UooSUhD/Rp3d1273q28AQCXUg3ILTTLGwwRERERUQUwIaVGSal1R1p+MQCgVRNJSP09tGjlq4MQQBTXIyUiIiKiBoAJKTVKrq17QQDw1bnA01Ujdzh1pk9JK+mJ6+y2S0RERET1HxNSapTc2vYF0HRaR+3uau0LgAkpERERETUMTEip0bEKAbd2toS0jb+7zNHUrX6tS2bavZGFYotV5miIiIiIiG6PCSk1OueS8qFy94FGqUALbze5w6lTHQP08HRVw1hkQXRSrtzhEBERERHdFhNSanQOXMkGALT01EDVBJZ7KU2pVKBvSbfd49fYbZeIiIiI6jcmpNToHCxJSEO8ms5kRqX1Lem2+8e1TJkjISIiIiK6PbXcARDVpPgsI2LTCyCsliaVkKalpUlft/eytQofvZKOlJQUKBQ3W4l1Oh08PDzqPD4iIiIiImeYkFKj8tv5VACAKT4a2r5DZY6m9pkKjYBCgW7dut3cqFKj1fPfIdMItOzcB8VZiVKRr58/rl+7yqSUiIiIiOoFJqTUqOw6nwIAKLh8DEDjT0jNpkJACMz9aDOaBbWQtv8vJhcphmJMWbIRnfxdAQD5OZlYNmssjEYjE1IiIiIiqheYkFKjYTAV4+gV27hJY+wxmaOpW+6ePtD7+EnvWzcDUgyZSDMp0bfUdiIiIiKi+oSTGlGjcSAmDUUWK0K8tSjOTJA7HFmF+OgAAHGZBRBCyBwNEREREZFzTEip0dhVMn50cDtveQOpBwK9tFArFSgwW5CZXyR3OERERERETjEhpUbBYhXYc7EkIW3rLW8w9YBaqUSwtxsAIC6rQOZoiIiIiIicY0JKjcKJ61nIzC+Cp6saPYM5YQ8AtPSxJaTxWUaZIyEiIiIico4JKTUKO/9MBgCM6BIItYrf1sDNcaTxWQWwWjmOlIiIiIjqH/7mTg2eEAK/nLMlpKO7NZc5mvojwFMLrVoJU7EVybmFcodDRERERFQGE1Jq8M4m5CAhuwA6FxUGd/CXO5x6Q6lQINTP1kp6NT1f5miIiIiIiMpiQkoNnr277vBOAXDVqGSOpn5p4+8OALiawYSUiIiIiOofJqTUoAkhpIR0ZLcgmaOpf0L93KEAkGEogqHIInc4REREREQOmJBSgxabasCV9Hy4qJQY3qmZ3OHUO24aFYK8XAEA8TlmmaMhIiIiInLEhJQaNHvr6KAO/tC7amSOpn5qXdJtN44JKRERERHVM0xIqUH7uSQhHdWV3XXL064kIU3IM0OhdZc5GiIiIiKim5iQUoN1I8OI6KRcqJQKjOgSKHc49ZafhxZ+7i6wCkDX4W65wyEiIiIikjAhpQbLvvZo/za+8HV3kTma+q1joB4A4N55iMyREBERERHdxISUGqydJQnpKM6ue0cdAz0AAK6teyHLyLGkRERERFQ/MCGlBik1txAnrmcBAO7vwoT0Trx1LvDXqaBQqvBbTJbc4RARERERAWBCSg2Uvbtu71be0rImdHttfbQAgB/+TIMQQuZoiIiIiIgAtdwBEFWEwWCA0WiU3v94Kg4AMCjUA6mpqQ77pqWl1WlsDUV7PxccvZ6Ni6nA6fgc9ArxljskIiIiImriZG0hTUtLw8KFC9GqVSv07dvXoSw/Px/Tpk2Dn58fwsPDceTIEYfymJgYDBkyBD4+Phg7dmyZpGTbtm3o0aMHAgMD8Y9//APFxcW1fj9UOwwGA0Jbt0FgYCACAwPRPLQ9jl2zdTt9+bFR0nb7q1u3bgAAs7lIzrDrHVe1EvkXDgIAvj5yXeZoiIiIiIhkbiGNi4tDbGwsPD09y5Q9+eSTuHbtGnbt2oWtW7di5MiRuHTpEgICAmAymXDvvfdi1KhRWLlyJRYsWIDx48fj8OHDAIDTp09jypQp+Pjjj9GnTx9MnToVOp0O77zzTl3fItUAo9GIzIx0zP98O9y9fHEpw4QD1/Ph66bCk59tLbN/atwVfBr5GMxm/hHiVnmndsCjWwR+OpOIhQ90gZdOI3dIRERERNSEydpC2qdPH2zatAkPPfSQw/bk5GRs2bIFK1asQO/evfHmm28iMDAQ33zzDQBg+/btyMnJwcqVK9GjRw+sWbMGR44cQVRUFABg3bp1GD58OGbPno3w8HAsXboU69atQ1ERW8waMncvX+h9/BCfb3vfIcgLeh+/Mi+dp7escdZnRYkX0N7fDYVmKzYeZSspEREREcmrXk5qdOjQIbi5uSE8PBwAoFAoEBERgT179gAA9u7di8GDB0OrtU3S0qJFC3Tq1MmhfMSIEdL5IiIikJ6ejnPnztXxnVBNMxVbcCPDNpa0fYCHzNE0TI/1tc1K/H8Hr6KgyCJzNERERETUlNXLhDQlJQUBAQFQqVTStuDgYKSkpEjlQUGOS33crjwgIABKpVIqv5XJZEJubq7Di+qna+lGWISAt04DP3cXucNpkO4P80OIrxsy8ovw7bEbcodDRERERE1YvUxIs7KyoNfrHbbp9XpkZmZWqVypVMLDw0Mqv9U777wDLy8v6RUSElKTt0M1KDbNAABo38wDCoVC5mgaJrVSgWeGtgcAfL7/CgrNbCUlIiIiInnUy4TU19cXeXl5Dttyc3Ph5+dXpXKr1Yq8vDyp/FavvPIKcnJypFdcXFxN3g7VkGKrwLV02wBSdtetnkl3tUCwlyuScwvxxcGrcodDRERERE1UvUxIg4KCkJKSAovlZstNYmKi1A03KCgISUlJDsfcrjwlJQVCiDLdfO20Wi08PT0dXlT/xOeaUWwV0LuqEaDXyh1Og6ZVqzB/VBgAYNWeWKTmFsocERERERE1RfUyIR00aBBMJhOOHj0KABBCYPfu3YiIiAAADB8+HAcOHIDJZAIAxMfHIyYmxqF8165d0vl2796NgIAAdOnSpY7vhGrS9WzbLMnsrlszxvUMRq8Qb+QXWfDe/7sodzhERERE1ATJmpBmZmYiOTkZBoMBZrMZycnJSEtLQ7NmzTB58mTMmzcPUVFReP3115GWloZHHnkEADBmzBj4+vpi7ty5OHPmDGbPno1Bgwahe/fuAIAZM2Zg3759WLNmDY4dO4aFCxdi1qxZ0Gi45mKDpVQjLscMgN11a4pSqcDrf7H9kWbziXj8mZAjc0RERERE1NTImpBOnDgRzZs3x/vvv48zZ86gefPm6NevHwBg7dq1aNeuHSIiIrBz50788ssv8Pf3BwC4uLhg165duHjxIoYMGQIA2Lp1q3Tebt26YfPmzfj0008xduxYjBs3DosXL67z+6Oa49q6J4osAjoXFZp7ucodTqPRp5UPxvcKhhDAm9ujIYSQOyQiIiIiakLUcl5879695Za5u7vjm2++Kbe8Q4cO2L9/f7nl48aNw7hx46oTHtUjuo73AADasbtutaWlpTm8n9HPHzv/TMaxq5n4z6GLiOjoK5XpdDp4eLBFmoiIiIhqh6wJKVFFFFsFdB3uBsDuutVhKjQCCgW6detWpsxr0KPwHvgoIjceQsK6ZwCLrXu0r58/rl+7yqSUiIiIiGoFE1Kq96Li86DSeUGrUqClt5vc4TRYZlMhIATmfrQZzYJaOJZZBLZEZ8PoHYQH39+JXs3dkJ+TiWWzxsJoNDIhJSIiIqJawYSU6r09sVkAgFbeGiiV7K5bXe6ePtD7lF2Td3BHF/xyLgVnUgrRq20g3GWIjYiIiIialnq57AuRndUqsPeSLSFt7e0iczSNW6dAPZp7uaLYKnD0aqbc4RARERFRE8CElOq1U3HZSMs3w2rKR7Cey/bUJoVCgcEdbDNZRyfmIrvQInNERERERNTYMSGlem3nn0kAgILY41Cxu26ta+7lhrb+7hAATiYa5Q6HiIiIiBo5JqRUb1mtAv87Y0tI82MOyRxN03FPO9v40mvZZmgC2socDRERERE1ZkxIqd7643oWEnMK4e6iQsHlP+QOp8nw89CiY6BtVl2vux+SORoiIiIiasyYkFK99UNUAgAgooOPtC4m1Y2+ob4AAF2ngYjLKpQ5GiIiIiJqrJiQUr1UVGzF/87auuuODPOVOZqmp5lei5aeGiiUKmz8I1nucIiIiIiokWJCSvXSwdg0ZBvN8PfQ4q4QT7nDaZJ6BLkCALZHpyM1l62kRERERFTzmJDWIlOxBYVmLp1RFd+fiAcA/KVnc86uK5MgDw0K48/BbBH44tBVucMhIiIiokZILXcAjVVangmzN55Ac70Gr93XCgpFxZIqnU4HDw+PWo6ufks3mPBrdAoAYErfEABsnZNL7pHv4fpQV3x95AbmDGsPLzeuBUtERERENYcJaS2JTTUg6kYWTghgw8dvI+/4tgod5+vnj+vXrjbppPS/JxNgtgj0DPFG5+aeSE1lQiqXgst/oJ2fGy5nFGDjkev4+/D2codERERERI0IE9JaMqCdH54f1grv77kBv4gZeGTWPLTwvH3rUn5OJpbNGguj0dhkE1IhBDYdvwEAeLhfiMzRECDw135BWLTzKv7v4FU8NagNXDUquYMiIqJ6TlmYU/ljCrId/q3LaxORfJiQ1qIpvQLwxkdfwKPHfTidakZYaJDcIdV7R69m4nJaPnQuKvylZ7Dc4RCA+zr54vMjyUjILsDmP+Lw+IDWcodERET1lJeXFzQuWuDKviqfw+3q/mrHoXHRwsvLq9rnIaLax4S0FikUCmQf+AoePe5Dcm4h8k3FcNfykd/OugO2yXPG92oBDz6rekGtUmLWkLZY9OM5fLb/Ch4JbwW1ivOhERFRWYGBgdj41Qbk5MjbSunl5YXAwEBZYyCiiuFv/LXMYsiEv06FdKMFV9Lz0b0F/1pXnitpBvx2wTaZ0VOD2sgcDZU2pW8IPv7tEuKzCvDj6URM7NNS7pCIiKieCgwMZDJIRBXGhLQOhHq7IN1YgMtpBiakt/HFwasQAhjROQDtA5rmGNr6KC0tDQAwpVczrD6UgA9+uYC7m6udtpJylmgiIiIiqgwmpHWglZcLTiQWID6zAEXFVrio2d3RYDDAaDRK79MMRfj+jzgAwEPdfJCamnqzrCQhorplKjQCCgW6desGAFBoXNHi6bWIhw86jf4bDKd3ljmGs0QTERERUWUwIa0D3q5KeLtpkF1gxvWMfHQI1MsdkqwMBgNCW7dBZka6tM33/r9D33s0CuOjMbrvWKfHmc1FdRUiATCbCgEhMPejzWgW1AIAcC61EEfjjQj5y7N46OXXoFbeXF+Xs0QTERERUWUxIa0DCoUCbfzdcSouGzcyjU0+ITUajcjMSMf8z7fD3csXOYUWbI3OgQAwMaI/gsb97rB/atwVfBr5GMzmYnkCbuLcPX2g9/EDAPT1siI6/TryCosRnQUMbO8nc3RERERE1JCx72gdaenjBgBIyC6QOZL6w93LF3ofP5xOs0AAaO2nQ4eQIOh9/BxeOk9vuUOlEmqlEkM7NgMAnLiRhbQ8k8wREREREVFDxoS0jgR72xLSLKMZ+Sa29NldSTcgNs0AhQK4p52/3OFQBbRr5oH2AR4QAth1PgXFVqvcIRERERFRA8WEtI64alTw93ABwFZSO7NFYM8F24RFfUJ80EyvlTkiqqhhHZtBq1YiNc+EPRfSIISQOyQiIiIiaoCYkNahlt46AEBCFhNSADgSnw+DqRhebhr0b+srdzhUCe5aNUZ3C4ICQHRSLk7eyJY7JCIiIiJqgJiQ1qEWHEcqce8+ApcyiqCAbd1RjZM1Lal+C/Vzx6AOtm7WB2PTcTqZ39dEREREVDnMAupQsLcrACAjvwgFRRaZo5HP+ZR8+N73DADg7rZ+aOmjkzkiqqreId4Ib21r3T6RWADf+/8OUzHHlBIRERFRxTAhrUM6FzX83G3jSOOzjDJHI49r6fl4fmsMlBotWnpq0K+1j9whUTUoFAoMaOeHwSUtpfreozFj03lcSsmTOTIiIiIiagiYkNaxkJLWwBtNMCGNzzLi8f87iqyCYpiSYzGsjQcUCoXcYVEN6NPKB/e384DFmIOLqUY88PFBfLonFsUWtpYSERERUfmYkNaxED/bONK4zKY13u5aej6mrDmMuMwCtPTSInXzYriomIw2Ji29XJD05XMY2MYLRRYrlv9yERNWHcL5pFy5QyMiIiKieooJaR1r6a2DQgHkFJiRU2CWO5w68ce1TExa/TsScwrRtpk71kwJg9WYLXdYVAsshgx8MKED3p/cE56uavyZkItxKw9ixa4YFHFsKRERERHdgglpHXNRKxHkaZvcKC6zcXfbFULgu+NxeHTtUWTkF6FrsCf+M2sAAvQucodGtSg9PR2DQ1yw6a9dMaSdN8wWgRW7LmHMir3Yf/YqUlNTpZfBYJA7XCIiIiKSkVruAJqiVr46JOUU4kamEd1aeMkdTo0xGAwwGm1JtsFUjOW7b+Dn8xkAgOHtfbB4dBuIghykpaXJGSbVElOhEVAo0K1bN4ftus5D4DviacSmA49/dRa5RzYj+/dNgKUYvn7+uH7tKjw8PGSKmoiIiIjkxIRUBq18dTh6NRNxWUZYrQJKZcMfS2kwGBDaug0yM9Lh1mEAfO+bDbXeD8JqQfb+r/Dvf23BvyEcjjGbi2SKlmqD2VQICIG5H21Gs6AWDmUFZisOxxlxLbsIXvc8jNCIaejrZ8EXz42F0WhkQkpERETURDEhlUGgpytcNUoUmq2IyzIi1M9d7pCqzWg0IsckcM+b25GQb9vmqVViUKgeQX1fAPCCtG9q3BV8GvkYzOZieYKlWuXu6QO9j5/DNj2A8QHApdQ87LmQhuxCC35LAHwiZiC7wIwAeUIlIiIiIpkxIZWBSqlAxwA9ziTk4HxyXoNPSLPyi/DJ/jgEz/oMCfmAUgHcFeqD8Na+UKvKDlM25GTKECXVBx0C9Gjpo8O+mDRcTM6DZ78JePCLM3hqcD5mDG4DT1eN3CESERERUR1iQiqTsOa2hPRyqgFFnaxwUTe8+aUyDCZsOHwdXxy8CoOpGEqNKwLc1RjRNRjN9Fq5w6N6yk2jwqiuQQj1EPjpYBQQ1B4f/3YJ63+/hmn9W2Ha3aFo4e0md5hEREREVAeYkMokyNMV3m4aZBeYcTnNgM7NPeUO6Y6EELicZsCRK5k4eCkdv11IgdliGxfaoZkbDq1+GX9bugKeTEapAlp6uiB5/Tys+d8RfPtnHq5lFmLV3stYs+8yhrTzxoM9AtAvRC+1sut0Oo41JSIiImpkmJDKRKFQICxIjyNXM3E2IQdhQXq5QyojNy8PZ29k4GR8Hk6VvLIKHMd9dg7U4fF+zdHduxg9Io9DoWj4EzRR3bDNygvMfqA/oFDCrUN/6HuPhVvrntgbm429sdmwFOSi8OopFMZHw9WQiNg/9sHLs/79rBARERFR1TAhlVGXYE/8cT0LSTmFiEkxILgeNCwWmi04FJuOn6LisfXoJShdHX/5t5pNKEq8gMK4P2G8dBTXU69gZ6lyzpxLFVXerLxZBRZcSC/E1awiFLp5wr3LULh3GQoA6L/sAFr7uqGVj2uZl4dW5XB+tqgSERER1X+NOiEVQuCtt97C2rVr4ebmhvnz52PGjBlyhyXRu2rQr7UvDl/JwIHYNEwMk6fbbo7RjIOx6fj5zyTsuZCK/CILAEDpqodaAQR4qBHkoUGQXo1mOh+olM0BDAfwrHQOzpxLVXXrrLx6H6BVMGAVAonZBUjILsD1lGwkZBpQCB0upBpxIdVY5jwWQxbMWQkozkqEOTsZrhYjtqxfg04t/OHv4cLWeyIiIqJ6qFEnpGvWrMGKFSuwZcsWZGRkYNq0aWjZsiVGjRold2iSPq28EZ2Ui5wCM/ZcNUChca2V61isAleSMnAlJQcJOSbEZxciPtuEq5kFiE0rcFghNMBDg/4tXLF28Vy8vGwNvHz973h+zpxLNU2pUKCljw4tfXRopcjE8kUP44nl30Gt90eOyYKcQityTRbkFFpQUCyg8vCBysMHCOkmneOxf0cBAFw1SgR5uiLQ0xVBXq4I8nRFgKft3yAvLQI9XdFMr4VWrSonGiIiIiKqDY02IRVCYNWqVXjppZcwfPhwAMD/+3//D2vWrKlXCalapcS9YQH44XQi4nPNCJq2DHsuZeFBX/9KzbxbaLYgLc+ElNxCxGUZEZ9ZgPisAsRnGxGfVYCErAIUW0W5x5sz4mC8dBTGmN9xPekSjpekqJZic7XvkahGCCuC/LwR2KJ5mSJTsQXZRjOyjEXIMZqRkWPA2agTCOncC+n5FhSarbiWYcS1jLItq6WplAq4qpVw1SihUSqgUNgSY6XCNu5bWfLeth3wdFXDT6eBv4cGfjoN/NxLXjoN3FxUUCsBlVIJD50b3N3dIQCIUj+GrholdC5qqJRsvSUiIqKmqdEmpJmZmfjzzz8xYsQIaVtERASefvppGaNyLsRXh0l9WuDHqAQgsC3++VMsXttxBaF+OvjoXOCiVsJFrYRKqYCp2AqT2YLCYisKTGYUFBUjq6AYBpPljtcRlmJ4aNXwdFPDU6uCp1YJT60KzdzV0Gl8gft6ApgFgF1wqWHRqlUI9FQh0NPWwyA9yYhdm15FihCAUg21pz9UHn5Q6f2g1vtJX6s87O99oVBpYLEK5BdZpG7rdRe/EjqNEm4uKripldBp1XB31cBNo4KbiwpatapUYmxPim8myJZiMyyWYihwM1lWKRVQKxXSv2qlAkqlAi4aDbRa5wPWb+3WfGuarFQASqXjte3JugIl75Vl41MAUjIuIEr+tf3hELduL1UmYDvQXlZYaIKpyCT16BDC1rW7yCJQbBEwW6w3v7ZaIRQqPH9/ZwRzGSEiIqJ6q9EmpCkpKQCAoKAgaVtwcDByc3NRUFAAN7ebv6CYTCaYTCbpfU5ODgAgNze3WjHk5eUBADKT41FozL/tvmoAQ33z8Z+t29Bq8ERkG624aDRU6nqiuAjF+Vmw5KaiOCcNltw0FOemoTg3FcW5qbAasjDnvY3w8gsoOcJqe5nNKLylIbSo0NaSlJWaACXKb1m1y05NrPD+ldm3vu1fn2Kp7P71KZbK7l/Zc6cnXAWEwGMLPoZvs8A77p+WFItNHy7CQy8ug963GYqtJUlRSfnNZArIyUjFb5s+w73T5kKr90FhMWCyAIWWm/8WWABrSXJlBVA2tXNUYAIK7hglVcX97d3h0b5sq3pF2esBIe78fUc29mdV3TqUiIgatorWoQrRSGvZQ4cOYdCgQcjKyoK3tzcA4NSpU+jTpw8SEhIQHBws7bt48WK88cYbMkVKRET1XVxcHFq2bCl3GA1CfHw8QkJC5A6DiIjqiTvVoY02IT1//jy6dOmCGzduSBXjvn37MGzYMBQUFMDV9ebkQbe2kFqtVmRmZsLPz69aM3Pm5uYiJCQEcXFx8PSUZwbd+ojPpXx8Ns7xuZSPz8a5mnouQgjk5eUhODgYSmXFx/U3ZVarFYmJidDr9axDawGfS/n4bJzjcykfn41zdV2HNtouu/auuklJSVJCmpiYCG9vb4dkFAC0Wm2ZMVX2VtWa4OnpyW9yJ/hcysdn4xyfS/n4bJyriefi5eVVQ9E0DUqlskZbk/m97RyfS/n4bJzjcykfn41zdVWHNto/9/r4+KBnz57YtWuXtG337t2IiIiQMSoiIiIiIiKya7QtpAAwZ84c/POf/8SAAQOQmZmJDRs2YPv27XKHRURERERERGjkCenMmTORkpKCxx9/HG5ubli1ahXuu+++Oru+VqvFokWLyl1ioanicykfn41zfC7l47Nxjs+l4eP/oXN8LuXjs3GOz6V8fDbO1fVzabSTGhEREREREVH91mjHkBIREREREVH9xoSUiIiIiIiIZMGElIiIiIiIiGTBhLSahBB48803ERISgo4dO2LdunXl7puSkoIHHngA3t7eGDp0KC5dulSHkdatij4Xi8WCJUuWoEePHvDx8cFDDz2EpKSkOo62blXme8Zu//79UCgUWLx4ce0HKJPKPBer1Yq3334bHTp0QLNmzTBt2jRkZGTUYbR1qzLP5uTJkxg0aBDc3d3Rs2dP/PLLL3UYad1KS0vDwoUL0apVK/Tt2/e2+zalz9+GhHWoc6xDy8c61DnWoeVjHepcvapDBVXLqlWrhI+Pj9i9e7fYvHmzcHFxET///HOZ/axWqwgPDxd/+ctfxOnTp8VTTz0lWrVqJUwmkwxR176KPpeXXnpJ3HPPPeK3334Tx48fF3379hUjRoyQIeK6U9FnY1dcXCx69eolPDw8xKJFi+ou0DpWmefy0ksvidDQUPHLL7+Is2fPimeeeUYcPXq0jiOuOxV9NkajUQQHB4uFCxeKmJgY8dZbbwmdTieSkpJkiLr2nThxQkydOlV07dpV3HXXXeXu19Q+fxsS1qHOsQ4tH+tQ51iHlo91qHP1qQ5lQloNVqtVdOvWTbz99tvStpkzZ4rx48eX2fePP/4QAERCQoIQQojCwkLh4eEh/vvf/9ZRtHWnMs8lOTlZGAwG6f1vv/0mAIisrKw6iLTuVebZ2K1du1a0atVKPPLII422Mq3Mc0lNTRVubm7iwIEDdRihfCrzbE6ePCm8vLyE1WoVQth+EQsMDBRbt26tq3BlsWjRottWpk3p87chYR3qHOvQ8rEOdY51aPlYh95ZfahD2WW3GjIzM/Hnn39ixIgR0raIiAjs2bOnzL579+5Fly5dEBwcDMC2vs/AgQOd7tvQVea5BAYGwt3dXXrv6+sLAMjLy6v9QGVQmWcDALm5uViwYAHefPNNuLi41FWYda4yz+Xnn3+Gv78/Bg4cWJchyqYyz6Zdu3YoLCxESkoKAEClUkGr1aJTp051Fm991JQ+fxsS1qHOsQ4tH+tQ51iHlo91aPXVxecvE9JqsH/DBgUFSduCg4ORm5uLgoKCMvuW3s++r/0cjUllnsutTp48CW9vb7Rs2bJWY5RLZZ/NkiVLEBYWhr/+9a91FqMcKvNcbty4gdDQUHz33Xfo2bMnOnbsiOXLl0M00iWVK/NsPD09MW/ePEREROCXX37Bf/7zH3Ts2BGdO3eu05jrm6b0+duQsA51jnVo+ViHOsc6tHysQ6uvLj5/1TV2piYoKysLAKDX66Vt9q+zsrLg5ubmsG/p/ez7xsfH10Gkdasyz6U0q9WKjz/+GE8++SQUCkXtByqDyjyb2NhYrFq1CkePHm20z8OuMs8lPj4eFy5cwDfffINPP/0UFy9exDPPPIMOHTpgwoQJdRp3Xajsz9OIESOwZcsWTJkyBQaDAQcOHGj03z930pQ+fxsS1qHOsQ4tH+tQ51iHlo91aPXVxecvW0irwVnXmNzcXIey0vve2oUmNzcXfn5+tRxl3avMcylt3bp1uHbtGiIjI2s3QBlV5tlERkbi2WefRdeuXesuQJlU5rno9Xr4+/tj8+bNGDRoEJ566ik8+OCD+OGHH+ou4DpUmWfz+++/Y86cOTh48CCuXbuGl156CaNHj0ZUVFSdxVsfNaXP34aEdahzrEPLxzrUOdah5WMdWn118fnLhLQa7M3XpadYT0xMhLe3N1xdXcvse+tU7ImJiWWawBuDyjwXuz/++APPPfccvvjiCzRv3rxO4pRDRZ9NQkICfvjhB6xevRr+/v7w9/fHt99+i2XLlqF37951Hndtq8z3TEhICJRKpcN4oNatWyM5Oblugq1jlXk2q1evxuTJkxEQEAAfHx+8++67uPfee7FixYq6DLneaUqfvw0J61DnWIeWj3Woc6xDy8c6tPrq4vOXCWk1+Pj4oGfPnti1a5e0bffu3YiIiCiz7/Dhw3H+/HkkJCQAAAoLC3Ho0CGn+zZ0lXkuAHD58mVMmDABzz//PCZNmlRXYcqios8mMDAQcXFx+PPPPxEVFYWoqCj06dMHs2fPxo4dO+o67FpXme+ZiIgIxMTEOFSeMTExaNeuXZ3EWtcq82zy8/Oh0WgctjVv3hw5OTm1Hmd91pQ+fxsS1qHOsQ4tH+tQ51iHlo91aPXVyedvjc3X20R99tlnwtvbW+zevVt8//33wsXFRfy///f/RGpqqmjVqpX48ssvpX0HDBggxo4dK63h07p1a1FUVCRf8LWoos/lypUrIiQkRDz++OMiLS1NJCUliaSkJJGdnS3vDdSiynzPlDZ06NBGO2W9EJV7LmPHjhUjR44Up0+fFl9++aXQaDQiKipKvuBrWUWfzaZNm4RerxcbN24Uly9fFps2bRI6nU5s2LBB3huoJRkZGSIpKUm8+OKLokePHiIpKUmkpqY2+c/fhoR1qHOsQ8vHOtQ51qHlYx3qXH2qQ5mQVpPVahVvvvmmaNGihWjfvr1Yt26dEMK2NlhISIj44osvpH1TUlLE6NGjhZeXlxgyZIi4dOmSXGHXuoo+l/vvv18AKPN64oknZIy+dlXme6a0xl6ZVua55OXlib/+9a/Cz89PdOzYsdGvEVaZZ/N///d/omvXrsLNzU106tRJrFq1SlpTrbEZOnRomc+O0NDQJv/525CwDnWOdWj5WIc6xzq0fKxDnatPdahCiEY6zzMRERERERHVaxxDSkRERERERLJgQkpERERERESyYEJKREREREREsmBCSkRERERERLJgQkpERERERESyYEJKREREREREsmBCSkRERERERLJgQkpERERERESyYEJK1Mht3boVbdu2RUFBQbX2qU+GDRuGyMjIap0jMjISDz30UA1FREREjRHrUOdYh1JNYkJK1Mj5+/sjLCwMGo0GAPDHH3/A39//tvs0BSEhIWjfvr30/r333mPlSkREDliHOsc6lGqSWu4AiKh2DRkyBEOGDKn2Po3NP/7xD7lDICKieo51qHOsQ6kmsYWUqJSLFy9i+vTpaNeuHXx9fTF9+nQYjUYMHDgQL7/8ssO+77zzDgYOHAgAyMvLw4wZM+Dv7w+FQiG9Hn744QpdV6FQ4Ndff8XEiRPh4+ODXr16Yc+ePQ77nDhxAsOGDYOnpyd69OiBTZs2OZRv2LAB7du3h6enJ0aOHIkzZ84AALZv3w6FQgEAWLx4Mfr164eMjAwpxlv3WbBgAe6++26Hcx8+fBiurq7Izc0FAJw8eRKDBw+GXq9H3759ceDAgQrdJwC0bt0a3377LaZPnw5/f3907ty5zL1cvnwZY8eOhbe3N8LCwvDxxx/DarWWe87jx49j8uTJCAkJQUBAACIjI2GxWBye7/nz5zF+/Hh4e3sjJycHkZGRGDZsGABb96WXXnoJW7ZsgUKhwLBhw/Dmm2+iT58+Dtc5duwY3NzcpOdAREQ3sQ5lHco6lKpEEJFk+fLl4q233hInT54U+/fvFwEBAeLdd98VH330kejUqZPDvv369RMffvihEEKIWbNmiU6dOomdO3eKXbt2ie7du4u3335bZGVlVei6AERwcLDYuHGjiI6OFvPmzRNarVYkJSUJIYS4fPmycHV1Fa+++qqIjo4WX375pdDpdGLTpk1CCCFu3LghlEqlePvtt0V0dLT48MMPxW+//SaEEOKnn34S9h/1vLw8sWLFCuHj4yOSkpKk85fe5/Tp0wKASEhIkOJ76aWXxPjx44UQQly5ckX4+PiIdevWiUuXLokvvvhCeHt7i7S0tArda2hoqPDy8hKrVq0S58+fF//6178EAHHq1CkhhBCZmZkiICBAPPnkk+Ls2bNi69atolmzZuLdd9+VzjF06FDx4osvSu8jIyPFihUrxNmzZ8X27duFq6ur9Gzsz7dTp05izZo14uLFi0IIIV588UUxdOhQIYQQGRkZYsqUKeKBBx4QSUlJIiMjQ9y4cUMoFApx7tw56Tzz588XEydOrNB9EhE1NaxDWYeyDqWqYEJKdBt/+9vfxKhRo0RCQoJQKBTi/PnzQggh4uLihEKhEHFxcUIIIXr27Cnefvtt6bgVK1aIsWPHVvg6AMT3338vvbdYLCIkJER88MEHQgghZsyYISIiIhyOWbRokejYsaMQQojo6GgBQKooSitdUQohxJdffin8/PzK3cdqtYqwsDCxevVq6X379u3F119/LYSw/eIwf/58h+Pvv/9+sX79+grda2hoqHjvvfcctg0cOFA899xzQgghlixZItq3by+Ki4sdYnZ3dxdFRUVCiLKV6a2GDx8uZs+eLb0HIJ3frnRlKoQQTzzxhJg0aZLDPqNHjxavvPKKEML2HNq1aye+++67Ct0nEVFTxzqUdSjrUKoIdtklKsVqtWLnzp2YNm0aevTogR9//BFpaWkIDg7GkCFDsG3bNgDAtm3bMGDAALRs2RIAMHr0aGzbtg2xsbG4evUqtmzZgi5dulTq2iqVSvpaqVSia9euuHz5MgAgKipK6hpjFxERgZiYGOTn5yMsLAyvvvoq7rnnHsybNw8JCQlVfgYKhQJTp06V7vXcuXOIi4vD2LFjAdi6Gq1YsQIeHh7S67fffqvUNUvfKwB0797d4V6HDBnisE9ERATy8/MRGxvr9HxmsxmbN2/GpEmT0K1bN5w4cQJpaWkO+9x3330Vjs9u5syZ+Prrr2G1WnHmzBkkJyfjgQceqPR5iIiaAtahrENLYx1KFcWElKiUuXPnIjIyEo8//jiOHz+OuXPnSmWlK5ht27ZhypQpUtkLL7yA2NhY9OzZE23btoVOp8Orr75arViMRiM8PDwAAEKIcvcTQkChUGDp0qU4deoUiouL0blzZ3z77bdVvvbUqVOxe/du5ObmYtu2bRg9ejQ8PT2l682ZMwdRUVHS68KFC3jmmWeqfL2K3quzMTBWqxUTJkzAJ598gnnz5iEqKgoPPvhglWMpbezYsTCZTDh48CB++OEHjBs3DjqdrkbOTUTU2LAOtWEdasM6lCqKCSlRidzcXHz22Wf45JNPMGrUKGi1WocP70mTJuHkyZO4fPkyDhw4gEmTJklly5cvx1NPPYX09HRkZWVh586d8PLyqnIsOTk5OHXqFHr06AEA6NWrF/bt2+ewz549e9ChQwepEgJs07B/8sknePHFF7F8+XKn51ar1SgsLLztBAedO3dGWFgYfv31V2zfvh2TJ0+Wynr27ImzZ8+iffv2Di9vb+8q3WtxcTEOHjzocK8HDhxwmFBhz5490Ol06NixY5njL1y4gB07dmD9+vUYNGgQ1Gr1be+tPGq1Gkaj0WGbRqPB9OnT8Z///Ac7d+7E1KlTK31eIqKmgHXoTaxDbViHUkUxISUqodVqodFosGnTJpw/fx6rV6/GqlWrpPKAgAAMHToUL7zwAvr16yd1NQKAzMxMnD59GlevXkVhYSGysrJu+1dKZ5YvX459+/bhzJkzeOyxxxAYGCit6fXyyy/j0KFDeO2113DhwgVs2LABy5cvxxtvvAEA+PLLLzF9+nT8/vvvOH78OHbu3IlWrVo5vU779u2Rn5+PDRs24Ny5c+XGOXXqVHz55Zc4e/Ys/vKXv0jbX3nlFRw6dAjz5s1DdHQ0jh8/jtdff92h8ruTtWvXYseOHYiOjsbs2bORlZWF2bNnAwDmzJmD7OxszJ49G9HR0fjxxx8xf/58LFy4UFrjTa/XIzk5GWazWfpr6/r16xEdHY133nkHW7durXAspZ/L0aNHceTIEcTExEjbn3zySWzatAkXL17EyJEjK31eIqKmgHWoI9ahNqxDqULkGbpKVD999dVXIigoSLRo0UI8//zzYv369eKuu+6SyteuXSsAiBUrVjgcd+jQIaHRaISLi4sAIACIZs2aia1bt1bougDE/PnzRb9+/YSnp6cYM2aMuHHjhsM+x48fF4MHDxYeHh6ia9eu0gQJQthmuJszZ45o06aN0Ov14oEHHpCOv3VCBqvVKubMmSM8PT1F27ZtRUJCQpl9hBDi0qVLAoCYMGFCmXhPnDghhg0bJvR6vWjdurWIjIwURqOxQvcaGhoqnn76aTF8+HCh1+vFkCFDxNmzZx32iYmJEaNHjxaenp6iQ4cO4oMPPhAWi0UqX79+vdDpdOLAgQNCCCGWLVsmfHx8RLt27cQbb7wh3n33XYfJFQCIn376yeEat07IkJ6eLu69916h0+nEfffd57Bv//79xRNPPFGh+yMiaqpYh97EOvQm1qF0JwohKvknKCJyYLVaERYWho0bNyI8PBwAkJ6ejieeeAImkwm7du264zkUCgV++uknadKDxqx169aIjIx0GFtUn+Xl5aFt27bYsmVLk1v4nIiotrEOrRzWodQYscsuUTUZjUZcu3YNP//8M44fP46zZ8/ihx9+QFRUFMaMGYMBAwY4zKZX+jV//ny5w69RDz/8cLn3WtEFzusLk8mEK1eu4Pnnn0f//v1ZkRIR1QLWoTexDqWmSi13AEQNnYeHB77//nu89dZbWLZsGTQaDTp16oQlS5Zg+vTpeOihh1BUVOT02OpM2lAfffjhh1iyZInTMnd39zqOpnqOHz+OkSNHIjw8HP/+97/lDoeIqFFiHXoT61Bqqthll4iIiIiIiGTBLrtEREREREQkCyakREREREREJAsmpERERERERCQLJqREREREREQkCyakREREREREJAsmpERERERERCQLJqREREREREQkCyakREREREREJIv/D22v8Y5uBwvPAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6wAAAGHCAYAAAC0z9+YAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAe0JJREFUeJzt3XlcVOX+B/DPzLDvm2yKWm5kiqVG10RN1NQytFK7ahmZmpkt3lzSTLNcuv68XStTUyszs7oumZpLoaJlaZZhlguuCQIDCLIzzPL8/oA5MDCDMwjMwfm8X695yZznnGeecyCevnyfRSGEECAiIiIiIiKSGaW9G0BERERERERkDgNWIiIiIiIikiUGrERERERERCRLDFiJiIiIiIhIlhiwEhERERERkSwxYCUiIiIiIiJZYsBKREREREREssSAlYiIiIiIiGSJASsRERERERHJEgNWInIYv//+Ozp27Fjj+Kuvvor4+PjGb1ADOHLkCFq3bm3vZhARkQMrLS2FQqHA5cuX7d0UugUwYKVbTnR0NN577z17N8MqR48eRfPmzZGSknJT58hJfHw8hg8fflN1vPfee4iOjq6nFlXSaDS4cuVKna5t06YNvvjii3pukW0uXryIoKAgk9fLL78MAPDy8uL/GBBRg2Hfal9y61svXLgALy8vk2OLFi3CM888Y1M9N+pbz5w5gxYtWiAoKAgqlQotWrSQXgqFAiEhIWjRogWKioqka4qKikzOq/5yc3PDsmXLbGon2RcDVrrlREZGIiwszN7NsIqvry8iIyPh4eEBAMjOzoZCoUBhYaHFcxxBWFgYIiMjpfebN29G9+7d61TXxx9/DD8/P/j5+aF///4oKiqS3vv5+VkVwBYXFyM1NRUXLlyo9bzhw4fDzc3NpP6qrx49epic/9NPP8HLy8viS6lUYsOGDdL5t99+O7Kzs6XXlClTTH5WiIgaCvvWpq8++1YhhEmQCABlZWXQaDRW12FN3xoZGYnU1FRs2LABERERSE1NlV4AcPDgQaSmpsLT01O6xtPT0+S86q/+/fvbeLdkb072bgBRfVu/fr29m2C1yMhI7Nu376bPudWMGDECI0aMqJe64uPjMXbsWIvlKpXqhnXs2rULALBt2zbMmjWr1mvmzJmDOXPmWNW2++67r9aAMzo6GgqFwqq6iIgaEvvWpq8++1aj8ePHS18fP37c7LQbS2zpW21RWloKPz8/hIaGwsmpZqhTVlaGYcOG1ctnUeNghpVk7f7778enn36KV199FeHh4ejatSsSExORnJyMwYMHw9fXF4888ggKCgqka7p374433ngDAHD58mW4urri4sWLeOSRR+Dv7497770X58+ft7oNrVu3xhdffIH4+HgEBQXhjjvuwJdffmlyzoULFzBkyBD4+fkhMjIS7733HgwGg1S+e/dudOrUCV5eXoiJicHBgwcBAH/++ac0x2PdunVo1qwZAMDb21s6XvWcNWvWICwszKTuq1evQqFQIDk5GUD5sNHBgwfDx8cHnTp1wtdff23T83733Xfx8ssvIzw8HLfffjveffddCCGkc9RqNUaPHo2goCDcdtttmDt3LsrKyizWefbsWcTHx6NNmzYICAhAfHw8iouLTZ7vwYMHER8fD39/f/z1119Yvny5NA8zPj4eI0aMwG+//QaFQoHWrVtj/fr1CA4OhlarlerJyMiAUqnEuXPnTD5fqVTCyckJX3/9NXr06IEWLVrgoYcewh9//AEnJ6cbBoSXLl3Cs88+i02bNkGhUGDWrFkmz6O6BQsWWMywWhvIGmk0Gri6ukrv1Wo17rrrLum1atUqk/M7duyIvn372vQZROR42Leyb73ZvtWoe/fu0is8PLxGeceOHREYGFjjuLV9a0pKClq0aIEnnnhC+tr4AoA+ffpIX1el0Wjw448/4vz58zVeV65cMQm0qQkQRDLWp08f4evrK9566y1x6tQpMWzYMBEWFibuuece8d1334kffvhBeHl5iXfeeUe6plu3bmLevHlCCCEuXbokAIiOHTuKrVu3ipMnT4qOHTuKESNGWN2GVq1aCV9fX7FixQpx+vRp8e9//1sAEL///rsQQoicnBwRHBwsxo0bJ06ePCm2bt0qmjVrJt5++20hhBClpaXC09NTvPjii+LUqVPi448/Fl988YUQQoiTJ08KAOLSpUuiuLhYbNmyRQAQ58+fF+np6UKn05mck52dLZycnMTPP/8ste+DDz4QXbp0EUIIcf36dREWFibefvttkZycLLZu3Sp8fX3FqVOnrH7ebm5uYuHCheLUqVPio48+Ei4uLmLbtm1CCCHKysrEnXfeKR566CFx/PhxkZCQINq0aSMmTZok1fHUU0+Jxx57THr/f//3f+Ktt94Sx48fF4cOHRLBwcHSszE+33bt2on58+eL06dPC61WK95//33RqlUr6Z5eeeUVERUVJdLT00VmZqYoKioSPj4+4ttvv5XqWbFihejatavZ+/ruu+9EcHCwOHDggMjPzxcfffSRCAgIEOnp6UIIIWbOnCmeeuopk2v0er348ssvRUBAgFi0aJEQovznqU2bNmL48OEiIyOjxuc89thj4q233rLqWVujQ4cO4ptvvpHep6SkCAAiKytLZGVlidTUVHHx4kVRVFQkPD09xaVLl8TPP/8sPTsiInPYt7Jvvdm+9dy5c6J6GDFv3jwxZswYIYQQJSUl0vOtqi59q62Mnx0UFCRCQkLMvqr3+SRvDFhJ1vr06SMmTpwovd+8ebMAIP744w/pWP/+/aVfkEKY71R/+uknqXzWrFmiTZs2VrehVatWYunSpSbHevbsKV588UUhhBALFiwQbdu2FTqdTir/5JNPhKenpygrKxP5+flCqVSKvXv31qi7aocphBAHDhwQAERBQYHFcwYNGiRmzpxpcv8LFy4UQgixaNEiMXLkSJPPmDhxopg/f75V99qnTx8xZcoUk2NjxowRcXFxQgghNmzYIHx9fUVeXp5UfuDAAaFQKERqaqoQomanWt3TTz8tBg0aJL1v1aqViIuLEwaDQTpWtVMVorwT7Natm0k9zz33nBg1apT0vl+/fmLJkiVmPzM+Pr5GIPnAAw+Ijz/+WAhRM2BNSkoSLVu2FLfffrvYsWOHyXVZWVli3LhxwtXV1eR/JoSo34DVYDAIT09PcezYMemYMWBt1qyZ8Pb2FkFBQaJ79+5i165dDFiJyGrsW9m3CnFzfasxYPX19ZVerq6utQastvatv/zyi0n9N3r9/fffZp/NJ598Inr27Gnx2ZH8cQ4ryZ63t7f0dbt27cweu9HCOVXPb9u2La5fv25TG6rPq+jcubO0SEBSUhJ69+5tck5sbCyKiopw/vx53HHHHXjvvfcwZswYPPzww3j11VfRvn17mz6/qscffxxvv/023n77beTm5iIxMRErVqwAUD5/5JtvvjFZuU+j0WDcuHE3da+fffaZdK/dunWDj4+PVB4TEwOVSoU//vgDzZs3r1GfwWDAd999h88++wwnT55EWlpajW1XBgwYYPNczfHjxyMmJgYFBQUoKyvDwYMH8dFHH5k9V6/X15jH4uzsDJ1OZ/b8O++8E5999hliYmKgVJrOnAgKCsJHH32EhQsXIisrq0adS5YswbJlyyCEkIb0Gu/N09MTf//9t1X3l5mZiaKiIpNn1bx5c2i1WqhUKovPKyIiAjNnzrTqM4jIcbFvNcW+tZy1fWvbtm1rnR6jUqnw/PPPm/yM2Nq33nPPPTb/TC1ZsgRLliwxOVZaWorS0lIEBQWZHA8LC8PJkydtqp/sgwErNSnVf8FZOmZrHbYqLi6WOq7afmEb58M8//zzePzxx7Fs2TLce++9mD17NqZPn16nzx42bBgmTpyIM2fO4Ndff0WnTp2k/9kQQmDo0KFYvHixyTW+vr51+izA9nutbsqUKTh06BCWLl2Kvn37YvHixdi5c2ed22PUtWtX3HHHHdI8onvuuQetWrUye+7IkSPx7LPPol+/fujSpQu+/vprHD58GCtXrjR7vpOTE3r37l3r54eGhiI0NNTkWNWl+QsLC+Ht7S0tyW+rK1euwMfHx2Tuj0KhMLuABFA+p8zf3x8qlQrPPfeczZ9HRI6LfSv7ViNb+tbaODs7Y/ny5SbH6tq3PvDAA7h48aLFawYNGiR91owZMzBjxgyT8osXL+Ly5cuIjY215RZIRhiwEtlIp9Phxx9/lPYau+uuu7B+/Xro9XrpL6gHDhyAh4eHyV97g4KCsGDBArRr1w5Tp04126kag5GqHVl1fn5+GDRoEL799lscO3bMZMW/Ll264PPPP0fr1q0tBja2SkxMRFRUlHSvH330EQoKCqS/mh4+fBg6nQ5dunSpcW1+fj4+/PBDJCQkSIsBWep8a+Pk5GSymITRhAkT8NVXX8HX1xePP/64xeuHDBmCt956C08++SRSU1PRuXNn7NixAxERETa3pbHcc889yM3NNfvX8XXr1uGZZ54x+cu1kU6nQ1BQEPdkJaImhX1r0+tb8/LyLAazQgjk5+fj0qVLNTK/tvruu+8sli1duhRHjhyp9fp33nkHn332GbKysuDi4nJTbSH74CrBRFZYs2YNdu3ahVOnTmHSpEnIzc3FpEmTAACTJ0/G9evXMWnSJJw6dQrbt2/HjBkzMGfOHDg7O2Pfvn14+OGHceDAAZw4cQJbtmxBy5YtzX7ObbfdBoVCgTVr1uDs2bMoKSkxe97jjz+Obdu2Ye/evSad6pQpU5CTk4OxY8ciKSkJJ06cwNy5c01WeryRLVu24KuvvsLZs2cxZ84c/Pzzz5g2bRqA8kxlWFgYxowZgxMnTuDAgQMYP348Jk6cKGURvb29kZ2djZKSEri6usLZ2RlffvklTp8+jZUrV0pDrGzRtm1bnD17Ft9//z1Onz4tHR81ahR+/PHHGs/BnHHjxuHMmTMoLCzEzz//jJiYGIvntm/fvtb9Uau+Fi1aZPP9AMB//vOfG9bt4+MDLy8vs9sE9OjRA9evX6/xqo+/sBMRNQb2rU27b/X19TXbD12/fh1qtbrG+XXtW3v16oXWrVsjMjKyxmv58uXw8/Oz2MY9e/bgyy+/xJ133olJkyZZnApE8saAlcgKvXr1wtKlS/GPf/wD586dw6FDhxAQEAAACAgIwOHDh3H16lX06NED06ZNw6uvvirNI+zRoweioqIwefJk9OzZE3l5ediwYYPZz2nevDkWLFiAd955B/369TPpQKqKi4vDr7/+itatW0tDlgDA398fP//8MwoKChAbG4uHHnoI6enptS6NX12PHj3wxRdf4J577sGePXuQkJAg/TXbxcUF+/fvh4eHB2JjYxEfH4+RI0fi/fffl65/5JFHcPz4cWzduhWurq5Yu3Yttm/fjgEDBiA5ORnvvPOO1W2pWuejjz6KRx55BCNHjpSW3Pf19cXgwYPRuXNns8vp11VycjIKCwutes2ePRsXLlyo0dmGhobC09MTkZGRNcp++OEHvPLKK1Z/xqlTp+rt3oiI5IJ9K/vW2vpWo4KCAixbtgxnzpyp8bp8+TLWrl1r8jmZmZnYvHkzHnnkETzzzDP46quvsGvXLmRnZyMyMhLLli3D77//Xm/3RQ1PIWobOE9EaN26NaZNm4YpU6bYuykN7v7770f37t2xdOlSezfFKnq9HlFRUZg5cybGjh1b53peffVVZGRkYN26dfXXuAa0bt06jBs3Dh4eHjXK9Ho9QkJCOCSYiGSNfat81UffWlpaCnd393oZEnzXXXchLS3NbJ9n9Ndff8HT0xMA8O233+Lrr7/GsGHD0K9fP7i7uwMoH6b8119/4ZtvvkF6enqNObYkX5zDSg7tn//8p8UhlEOGDKmxiXlTNmPGDItDhjp37oyff/65kVtUd0IIXLp0CevWrYNKpcKYMWNuqr633367nlrWOOLj4xEfH2/vZhARmcW+tZwj961ubm61LiZli6SkJJvOf+ihh/DQQw/VOK5QKNCpUyd06tSpXtpFjYcBKzm0//73v1iwYIHZMuNf6m4V06dPx8SJE82WNbVFCK5cuYKoqCh06NABn332WY3tAoiIyH7Yt5Zj30pUPzgkmIiIiIiIiGSJiy4RERERERGRLDFgJSIiIiIiIlliwEpERERERESyxEWX6sBgMCAtLQ3e3t5QKBT2bg4REdmJEAIFBQUIDw+HUsm/AVuDfSgREQHW96EMWOsgLS0NERER9m4GERHJREpKClq0aGHvZjQJ7EOJiKiqG/WhDFjrwNvbG0D5w/Xx8bFza4iIyF7y8/MREREh9Qt0Y+xDiYgIsL4PZcBaB8YhTD4+PuxsiYiIQ1ttwD6UiIiqulEfygk3REREREREJEsMWImIiIiIiEiWGLASERERERGRLDFgJSIiIiIiIlliwEpERERERESyxICViIiIiIiIZIkBKxEREREREckSA1YiIiIiIiKSJbsGrFlZWZgzZw5atmyJ7t27mz3n3LlzcHFxQXx8vMnxn376CdHR0QgMDMQTTzyBoqIik/LVq1ejXbt2iIiIwIIFCyCEkMp0Oh1eeOEFhISEICoqCtu3b6/3eyMiIiIiIqKbY9eANSUlBefPn4ePj4/Fc6ZNmwZXV1eTYxkZGRg0aBAGDRqEhIQEJCcnY/z48VL5rl278MILL+Dtt9/Gp59+iv/85z9YvXq1VP7aa6/h22+/xY4dO/Dcc89hxIgROHnyZP3fIBEREREREdWZXQPWrl274ssvv8Tw4cPNlickJGDfvn146qmnTI5//vnnCA8Px/z583H33Xdj2bJl2Lx5MzIzMwEAK1euRHx8PB577DHExsZi+vTpWLlyJQBAo9Fg7dq1WLhwIaKjo/Hcc8+hT58++Oijjxr2ZmVu82+p+OTwJXs3g4iIiIiISCLbOaw6nQ5Tp07F9OnTERQUZFKWmJiIfv36QaFQAACio6Ph4uKCw4cPS+X9+/eXzo+NjcWJEyeQm5uLP//8Ezk5OTXKDxw40Ah3JU8anR6vbvkD83ecwvXiMns3h4iIiIiICICMA9bVq1ejtLQUM2fOrFGmVqsRGhoqvXdyckJISAjUajWKiopQWFhoUh4eHi5dp1aroVKpTILg8PBwqNVqi23RaDTIz883ed1K1Hka6Azlc3yLyvR2bg0REREREVE5WQasubm5mDt3LlasWAE3Nzez5d7e3ibHvL29kZOTg+vXr0vvq5YBQE5ODnJzc+Hl5SVlZ6tea8nixYvh6+srvSIiIm7m9mQnLa9E+lqjZcBKRERERETyIMuAdf78+RgwYAAGDBhgtjwgIAAFBQUmx/Lz8xEYGIiAgAAAMCk3ZkSN5YWFhSarBhuvtWTWrFnIy8uTXikpKXW+NzlKrxKwlukNdmwJERERERFRJSd7N8CcDz74AG5ubtKw3eLiYuj1euzcuRPZ2dkIDQ1Fenq6dL5Op0NmZiZCQ0Ph7u4OHx8fk/K0tDQAQEhIiFRXVlYWgoODpfKqQ4irc3V1rbFS8a0k7Xqp9LVGy4CViIiIiIjkQZYZ1kuXLuH06dNISkpCUlISHnroIcTFxSEpKQkA0LdvXyQkJEhZ0qNHj0Kr1SImJsak3Gj//v3o2rUr/Pz8cOeddyIoKKhGeWxsbOPdoMykXWeGlYiIiIiI5MeuGdacnByUlZWhsLAQWq0WGRkZUKlUaNGihcl5np6eACAdHz16NObNm4d58+bh0UcfxdSpU/H4449Lw3qfe+45xMXFYdCgQfDz88PSpUuxdOlSAICLiwsmTJiAOXPmoG3btvjtt99w6NAhLFu2rPFuXGbS85hhJSIiIiIi+bFrwProo4/i4MGD0vuwsDC0atUKly9frvW64OBg7NmzBy+++CKWL1+OBx98EB9++KFUPnDgQCxfvhwzZsyARqPBtGnTMG7cOKn8zTffREFBAYYMGYKQkBBs2bIFd955Z73fX1NhmmHloktERERERCQPClF19SGySn5+Pnx9fZGXlwcfHx97N+emdZn/HfJKtACAlWO6YnDnMDu3iIioabjV+oPGwGdGRESA9f2BLOewUuMp0uikYBXgHFYiIiIiIpIPBqwOruqWNgDnsBIRERERkXwwYHVwVbe0AQANM6xERERERCQTDFgdXM0MKxddIiIiIiIieWDA6uCuVsuwcg4rERERERHJBQNWB5d+nXNYiYiIiIhInhiwOrj0vPIMq5+HMwBmWImIiIiISD6c7N0Asq9CjQ4AEOztiuvFWmZYiYjIoajVauTl5d10Pb6+vggJCamHFhERUVUMWB2ctiKj6ula/qNQpueiS0RE5BjUajWeeHIstGWam65LqVRhxYoPEBkZWQ8tIyIiIwasDs4YsHpVBKzMsBIRkaPIy8uDtkyDktv7wODmW+d6VHmpcLt6HCkpKQxYiYjqGQNWB6fVCwCAp4sxw8qAlYiIHIvBzRcGz6A6X68suV5/jSEiIhNcdMnBVR8SzAwrERERERHJBQNWB1c5JFgFgBlWIiIiIiKSDwasDs44JNjLrWJIsI4BKxERERERyQMDVgdXY0iwjqsEExERERGRPDBgdXDVVwlmhpWIiIiIiOSCAauDq75KsIYBKxERERERyQQDVgdmMAjoDZzDSkRERERE8sSA1YFpDZXBqZcrM6xERERERCQvDFgdmHE4MFB10SUGrEREREREJA8MWB2YVlc1w1q+DytXCSYiIiIiIrlgwOrAjCsEKxWAuwvnsBIRERERkbwwYHVg2ooFl5xVSrioyn8UNDoDhBC1XUZERERERNQoGLA6MOOQYGeVEq7OlT8KVee2EhERERER2QsDVgdmHBLsrFJIGVaA81iJiIiIiEgeGLA6sDJ9ZYa1asDKeaxERERERCQHDFgdmE5fOYdVqVSYzGMlIiIiIiKyNwasDqzqkGAAcHEq/3FghpWIiIiIiOSAAasDqzokGABcnZhhJSIiIiIi+bBrwJqVlYU5c+agZcuW6N69u3RcCIFVq1YhOjoaPj4+eOCBB5CcnGxybXJyMnr37g1/f38MGTIEmZmZJuXbtm1DVFQUQkJC8NJLL0Gn05nU/+abbyIiIgLt27fH2rVrG/ZGZarqkGCAGVYiIiIiIpIXuwasKSkpOH/+PHx8fEyOr1y5Eu+++y7mzp2Lw4cPw8nJCUOHDoVWqwUAaDQa9OvXDx06dMDBgwehUCgwdOhQ6foTJ05g5MiRmDx5Mnbs2IHt27fj9ddfl8pXrVqFZcuWYf369Vi0aBGef/557Nmzp3FuWkaqDwmuzLBylWAiIiIiIrI/uwasXbt2xZdffonhw4ebHB87diwOHjyIIUOGoHPnzli+fDnOnDmDU6dOAQB27tyJvLw8LF++HFFRUVi1ahWOHDmCpKQkAMDatWvRt29fTJo0CdHR0Vi4cCHWrl2LsrIyCCGwYsUKTJ8+HX379sXw4cPx1FNPYdWqVY19+3anrTYkmBlWIiIiIiKSE1nOYfXy8kJwcLD0PiAgAACQn58PAEhMTESvXr3g6uoKAGjevDk6dOiAAwcOSOX9+/eXro+NjUV2djb++usv5OTk4M8//6xRbrzWkZRVGxLs6qQCwDmsREREREQkD072boA1jh8/DgDo1KkTAECtViM0NNTknPDwcKjVarPlwcHBUCqVUKvVUpBbtTw8PBz5+fkoKSmBu7t7jc/XaDTQaDTSe2Pg3NTpjBlWJ9MMKwNWIiIiIiKSA1lmWKtbtmwZHn30Ufj7+wMAcnNz4e3tbXKOt7c3cnJyzJYrlUp4eXkhJycHubm50vlVrzVeZ87ixYvh6+srvSIiIurv5uxIGhKs5BxWIiIiIiKSH9kHrHv37sWePXswb9486VhAQAAKCgpMzsvPz0dgYKDZcoPBgIKCAgQGBkrDi6uWGzOmxrLqZs2ahby8POmVkpJSPzdnZ9WHBHMOKxERERERyYmshwRfvnwZo0ePxuLFixEVFSUdDw0Nxblz50zOTUtLk4b5hoaGIj09XSpTq9UQQiA0NFQ6Jz09XcqUpqWlwc/PD25ubmbb4erqKg0lvpVodaZDgrkPKxERERERyYlsM6zZ2dl4+OGH8cADD2Dq1KkmZX379sUPP/wgzStNTU1FcnIyYmNjpfKEhATp/P379yM4OBgdO3aEv78/unTpUqPceK0j0RlMt7VxqVh0iRlWIiIiIiKSA7tmWHNyclBWVobCwkJotVpkZGRApVJBqVSif//+CAkJwX//+19kZmYCAFxcXBAQEIAHH3wQAQEBmDJlCl544QXMnj0bMTEx6Ny5MwBg/Pjx6NatG1atWoWuXbtizpw5mDhxIpydnQEAkydPxsyZM9GjRw/k5ORg/fr12Llzp92eg71ojUOClcywEhERERGR/Ng1YH300Udx8OBB6X1YWBhatWqFgQMH4sSJE9Ixoz59+iAxMREuLi5ISEjAM888g969eyMmJgZbt26VzuvUqRM2bdqE1157DWq1GqNGjcIbb7whlU+YMAFqtRpPPvkk3N3dsWLFCgwYMKDhb1hmyqQhwcYMK+ewEhERERGRfNg1YE1MTLRY9uGHH9Z6bbt27XDo0CGL5XFxcYiLizNbplAo8Prrr+P111+3qp23KmmVYFX1DCtXCSYiIiIiIvuT7RxWang6Q/mQYBeuEkxERERERDLEgNWBGQNTJ5VxH9byRZc4h5WIiIiIiOSAAasDszQkmBlWIiIiIiKSAwasDkxnXCWYc1iJiIiIiEiGGLA6MGOGtcYcVj0zrEREREREZH8MWB2YMTCtnMNakWHVMmAlIiIiIiL7Y8DqwKrPYWWGlYiIiIiI5IQBqwMzzmF1keawcpVgIiIiIiKSDwasDqz6kGBj4MqAlYiIiIiI5IABqwOrsa2NM7e1ISIiIiIi+WDA6sC01ba1qcywclsbIiIiIiKyPwasDkxn3NbGqXxIsHPFoktaLrpEREREREQywIDVgZVVZFidlOU/Bs4V/xoXYyIiIiIiIrInBqwOrPocVuPiS1oGrEREREREJAMMWB2YttqQYCdl+b86A4cEExERERGR/TFgdWC6aosuOVX8q2eGlYiIiIiIZIABqwOT9mGtmLtqzLBqmWElIiIiIiIZYMDqwKoPCTZmWrnoEhERERERyQEDVgdWc0iwcQ6rgBAMWomIiIiIyL4YsDqwsmqrBBu3tQHKg1YiIiIiIiJ7YsDqoIQQ0pBgY2ZVVfEvwGHBRERERERkfwxYHZTeIGAc9euiMl10CeDCS0REREREZH8MWB1U1SG/0pBgVeWPA7e2ISIiIiIie2PA6qCM81eBykBVpVRAUZFkZYaViIiIiIjsjQGrg9LqqgaslUOBjQsvcQ4rERERERHZGwNWB6WtCEidlAooFJUBq7S1DQNWIiIiIiKyMwasDkpbbUsbI+PCSxwSTERERERE9saA1UFV39LGyEnFIcFERCR/paWlSE5ORmlpqb2b0qAc5T6JiCxhwOqgjEOCXSxlWPXMsBIRkXxduXIFEydOxJUrV+zdlAblKPdJRGQJA1YHZWlIsPG93sAMKxERERER2ZddA9asrCzMmTMHLVu2RPfu3U3KioqKMGbMGAQGBiI6OhpHjhwxKU9OTkbv3r3h7++PIUOGIDMz06R827ZtiIqKQkhICF566SXodDqpTAiBN998ExEREWjfvj3Wrl3bcDcpU1LA6lR9SHDFokucw0pERERERHZm14A1JSUF58+fh4+PT42ycePG4fz580hISMDAgQMxcOBAKSjVaDTo168fOnTogIMHD0KhUGDo0KHStSdOnMDIkSMxefJk7NixA9u3b8frr78ula9atQrLli3D+vXrsWjRIjz//PPYs2dPw9+wjBiHBBu3sTGqHBLMDCsREREREdmXXQPWrl274ssvv8Tw4cNNjmdkZGDLli1YtmwZ7r77brz55psICQnBxo0bAQA7d+5EXl4eli9fjqioKKxatQpHjhxBUlISAGDt2rXo27cvJk2ahOjoaCxcuBBr165FWVkZhBBYsWIFpk+fjr59+2L48OF46qmnsGrVqsa+fbu60ZBgLrpERERERET2Jss5rIcPH4a7uzuio6MBAAqFArGxsThw4AAAIDExEb169YKrqysAoHnz5ujQoYNJef/+/aX6YmNjkZ2djb/++gs5OTn4888/a5Qbr3UUloYEq7itDRERERERyYSTvRtgjlqtRnBwMFQqlXQsPDxcyqCq1WqEhoaaXBMeHg61Wm22PDg4GEqlEmq1Wgpyq5aHh4cjPz8fJSUlcHd3r9EejUYDjUYjvc/Pz7/5m7QzaUhw9VWCmWElIiIiIiKZkGWGNTc3F97e3ibHvL29kZOTU6dypVIJLy8v5OTkIDc3Vzq/6rXG68xZvHgxfH19pVdERMRN3qH9SRnWanNYnSsyrHpmWImIiIiIyM5kGbAGBASgoKDA5Fh+fj4CAwPrVG4wGFBQUIDAwEAEBAQAgEm5MWNqLKtu1qxZyMvLk14pKSk3eYf2d6NVgrnoEhERERER2ZsshwSHhoZCrVZDr9dLw4LT0tKkYbyhoaE4d+6cyTXVy9PT06UytVoNIQRCQ0Olc9LT06VMaVpaGvz8/ODm5ma2Pa6urtJQ4luFpSHB0qJLzLASEREREZGdyTLDGhMTA41Gg6NHjwIo3zd1//79iI2NBQD07dsXP/zwgzSvNDU1FcnJySblCQkJUn379+9HcHAwOnbsCH9/f3Tp0qVGufFaR2FplWBua0NERERERHJh1wxrTk4OysrKUFhYCK1Wi4yMDKhUKjRr1gwjRozA1KlT8eGHH2LLli3IysrCqFGjAAAPPvggAgICMGXKFLzwwguYPXs2YmJi0LlzZwDA+PHj0a1bN6xatQpdu3bFnDlzMHHiRDg7OwMAJk+ejJkzZ6JHjx7IycnB+vXrsXPnTrs9B3uoDFirDwnmoktERERERCQPdg1YH330URw8eFB6HxYWhlatWuHy5ctYs2YNJkyYgNjYWLRp0wZ79+5FUFAQAMDFxQUJCQl45pln0Lt3b8TExGDr1q1SPZ06dcKmTZvw2muvQa1WY9SoUXjjjTek8gkTJkCtVuPJJ5+Eu7s7VqxYgQEDBjTafctBma72DCuHBBMRERERkb3ZNWBNTEy0WObp6YmNGzdaLG/Xrh0OHTpksTwuLg5xcXFmyxQKBV5//XW8/vrrVrf1VqMz1L6tDYcEExERERGRvclyDis1PK2FDCu3tSEiIiIiIrlgwOqgLM9h5aJLREREREQkDwxYHVRZRUDqpDQ/JJiLLhERERERkb0xYHVQOmOG1ck0w+rMRZeIiIiIiEgmGLA6KGnRpWoZVpWSiy4REREREZE8MGB1UMY5rE7V5rAa57QaM7BERERERET2woDVQRnnqNbc1sY4JJgZViIiIiIisi8GrA5KWzFH1UlZbZXgiiHBnMNKRERERET2xoDVQRnnqDpV34dVGhLMDCsREREREdkXA1YHpbO4DysXXSIiIiIiInlgwOqgtJbmsHJbGyIiIiIikgkGrA5KZ3EOK4cEExERERGRPDBgdVCWVwk2DglmhpWIiIiIiOyLAauDuuE+rNzWhoiIiIiI7IwBq4MyBqTGbWyMKre1YcBKRERERET2xYDVQVleJVhhUk5ERERERGQvDFgdlOV9WCsyrFx0iYiIiIiI7IwBq4MyrhLsXG2VYFXFey23tSEiIiIiIjtjwOqgdBYzrNzWhoiIiIiI5IEBq4MyZlCrrxJsXHSJ29oQEREREZG9MWB1UNI+rNVXCea2NkREREREJBMMWB1U5aJL1fdhLf+R0DNgJSIiIiIiO2PA6qC0lra1MS66xCHBRERERERkZwxYHZRxn1UnJbe1ISIiIiIieWLA6qC0FUN+nZ1MfwSM29rouK0NERERERHZGQNWB2XMsFbfh9U4RFjLDCsREREREdkZA1YHZDAIGNdUqr4Pq3GIsI5zWImIiIiIyM4YsDogbZXhvjX2YTVmWLlKMBERERER2RkDVgdUdUGl6vuwclsbIiIiIiKSCwasDqhqwFojw1oxp1VvEBCCQSsREREREdlPvQesGRkZ9VZXUVERJk2ahKCgIISHh2PGjBnQ6XRS2ZgxYxAYGIjo6GgcOXLE5Nrk5GT07t0b/v7+GDJkCDIzM03Kt23bhqioKISEhOCll16S6nUEJkOCldUD1sofCS68RERERERE9lSngFWlUtUIAAHg1KlT6NWr1003ymju3Lk4fvw49u3bhw0bNuDTTz/FypUrAQDjxo3D+fPnkZCQgIEDB2LgwIFSmzQaDfr164cOHTrg4MGDUCgUGDp0qFTviRMnMHLkSEyePBk7duzA9u3b8frrr9dbu+XOmGF1UiqgUJifwwpwaxsiIiIiIrKvOgWsQogagQ4AHDt2DNnZ2TfdKKOEhATMnj0bXbp0QWxsLJ566ikkJCQgIyMDW7ZswbJly3D33XfjzTffREhICDZu3AgA2LlzJ/Ly8rB8+XJERUVh1apVOHLkCJKSkgAAa9euRd++fTFp0iRER0dj4cKFWLt2LcrKyuqt7XKmrVgBuPpw4OrHmGElIiIiIiJ7silgbdasGYKDg6FQKHDHHXcgODhYevn4+GDcuHF44okn6q1xkZGROHfunPTe3d0dHTp0wOHDh+Hu7o7o6GgAgEKhQGxsLA4cOAAASExMRK9eveDq6goAaN68OTp06GBS3r9/f6ne2NhYZGdn46+//qq3tsuZrmJBpeoLLlU/xq1tiIiIiIjInpxsOXnPnj0QQiA6Ohpz586Fr69vZUVOTmjbti3uvffeemvc9OnTMXjwYGi1WowZMwabN2/G5s2bceDAAQQHB0OlUknnhoeHSxlUtVqN0NBQk7rCw8OhVqvNlgcHB0OpVErl1Wk0Gmg0Gul9fn5+fd2iXehqybAqlQooFYBBVAa2RERERERE9mBTwNqtWzcAwLx58zB+/Hh4eHg0SKOMbr/9dtx+++34/PPP8frrr2P8+PG44447sHXrVnh7e5uc6+3tjZycHABAbm4uwsPDay2ver1SqYSXl5dUXt3ixYsxf/78+rw1uzIO9XVSmU+wO6mUKNMZGLASEREREZFd1WkO67x58xo8WNXpdIiNjcW0adNw8uRJbNmyBTt27MC8efMQEBCAgoICk/Pz8/MRGBgIADaXGwwGFBQUSOXVzZo1C3l5edIrJSWlPm+10RnnsDora2ZYgcqVgzkkmIiIiIiI7MmmDKvRxYsXMXv2bBw/fhzXr1+vUW5uBWFbHTx4ENnZ2Rg+fDgUCgWGDRsGpVKJ4cOH47PPPoNarYZer5eGBaelpUnDfENDQ03mvporT09Pl8rUajWEEDWGERu5urpK82FvBcbVfy1mWCsCVi66RERERERE9lSngHX06NEoLCzEiBEj0LZtWyjNLN5zs4qKiuDi4mKyInFYWBi0Wi1iY2Oh0Whw9OhR3HfffRBCYP/+/XjxxRcBAH379sXHH38MjUYDV1dXpKamIjk5GbGxsVJ5QkICZsyYAQDYv38/goOD0bFjx3q/DzkyBqLO1eawFhYWori4GMbDmVnZ8EGxxXo8PDzg5eXVYO0kIiIiIiLHVqeANTk5GT/++GODBni9evVCSUkJnnvuObz88ssoKirC1KlTERsbi2bNmmHEiBGYOnUqPvzwQ2zZsgVZWVkYNWoUAODBBx9EQEAApkyZghdeeAGzZ89GTEwMOnfuDAAYP348unXrhlWrVqFr166YM2cOJk6cCGdn5wa7HznRSQFr5R8aCgsL0ar1bci5lo3mkz+Fk3cg+vbrjzL1BYv1BAQG4e/Llxi0EhERERFRg6hTwHr//ffj/PnzDRqw+vv7S1nQHj16wM3NDYMHD8aSJUsAAGvWrMGECRMQGxuLNm3aYO/evQgKCgIAuLi4ICEhAc888wx69+6NmJgYbN26Vaq7U6dO2LRpE1577TWo1WqMGjUKb7zxRoPdi9xoDTVXCS4uLkbOtWzMWL0T36YoUVhmwITFnyDY0/yPSFFeDpZMHILi4mIGrERERERE1CDqFLD+5z//QVxcHEJDQ80GK/UVyN5555349ttvzZZ5enpi48aNFq9t164dDh06ZLE8Li4OcXFxN93GpsiYYXUyM5Tb0zcAqrQCAAa4efnA28+9kVtHRERERERUrk4B6x133IGysjL84x//kI4pFAppvqler6+3BlL9M67+W30Oq5GqYs6wEFx0iYiIiIiI7KdOAevZs2frux3UiLQGyxlWAFBUHNZzH1YiIqIm4/77769xbNiwYdi2bZvJ+zFjxmDKlCnIy8uDr68vli9fjmbNmtVaT2JiosXP/fjjj7F+/Xrp/dixY3Hu3Dn8/PPP0rEePXpg8eLFFuv48ssvsWrVKun9pEmT8M9//hPr1q3DunXrTM719/fHu+++i61btyI1NRVZWVn4+++/pfLHHnsML7zwgsk1CxcuxPfffy+9HzBgAF566SUsXrwYaWlpCA8Px6xZs+Dl5YW0tDRMnDgRJSUlcHd3x+rVqxEeHo6SkhJ8+OGHSE1NRYsWLfDss8/C3d32kWixsbEwGCq3DlQqldi/f7/N9dSH1NRUjB8/XlqodO3atWjRooVd2pKXlydN1wsJCcHChQvh6+trl7YUFhaa/dkg+3yfFIJpNJvl5+fD19cXeXl58PHxsXdzbLb1eCr+9b8T6NUuCJ89cy+A8q2IQkJCMP+rn7DzXBEyCzSI6xKO24I8zdZRkHsN8x6/D2q1GsHBwY3ZfCIi2Wjq/YE91NczS05OxsSJE7F69Wq0b9/+puoo6hgHg2dQndvilH0e7pcO4bXXXsOAAQPqXI851t6nuSDTFl5eXti5c2et9ZgLWm393Pqow9bPqo/6jSMJq+vZsycWLlxodT22Pt+G1K9fP7OjIlUqFfbt29eobRkzZgyuXr1a43jz5s3x+eefN2pbJk2ahDNnztQ4HhkZafJHFUdU398na/uDOu1Hs379+lpfJG/mVgmuSlkxJNjAv2UQERHJnjUBmbmdEBYtWoTWrVsDKM8o3aie6uXV31vzB4gb1REYGHjDOqx1//33W/Vs1q5diwceeMDkWEBAAGbNmoWAgAAAldOkRo8ejQ0bNmD06NFwdnbG4cOH8dprr1ndnpspr09Vg1UfHx+88sor0vdPr9ejX79+jdaWqkFQdHQ0li9fjujoaADA1atXMWbMmEZrizFYVSgUeOCBB6SfDYVCgTNnzmDSpEmN1ha5sef3qU4B6yuvvFLjNXnyZLzyyiv47LPP6ruNVM+kVYKV5uewGg8bOCSYiIhI1qoHOYmJiRg2bFiN87RabY1jv/zyC9atW4ft27fXKEtMTJRe5j7v448/lo7961//QmJiIu68884a9YwdOxaJiYl49NFHpWOzZs0CUD4M2GjmzJlITEzEli1bEB8fX6Oebdu2Yffu3TWOA8BTTz2F7777TgrKzQUVxnbs3LmzRtlXX31V4zPXr1+PgQMH1sgaDRkyBC1atMDEiRPx7bffSkFrSUmJ2bYZxcbGSl83b97c5Pk2b97c7HkNJTU1VQpWN2/ejO3bt+Phhx/G9u3bsXnzZgDlQWtqamqDtyUvL08Kgnbt2oUlS5agU6dOWLJkCXbt2gWgPBjKy8tr8LYUFhZKweru3bsxe/ZstG3bFrNnz8bu3buloLWwsLDB2yI39v4+1SlgzcrKqvH6+++/ERkZiTfffLO+20j17IYZVqUxw9poTSIioluURqNBfn6+yas+/f3330hOTq7Tq+q8x/qQnp5e57bURxuNwWXVOavVGTOqVc+rnhmtHqSaG6padUSdcdeFqnNWq5/34osvSseM51UdXjl48GDp6+pzVgHAz88PH374YY3jALB9+3a4uLhg+PDhAICMjIwa54wbNw4ApHm0VTOq33//PSZOnAigMhNtPM/4ma6urgAgnQfA5DMttc2o6pzV6kFw1fdVz2so48ePB1D+fTduCWkUFBQEb29vk/MakjE7HR0dDQ8PD5MyDw8P3HPPPSbnNSTj93zAgAFwc3MzKXNzc0P//v1NznMk9v4+1WnRJXMCAwMxd+5czJgxAz/88EN9VUsNQKuvuQ9rVRwSTERE9WXx4sWYP39+g9Vvy/zBhvbxxx+bZB7tzdnZuUZmdeLEiZgzZ069BUaWhgF7enqiqKjIqjqsHQZszPi5u7ubZDSNGa8HH3wQX3zxBVJTU83eOwCkpaUBAEaOHInvvvtOOm6sb8yYMVi3bp10nvEzn3jiCXz00Uc1MqlVP7Op0Gg0AIAJEyaYLX/66afx3nvvSec1JLVaDaA8A27Ok08+iWPHjknnNaSqPxvmjBgxAt9//710niOx9/ep3gJWACgtLcXJkyfrs0pqALobrBIsDQlmwEpERDdp1qxZ+Ne//iW9z8/PR0RERL3V/9prr6FVq1Z1uvbvv/+u14B33LhxJlv+1YebaaO5gG316tX1msWzlDG3NlgFgGvXrll1XosWLfDrr7/WCBqNq7cahyYazzMnPDwcly5dwv/+9z+T4+7u7igsLJSyneHh4SZ1bdiwQTqvqqqf2VS4urqitLQUa9aswcMPP1yj/JNPPpHOa2ghISHIysrC+vXrsWTJkhrlxqmGISEhDd6Wqj8bs2fPrlG+adMm6TxHY+/vU50C1up/eTAYDLhw4QL++usvi3+VIPnQ6mrfh1XKsDb8qBQiIrrFubq6Nuj/+LZq1arOqwTXt7CwMLu25f7775fmsFoaFnz58mXpa+Nc1+pBp7Gequ+rGzt2rDTcd/v27YiLi0OPHj1qDAs2ZmTee+896ViPHj0AlM81NQ4L3r17tzQsOD4+vsaw4OvXr+PZZ581e19xcXEoKyuT5l+GhobWOOfjjz/GuHHjMGvWLAwZMsQkuzpgwAA8/fTTGD16tBTkG+fZGj/TmG1cvXq1dF3Vz3z22WdrfGZVSqVS+kPBmDFjTIYBV12sRmkhmVCf1q5diyeeeAL5+fnIzs42GRacnZ2NgoIC6byGtnDhQgwdOhS//PILiouLTYabFhcX49ixY9J5Dc34s/H999/jX//6l8mw4NLSUiQkJEjnORp7f5/qFLB6etbc6qRXr1548cUXMXr06JtuFDUsaR9WSwGrkkOCiYiImoLExESTgNLSSrNVAyajrl274qmnnjI7T9ZSPcZAdty4cVLA+s477+Cdd94x+/+H5naQMM4B/Oc//ykFrP/+97/x73//G35+frh+/XqNeswtJGX06aef4tNPP5Xem9t6pLadLEaMGCFlFY3Gjh2LcePG1RjivWPHDjz44IPYtWsXNm/eDK1Wi549e95wP9b9+/dLz/Tq1asWn29j7MfaokULqFQq6PV6DB8+HN7e3nj66afxySefSMGqSqVqlKyxr68vmjdvjqtXr+LBBx/EPffcgyeffBKfffaZFAQ1b968UfZj9fLyQmRkJM6cOYPBgwejf//+GDFiBDZt2oSEhAQIIRAZGemQ+7Ha+/tUp4C1+n/U1LTo9MYMa+1DgvUMWImIiGSvetBqjrlhwHPnzpW+rss+rNU/15phwDeqw1ywWlfW7sNadSElo5ycHCxdulR6b9yH9YsvvsAXX3whHbdlH9YbfZ8acx/Wffv2SVvbFBQUmGTAG3sf1s8//1zaMuXYsWNSAAQ0/j6sq1atkra2+f777/H9999LZY6+D6s9v083Ne7g6NGj+PDDD7Fy5UocPXq0vtpEDcw4h9VSwKrioktERERNiqVgp3pmctiwYdi0aRNCQkLg5uaGkJAQbNq0SdruxVI9tR2vvhDL2LFjpWG/Rj169Ki1jupb0UyaNAmJiYlmt7jx9/fH+vXrMWzYMHTv3r3GHObHHnvM5LMSExMxYMAAk3MGDBiAnTt3omfPnrjtttvQs2dP7Ny5E4mJidi4cSO8vLygUqng5eWFjRs34sCBA9i9e7f0mcOGDcPu3bttHgKZmJhYY9ivUqls1GDVaN++fdiwYQPc3NygUCjg5uaGDRs2NGqwavT555/jm2++QadOndCsWTN06tQJ33zzTaMGq0arVq0y+7PhyMGqkb2+TwohbI9KioqKMGLECOzdu1daHv3y5csYOHAgNm3aZHZIyK0kPz8fvr6+yMvLs2qTbLmZv+MvfHL4Mibf3wYzBkUCADIzMxESEoL5X/2Eoxk6/JWWjx63ByL6tgCzdRTkXsO8x++DWq1GcHBwYzafiEg2mnp/YA/19cySk5MxceJErF69us7zRo11FHWMg8Ez6MYXWOCUfR7ulw7htddeqxEY3az6uE8iIjmytj+oU4Z15syZuHbtGs6fP48LFy7gwoULOH/+PHJycjBz5sw6N5oah3EfVieLQ4KZYSUiIiIiIvurU8C6bds2vP/++7jtttukY7fddhveffddfP311/XWOGoYuop5LM5KS6sEl//LgJWIiIiIiOypTgGrEAIKRc1gpzGW4qabp7U6w9poTSIiIiIiIqqhThHm0KFD8eKLL+LKlSvSsStXruDll1/G0KFD661x1DAqVwm+wbY2jFiJiIiIiMiO6hSwLlmyBD4+PmjTpg3atWuHdu3aoU2bNvD09MSSJUvqu41Uz6R9WDkkmIiIiIiIZKxO+7B6eXlh7969OHz4ME6cOAEhBO666y6H3Uy3qTFmWG80JJj7sBIRERERkT1ZnWFNSkpC//79TTae7tmzJyZPnoznn38ebdq0Qfv27XHixIkGaSjVH+MqwTceEtxoTSIiIiIiIqrB6oD19ddfx/33329xYaXQ0FBMnToVr732Wr01jhpG5ZBg899LFbe1ISIiIiIiGbA6YP3pp5/w6KOP1nrOsGHDcPTo0ZtuFDUsrc44JPgGc1i56BIREREREdmR1QFrSEgIMjIyaj0nKysL/v7+N90oaljSPqwW5rCqlJzDSkRERERE9md1wNq/f38sWLAAer3ebLlOp8Pbb7+Nvn371lvjqGFI+7BaWiXYGLAyw0pERERERHZkdcC6aNEipKWloXv37ti4cSPOnDmDnJwcnD59Gp9//jmio6Nx8eJFbmvTBEgZVifz334naQ5rozWJiIiIiIioBqu3tfHy8sLRo0exaNEiTJo0CYWFhVAoFBBCwMPDA88++yxmz54NX1/fhmwv1QNplWALiy4xw0pERERERHJg0z6svr6++Pe//41///vfSEtLQ2pqKpo3b47w8HAoFOaHl5L8aPW1L7pknMPKVYKJiIiIiMiebApYqwoPD0d4eHh9toUaic5wg31YFcywEhERERGR/Vk9h5VuHTr9DfZh5ZBgIiIiIiKSAQasTcj8HX/hX18l3XQgecMhwQpua0NERERERPYn+4D1yJEj6NmzJ3x8fHDvvffiwIEDAICioiKMGTMGgYGBiI6OxpEjR0yuS05ORu/eveHv748hQ4YgMzPTpHzbtm2IiopCSEgIXnrpJeh0uka7p7ooKNXik8OXsfX3qzhwJvPGF9Sickhw7RlWAzOsRERERERkR7IOWH///XfExsYiLi4Ov/zyCyZOnIiDBw8CAMaNG4fz588jISEBAwcOxMCBA6WgVKPRoF+/fujQoQMOHjwIhUKBoUOHSvWeOHECI0eOxOTJk7Fjxw5s374dr7/+ul3u0VoZeaXS158d+fum6pIyrBb3YS3/lxlWIiIiIiKypzovutQYFi1ahNGjR2PmzJkAgMjISABARkYGtmzZgh9++AF333037rrrLnz11VfYuHEjXn75ZezcuRN5eXlYvnw5XF1dsWrVKrRo0QJJSUm46667sHbtWvTt2xeTJk0CACxcuBAvvfQS5s+fDxcXF7vdb23SqwSsB5Oz8Pe1IrQK9KxTXdK2NpYyrFx0iYiIiIiIZEC2GVa9Xo9t27Zh9OjRNcoOHz4Md3d3REdHAwAUCgViY2Ol4cKJiYno1asXXF1dAQDNmzdHhw4dTMr79+8v1RcbG4vs7Gz89ddfDX1bdVY1wwoAG24iy6oz1D6HVSkNCa7zRxAREREREd002Qas6enp0Ol0UCgUePjhh9G8eXOMGDECarUaarUawcHBUKlU0vnh4eFQq9UAALVajdDQUJP6aisPDg6GUqmUyqvTaDTIz883eTW2jPzygDXUxw0AsOevjDrVI4SA1tpVgjkkmIiIiIiI7Ei2AWtqaioA4KWXXsKTTz6Jr776CsnJyXj22WeRm5sLb29vk/O9vb2Rk5MDADaXK5VKeHl5SeXVLV68GL6+vtIrIiKi3u7TWsYhwf07BgMAUnNLUKrV21yPrsowX0v7sFYdEiwYtBIRERERkZ3INmA1BpQffPABRo4ciZiYGLz99tvYuXMnfHx8UFBQYHJ+fn4+AgMDAQABAQE2lRsMBhQUFEjl1c2aNQt5eXnSKyUlpd7u01oZeSUAgDvDfeHr7gwhgItZRTbXY5y/CgBON1glGAAYrxIRERERkb3INmA1ZjHd3NykY61bt4Zer0dwcDDUajX0+soMY1pamjTMNzQ0FOnp6Sb11VauVqshhKgxjNjI1dUVPj4+Jq/GZsywhvm6oW2wFwDgQlahzfVoq0xMtbhKsKLyOIcFExERERGRvcg2YPXx8UH37t2lbWyA8r1Vvby8EBsbC41Gg6NHjwIon5e5f/9+xMbGAgD69u2LH374ARqNBkD58OLk5GST8oSEBKne/fv3Izg4GB07dmys27OZcQ5rmK872jQrXx24LgFr1QzrjfZhBbhSMBERERER2Y+st7WZOXMmJk6ciMjISISFhWH27Nl49tln0axZM4wYMQJTp07Fhx9+iC1btiArKwujRo0CADz44IMICAjAlClT8MILL2D27NmIiYlB586dAQDjx49Ht27dsGrVKnTt2hVz5szBxIkT4ezsbM/btahUq8f1Yi0AINTXDW2aGTOsdRkSXJ5hVSpMA9Oqqh5mwEpERERERPYi64B1+PDhyM/Px/Tp05GZmYlRo0ZhwYIFAIA1a9ZgwoQJiI2NRZs2bbB3714EBQUBAFxcXJCQkIBnnnkGvXv3RkxMDLZu3SrV26lTJ2zatAmvvfYa1Go1Ro0ahTfeeMMet2gV45Y2Hi4q+Lg5SQHr+cy6DAmuWCHYQnYVKN8mSKVQQC8EDBwSTEREREREdiLrgBUAxo0bh3HjxtU47unpiY0bN1q8rl27djh06JDF8ri4OMTFxdVLGxuacf5qqI8bFAqFNIf1YlYhDAYh7ZtqDWOG1fkG1yiVgF7PDCsREREREdmPbOewUqWM/PIVgkN9yxegauHvDheVEhqdAVevl9hUl7QHay0ZVqByaxvGq0REREREZC8MWJsAKcNaEbA6qZRoHeQBwPaFl3QVqwRb2oPVyJi1ZYaViIiIiIjshQFrE5BRZUsbo7rOYzWuEuykvEGGlQErERERERHZGQPWJiBDyrC6S8fqGrBqK+awOt0gw2ocEsx9WImIiIiIyF4YsDYB6oLy/WSDvV2lYx1CvQEApzMKbKpLV5ExtbQHq5Exw2pghpWIiIiIiOyEAWsTUKTRAQC83SoXde4Y7gMAOJuRb9OwXSnDesNVgplhJSIiIiIi+2LA2gQYA1Yv18qAtXWgJ9ydVSjVGnApu8jqunQ2rhLMOaxERERERGQvDFibgMKKgNWzSsCqUioqhwWn51tdlzHDeqNVgjkkmIiIiIiI7I0Bq8wJIVBcpgdgmmEFKocFn7IpYLVuDqtxEWEOCSYiIiIiInthwCpzGp1BGpbr4aIyKesYVhGwplkfsGp05cGvqxOHBBMRERERkbwxYJU543BgAPB0Mc2w3lERsNoyJFijKx8SfMOAVRoSbHXVRERERERE9YoBq8wVa8ozoh4uKmnlXqPIUG8oFEBmgQZZFVvf3EhlwKqq9Twl92ElIiIiIiI7Y8Aqc8YMq0e17CpQvgjTbYGeAKzPspYZA1Zn6zKsHBJMRERERET2woBV5orKjFvamM+IGocFn8mwLmC1eg4r92ElIiIiIiI7Y8Aqc+a2tKmqfUj51jbJ6kKr6tNoyzOsLjcIWI1DgrmtDRERERER2QsDVpkzzmG1FLB2CPUCACSrC6yqz9o5rBwSTERERERE9saAVeaKjBlWF/MBZruKDOs5daFV2VBbt7UxcEgwERERERHZCQNWmbvRkOBWAR5wcVKiRKtHam7JDesrY4aViIiIiIiaCAasMlcsLbpkPmB1UinRtln5sOCzVgwL1li5SrCyopgBKxERERER2QsDVpkrlPZhNR+wAkCHUOPCSzYErFYOCeYqwUREREREZC8MWGXOOIfV0rY2QOVKwWczrAhYteUB8A1XCVYaVwm2qplERERERET1jgGrzBXdYA4rYNtKwVavEswMKxERERER2RkDVpkrKrtxwNouuDzDejGrCFp97SnRMmuHBHPRJSIiIiIisjMGrDJXJO3Dajkj2tzPHR4uKpTpDbiSU1xrfVZvayMNCWbASkRERERE9sGAVeakbW1qWXRJqVSghb87ACDteu1b21SuElz7kGAlhwQTEREREZGdMWCVuRtta2MU5mtbwOqi4pBgIiIiIiKSt9qjIGpwhYWFKC62PIw3v7gMAKApykdhoRu8vLzMnhfu5wYASLteWuvnSUOCrdyH1cAMKxERyVDLli2xevVqtGzZ0t5NaVCOcp9ERJYwYLWjwsJCtGp9G3KuZVs8p8WLX0Dl7o0H+vWBD0rx9+VLZoPW8IoMa3pe7RlWqxddUjDDSkRE8uXm5ob27dvbuxkNzlHuk4jIEgasdlRcXIyca9mYsXonPH0DapQLIbDu91wIAJPeWoXlk4eguLjYbMAa5mcMWG+UYbVyWxsOCSYiIiIiIjtrEnNYy8rK0KFDB7Ru3Vo6plar8dBDD8HPzw99+vTBuXPnTK756aefEB0djcDAQDzxxBMoKioyKV+9ejXatWuHiIgILFiwAMKOQ189fQPg7R9Y4+XhFwBjq3z9/GqtI9y3fEjw1RvNYdXatq0N41UiIiIiIrKXJhGwfvDBB0hLS5PeCyEQFxcHlUqFQ4cOoV27dujfvz/Kysrne2ZkZGDQoEEYNGgQEhISkJycjPHjx0vX79q1Cy+88ALefvttfPrpp/jPf/6D1atXN/p93YhWVxktOlUEkJZIGdbrpRaDbyGE1dvaKDkkmIiIiIiI7Ez2AWtWVhbmz5+PKVOmSMeOHz+OX375BatWrUJUVBQ++OAD5OTkYNeuXQCAzz//HOHh4Zg/fz7uvvtuLFu2DJs3b0ZmZiYAYOXKlYiPj8djjz2G2NhYTJ8+HStXrrTL/dWmTF+eDXVSKqQA0pKwigxriVaPvBKt2XN0BiFlTDkkmIiIiIiI5E72AevcuXNx991344EHHpCOJSYmomPHjggPDwcAuLq6omfPnjhw4IBU3q9fPygqgrzo6Gi4uLjg8OHDUnn//v2l+mJjY3HixAnk5uY21m1ZRVsRsDrfYAsaAHBzViHQ0wWA5ZWCjfNXgRuvElw5JJgBKxERERER2YesA9aTJ09i3bp1WLFihRR8AuXzV0NDQ03ODQ8Ph1qtNlvu5OSEkJAQqNVqFBUVobCw0KTcGPgar69Oo9EgPz/f5NUYjCv6utxg+K5RmLS1jfl5rGVVAtYb7sPKIcFERERERGRnsg1YhRB4+eWX8corr+COO+4wKcvNzYW3t7fJMW9vb+Tk5Nyw/Pr169L7qmUApOurW7x4MXx9faVXRETETd2btSozrLUPBzYKu8HWNsb5qy4qJZQ3mBNrLNczw0pERERERHYi24D1m2++waVLlzB79uwaZQEBASgoKDA5lp+fj8DAwBuWBwSUbx9TtdyYMTVeX92sWbOQl5cnvVJSUup+YzYwzmG9UTbUqHnFwktpFra2sXaFYKAywyoEhwUTEREREZF9yHYfVuPKwC1btgQAaLVaFBQUICgoCFOnTkV6errJ+WlpaejYsSMAIDQ01KRcp9MhMzMToaGhcHd3h4+Pj0m5cQXikJAQs21xdXWFq6trvd6fNYyrBDtbOyTYt/YhwRobhhgrq5xiMAgorczyEhERERER1RfZZlg3btyI8+fPIykpCUlJSZg3bx7Cw8ORlJSE/v374/Tp07h69SoAoLS0FIcPH0ZsbCwAoG/fvkhISJC2dzl69Ci0Wi1iYmJMyo3279+Prl27wu8Ge502NlszrFW3tjHH2i1tgMoMK8BhwUREREREZB+yDVibNWuGFi1aSK+AgAA4OTmhRYsWuPfee9GjRw9MmjQJf/zxB6ZMmYJmzZph0KBBAIDRo0dDrVZj3rx5SEpKwtSpU/H4449LQ36fe+45fPrpp9i6dSv279+PpUuXYvLkyfa8XbPKbFglGADCjRlWC3NYjYsuuTrXvqUNULlKMMCFl4iIiIiIyD5kG7DeyLZt26DX69G7d2+cO3cO33//PZydnQEAwcHB2LNnD3bv3o3Y2Fi0b98eH374oXTtwIEDsXz5csyYMQNPPfUUpk2bhnHjxtnrVizS2rhKcIhPecCama+RsstVGYcEW5NhVSgUMMasBkPt5xIRERERETUE2c5hrS4+Ph7x8fHS++DgYOzatcvi+T169MCxY8cslk+YMAETJkyozybWO1tXCW7mXT7PtkxvQH6pDr7uzibltgwJBgClQgGDEBwSTEREREREdtFkAlZHZDqHtTxozMrKqvUaTxclisoMSP47DXe0CISXl5dUZlwl2NqMrUqpgM4gYOCQYCIiIiIisgMGrDJWdZVgTWkBoFCgU6dOtV4TPmE1nAPC0WvAQ/AsUePvy5ekoLVySPCN57AC5RlWgIsuERERERGRfTBglbGqGVatphQQAlPe3YRmoc0tXrPzbD4yi3R49F//xhf/GoLi4mIpYC2zYQ4rULnwEhddIiIiIiIie2DAKmNaM6sEe/r4w9s/0OI13h4aZBbpYHD2qFEmzWF1ZsBKRERERETy12RXCXYEZTauEgwAHi7lf4Mo0dVc2tfWIcHGvVgNHBJMRERERER2wIBVxrQmiy5Zx8OlPBgt0Vre1sba+pQVpzHDSkRERERE9sCAVcbKbNzWBqgMWEvNZVi1dRwSzAwrERERERHZAQNWGau6SrC1pCHBWjMBq97GRZeMQ4JrVkVERERERNTgGLDKlN4gpMxmnYYE68wMCdbauK0NF10iIiIiIiI7YsAqU8b5q4DpKsE3UjmHtbZFlzgkmIiIiIiI5I8Bq0wZVwhWKRVS4GgN45BgvQAULu4mZTZva6NghpWIiIiIiOyHAatM1WWFYKB8CxynigBX5eFnUmbrKsHGubNaMws4ERERERERNTQGrDJVlxWCjYzDglWefqZ1GocEO1s3h9UY2Gr0DFiJiIiIiKjxMWCVKWNwacsKwUbGYcHKagGrrXNYjeeVmcmwphdoETJqMS5eK7G5fURERERERNZgwCpTWr3tKwQbSRnW6kOCjfuwWrlKsEstAeuprFK4teyMT39Jt7l9RERERERE1mDAKlN1ncMKAB6uxiHB/ibHbc2wGj/bXMCaW1Ie/P548brZciIiIiIiopvFgFWmpCHBdcqwlg8Jrj6HVVp0ydYhwdXmsOr0BhRoyo8VaPT4+eI1m9tIRERERER0IwxYZUpadMnpJhZdqrFKsHFIsJUZ1orzjNcZ5RSXoepGN3v+zLC5jURERERERDfCgFWmbmpIcEXAWn3RJZtXCbYwh/VaYRkAwFBWvuDS96cyuFcrERERERHVOwasMqXVVSy6dBOrBFsaEmxrhrX6kOBrReUBa9Gpg/B2VSG7sAwnr+bZ3E4iIiIiIqLaMGCVqcp9WBtileCbW3TpWqGm/Lj6AtoEuQMAUnKKbW4nERERERFRbRiwylRZfQwJdvVAqbZy/qmmjkOCtXoBg6gc8mvMsGqzryDYywUAoM4vtbmdREREREREtWHAKlNaadEl279FLiolVBVrNV0r1gEAhBCVqwRbGQRXHY5szLKW6QwoKC2vU5v9txSwpucxYCUiIiIiovrFgFWmKre1sX2VYIVCAXfn8m9tTpEWQHmW1MjV2bpvu5NSCZVSYdKenIrsqruzAobSQjTzdgYAZDDDSkRERERE9YwBq0zdzCrBAOBWsR1OTnF5wFp1axpr57BW/XzjEGVjwOrvVj6sWBoSzAwrERERERHVMwasMmXMiNZllWAAlRnWioC1SFMesKqUCpuC4Mq9WMsD1oLS8vq8KubJNuOQYCIiIiIiaiAMWGWqckhwHQNWJ2PAWj7fNLtiZd8gLxcoFNYPM3atthdrgaa8Pk+X8uPBXuVDgjMLSmHgXqxERERERFSPnOzdADLvZlYJBsrnmALA1Wv5yMzMxLmU6wAAPzcVMjMza5yflZVltp7qW9sUGgPWigxukKczFIryjHBOcRmCvFzr1F4iIiIiIqLqGLDKkMEgoK/IVtZllWAAcEZ5YPnVN7vxwbg+8OzcH0EPvoykI4cQ8kqsxeu02jKT9y7VMqyFpaYZ1tycawjwcMa1Ii1OXUpDZIin2Xo9PDzg5eVVp3shIiIiIiLHxIBVhowLLgF1z7A6i/LAMiKqB14Y/RNOZJTgt7QSRHW/D72G/1Tj/MyUi/hg2hPQanUmx6WAVW86JNhZaACFAp06dULo2P/CNawdBj3yT5Rc+MVsewICg/D35UsMWomIiIiIyGqynsN64MABDBw4EL6+vujSpQt2794tlRUVFWHMmDEIDAxEdHQ0jhw5YnJtcnIyevfuDX9/fwwZMqTGMNht27YhKioKISEheOmll6DTmQZq9mQMDpUKSNvK2Mq1fE0kaOEEb/9A6FXlQ3V9vD3h7R9Y4+Xh42e+nioZVuMLAJwNZYAQmPLuJrSL7AgAGDb1bcz/6qcarxmrdyLnWjaKi4vrdC9EREREROSYZBuwnjhxAsOHD8fw4cNx9OhRDBkyBMOGDcOFCxcAAOPGjcP58+eRkJCAgQMHYuDAgVJQqtFo0K9fP3To0AEHDx6EQqHA0KFDTeoeOXIkJk+ejB07dmD79u14/fXX7XKf5hiDwrpmVwGgYtcZlOrKhxYXS4slqWyqp3KVYL20QrCrkxLGrVw9ffzh5+UBANCp3MwGw56+AXW+DyIiIiIiclyyDVijoqLw66+/YsKECYiMjMSCBQsQGhqKnTt3IiMjA1u2bMGyZctw9913480330RISAg2btwIANi5cyfy8vKwfPlyREVFYdWqVThy5AiSkpIAAGvXrkXfvn0xadIkREdHY+HChVi7di3KyspqaVHjMW5pU9f5q0BlwKo1ADq9AcVl5dvaeLjYNgq86j6sxgWXvFxN6/B0K39foNHWub1ERERERETVyTZgVSgUuO2220ze+/v7Iz8/H4cPH4a7uzuio6OlstjYWBw4cAAAkJiYiF69esHVtXwYbPPmzdGhQweT8v79+0t1x8bGIjs7G3/99Vdj3V6tbnaFYABwVgJCVx5AFmv1VQLWumVYy3QGaf6ql5tpwGoMYI17vRIREREREdUH2Qas1ZWUlOD06dPo3Lkz1Go1goODoVJVBl/h4eFQq9UAALVajdDQUJPraysPDg6GUqmUyqvTaDTIz883eTUk46JLdd2DFQAUCkBffB0AUFymR1FZebBZ14BVozNIKwR7u5oPWI0ZWCIiIiIiovrQZALWlStXIjAwEIMGDUJubi68vb1Nyr29vZGTkwMANpcrlUp4eXlJ5dUtXrwYvr6+0isiIqI+b62GkopsqJvzzX179EXXAZRvRaOpmBfr4WrjkOAqGdbCG2RYjQEtERERERFRfWgSAevVq1exaNEizJ07F25ubggICEBBQYHJOfn5+QgMDAQAm8sNBgMKCgqk8upmzZqFvLw86ZWSklKft1dDibY8YHW3MRtanTHDml2oAVC+6rCbjfNiXSuy2GU6AwpKzc9hNb4v01euIkxERERERHSzZL8Pa1lZGUaMGIEePXrg2WefBQCEhoZCrVZDr9dLw4LT0tKkYb6hoaE4d+6cST3Vy9PT06UytVoNIUSNYcRGrq6u0nzYxmDMsHo439y3R19wDQCQnldaXp+LExQK27bJqboPqzHD6u3mDJSZnuPqpIRGZ0BBqRaBXo33rIiIiIiI6NYl6wyrXq/HuHHjcP36dXz66adSsBUTEwONRoOjR48CAIQQ2L9/P2JjYwEAffv2xQ8//ACNpjyzmJqaiuTkZJPyhIQE6XP279+P4OBgdOzYsTFvz6L6yrBq0s4CAFJyy/c/tXX+KlBtSLCFOaxA5TDhAs5jJSIiIiKieiLbgNUYrB48eBCbN29GWVkZMjIykJGRgWbNmmHEiBGYOnUqkpKSMHfuXGRlZWHUqFEAgAcffBABAQGYMmUK/vjjD0yaNAkxMTHo3LkzAGD8+PE4ePAgVq1ahV9++QVz5szBxIkT4ezsbM9blhgzrO7ONxmwpvwJABDlu+TUKWB1rQhYdQYhrV5cfQ4rUBnEch4rERERERHVF9kGrP/73/+wfv16pKam4s4770RYWJj0AoA1a9agTZs2iI2NxZ49e7B3714EBQUBAFxcXJCQkICzZ8+id+/eAICtW7dKdXfq1AmbNm3CBx98gCFDhiAuLg5vvPFGo9+jJfWVYdVdT4d7ldjS1j1YgZorFXu5OpldvdjbrTzYL2DASkRERERE9US2c1hHjRolZUzN8fT0xMaNGy2Wt2vXDocOHbJYHhcXh7i4uJtqY0MprqcMKwA081DiSn7FCsF1CIBVStM5r/3uCDZ7nrc0JFhr82cQERERERGZI9sMq6MSQkgZ1roEmNUFu1d+i+taX7B3+SJKD3YKRetAT7PnGIcEM8NKRERERET1RbYZVkel1QvoDeWTTm92SDBQnmE1qsuQYAB4rGsLaHR6adivOdKiSwxYiYiIiIionjDDKjPG7KqTUmF2rqitfFwU0tDi6vunWsvFSVlrsApUzmEt1OggjKs8ERERERER3QRmWGVGWiG4HrKrAKBQKNDvjmCkXS9BmK9bvdRpjjEY1hvKhzTXNZtLRETU2JSleTd1vaKssJ5aQkRE1TGqkJlibfmQ2vpYcMmoTTMvtGnmVW/1maNSKuDpokJRmR4FpToGrEREJHu+vr5wdnEFLh686bqUShUiIiLqoVVERFQVowqZqe8Ma2PycnNCUZkehRodQuzdGCIiohsICQnBhs/WIy/v5jKsQHnwGxLC3o+IqL4xYJUZaQ/WesywNhZvN2eo8zVceImIiJqMkJAQBppERDLGRZdkpilnWCu3tuFerEREREREdPMYsMqMtAdrE8ywcmsbIiIiIiKqTwxYZaa4KWdYKwLWfGZYiYiIiIioHjBglRlpSHATzLA283IFAGQVaKDTG+zcGiIiIiIiauoYsMqMtOhSE8yw+ro7w8NFBYMA1PkaezeHiIiIiIiaOAasMtOUM6wKhQLhvu4AgLS8kjrXI4TAxaxCCCHqq2lERERERNQEMWCVEa3eAJ2hPEhrihlWAAj3cwMApF2vW8AqhMDUr5IQ+5+D+OTw5XpsGRERERERNTUMWO1Eo9NjccJlOPlW7v2WWVA+jNbVSQkXVdP81oT5lWdY0/NK65Qh/e/3ydiWlAYAeHffOeSVcAEnIiIiIiJH1TSjolvAom9P4+s/shDyz4UorBgGfCGrEABwW5AnFAqFPZtXZ828XOGkVECjMyCnqMyma3++cA3v7T8PAPD3cEZeiRarD11oiGYSEREREVETwIDVTp7v2xYRfq5w8gvFnnMFKNLocDGrCABwezNPO7eu7lRKBUJ9y4cFp9o4LHjHH+WZ1ce6tsC/H4sCAHz842VkFXABJyIiIiIiR8SA1U6CfdzwwfAO0OWpka8x4OvfryKvRAuVUoFWAU03YAWAiAAPAOUZU2uzrEII7D+dCQB4uEsYBnQMQafmPijR6rH3r4wGaysREREREckXA1Y7CvVxhXrTPDgpgWsVgV3LAA+4ODXtb8vdEX4I9XGDRmfAtqSryC/V3/CaU+n5yMgvhbuzCv+4PRBFRUXo1dobALD3j1RkZmbW+iosLGzo2yIiIiIiokbmZO8GODrdtVT0iPDED3+XDwdu04SHAxs5q5SI6xKO//2WguvFWmw/mw+31nfXeo0xuxrTLgg6TQlatb4NBSpvhD/9Pg6eyUBIeF9Ab3kBpoDAIPx9+RK8vLzq9V6IiIiIiMh+GLDKQLtAV5QqXJCRX4p2wd72bk69cHdRYXjXFtj5Rzoy8ksR/NhcXMktRXCw+fP3nSkPWPtFBqO4uBg517Ix/cN12JGiQAncMHFVApr7OJu9tigvB0smDkFxcTEDViIiIiKiWwgDVpm4r02QvZtQ7zxdnfBYt+bY+uvfSC8AVvyYio87tKxxXlaBBidSrwMA+kYGA6X5AAAvvwDcXqrDX2n5UGtUiPQPbMzmExERERGRnTXtyZIke05KJf7RwgPCoMf+c7n49XJOjXO+P6WGEEBUC1+E+LiZlLUOLB8ifTm7qFHaS0RERERE8sGAlRqcv7sTCk8mAAAW7z4DIYRJuXEV4IF3hta4NiLAHUoFcL1Ei2uF3N6GiIiIiMiRMGClRpH34+dwUSnw29+5+OVSZZY1v1SLny5kAzAfsLo6qdCyYpuc85m2rQSsNwhodDdeoZiIiIiIiOSJASs1Cn1hDobcWT5Pd+XBC9LxA2cyodULtGnmibbB5hdMahdSvhDVOSsDVnV+KV7f9ieiFyYgeuE+/PZ3zWHIREREREQkfwxYqdE80T0USgWQeDYLp9LKF1ba86fl4cBGtwd5Qqko36s2p2K/Wks0Oj2e+vgXfHbkb1wrKkNeiRZjP/rF7NxZIiIiIiKSNwas1Gha+LnhoahwAMD0zSfw1bEr2F0RsA7uFGbxOjdnFSKsHBb8znfJOJNRgEBPF3z0VHfc1yYQRWV6jF//K7I5B5aIiIiIqElhwEqNJisrC/FdA+Hn7oS/0vIxc8tJAMCILsEIcdEgMzMTmZmZyMrKqnFtu4rhwqfS86E3iBrlALD/zxSsPnQRADCrf0t0DlTg7QdboV0zd1wv1uL1Lb9Ln1FYaNt8WCIiIiIianwMWKnBaUqLAYUCnTp1QvfIVji1/Fnoi64DAIovHMPSMf9ASEiI9OrUqRMAQKutHP7bLtgbHi4q5JVo8UfFnq1V61e4emLapj8gABSc2IsR90UiJCQErVqE49DSSRDCgN2nr6FV9APlx1vfxqD1JuQWlSFZXYDzmYUW/4BARERERHSznOzdAHsRQuCtt97CmjVr4O7ujhkzZmD8+PH2btYtSaspBYTAlHc3oVlocwBAgUaPqwVatOnyAJxHDDQ5PzPlIj6Y9gS0Wp10zMVJiR63B2LfmUwcvZSDO8J84OasAgCUlZYioN9EOPkGw9tFiSfH/hPOT48yqfNIShFOZWlw25OL0D9c4P3JQ1BcXAwvL/MLPRkMApkFGugMBoT6uMFJxb/tAOWrOi/ffx7rDl9Gmd4AAGgb7IXXHrwDfSOD7dw6IiIiIrrVOGzAumrVKixbtgxbtmzBtWvXMGbMGLRo0QKDBg2yd9NuWZ4+/vD2DwQAeAMIt7DOUmGe+QWSOob7ICnlOq4VlWHnH+kY3CkUbs4q/JYFeHXuBwWAQZ3DEeDnXuPaPt7+yDiWgpziMhzNdgKUpj/6QghcyCrC/jNqJJzORNKV61JA5qRUoEu4Fx7t0gz3t/WH8w2CVw8PD3h5eUGnN6BIo0eBRguFQgE3JyX8PFygUiosXiuEgEJhWq43CKTmFuPva8XQGQxwd3ZCVAtfeLo27n++P1+4hqlfJSEjvxQA4OfhjNIyPc5nFuLpdccwKDIAM/q1hperymIdxmdDRERERGQNhwxYhRBYsWIFpk+fjr59+wIAvvvuO6xatYoBq4wpFQr0jQzGN0lXcfV6Cdb9dBlKhQJlekAIA7qGuiDcTLAKlGdoh0SF4ctjKVAX6RA+7n1sO5mFkEAt/kjNw77Taly+VmxyjUqpgFIBaPUCv6UW4LfUAugLc1H4x3covvALyjLOA0JA5R0El2at4dysFVyatYZbaBt4hbREidZg5h6AAE9XNPN2hZuzEiVlehRqdCgu06NIo4POIBDi7YpgHzd4uTohp6gMF7IKodEZarStU5gn+rXzR8ztfmju61oj0AXqJ0DMLCjFB/vPY/2RvyEEcFuQJ+YO6Yjuzd3RukMnGCL7w6f7UOw5k4MdR07h+sF1KD5zGEDNocIBgUH4+/IlBq1EREREZBWHDFhzcnLw559/on///tKx2NhYPPvss3ZsFVmjuZ87Rt3TEt/+mY5rhWUABJyVQOpXb6L962/Xeq2/pwse7ByK3SfTgcAILPr+skm5k1KBbhHe6HW7H/7R2hfhvq64lp2Frr0ewAOvrsHFAgVKvPzhe9/j8L3v8Vo/q2qw6qIqDyTL9AIGAWQXampdsTgtrxRpeaUmx1xUCrTwc4OzUuD0pauAVyBOXC3EiauFeCcxBbqCa9DlpkFffB1CrwMMegiDHq4uzoh/8gm4ubrCSaWASqmAs1IBlVIpvXeqeKlUyvJ/lQpodAZkF2iQlHIdRy9dQ2nF/Yzs3gJvxN0JDxcnZGZmIif9CmbMfxSFKm8kXi5CkV8omg19FV4jlAj3doavmxKuTkooAWhKirBz7RJsO54CPz9fKBUKKFAe1goBCIiKf8v/qITqxyvKdHoDSrV6aHQGlGoN0OiMX+sBAO7OKrg5q+DuUvGvswruLko4q5Qo/8T6UbW9uEGbjRQAlEpAAQUUCkChKP+jiALl/1Z9FgZhWqdBVH6eoeIL4+cYTD5TmH5mxXNWVPkcpVIBlUIBpbL8D0GqiveV91blPkXl/VY/VvVcUfWgmeur1mFy7AZ1mdRqdVtqfiZQ/px0egN0egGtwQCVQoFn+7Qx224iIiKSB4cMWNVqNQAgNLRyTGp4eDjy8/NRUlICd3fTLJ1Go4FGUxlg5OXlAQDy8/Nvqh0FBQUAgJyMVJQWF9V67vXMNABAbuZVKM1kruR8fkPU/UBzIE+jgkKhQEnWFXxy8Ver6vcA0McnG19t2wnXFh0hDAboC7JQevE4Sq78gQvaUvzPzHWtnfIQ1aYZruRpcfm6BhmFemj15Z+lVAA+rkr4u6ngqi/GoQ1LoctJg6G0CKKsBBD6iloUUHr4QunhC5WnHxQqFwhtCYSuDAZNCQzaYkAYoPIMgMrTD0pndxi0JdBeS4U+LxPnqtzb43NWosAlAGnFQG4poHRxh0uI+f/x3nD4fK3PxBp3hHhg/D/C0TXCC+kpfwMAsrOzAQCakmL4+rjioVZOOJVditOZpcjXAPkFNesJHDgFs7/+86bbQ1Qf3JyVGHV3s5uqw9gPWArYqSbjs7rZPpSIiJo2a/tQhXDAXvbw4cOIiYlBbm4u/Pz8AAC///47unbtiqtXryI8PNzk/DfeeAPz58+3Q0uJiKgpSElJQYsWLezdjCYhNTUVERER9m4GERHJxI36UIcMWE+fPo2OHTviypUrUqd58OBB3H///SgpKYGbm5vJ+dUzrAaDATk5OQgMDDQ7b9Ba+fn5iIiIQEpKCnx8fOpcz62Gz8UyPhvz+FzM43OxrL6ejRACBQUFCA8Ph1LJ1cStYTAYkJaWBm9vb/ahDYDPxTI+G/P4XMzjc7GssftQhxwSbBwKnJ6eLgWsaWlp8PPzqxGsAoCrqytcXV1Njhkzs/XBx8eH/yGYwediGZ+NeXwu5vG5WFYfz8bX17eeWuMYlEplvWaj+fNtHp+LZXw25vG5mMfnYllj9aEO+edgf39/dOnSBQkJCdKx/fv3IzY21o6tIiIiIiIioqocMsMKAJMnT8bMmTPRo0cP5OTkYP369di5c6e9m0VEREREREQVHDZgnTBhAtRqNZ588km4u7tjxYoVGDBgQKO2wdXVFfPmzasx3NjR8blYxmdjHp+LeXwulvHZNH38HprH52IZn415fC7m8blY1tjPxiEXXSIiIiIiIiL5c8g5rERERERERCR/DFiJiIiIiIhIlhiwEhERERERkSwxYG1gQgi8+eabiIiIQPv27bF27VqL56rVajz00EPw8/NDnz59cO7cuUZsaeOy9rno9XosWLAAUVFR8Pf3x/Dhw5Gent7IrW1ctvzMGB06dAgKhQJvvPFGwzfQTmx5LgaDAYsWLUK7du3QrFkzjBkzBteuXWvE1jYuW57N8ePHERMTA09PT3Tp0gV79+5txJY2nqysLMyZMwctW7ZE9+7daz3XkX73NjXsQ81jH2oZ+1Dz2Idaxj60Jtn1oYIa1IoVK4S/v7/Yv3+/2LRpk3BxcRG7d++ucZ7BYBDR0dHi4YcfFidOnBDPPPOMaNmypdBoNHZodcOz9rlMnz5d3HfffWLfvn3i2LFjonv37qJ///52aHHjsfbZGOl0OnHXXXcJLy8vMW/evMZraCOz5blMnz5dtGrVSuzdu1ecPHlSPPfcc+Lo0aON3OLGY+2zKS4uFuHh4WLOnDkiOTlZvPXWW8LDw0Okp6fbodUN67fffhOPP/64uPPOO0W3bt0snudov3ubGvah5rEPtYx9qHnsQy1jH1qT3PpQBqwNyGAwiE6dOolFixZJxyZMmCCGDh1a49xff/1VABBXr14VQghRWloqvLy8xNdff91IrW08tjyXjIwMUVhYKL3ft2+fACByc3MboaWNz5ZnY7RmzRrRsmVLMWrUqFu2s7XluWRmZgp3d3fxww8/NGIL7ceWZ3P8+HHh6+srDAaDEKL8f9RCQkLE1q1bG6u5jW7evHm1draO9Lu3qWEfah77UMvYh5rHPtQy9qG1k0sfyiHBDSgnJwd//vkn+vfvLx2LjY3FgQMHapybmJiIjh07Ijw8HED5/kY9e/Y0e25TZ8tzCQkJgaenp/Q+ICAAAFBQUNDwDbUDW54NAOTn5+O1117Dm2++CRcXl8ZqZqOz5bns3r0bQUFB6NmzZ2M20W5seTZt2rRBaWkp1Go1AEClUsHV1RUdOnRotPbKjSP97m1q2Ieaxz7UMvah5rEPtYx96M1prN+9DFgbkPEHOjQ0VDoWHh6O/Px8lJSU1Di36nnGc4113EpseS7VHT9+HH5+fmjRokWDttFebH02CxYsQGRkJMaOHdtobbQHW57LlStX0KpVK/zvf/9Dly5d0L59e/zf//0fxC265bQtz8bHxwdTp05FbGws9u7di6+++grt27fHHXfc0ahtlhNH+t3b1LAPNY99qGXsQ81jH2oZ+9Cb01i/e53qtTYykZubCwDw9vaWjhm/zs3Nhbu7u8m5Vc8znpuamtoILW1ctjyXqgwGA9577z2MGzcOCoWi4RtqB7Y8m/Pnz2PFihU4evToLfs8jGx5LqmpqThz5gw2btyIDz74AGfPnsVzzz2Hdu3aYdiwYY3a7sZg639P/fv3x5YtWzBy5EgUFhbihx9+uOV/fmrjSL97mxr2oeaxD7WMfah57EMtYx96cxrrdy8zrA3I3NCb/Px8k7Kq51YfopOfn4/AwMAGbmXjs+W5VLV27VpcvnwZ06ZNa9gG2pEtz2batGl44YUXcOeddzZeA+3Elufi7e2NoKAgbNq0CTExMXjmmWfwyCOP4Jtvvmm8BjciW57NTz/9hMmTJ+PHH3/E5cuXMX36dAwePBhJSUmN1l65caTfvU0N+1Dz2Idaxj7UPPahlrEPvTmN9buXAWsDMqbIqy4hn5aWBj8/P7i5udU4t/pS82lpaTXS7LcCW56L0a+//ooXX3wRH330EcLCwhqlnfZg7bO5evUqvvnmG6xcuRJBQUEICgrCF198gSVLluDuu+9u9HY3NFt+ZiIiIqBUKk3mI7Vu3RoZGRmN09hGZsuzWblyJUaMGIHg4GD4+/vj7bffRr9+/bBs2bLGbLKsONLv3qaGfah57EMtYx9qHvtQy9iH3pzG+t3LgLUB+fv7o0uXLkhISJCO7d+/H7GxsTXO7du3L06fPo2rV68CAEpLS3H48GGz5zZ1tjwXALhw4QKGDRuGl19+GY899lhjNdMurH02ISEhSElJwZ9//omkpCQkJSWha9eumDRpEnbt2tXYzW5wtvzMxMbGIjk52aRzTU5ORps2bRqlrY3NlmdTVFQEZ2dnk2NhYWHIy8tr8HbKlSP97m1q2Ieaxz7UMvah5rEPtYx96M1ptN+99brmMNXw4YcfCj8/P7F//36xefNm4eLiIr777juRmZkpWrZsKT755BPp3B49eoghQ4ZI+xi1bt1alJWV2a/xDcja53Lx4kUREREhnnzySZGVlSXS09NFenq6uH79un1voAHZ8jNTVZ8+fW7ZJfmFsO25DBkyRAwcOFCcOHFCfPLJJ8LZ2VkkJSXZr/ENzNpn8+WXXwpvb2+xYcMGceHCBfHll18KDw8PsX79evveQAO4du2aSE9PF6+88oqIiooS6enpIjMz0+F/9zY17EPNYx9qGftQ89iHWsY+tCa59aEMWBuYwWAQb775pmjevLlo27atWLt2rRCifG+0iIgI8dFHH0nnqtVqMXjwYOHr6yt69+4tzp07Z69mNzhrn8sDDzwgANR4PfXUU3ZsfcOy5Wemqlu9s7XluRQUFIixY8eKwMBA0b59+1t6jzQhbHs2H3/8sbjzzjuFu7u76NChg1ixYoW0p9ytpE+fPjV+b7Rq1crhf/c2NexDzWMfahn7UPPYh1rGPrQmufWhCiFu0XWqiYiIiIiIqEnjHFYiIiIiIiKSJQasREREREREJEsMWImIiIiIiEiWGLASERERERGRLDFgJSIiIiIiIlliwEpERERERESyxICViIiIiIiIZIkBKxEREREREckSA1aiBhAdHY333nvP3s2wytGjR9G8eXOkpKTc1DlyEh8fj+HDh99UHe+99x6io6PrqUVERGQt9qH2xT6U5IYBK1EDiIyMRFhYmL2bYRVfX19ERkbCw8MDAJCdnQ2FQoHCwkKL5ziCsLAwREZGSu83b96M7t2727FFRESOgX1o08c+lOqTk70bQHQrWr9+vb2bYLXIyEjs27fvps+51YwYMQIjRoywdzOIiBwO+9Cmj30o1SdmWImscP/99+PTTz/Fq6++ivDwcHTt2hWJiYlITk7G4MGD4evri0ceeQQFBQUAgO7du+ONN94AAFy+fBmurq64ePEiHnnkEfj7++Pee+/F+fPnrf781q1b44svvkB8fDyCgoJwxx134MsvvzQ558KFCxgyZAj8/PwQGRmJ9957DwaDQSrfvXs3OnXqBC8vL8TExODgwYMAgD///BMKhQKXL1/GunXr0KxZMwCAt7e3dLzqOWvWrEFYWJhJ3VevXoVCoUBycjIA4OLFixg8eDB8fHzQqVMnfP311zY963fffRcvv/wywsPDcfvtt+Pdd9+FEEI6R61WY/To0QgKCsJtt92GuXPnoqyszGKdZ8+eRXx8PNq0aYOAgADEx8ejuLjY5PkePHgQ8fHx8Pf3x19//YXly5ejdevWAMqHR40YMQK//fYbFAoFWrdujfXr1yM4OBharVaqJyMjA0qlEufOnbP6fomIbnXsQ9mHsg+lm8GAlchKL730Ery8vLBv3z60atUKo0ePxhNPPIF//etf+Pbbb5GQkIC1a9eavbasrAwPP/wwxo4dix9++AGFhYWYPXu2TZ//3HPP4d5778WPP/6Ip59+GqNGjUJSUhIAIDc3F/fddx9CQkLw448/YvHixViwYAH+7//+DwCg0WgwYsQI9OvXD8eOHcMzzzyD9PT0Gp/x+OOPY8uWLQCA8+fPIz09HRERESbnPProo8jOzsYvv/wiHfvmm2/QpUsXtG/fHnl5eYiJicH999+P3377DW+99RaefvppnD592up7ffXVVxEcHIx9+/Zhzpw5mDFjBrZv3w4A0Gq16NevH/Lz8/H9999j7dq12LhxI1566SWL9e3YsQNt27bF5s2b8c0332D37t14//33Tc6ZMGECbr/9dvz888/o0KGDSdm7776LV155BVFRUUhPT8exY8cwfPhwaDQafP/999J5X3/9Ne6++260a9fO6nslInIE7EPLsQ9lH0p1IIjohvr06SMmTpwovd+8ebMAIP744w/pWP/+/cWYMWOEEEJ069ZNzJs3TwghxKVLlwQA8dNPP0nnzpo1S7Rp08bqz2/VqpVYunSpybGePXuKF198UQghxIIFC0Tbtm2FTqeTyj/55BPh6ekpysrKRH5+vlAqlWLv3r016j558qQAIC5duiSEEOLAgQMCgCgoKLB4zqBBg8TMmTNN7n3hwoVCCCEWLVokRo4cafIZEydOFPPnz7fqXvv06SOmTJlicmzMmDEiLi5OCCHEhg0bhK+vr8jLy5PKDxw4IBQKhUhNTRVCCPHUU0+Jxx57zOJnPP3002LQoEHS+1atWom4uDhhMBikY++//75o1aqV9H7evHmiW7duJvU899xzYtSoUdL7fv36iSVLllh1n0REjoJ9KPtQ9qF0M5hhJbKSt7e39LXxr3/Vj12/ft2q69u2bVvrueaoVCqT9507d8aFCxcAAElJSejdu7fJObGxsSgqKsL58+fh7e2N9957D2PGjMG4ceOkYUd19fjjj2Pbtm0Ayv8ynZiYKM1VOX78OL7++mt4eXlJr48//hhXr16tt3vt1q0bfHx8pPKYmBioVCr88ccfZuszGAzYs2cPxowZg6ioKGzfvh1ZWVkm5wwYMAAKhcLqNgLA+PHjsW3bNhQUFODatWs4ePAgRo4caVMdRESOgH1oJfah5diHkrUYsBLVgVJZ8z8dc8dsud5WxcXF8PLyAgCTuSnVGefJPP/88zh9+jTCw8Nx7733SkOd6mLYsGG4ePEizpw5g2+//RadOnWS/gdECIGhQ4ciKSlJep0+fRoLFiyo8+fZeq/VTZkyBdOmTcOTTz6JY8eOYcqUKXVuS1Vdu3bFHXfcga+//hrffvst7rnnHrRq1ape6iYiulWxD2UfCrAPJetxlWCiJkin0+HHH3/EM888AwC46667sH79euj1eukvqwcOHICHhwfat28vXRcUFIQFCxagXbt2mDp1KqZPn16jbien8l8LVTu46vz8/DBo0CB8++23OHbsmMlKgF26dMHnn3+O1q1bS3XdrMTERERFRUn3+tFHH6GgoED6i/vhw4eh0+nQpUuXGtfm5+fjww8/REJCAvr27QvAcqdcGycnJ5NFJowmTJiAr776Cr6+vnj88cdtrpeIiBoX+1D2odS0MMNK1ESsWbMGu3btwqlTpzBp0iTk5uZi0qRJAIDJkyfj+vXrmDRpEk6dOoXt27djxowZmDNnDpydnbFv3z48/PDDOHDgAE6cOIEtW7agZcuWZj/ntttug0KhwJo1a3D27FmUlJSYPc84pGnv3r0mne2UKVOQk5ODsWPHIikpCSdOnMDcuXOl1R+tsWXLFnz11Vc4e/Ys5syZg59//hnTpk0DAIwcORJhYWEYM2YMTpw4gQMHDmD8+PGYOHEiWrRoAaB86Fh2djZKSkrg6uoKZ2dnfPnllzh9+jRWrlyJFStWWN0Wo7Zt2+Ls2bP4/vvvTRa/GDVqFH788ccaz4GIiOSDfSj7UGq6GLASNRG9evXC0qVL8Y9//APnzp3DoUOHEBAQAAAICAjA4cOHcfXqVfTo0QPTpk3Dq6++ipkzZwIAevTogaioKEyePBk9e/ZEXl4eNmzYYPZzmjdvjgULFuCdd95Bv379LK5MGBcXh19//RWtW7c2WdHP398fP//8MwoKChAbG4uHHnoI6enptS6ZX12PHj3wxRdf4J577sGePXuQkJAg/ZXbxcUF+/fvh4eHB2JjYxEfH4+RI0earFj4yCOP4Pjx49i6dStcXV2xdu1abN++HQMGDEBycjLeeecdq9tStc5HH30UjzzyCEaOHCktxe/r64vBgwejc+fOCA8Pt7leIiJqeOxD2YdS06UQtQ1mJyJZaN26NaZNm1Zv80bk7P7770f37t2xdOlSezfFKnq9HlFRUZg5cybGjh1r7+YQEVE17EPli30oWYMZViI7++c//2myGmDV1z//+U97N69ezZgxw+K99ujRw97Ns4kQAhcvXsT8+fOhUqkwZswYezeJiMjhsA9lH0q3Pi66RGRn//3vfy2u/ufp6dnIrWlY06dPx8SJE82Wubi4NHJrbs6VK1cQFRWFDh064LPPPquxjQARETU89qHl2IfSrYxDgomIiIiIiEiWOCSYiIiIiIiIZIkBKxEREREREckSA1YiIiIiIiKSJQasREREREREJEsMWImIiIiIiEiWGLASERERERGRLDFgJSIiIiIiIlliwEpERERERESy9P/gshasO8KI1AAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAGHCAYAAACnGJ9sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAcLBJREFUeJzt3XlcVFX/B/DPsA3LwLAvI+CCqOGGppSJomihZWqZ+pQtPuaeleaSPlpmplk99lg/TbPFpSLL3MrHJc1d0+xR3BXcWYcdZhgYYOb8/iAmR0AWYS7L5/163ZfMPefe+d4rzvE759xzZEIIASIiIiIiIiILs5I6ACIiIiIiImqamJASERERERGRJJiQEhERERERkSSYkBIREREREZEkmJASERERERGRJJiQEhERERERkSSYkBIREREREZEkmJASERERERGRJJiQEhERERERkSSYkBJRo/PSSy9h9erVZvsKCgogk8lw8+ZNaYKqZQMGDMDatWulDoOIiJqw2bNnY/To0VKHQQ0cE1IiC3jmmWcwY8aM+65TX9y8eRMymQznz5+v8Tny8/PRqlUrbNmypRYjK6FWq5Gbm1vt47755hu0bdu21uOprokTJ8LT09NsS0pKwr///W82/EREf2HbWlZtt60vv/wyFi9ebLZPoVDg2rVrVT5HVdrW8ePHw9/fH3Z2dnBzc4O/vz/8/f3h4uICBwcH+Pv7Y+HChWbHLFy40FTv7s3b2xuurq5VjpGkxYSUyAKCgoIQGBhoej1lypQyDeTddRo7W1tbtGvXDp6enqZ9HTp0wPbt22t0vjZt2sDV1RWurq7Yu3cv5s2bZ3o9efLkKp0jLi4O8fHx0Ov1FdY5f/48ZDKZ6dzlbdu2bTM7ZtCgQVAoFOVu9vb2aN26tVn9VatWIT093bRlZGTAaDRW/6YQETVibFvLqu22Va/Xo7Cw0GxfXl4ehBBVPkdV2tbVq1cjISEBXbt2xX/+8x8kJCQgISEBb7zxBoYMGYKEhAS89dZbZse89dZbpnp3bzt27KjehZKkbKQOgKgp+OCDD2qlTmNiY2NTqw3GxYsXKyyTyWQoKiq65/HFxcXYtm0bDAYDdu3ahSFDhlRY19raGtnZ2VWO7V7/EdixYwdef/31Kp+LiIhKsG0tq7bbVgD4+eefkZCQUKNjq9O2Vte8efPw2Wefwd3dvUyZ0WiESqWqtfeiusUeUpJMnz59sG7dOsyePRsqlQpdu3bFgQMHEBsbi4EDB0KpVOKpp56CRqMxHXPy5EkMHz4cAQEB8Pb2xowZM2AwGAAAM2bMQGhoqOn1li1b4OXlVaXEYe3atejVqxd++OEHdO3aFW5ubhgxYgQyMzNNdYxGI5YtW4Z27drB1dUVTz75JK5fv24q1+l0eOmll+Du7o7AwEBMnz4deXl5AEqGDJUOtWzRogVWrFiBpUuXQiaTmfaX1jEajfD398eqVavMYpwwYQJGjRoFABBC4D//+Q9at24NDw8PPP/882ax3suBAwcQEBCA3377DT179oRSqURUVBRu375tVi86OhqdOnWCi4sL+vbti9OnT1d4zqKiInz++efo1asXXFxcEBISgl9//dXs/g4YMAC7du1CSEgInn/+eQCAp6cn1q5daxqmdOHCBTz55JOQyWT4+uuvERQUhE8//dTsvSZPnmw6/k42NjbIzMzE+PHj0apVK3Ts2BGLFi2C0WiEtbX1Pe+JEAKzZs2Cvb09fvjhB4wdO/aez5oaDIZ79pBqtdp7vt+d9Ho95HK52b4FCxYgNDTUtN3p22+/hUKhwJ49e6r8HkTUdLBtZdtam20rAKhUKnTr1s203a20XVq6dKnZ/uq0rdOmTYO/vz9OnTpl+tnf3x8ff/wxtm3bZvr5TsXFxXj66adx9erVMtv169fv+UU11TOCSCIRERFCqVSKhQsXiosXL4qhQ4cKPz8/0b17d/Hrr7+Kw4cPC4VCIT7++GPTMTNmzBDLli0T586dE9u3bxf29vZiw4YNQgghcnNzhZ+fn1i1apUoKioS7dq1E6tXr65SLGvWrBEymUwMHDhQ/P777+LYsWOiU6dOYujQoaY6ixcvFt7e3mLLli3i3LlzYvTo0cLHx0dkZ2cLIYRYtGiR8PX1Ffv37xcnT54U06dPF1qtVgghxLBhw8RLL70khBAiNTVV9OjRQ0ycOFEkJyebjr+zzrRp00RUVJTpvQ0Gg/Dx8RFbtmwRQgjxwQcfiNDQUHHo0CFx8eJF8cwzz4jnnnuuSte6f/9+AUA89NBD4rfffhOnTp0S/fr1E6GhocJoNAohhIiOjhZOTk5i7dq14uLFi2L27NnCwcFBXL9+XQghxI0bNwQAce7cOSGEEOnp6WLIkCHi559/FleuXBGvvvqqcHd3N13/mjVrhK+vr3j44YfF4cOHRWJiohBCCA8PD7FmzRpRXFwskpOTBQCxfv16kZycLHQ6nVi0aJHo3r272X3w9fUVP//8c5nrMhqNIjw8XIwbN06kpaWJuLg48cgjj4g5c+YIIYTIz88XAMSNGzfMjktOThZPPfWUCA4OFrdu3RJCCLFw4ULh4eEhfvzxR2EwGMzqnzt3TlhbW1fpXlfF999/L7p06WK27+WXXxYzZ84UaWlpQq1Wi+vXr4ukpCTx0UcfmX5HoqKixJo1a2otDiJqHNi2sm2tzbZ11KhRYv78+Wb7AIi4uDghhBBvvvmm6f7eqbpta028+eabwt7eXvj4+FS4ld5Lqt+YkJJkIiIixPjx402vf/rpJwFAnD171rSvf//+YtSoURWeo2/fvmLixImm19HR0cLHx0d89tlnIjQ0VBQXF1cpljVr1ghnZ2ezD8g9e/YIACI9PV3o9Xrh6Ogo1q9fbyovKioSrVq1EosXLxZCCDFr1izRs2fPcj9k72wQS699+vTpFdY5fvy4sLW1NTWoR44cEQqFQuh0OlFYWCicnJzEhQsXTMcmJSUJW1tbUVRUVOm1ljaa6enppn1xcXECgDh16pQQQojWrVuLd9991+y4O/++7m4073br1i0BQBw/flwI8fd/Sk6ePGlWr7TRLAVA/PLLL6bXiYmJwtraWly+fFkIIcThw4eFq6urKCgoKPOeN2/eFA4ODkKv15v2HTt2TAQGBgohyk9IJ0+eLORyuRg3bpzZ/RBCiK1bt4oWLVqIwMBA030RovYT0o8++kg88cQTZvtefvll4eTkJNzc3IRCoRCtW7cWw4cPZ0JKRJVi28q2tTbb1lGjRgm5XC6USqVpqywhrW7b+vTTT5ud/17bpEmTyr03QgjRvHlzsWfPngrLqf7ikF2SlLOzs+nn4ODgcvfdOSyoqKgIGzduxLBhw9ChQwf873//Q1pamqn8H//4B4KDgzFlyhR88sknlQ7VvJOVlRWsrP7+J9GxY0cAwI0bNxAXFwedToc+ffqYym1sbBAREYGYmBgAwNSpUyGEQIcOHfD1119X+szivYSFhaFZs2bYuXMngJIhUoMHD4aDgwNiY2ORl5eHsLAw08Q4rVu3RlFRkdm9qMyd9yYoKAgODg64du0atFotrl69anatABAZGWm61vIkJydj0aJF6NOnDx577DEAMIvH3d293KE+96JSqfDEE0/gu+++AwBs2rQJTz31VJkhrkDJMFqZTGb2d2hra4vi4uIKzz9x4kTcunULq1evhoeHh1nZkCFDcPXqVaxfv970uwCUPI9qY2Njmv1WqVRCqVSazYi7YsWKKl/jtWvX0KJFC7N9n3/+OXJycpCZmQmNRoO4uDj8+OOPZnX++c9/4sEHH6zy+xBR08G2tXxsW0tUp2399ttvUVBQgOzsbNMmhDBNxtejRw/069fP7Jjqtq2bNm0yO/+9ts8++wwAysxE7+npidu3b+Ppp58us/+bb76p1v0hy+OkRlRv3NlglbfPaDRi6NCh0Gg0WLx4MR5++GGMHTvW7Hk9jUaD69evQ6FQmD0fUxM6nQ5AyfTm90pqSmc/9fPzw5EjR7Bnzx68++67WLJkCQ4dOgRfX99qv7dMJsOIESOwdetWjBw5Elu3bjU9myH+mtlu586d8PPzMzvO29u72u8FlDyHodfroVAo7jlzXkUzvV68eBEREREYP3481q9fj4CAgHL/Pmti7NixmDp1KhYsWIBt27aVef6nVMuWLRESEoKZM2di4cKFyMnJwZtvvomRI0dWeO47E83yWFtbIyIiwmxf+/btUVBQYHo9Y8YMZGdn48svv6zGVf0tMTERvXv3LvO+5Zk8ebLpOa57XRcRUSm2rX9j2/q3qratlSlvkqKatK3bt2/H1KlT73ncjh070KZNGwBAenp6mfLt27ejW7duNfrdIGmxh5QajMuXL2PHjh1Yt24dwsPDYWNjU+ZDfMGCBYiIiMAHH3yAKVOmmBq+mjhw4AAcHR3RqlUrtGnTBvb29jh48KCp3GAw4NChQ+jSpYtpn0wmw2OPPYaDBw/CaDSW6dUqZWNjU2lsI0eOxO7du3HhwgWkpqYiKioKQMk32w4ODrh16xZat25ttlXnW+s7HTlyxPQNtLOzM4KCgsyuFQD2799vdq13Wrt2LUJCQrBo0SIEBgZWazr4O1lbW5e5LwMHDkRBQQGio6Oh0WgQGRlZ7rEymQxbtmxBfHw8AgMD0b17d4SGhmLRokU1isVSfv75Z7zxxhvllrVo0QJOTk6myZJUKhUCAgLg6uoKGxsbrF271rLBElGjw7aVbeu92lYAmDNnToWT+Mnl8lpZH3vQoEHlTk5UuqWkpJRZfuZOqampGDx4MNatW3ffsZDlsYeUGgxHR0cAwLp16zBixAhs27YNmzdvxoABAwCUrA+5atUqnD17Fs2bN8eqVauwcOFCvP/++1U6f25uLmbPno0XX3wR165dw+zZszFz5kzY2dkBKJlefMaMGVAqlWjVqhU+/vhjaDQaTJo0CUIIPP3004iMjES/fv1w4cIF3Lx5s8K1z1q3bo29e/fizJkzcHZ2RqtWrcrU6dKlCzw9PTF37lwMHjwY9vb2AAC5XI558+Zh6tSpsLGxQbdu3XD+/Hnk5eWZZgqsiunTp+P11183zUz74osvwt/fH0DJfz4mTJiA5s2bo3v37vjmm29w/PhxfPXVVwBKvtkGgPj4eHTo0AEKhQKnT5/GkSNHYG1tjUWLFtWoAW/dujU2bNiALl26wNnZGb6+vrCxscE///lPTJs2DcOGDYONTcUfW/7+/vjpp5+q9F5r167FlClTqhxbdWbOvZOHh8c9116706pVq8rMcrht2zb079+/TN27h30REdUE21a2rZW1re+//36Ff9+zZ89GSkqK6XVN29b//ve/eOmll8zWT72TQqGoMEadTofRo0cjIiICH3/8MSIjI9G9e/cqx0D1gCRPrhKJspMPnDt3rsykM6+88orZhC8ffvihcHNzE0FBQWLBggViyZIlYtiwYcJoNIqIiAjx2muvmeoeOXJE2NraivPnz1cay5o1a4Srq6tYuHChUKlUolmzZmL+/PlmEzcYDAaxdOlS0bp1a+Hi4iIef/xx00P9QpRMoPPoo48KpVIpVCqVeOedd0wz69098cL169dF9+7dhZOTkxgzZky5dYQQYu7cuQKA2Lp1q9l+g8Egli1bJtq1ayecnJxE9+7dTbMEVqZ04oWlS5eKli1bCi8vL/HKK68InU5nqmM0GsU333wj2rdvLxQKhejdu7f4888/zc4zYMAA0bZtWyGEEJmZmSIqKko4OTmJ8PBwsW/fPtGxY0fTJApr1qwRHh4eZWK5e+KFnTt3isDAQOHq6io+//xz0/5Lly4JAGL//v1VusbyVDTLbnWMGTNGODk5VWkbMGBAjd+n1L0maIiIiOCkRkRUBttWtq2WbFsrmmW3urZs2SI6d+5c5foGg0EcP35cLF68WPj5+YkXX3xR5Ofni59++kl4eXmJF154QWzbtk1kZmbed2xU92RC1LD/n6gRWbt2LWbMmFHuMwmNzYEDB9C3b19oNBrTt7H13YYNG/DWW2/h8uXLNR46VVBQAAcHB9y4caPMJEL1VYsWLZCSklLut8L5+fn46quvamWoFBFRXWDbWr/VRtta2kN6v4+QbN26FSNGjIBKpaqwzrhx4zB37lwAJc/8vvTSS+jUqROGDBlimrwLKOl13bVrF7Zt24ZXXnkFDz/88H3FRnWPQ3ap0Tt79iweeeSRCsuPHTtmwWjqVnZ2tmloUHm+//57s5kW67v09HTcvn0bc+bMwUcffVTjBhMA7O3ta/z8jVQqWkCciEhqbFv/1pTb1iVLltRKTEOHDr3nM6J3k8lkWL9+fbllCoUCzzzzDJ555plaiY3qHhNSavTatm17zynVAwICcOrUKcsFVIecnZ3vea2+vr74888/LRfQfXr77bfxww8/4OWXX8awYcOkDoeIiP7CtvVvbFuJ7g+H7BIREREREZEkuOwLERERERERSYIJKREREREREUmCCSkRERERERFJgpMalcNoNCIpKQnOzs6QyWRSh0NERBIRQkCj0UClUsHKit/hVgXbUCIiAqrehjIhLUdSUhICAgKkDoOIiOqJ+Pj4ey77QH9jG0pERHeqrA1lQlqO0rWk4uPj4eLiInE0REQkldzcXAQEBDSoNQalxjaUiIiAqrehTEjLUTrEyMXFhY0pERFx6Gk1sA0lIqI7VdaG8oEYIiIiIiIikgQTUiIiIiIiIpIEE1IiIiIiIiKSBBNSIiIiIiIikgQTUiIiIiIiIpIEE1IiIiIiIiKSBBNSIiIiIiIikgQTUiIiIiIiIpIEE1IiIiIiIiKSBBNSIiIiIiIikoSN1AEQERFVhVarhU6nq3J9R0dHKBSKOoyIiIiI7hcTUiIiqve0Wi2at2iJzIz0Kh/j7uGJWzdvMCklIiKqx5iQEhFRvafT6ZCZkY5Zq7fDSeleaf28nEx8OH4QdDodE1IiIqJ6jAkpERE1GE5Kdzi7eUgdBhEREdUSTmpEREREREREkmBCSkRERERERJJgQkpERERERESSYEJKREREREREkmBCSkRERERERJJgQkpERERERESSYEJKREREREREkmBCSkRERERERJJgQkpERERERESSYEJKREREREREkmBCSkRERERERJJgQkpERERERESSkCwhXbt2LWQyWZnNxsYGAKBWq/HEE0/A1dUVERERiIuLMzv+2LFjCAsLg4eHB55//nnk5eWZla9evRrBwcEICAjAe++9ByGExa6NiIiIiIiIKmcj1RuPHDkSAwYMMNv3z3/+Ex06dIAQAoMHD4aPjw8OHTqETz/9FP3790dcXBzs7OyQkpKCAQMGYOrUqXjqqacwYcIEjB07Ft9//z0AYMeOHXj11VcRHR0NNzc3DBs2DF5eXpgwYYIUl0pEREREJBm1Wo2cnBypw6AGRKlUwsfHxyLvJVlC6uDgAAcHB9Prw4cP4+LFi9i4cSNOnTqFP/74A4mJiVCpVFixYgU8PT2xY8cODB06FN999x1UKhUWLFgAmUyGZcuWISIiAp988gm8vb2xcuVKjB49GsOGDQMAzJw5EytXrmRCSkRERERNilqtxvMvvIiiQr3UoVADYmsnx7ffrLdIUipZQnq3OXPmYN68eVAoFDhw4ABCQkKgUqkAAHK5HD179sT+/fsxdOhQHDhwAP369YNMJgMAhIWFwc7ODkePHsVTTz2FAwcO4OuvvzadOzIyEnPnzkVWVhbc3NwkuT4iIiIiIkvLyclBUaEe+a0iYLRXSh1OvWKVnw2HG4eQ37I3jA6uUodTb1gV5ADXDyInJ6fpJKRXrlzBqVOn8N///hdAyTc5vr6+ZnVUKhXUarWpPCwszFRmY2MDHx8fqNVq5OXlQavVmh1fmtiq1epyE1K9Xg+9/u9vjXJzc2vv4oiIiIiIJGa0V8Lo5Cl1GPWS0cGV90ZC9WKW3e+++w4DBw6EUlnyrU1WVhacnZ3N6jg7OyMzM7PS8uzsbNPrO8sAmI6/2/vvvw+lUmnaAgICauW6iIiIiIiIqGL1IiH9/fff0bVrV9Nrd3d3aDQaszq5ubnw8PCotNzd3R0AzMpLezxLj7/bnDlzkJOTY9ri4+Pv/6KIiIiIiIjoniRPSIUQOH36NEJCQkz7fH19kZycbFYvKSnJNAz37vLi4mKkpqbC19cXDg4OcHFxMStPSkoCgArHQMvlcri4uJhtREREREREVLckT0g1Gg0yMjJMz3kCQN++fXHp0iUkJiYCAAoKCnD06FFERkaayvfu3WtaW/TEiRMoKipCeHi4WXmpffv2oWvXrnB1dbXQVREREREREVFlJE9ItVotAJgtARMaGooePXpg4sSJOHv2LKZMmQIvLy/TuqXPPfcc1Go15s+fj5iYGEybNg0jR440DcmdNGkS1q1bh82bN2Pfvn3497//jcmTJ1v+4oiIiIiIiKhC9TIhBYCtW7fCYDCgd+/eiIuLw549e2BrawsA8Pb2xq5du7Bz505ERkaiTZs2+Pzzz03HRkVFYfny5Zg1axZeeuklzJgxA2PGjLHcRREREREREVGlJF/2pU2bNqaht3fy9vbGjh07KjyuR48eOHnyZIXl48aNw7hx42olRiIiIiIiIqp9kveQEhERERERUdPEhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJCF5Qnr8+HH07NkTLi4ueOihh7B//34AQF5eHkaNGgUPDw+EhYXh+PHjZsfFxsaid+/ecHNzw6BBg5CammpWvnXrVnTq1Ak+Pj54/fXXUVxcbLFrIiIiIiIiospJmpCePn0akZGRGDx4MP744w+MHz8eBw8eBACMGTMGV69exd69exEVFYWoqChT0qnX69GvXz+0bdsWBw8ehEwmw5AhQ0znPXPmDEaMGIHJkyfjl19+wc8//4y33npLkmskIiIiIiKi8tlI+eaLFy/Gc889hzfffBMA0K5dOwBASkoKNm3ahMOHD6NLly4IDQ3FDz/8gOjoaEydOhXbt29HTk4Oli9fDrlcjlWrVsHf3x8xMTEIDQ3Fl19+ib59+2LixIkAgEWLFuH111/HggULYGdnJ9n1EhERERER0d8k6yE1GAzYunUrnnvuuTJlR48ehYODA8LCwgAAMpkMkZGRpuG8Bw4cQK9evSCXywEAzZo1Q9u2bc3K+/fvbzpfZGQk0tPTceHChbq+LCIiIiIiIqoiyRLS5ORkFBcXQyaT4cknn0SzZs0wfPhwqNVqqNVqeHt7w9ra2lRfpVJBrVYDANRqNXx9fc3Od69yb29vWFlZmcrvptfrkZuba7YRERERERFR3ZIsIU1ISAAAvP7663jhhRfwww8/IDY2FhMmTEBWVhacnZ3N6js7OyMzMxMAql1uZWUFhUJhKr/b+++/D6VSadoCAgJq7TqJiIiIiIiofJIlpKUJ44oVKzBixAiEh4djyZIl2L59O1xcXKDRaMzq5+bmwsPDAwDg7u5erXKj0QiNRmMqv9ucOXOQk5Nj2uLj42vtOomIiIiIiKh8kk1qVNoLaW9vb9rXokULGAwGeHt7Q61Ww2AwmIbtJiUlmYbh+vr6Ii4uzux8d5cnJyebytRqNYQQZYb5lpLL5abnUYmIiIiIiMgyJOshdXFxQbdu3UzLvAAla4sqFApERkZCr9fjxIkTAAAhBPbt24fIyEgAQN++fXH48GHo9XoAJcN/Y2Njzcr37t1rOu++ffvg7e2NkJAQS10eERERERERVULSZV/efPNNjB8/Hu3atYOfnx/+9a9/YcKECfDy8sLw4cMxbdo0fP7559i0aRPS0tLw7LPPAgAef/xxuLu7Y8qUKXj11Vfxr3/9C+Hh4ejYsSMAYOzYsXjwwQexatUqdO3aFfPmzcP48eNha2sr5eUSERERERHRHSRNSJ955hnk5uZi5syZSE1NxbPPPov33nsPAPDFF19g3LhxiIyMRFBQEHbv3g1PT08AgJ2dHfbu3YuXX34ZvXv3Rnh4ODZv3mw6b4cOHbBx40bMnTsXarUazz77LN555x0pLpGIiIiIiIgqIGlCCgBjxozBmDFjyux3cnJCdHR0hccFBwfj0KFDFZYPHjwYgwcPrpUYiYiIiIiIqPZJ9gwpERERERERNW1MSImIiIiIiEgSTEiJiIiIiIhIEkxIiYiIiIiISBJMSImIiIiIiEgSTEiJiIiIiIhIEkxIiYiIiIiISBJMSImIiIiIiEgSTEiJiIiIiIhIEkxIiYiIiIiISBJMSImIiIiIiEgSTEiJiIio3ikoKEBsbCwKCgqkDoWIqGkxFgMA9Hq9Rd6OCSkRERHVO7dv38b48eNx+/ZtqUMhImpSrPRaAEBKSopl3s8i70JERERERER0FyakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAlJE9LRo0dDJpOZbe+88w4AIC8vD6NGjYKHhwfCwsJw/Phxs2NjY2PRu3dvuLm5YdCgQUhNTTUr37p1Kzp16gQfHx+8/vrrKC4uttRlERERERERURVI3kM6cuRIJCcnm7YZM2YAAMaMGYOrV69i7969iIqKQlRUlCnp1Ov16NevH9q2bYuDBw9CJpNhyJAhpnOeOXMGI0aMwOTJk/HLL7/g559/xltvvSXJ9REREREREVH5JE9IAwIC4Ovra9oUCgVSUlKwadMmLFu2DF26dMG7774LHx8fREdHAwC2b9+OnJwcLF++HJ06dcKqVatw/PhxxMTEAAC+/PJL9O3bFxMnTkRYWBgWLVqEL7/8EoWFhRJeKREREREREd1J8oTU09OzzL6jR4/CwcEBYWFhAACZTIbIyEjs378fAHDgwAH06tULcrkcANCsWTO0bdvWrLx///6m80VGRiI9PR0XLlyo68shIiIiIiKiKpI8Id2+fTs6dOiA4OBgvPXWWygsLIRarYa3tzesra1N9VQqFdRqNQBArVbD19fX7Dz3Kvf29oaVlZWp/G56vR65ublmGxEREREREdUtGynf/NFHH0WXLl3Qu3dvnDp1ClOnToW1tTVsbW3h7OxsVtfZ2RmZmZkAgKysLKhUqnuW33m8lZUVFAqFqfxu77//PhYsWFCbl0ZERERERESVkDQhHTVqlOnnLl264Pbt24iOjsa0adOg0WjM6ubm5sLDwwMA4O7uXm55SEhIueVGoxEajcZ0/N3mzJmDN954w+xcAQEB93dxREREREREdE+SJqR3CwkJQWJiInx9faFWq2EwGEzDdpOSkkzDcH19fREXF2d27N3lycnJpjK1Wg0hRJlhvqXkcrnpeVQiIiIiIiKyDMmeIS0uLkZeXp7ZvpiYGLRr1w7h4eHQ6/U4ceIEAEAIgX379iEyMhIA0LdvXxw+fBh6vR4AkJCQgNjYWLPyvXv3ms67b98+eHt7m3pQiYiIiIiISHqSJaTR0dF46KGH8MMPPyA2NhZr1qzBJ598glmzZsHLywvDhw/HtGnTEBMTg7fffhtpaWl49tlnAQCPP/443N3dMWXKFJw9exYTJ05EeHg4OnbsCAAYO3YsDh48iFWrVuGPP/7AvHnzMH78eNja2kp1uURERERERHQXyYbsvvDCC8jLy8PKlStx6tQpNGvWDCtXrsSIESMAAF988QXGjRuHyMhIBAUFYffu3aYlYuzs7LB37168/PLL6N27N8LDw7F582bTuTt06ICNGzdi7ty5UKvVePbZZ/HOO+9IcZlERERERERUAckSUplMhkmTJmHSpEnlljs5OSE6OrrC44ODg3Ho0KEKywcPHozBgwffd5xERERERERUNyRfh5SIiIiIiIiaJiakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJIlaT0hTUlJq+5RERERERETUCNUoIbW2tkZqamqZ/RcvXkSvXr3uOygiIiIiIiJq/GqUkAohIJPJyuw/efIk0tPT7zsoIiIiIiIiavxsqlPZy8sLMpkMMpkMDzzwAKys/s5nCwoKkJeXh8mTJ9d6kERERERERNT4VCsh3bVrF4QQCAsLw9tvvw2lUvn3iWxs0Lp1azz00EO1HiQRERERERE1PtVKSB988EEAwPz58zF27Fg4OjrWSVBERERERETU+FUrIS01f/782o6DiIiIiIiImpgaJaTXr1/Hv/71L5w6dQrZ2dllysubgZeIiIiIiIjoTjVKSJ977jlotVoMHz4crVu3NpvciIiIiIiIiKgqapSQxsbG4siRIwgJCanteIiIiIiIiKiJqFHXZp8+fXD16tVaC6KwsBBt27ZFixYtTPvUajWeeOIJuLq6IiIiAnFxcWbHHDt2DGFhYfDw8MDzzz+PvLw8s/LVq1cjODgYAQEBeO+99yCEqLV4iYiIiIiI6P7VKCFdunQp5s6diz/++AMXL14ss1XXihUrkJSUZHothMDgwYNhbW2NQ4cOITg4GP3790dhYSEAICUlBQMGDMCAAQOwd+9exMbGYuzYsabjd+zYgVdffRVLlizBunXrsHTpUqxevboml0pERERERER1pEZDdh944AEUFhbi4YcfNu2TyWQQQkAmk8FgMFT5XGlpaViwYAGmTJmC77//HgBw6tQp/PHHH0hMTIRKpcKKFSvg6emJHTt2YOjQofjuu++gUqmwYMECyGQyLFu2DBEREfjkk0/g7e2NlStXYvTo0Rg2bBgAYObMmVi5ciUmTJhQk8slIiIiIiKiOlCjHtIrV67gxo0bZtv169dNf1bH22+/jS5duuCxxx4z7Ttw4ABCQkKgUqkAAHK5HD179sT+/ftN5f369YNMJgMAhIWFwc7ODkePHjWV9+/f33S+yMhInDlzBllZWTW5XCIiIiIiIqoDNeohbd68ea28+blz57B27VqcOnUKarXatF+tVsPX19esrkqlMtVRq9UICwszldnY2MDHxwdqtRp5eXnQarVmx5cmtmq1Gm5ubmXi0Ov10Ov1pte5ubm1cn1ERERERERUsRolpOvXr79n+YsvvljpOYQQmDp1KqZPn44HHnjALCHNysqCs7OzWX1nZ2ckJCTcszwzM9O0Luqd5aU/Z2ZmlhvL+++/jwULFlQaMxEREREREdWeGiWk06dPL7MvPz8fDg4OCA0NrVJCum3bNty4cQO//PJLmTJ3d/cyQ39zc3Ph4eFhKtdoNOWWu7u7A4BZeWmPZ+nxd5szZw7eeOMNs/oBAQGVXgMRERERERHVXI0S0rS0tDL7MjIyMHToULz77rtVOkfpzLqBgYEAgKKiImg0Gnh6emLatGlITk42q5+UlGRa99TX19esvLi4GKmpqfD19YWDgwNcXFzMyktn8PXx8Sk3FrlcDrlcXqW4iYiI6G91/djLrVu3avV8RE0N/w1RfVejhLQ8Hh4eePvttzFr1iwcPny40vrR0dFmDdiPP/6Ijz/+GMePH0diYiLmzZuHxMRENGvWDAUFBTh69CimTJkCAOjbty8+++wz06y+J06cQFFREcLDw03le/fuxYgRIwAA+/btQ9euXeHq6lpbl0tERESo+8deFi1aVGfnJiIi6dVaQgoABQUFOHfuXJXqenl5mb12d3eHjY0N/P394e/vjx49emDixIlYtGgRPv30U3h5eWHAgAEAgOeeew7z58/H/Pnz8fTTT2PatGkYOXKkaUjupEmTMHjwYAwYMACurq7497//jX//+9+1ealERESEun/sZe7cubU2mSJRU3Tr1i1+sUP1Wo0S0tKex1JGoxHXrl3DhQsXypTV1NatWzF69Gj07t0bnTt3xp49e2BrawsA8Pb2xq5du/Daa69h+fLlePzxx/H555+bjo2KisLy5csxa9Ys6PV6zJgxA2PGjKmVuIiIiOhvdf3YS/PmzdGmTZs6Oz8REUmrRgmpk5NTmX29evXCa6+9hueee65GgYwePRqjR482vfb29saOHTsqrN+jRw+cPHmywvJx48Zh3LhxNYqFiIiIiIiI6l6NEtI1a9bUdhxERERERETUxNzXM6QnTpxATEwMjEYjunbtioceeqi24iIiIiIiIqJGrkYJaV5eHoYPH47du3ejRYsWAICbN28iKioKGzduLHdILxEREREREdGdrGpy0JtvvomMjAxcvXoV165dw7Vr13D16lVkZmbizTffrO0YiYiIiIiIqBGqUUK6detW/N///R9atmxp2teyZUt88skn2LJlS60FR0RERERERI1XjRJSIQRkMlnZk1nV6HRERERERETUBNUogxwyZAhee+013L5927Tv9u3bmDp1KoYMGVJrwREREREREVHjVaOE9MMPP4SLiwuCgoIQHByM4OBgBAUFwcnJCR9++GFtx0hERERERESNUI1m2VUoFNi9ezeOHj2KM2fOQAiB0NBQtGvXDgqForZjJCIiIiIiokaoyj2kMTEx6N+/P4xGo2lfz549MXnyZLzyyisICgpCmzZtcObMmToJlIiIiIiIiBqXKiekb731Fvr06VPhxEW+vr6YNm0a5s6dW2vBERERERERUeNV5YT02LFjePrpp+9ZZ+jQoThx4sR9B0VERERERESNX5UTUh8fH6SkpNyzTlpaGtzc3O47KCIiIiIiImr8qpyQ9u/fH++99x4MBkO55cXFxViyZAn69u1ba8ERERERERFR41XlhHTx4sVISkpCt27dEB0djcuXLyMzMxOXLl3Cd999h7CwMFy/fp3LvhAREREREVGVVHnZF4VCgRMnTmDx4sWYOHEitFotZDIZhBBwdHTEhAkT8K9//QtKpbIu4yUiIiIiIqJGolrrkCqVSnzwwQf44IMPkJSUhISEBDRr1gwqlQoymayuYiQiiWi1Wuh0uirXd3R05FrERERERFRl1UpI76RSqaBSqWozFiKqR7RaLZq3aInMjPQqH+Pu4YlbN28wKSUiIiKiKqlxQkpEjZtOp0NmRjpmrd4OJ6V7pfXzcjLx4fhB0Ol0TEiJiIiIqEqYkBLRPTkp3eHs5iF1GERERETUCFV5ll0iIiIiIiKi2sSElIiIiIiIiCTBhJSIiIiIiIgkwYSUiIiIiIiIJMGElIiIiIiIiCTBhJSIiBolmZ2j1CEQERFRJZiQEhFRo2I0Cvwenwe/Fz9GbkGx1OEQERHRPUiakO7Zswd9+vSBs7MzHnjgAXz33XemMrVajSeeeAKurq6IiIhAXFyc2bHHjh1DWFgYPDw88PzzzyMvL8+sfPXq1QgODkZAQADee+89CCEsck1ERCQdfZEBW88k4lKaHrYe/jh+M0fqkIiIiOgeJEtIs7KyMHr0aIwYMQJ//vknJk2ahBdeeAEnTpyAEAKDBw+GtbU1Dh06hODgYPTv3x+FhYUAgJSUFAwYMAADBgzA3r17ERsbi7Fjx5rOvWPHDrz66qtYsmQJ1q1bh6VLl2L16tVSXSoREVnIgdg0xGfmw8YKSN38Hh5r5yF1SERERHQPNlK9sZubG65evQoHBwcAQNu2bfHFF19gz549sLGxwR9//IHExESoVCqsWLECnp6e2LFjB4YOHYrvvvsOKpUKCxYsgEwmw7JlyxAREYFPPvkE3t7eWLlyJUaPHo1hw4YBAGbOnImVK1diwoQJUl0uERHVMV1hMWLVGgDAY62dsTLuuMQRERERUWUkHbJbmowCgNFohFarhUKhwIEDBxASEgKVSgUAkMvl6NmzJ/bv3w8AOHDgAPr16weZTAYACAsLg52dHY4ePWoq79+/v+nckZGROHPmDLKysix1aUREZGEXk3NhFICPixy+ClupwyEiIqIqkHxSIyEEkpOT8cYbbyA/Px/PPfcc1Go1fH19zeqpVCqo1WoAKFNuY2MDHx8fqNVq5OXlQavVmpWXJralx99Nr9cjNzfXbCMiooZDCIHziSWf3R2bKSWOhoiIiKpK8oR0+vTpUKlU+Oqrr7Bx40Z4e3sjKysLzs7OZvWcnZ2RmZkJAPcsz87ONr2+swyA6fi7vf/++1AqlaYtICCgti6PiIgs4HamDjn5RbCzsUIbH+fKDyAiIqJ6QfKEdObMmThw4ABeffVVPP744/jtt9/g7u4OjUZjVi83NxceHiWTU9yr3N3dHQDMykt7PEuPv9ucOXOQk5Nj2uLj42vt+oiIqO7dSC+Zab2NtwK21pI3bURERFRFkk1qVMrPzw9+fn6IiIiARqPBu+++i6FDhyI5OdmsXlJSEkJCQgAAvr6+ZuXFxcVITU2Fr68vHBwc4OLiYlaelJQEAPDx8Sk3BrlcDrlcXtuXRkREFpKQlQ8ACHR3lDgSIiIiqg7JvkYuKiqCTqcz2+fq6gqdToe+ffvi0qVLSExMBAAUFBTg6NGjiIyMBAD07dsXe/fuNa0teuLECRQVFSE8PNysvNS+ffvQtWtXuLq6WuDKiIjIknSFxcjIK1kWrJmbQyW1iYiIqD6RLCH99ttv8dBDD+HHH39EXFwcNm3ahOXLl2P48OEIDQ1Fjx49MHHiRJw9exZTpkyBl5cXBgwYAACmiY/mz5+PmJgYTJs2DSNHjjQNyZ00aRLWrVuHzZs3Y9++ffj3v/+NyZMnS3WpRERUh0p7Rz0UdnC0k3zgDxEREVWDZC336NGjodVqsXz5cpw+fRre3t548803MWPGDADA1q1bMXr0aPTu3RudO3fGnj17YGtbMo2/t7c3du3ahddeew3Lly/H448/js8//9x07qioKCxfvhyzZs2CXq/HjBkzMGbMGEmuk4iI6lZpQhrgxuG6REREDY1kCalMJsOrr76KV199tdxyb29v7Nixo8Lje/TogZMnT1ZYPm7cOIwbN+6+4yQiovotPqvk8Q9/DtclIiJqcDgVIRERNVhafTGydUWQAfB3ZUJKRETU0DAhJSKiBislpwBAyfOjcltriaMhIiKi6mJCSkREDVaaRg8A8Ha2lzgSIiIiqglOR0hE1MBptdoyy2hVxNHREQqFoo4jspxUTUkPqbcz15ImIiJqiJiQEhE1YFqtFs1btERmRnqV6rt7eOLWzRuNIikVQiC1tIfUhQkpERFRQ8SElIioAdPpdMjMSMes1dvhpHS/Z928nEx8OH4QdDpdo0hI8woN0BUaIAPgqWBCSkRE1BAxISUiagSclO5wdvOQOgyLSs0tGa7r7mQHW2tOiUBERNQQsQUnIqIGqXS4rhefHyUiImqwmJASEVGD9PcMu0xIiYiIGiompERE1CClcskXIiKiBo8JKRERNTgFRQZo9cUAAE9nO4mjISIiopripEZERHWsOuuEAo1vrdC6kKEtBAA429tAbmMtcTRERERUU0xIiYjqUHXXCQUa11qhdSUjr2S4rrsTe0eJiIgaMiakRER1qDrrhAKNb63QupKZV9JD6unECY2IiIgaMiakREQW0BTXCa1LpUN23RXsISUiImrIOKkRERE1OBl/9ZB6cMguERFRg8aElIiIGhRdYTHyiwwA+AwpERFRQ8eElIiIGpTS4bpKB1vYWrMZIyIiasjYkhMRUYPC4bpERESNBxNSIiJqUEqXfPHghEaNWmBgIFavXo3AwECpQyEialKM8pJZ/n19fS3yfpxll4iIGpTM0hl22UPaqNnb26NNmzZSh0FE1PRYlaSIcrllllZjDykRETUoWboiAExIiYiIGgMmpERE1GAUFBtNM+y6OTIhJSIiauiYkBIRUYORXVCSjDrb23CGXSIiokaArTkRETUYOX8lpO7sHSUiImoUmJASEVGDkV1gBAC48flRIiKiRoEJKRERNRjsISUiImpcuOwLETUIWq0WOp2uyvUdHR2hUCjqMCKSgikhZQ8pERFRoyBpD+n+/fsRFRUFpVKJzp07Y+fOnaayvLw8jBo1Ch4eHggLC8Px48fNjo2NjUXv3r3h5uaGQYMGITU11ax869at6NSpE3x8fPD666+juLjYItdERLVPq9WieYuW8PHxqfLWvEVLaLVaqUOnWiSzsYOmsHTIrq3E0RAREVFtkKyH9MyZM3jmmWewZMkSfPLJJ/jmm28wdOhQXLx4EUFBQRgzZgxu3ryJvXv3YvPmzYiKikJcXBy8vb2h1+vRr18/DBgwAMuXL8fcuXMxZMgQ/P7776ZzjxgxAp9++im6du2KkSNHwtHREe+//75Ul0tE90Gn0yEzIx2zVm+Hk9K90vp5OZn4cPwg6HQ69pI2IjZuKgCA3MYKDrbWEkdDREREtUGyhLRTp074888/0bJlSwDAe++9h2+//Rbbt2/HyJEjsWnTJhw+fBhdunRBaGgofvjhB0RHR2Pq1KnYvn07cnJysHz5csjlcqxatQr+/v6IiYlBaGgovvzyS/Tt2xcTJ04EACxatAivv/46FixYADs7DvMiaqiclO5wdvOQOgySiK1HAICS4boymUziaIiIiKg2SDZkVyaTmZLR0tdubm7Izc3F0aNH4eDggLCwMFNZZGQk9u/fDwA4cOAAevXqBblcDgBo1qwZ2rZta1bev39/07kjIyORnp6OCxculBuLXq9Hbm6u2UZERPWLrYc/AMCNExoRERE1GvVmlt38/HxcunQJHTt2hFqthre3N6yt/x6SpVKpoFarAQBqtRq+vr5mx9+r3NvbG1ZWVqbyu73//vtQKpWmLSAgoLYvj4iI7tOdPaRERETUONSbhHTlypXw8PDAgAEDkJWVBWdnZ7NyZ2dnZGZmAkC1y62srKBQKEzld5szZw5ycnJMW3x8fG1eGhER1YLShJQTGhERETUe9WLZl8TERCxevBjvvfce7O3t4e7uDo1GY1YnNzcXHh4lz45VVB4SElJuudFohEajMR1/N7lcbhr+S0RE9Y/BKEyTGnENUiIiosZD8h7SwsJCDB8+HD169MCECRMAAL6+vlCr1TAYDKZ6SUlJpmG4vr6+SE5ONjvPvcrVajWEEGWG+RIRUcOQkquHla0c1jLAxYE9pERERI2FpAmpwWDAmDFjkJ2djXXr1plmTQwPD4der8eJEycAAEII7Nu3D5GRkQCAvn374vDhw9Dr9QCAhIQExMbGmpXv3bvX9D779u2Dt7e3qQeViIgalhuZBQAAF3trWHGGXSIiokZDsoS0NBk9ePAgfvrpJxQWFiIlJQUpKSnw8vLC8OHDMW3aNMTExODtt99GWloann32WQDA448/Dnd3d0yZMgVnz57FxIkTER4ejo4dOwIAxo4di4MHD2LVqlX4448/MG/ePIwfPx62tvxWnYioIbqZmQ8AcJVz/VEiIqLGRLKE9Mcff8T69euRkJCA9u3bw8/Pz7QBwBdffIGgoCBERkZi165d2L17Nzw9PQEAdnZ22Lt3L65cuYLevXsDADZv3mw6d4cOHbBx40asWLECgwYNwuDBg/HOO+9Y/BqJiKh23Pyrh1RpL/mTJkRERFSLJJvU6NlnnzX1eJbHyckJ0dHRFZYHBwfj0KFDFZYPHjwYgwcPvq8YiYiofihNSF3t2UNKRETUmNSLWXaJiMicwShwKTkXWn0x5DZWaK9SwtqqaT47KYTAzYySIbtKJqRERESNChNSIqJ6pNBghGvvFzHkyzNI1RaZ9nsq5Hi6azNM7R8MR7um9dGdri1Ert4AIYxMSImIiBoZPoxDRFRP3MzIw5aLuVD2GIFUbREUchu09HSCs70N0rV6rD50HU98egTnEnKkDtWirqVpAQDF2WrYNNFeYiIiosaqaX3NTkRUT52Jz8aB2DQAQFFmEpa+2AtPPRwMuY01CouN2H8lFe/8fAE30vPw7BfHsWH8w+jQTClx1JZxNbUkIS3KiAfQXtpgiIiIqFaxh5SISGJ/3Mg0JaNtPeVIXvMqItu4Q25TMjzVzsYKUe19sev13niopTu0+mKMXvMHbqbnSRm2xZgS0swEiSMhIiKi2saElIhIQucScvD79QwAwEMt3fFIgCNEsb7cukpHW3z5Uje0V7kgXVuIV6JPodhgtGS4kjAN2c2IlzgSIiIiqm0csktEVE1arRY6na5KddPS0iosu5Geh/1XUgEAYS3d8XArD2iyMu55Pmd7W6wZ3R2PLTuEC0m5+PpEctUDb6CumQ3ZJSIiosaECSkRUTVotVo0b9ESmRnp1TquqKjQ7HW2rhC7LqRAAAjxc8HDLd2rfC5vF3ssHNIBr35/GmtOJMHWu1W1YmlI8vTFSMopWYO0KINDdomIiBobJqRERNWg0+mQmZGOWau3w0lZeRKZGn8dK2Y8j6KiYtO+IoMR/z2XjMJiI/yU9ohs5w2ZrHqzxz7ZWYWd55Ox41wK3CPHQghR7WtpCEqH67o72uBWgVbiaIiIiKi2MSElIqoBJ6U7nN08Kq2nzckss+/o1XSkawvhYGuNxzv4wbqGS5n86/EHsPeiGmjeCQm5RQipeidrg1E6oVELdwecljgWIiIiqn2c1IiIyIJuZ+pw5q91RKPa+0BhX/PvBf3dHDGiiw8A4GRiPoyNsJe0tIe0hbu9xJEQERFRXWBCSkRkIfoiA/ZcVAMAOjVTormH032fc3SYHwz5GmQXGEy9iY3J3z2kTEiJiIgaIyakREQWcjA2DVp9MZQOtggP9qyVc7rY20Dzv58BAH/eymp0z5LeOWSXiIiIGh8mpERURpHBiLg0Heybd0ZeYeNf59ISEjQGXErRQIaSobq21rX38av533bYWAFpGj1uZ1ZtOZqGoMhgxK2Mkutp6cEeUiIiosaICSkRmYlVa/Doxwcx6psL8PnHImy8kI0T1zNQbGRiWlNWjkqcTCkCADzY3A1+ytrt7TMWaNDWUw6gpJe0sbielodio4BCbgNvhZ3U4RAREVEdYEJKRCZH4tLx1IqjuJmhg5OdFYqzU2AUwPEbmdh+NrlRTppT14QAPKKmQG8APBV2eKhV3UyF297bHjIZkJCVj3Stvk7ew9Iup+QCANr6Old7WRwiIiJqGJiQEhEAIF2rx2sbTiOv0IAerTyw5eVOSPx8LCJaOMHGSoZbGTqcuF52CRO6t1sawLFND1gBeCzEFzZWdfOxq7CzRpCXAgBwJiG7Tt7D0i6naAAA7XydJY6EiIiI6goTUiICALy97Twy8wrRztcZa8d0h6uDLQAgyF2Ofu28AQB/3MzErYw8KcNsUHILinA6o+TnDl428HKW1+n7dfZXAgAuJ2ugLzLU6XtZwhUmpERERI0eE1Iiwq8XUrDjXApsrGT49/DOkNtYm5W383NBx2Ylyc7B2DQYjBy6WxkhBPZcVKPICBQkXEQ7d+vKD7pPzVwd4OFkh2KjwMXk3Dp/v7p2+a9raOfnInEkREREVFeYkBI1cUII/GdvHABgXO9W6PBX4nm3nq094GBrjSxdES4k5VgyxAYpJj4bCVn5sJYBGf/9D6ws8AykTCZDp796Sc8l5jToJWBy8ouQlFMAAGjjwx5SIiKixooJKVETt+9yKi4l58LJzhrje7WqsJ7cxhoPtSyZkOf49Uzoixv+kNC6kqbR4+jVkrG6nT2A4uxki713W19n2FjJkKUrQvJfCV1DVDpcV6W0h/Kv4eNERETU+DAhJWrChBD4v31XAQDP92gON6d7L63RoZkSro62yC8y4Hxiwx8SWheKDUbsvpACgxBo6emEIAuPNpXbWJt6FM834J7sKykcrktERNQUMCElasJO3sxCTHw25DZWGBtece9oKWsrGbo3L+klPX07i2uTluPI1XRk5BXC0c4a/R/whhSrlbRXlSRxcWptg+3JLp1hty0nNCIiImrUmJASNWHRJ24BAJ7q0qzKM8C29XWGQm6DvEKDaVgllbiRnoczCSW9ko+G+MDRzkaSOPyU9nB3LJncKDZFK0kM94tLvhARETUNTEiJmqisvELsOJ8CAHjuocAqH2dtJUOXAFcAwP9uZTXoiXNqk66wGHsuqgEAoQGuaOHhJFksMpkM7ZuV9JI2xGG7BqPAxaSSIbulvb1ERETUODEhJWqiNp1KQGGxEe1Vfy/pUlXtm7nAztoKWboi3MrU1VGEDUfpEi/5RQZ4ONmhZ5CH1CGhna8zrGRAqkaPNI1e6nCq5VqaFvlFBjjZWaOlp0LqcIiIiKgOMSElaoKEENhwMh5ASe+orJoPOsptrE09VzHx2bUdXoNzNjEHNzN0sLaSYUAHX9hYS//R6mhngyCvkmSuoS3Tc/avYc/tVUpYW0nwEC4RERFZjKT/a0pLS8O8efMQGBiIbt26mZXl5eVh1KhR8PDwQFhYGI4fP25WHhsbi969e8PNzQ2DBg1CamqqWfnWrVvRqVMn+Pj44PXXX0dxcXGdXw9RQ3EuMQdXU7Wwt7XC4M6qGp2jdL3LWxk6ZOkKazO8BiVDq8fhuHQAQHhrT3gqqvYsriWUfmlwOUWDYkPDmYDqfGJJQtrRv3o990RERNTwSJqQxsfH4+rVq3BxKfuM0JgxY3D16lXs3bsXUVFRiIqKMiWder0e/fr1Q9u2bXHw4EHIZDIMGTLEdOyZM2cwYsQITJ48Gb/88gt+/vlnvPXWWxa7LqL6bltMEgDg0RBfONvXbI1HV0c7tPQseU7yTBPtJS02GrHrQgoMRoHmHo7oXM8SqEB3Rzjb20BfbMTVtIYzudHZhGwAqPZQciIiImp4JE1Iu3btig0bNuCZZ54x25+SkoJNmzZh2bJl6NKlC9599134+PggOjoaALB9+3bk5ORg+fLl6NSpE1atWoXjx48jJiYGAPDll1+ib9++mDhxIsLCwrBo0SJ8+eWXKCxsur04RKUMRoFfzpQkpENq2DtaqjQBu5ici0JD05vc6Ni1DKRrC+Fga41HH/Cp9tDnuiaTydD+r3U8LzSQdWOLDUZcTC6JlT2kREREjZ/0DzqV4+jRo3BwcEBYWBiAkv9URUZGYv/+/QCAAwcOoFevXpDLS4bGNWvWDG3btjUr79+/v+l8kZGRSE9Px4ULFyx8JUT1z/HrGUjV6OHqaIvebbzu61yB7o5wd7RDkUEgLqNhTZxzvxJzi3D6djYAoH+IN5zk0izxUpmQv4btJmTnI6eg/q9JejVNi4IiIxRyG7SUcKZiIiIisox6mZCq1Wp4e3vD2tratE+lUkGtVpvKfX19zY65V7m3tzesrKxM5XfT6/XIzc0124gaq62nEwEAj3f0g53N/X0EyGQydA4o6cW6lFYAoH71ENYVKwcXHLpVMgS2UzMlWtXjmWCd7W3R3MMRABrElwZ/T2jkAitOaERERNTo1cuENCsrC87O5ouhOzs7IzMzs0blVlZWUCgUpvK7vf/++1AqlaYtICCgNi+HqN4oKDJg119rj97vcN1S7XxdYGdjhVy9EfatHqyVc9ZnQgi4PzYJ+UUC7o52CA/2lDqkSnVQlXxpEJehB6ysK6ktLT4/SkRE1LTUy4TU3d0dGo3GbF9ubi48PDxqVG40GqHRaEzld5szZw5ycnJMW3x8fG1eDlG9sf9yKjT6YqiU9ujewr1WzmlnY2WazdWl+1O1cs767NcrmXBq1wsyAFHtfWBbD5Z4qUxLTyc42Fojv1jAoVW3yg+Q0MkbWQCAbi3cJI6EiIiILKFePvTk6+sLtVoNg8FgGrablJRkGobr6+uLuLg4s2PuLk9OTjaVqdVqCCHKDPMtJZfLTc+jEjVmpbPrPhmqqtXhkKEBroi5nQ2HFp1xIVkLb2/v+z5nfqEB3x6/hX2XU3E2IQuqCV/i16sadA9yQKC7Yy1EXX3q3AJ89NstAEConwO8XewliaO6rK1kCPFzwf9uZ0HROUrqcCqUrSvEFXXJl4ndaukLEyIiIqrf6uVX++Hh4dDr9Thx4gSAkiFy+/btQ2RkJACgb9++OHz4MPT6kuehEhISEBsba1a+d+9e0/n27dsHb29vhISEWPhKiOqPnPwi7LtcsnTS0NBmtXpuF3tbBLnbAQDWnUyupHbl9l9JxaP/OYhFOy7h9+sZyCs0wtbVFwm5RdhyOhFbTyciv8iyE/QIITDrp7PI1RugT45DZ9+GkYyWKu3Fdmj1IFI19XPG8ZM3S3pHg7yc6tV6rkRERFR3JE1IMzMzkZKSAq1Wi6KiIqSkpCAtLQ1eXl4YPnw4pk2bhpiYGLz99ttIS0vDs88+CwB4/PHH4e7ujilTpuDs2bOYOHEiwsPD0bFjRwDA2LFjcfDgQaxatQp//PEH5s2bh/Hjx8PWtmbrLRI1BrvPp6DQYEQbHwXa+TpXfkA1dfJxAAAcuJqNOLWmktrlE0Jg1cFr+Oeak0jIykczVwe882QIvnuhPVK+nYUQLzmsZTLcytRh45/xyM0vqs1LuKcNJ+NxMDYNdtYyZPz3Y1jVsyVeKuPmZAcfhQ1kVtbYfjFd6nDK9ceNDABAWEv2jhIRETUVkiakTz/9NPz8/LB06VKcPXsWfn5+6N69OwDgiy++QFBQECIjI7Fr1y7s3r0bnp4lk4fY2dlh7969uHLlCnr37g0A2Lx5s+m8HTp0wMaNG7FixQoMGjQIgwcPxjvvvGPx6yOqTzafTgAADAltVifrZbo6WCPvylEAwAe7Llf7eCEEFvxyEUt2lhz7/MOB2PNGb4zu2RLBXo7QJ17EwwFOeDYsAAq5DbJ0RfjpVALy9MW1eh3lic/U4b3tFwEAk8L9UZTRMJ8zb+NR0uu49Wwaig1GiaMp64+/ekhr6/lmIiIiqv8kfYb0wIEDFZY5OTkhOjq6wvLg4GAcOnSowvLBgwdj8ODB9xMeUaORmJ2P49dLZpke2qV2h+veKfvQerg8EI69l1Jx7Go6HmldtRlohRB45+cLWPf7LchkwFtPhGBMeMty63oo5BjZLQCbTiUgO78Iv5xNwjNd/WFTR5MLGY0C0zeeQV6hAWEt3fFsVx+8USfvVPdautnhwKUkpECJPRfVGNjRT+qQTPL0xTifWLLkC3tIiYiImo56OakREdWu0rVHH27ljmauDnX2PsWZiRjWyQs/xqRi4X8v4ZcpPStNFIUQeHf7RVMy+sGwThjR7d5LLynsbTA4VIUfT8ZDnavH3supiArxqZOe36+P3sAfNzLhaGeNpcM7w6pYW+vvYSk2VjJoY3ZC+cg/sObozXqVkJ66nQWDUUCltIe/mzSTVhERNWZWBTlSh1DvWOVnm/1JJSz9u8KElKiRE0Jgy18J6dNd/Ov8/cb2aIZdlzNxKTkXH++JxawB7e4Z26L/XsKaozcBAEue7lhpMlrKzdEOT3Tyw+bTibiSooG/m4Npvc3aEqfW4MPdVwAA854IQYC7I1JTG25CCgCa0zvgHv4s/riZifOJOehQT9b7PHglDQDwcFD5y3MREVHNKJVK2NrJgesHpQ6l3nK4UfGoy6bK1k4OpdIy/0dgQkrUyJ1PzMXVVC3kNlYY2LH8pY9qk6uDDRY/3RFTok/jswPX0L2lO/q2LbsMTJHBiLlbzuHHP0uebV38VEeM7B5Yrffyd3NEj1YeOHYtAwevpMHXxb7WZmctMhgxfeMZFBYb0aetF54Nq1qiXN8ZtJnoF+yGX69kYvWh6/j02S5ShwQA+O2vGaD7P+AjcSRERI2Lj48Pvv1mPXJy2ENKVadUKuHjY5k2mQkpUSNXOpnRoyE+cLa3zEzTgzqpcOJ6Jr45fguTvv0fPnqmM57srDKV38rIw+xN5/D79QxYyYD3hnbEcw9VLxkt1a25GxKz83ErQ4ed51Lwj1pKHD/bfw1nE3KgdLDFB8M61clwYKm80N0Xv17JxPazSZj2aBu09HSSNJ7raVrcSM+DrbUMvYKr9twxERFVnY+Pj8WSC6LqqpfrkBJR7Sg2GPHLmSQAwNNd624yo/LMfeIB9GnrhYIiI179/jRe+OoEPt4Ti9e+P41HPz6E369nwMHWGl+82K3GySgAyGQyPBbiAye5NTJ1hdh/JfW+Yz+XkIP/2xcHAHh3SHv4uDSsNUcr09bbCZHtvGEUwGf7r0odDn67VPJ39lBLD4t9aUJERET1AxNSokbscFw60rWF8HCyQ69gL4u+t72tNb56qTvG925liuXT3+Lw85kkFBqM6BXsiV9eDUe/Whii6Whng4Ht/SADcClZg7gMfY3PVVBkwPSNMSg2Cjze0ReD7+jZbUymRLYGAGw5nYj4TJ2ksfx2WQ0A6PdA2aHdRERE1LhxyG4d02q10Omq/p89R0dHKBSKOoyImpLNf01m9GRnFWzraFmUe7G2kuFfjz+Akd0D8NslNS6naNDSwwndWrjj4VbutToMtpmbAx5u5YHfr2fg9/g82HrUbOjugl8uIFathadCjveGdmxUQ3Xv1DXQDb2CPXE4Lh0f7r6C/5PoWdJsXSFO/rX+aL92HE5GRETU1DAhrUNarRbNW7REZkZ6lY9x9/DErZs3mJTSfcvJL8KeiykALD9c925BXgoEedX973T3FiXPk97O1MFzyGwUFBmqdfyW0wn4/o94yGTAspGhcHeyq6NI64fZA9vhyNUj+OVMEkY/0gIPNnezeAxbTyfCYBQI8XNBoAeXeyEiImpqmJDWIZ1Oh8yMdMxavR1OysoXes/LycSH4wdBp9MxIaX79tP/ElBQZERbH2d0rCdLe9S10udJvzt+E/Bqjo/23cb/vVC1tTb/dysLszedAwC8FhmM8CYwuU57lRLDH/THj38m4L3/XsSmiY/AyspyPcJCCGw4GQ8AGNm9ccxiTERERNXDhNQCnJTucHbj2npkOUajwDe/3wQAvPhI80Y77LQ8TnIb9GmpwI4r2fjlQjp6/Rlf6dqm19K0GLvuJPTFRkS288Zr/YItFK30ZjzWFtvPJuP07Wx8c/wWXnqkxX2drzqPKVxMycPlFA3sbKwwNFTaXnwiIiKSBhNSokbo8NV03MzQwVlu0yT/o+/nbIucYxvgGj4KszedhdEo8I+w8mfyjYnPxth1J5GlK0JnfyWWP9cF1hbsJZSat4s9Zg9sh7e3XcCSnZcR0cYLLWq4DEx1H1Nwf+wVOHcZiEfbekLpyNl1iYiImiImpESN0PpjNwEAwx70h5O8af4zzzn2A/45aSq2nEvD7M3ncC4xBzMeawu3v54LLSgy4Jvfb2HpnisoKDIixM8FX43uDke7pne/nn+oOXadT8GxaxmY+kMMNox/GPa21tU+T3UeU9AVGbHxfDYMAnj8Acs/u0pERET1Q9P7nxdRI3cpORe/XU6FTAa82KO51OFIRxgxu39z+Hq4YOWBa/juxG1sOZ2I0ABXyG2scC4xB+naQgBAn7ZeWP5cVyiaaPJuZSXDh890wuOfHEZMfDZmbzqL/4wMrfFQ76o8pnDycioMAtAnXka3gG41eh8iIiJq+LgOKVEjs3zfVQDAEx390MoCM9vWZzKZDG8OaIcN4x9GO19n6AoNOHYtA/uvpCFdWwhfF3t8OKwTvnqpe5NNRkv5uzli5fMPwtpKhq0xSViy6zKEEHXyXtm6QlxIygEAZB1c26SecSYiIiJzTft/YESNzNVUDXacTwYATIlsLXE09cfDrTyw47VeuJSSi7MJOSg2CrTzLZl9uCZDUxurnq09sXBIB/xryzl8fvA6cvOLsXBIe9jU4hq2RiGw/0oajALwd7HFrfjztXZuIiIianiYkBI1Ikt/jYUQwGMhPmjn6yJ1OPWKlZUM7VVKtFc1jSVwauq5h0omf5q39Ry+/+M2rqTkYumIULSs4URHdztxPRO3M3WwsZKhezNHHK2VsxIREVFDxSG7RI3Ekbh07DyfAmsrGd54rI3U4VAD9txDgfhs1INwltvg1O1sRC07hHd+voCk7Pz7Ou/ZhGz8cTMTANDvAW+4ObB3moiIqKljDylRI1BkMOKdXy4AAF54uDl7R+me0tLSKq3T1dsK370Qgvd/i8fvN7Kw9thNrPv9Jnq08kD/B3zwcCsPBPsoYFuF4bx5+mL8fj0DF5JyAQChAa5o5+sCTVbGfV8LERERNWxMSIkagU9/i8PVVC08nOww7VH2jlL59AU6QCZDhw4dqnyMu4cnfth/Cl/+noDj1zNx7FoGjl0rSSRtrGRo6emEYB8FAtwdYWsshKLTo7iZVQhbfS60+mKk5BTgZroOhr8mSHokyAPdmnOZFyIiIirBhJSogTsUm4bl+0tm1p0/uD2UDrYSR0T1VZG+ABACUz7ZCC/fZpXWz8vJxIfjB6GTjxwbxvdAfKYO/z2XjGPXMnDqVha0+mLEpWoRl6o1HeMx8HXsu6EFoDU7l5/SHj1aeSDA3bG2L4uIiIgaMCakdSQnvwifHLwNZY+RuJhWAD+hg7eznDN6Uq26lqbFtB9iIETJc3+DO6ukDokaACcXt0rXCS1PgLsjJkYEYWJEEIQQSM4pQKxagzi1Fim5BUjOzMVPv+xCi049IJfbwsHGGl4ucvi7OcLXxb4OroSIiIgaOiakdSRdq8d3/1PDtfcLOB6vA+J1AEp6CYK9FXjAz4XJKZVLq9VCp9NVWu9WZj4mbbyCjLwitFe54O1BIRaIjqiETCaDytUBKlcH9GnrDQBITU3FypcWYMrwYzVKeImIiKjpYUJaRxRyGzzfzRcrv1qH9r0HIVsvkFtQjOScAiTnFODotQw84OuM7i3c4cIhlvQXrVaL5i1aIjMj/Z71HIJ7wGPgq7B2cIEhMx4rp43gFxxERERE1OAwIa0jPi72eK13ABYN/z/0/+ezcHbzgKagCNfS8nAhKQfp2kKcT8rFxeRchKhc0L2FO2RSB02S0+l0yMxIx6zV2+GkdC9TnqErxpmUfNzMLgIAuMmBs9++Cfv3nrZ0qERERERE940JqQU529siNMAVnf2VSMouwImbGYjPzMf5xFxcStKgracdrBxdpQ6TpCazgrWTK4rlztAXGZGTX4Q0rR43M/KQoS00VXsw0A0d3IGY/FwJgyUiIiIiqjkmpBKQyWRo5uaAp938kZiVj+M3MpCQlY+LaXo0m/AFVh5NwNQBbpwttZFJ1+pxLVWLpJz8kqHb2QVI0+iRk19k2rJ1hWg+62dEn80GkF3mHDIZEOytQPcW7vBUyLmOIxERERE1aExIJdbMzQHD3PxxO1OHw1dSkA4HrDmRjJ/OpOG5hwLxz0dawlfJ2SkbCiEE0rWFiFNrEJeqLZmBNFWLq6laZOYVVn6CO9hayyC3sYZCbgNPZzs0UzqghacTnxUlIiIiokajUSekQggsXLgQX3zxBRwcHDBr1iyMHTtW6rDKFejuiCfbuuCDt6bj4XGLcD0jH58fvI6vDt/A4M4qjOgegO4t3GFtxSdN64MigxG3M3W4npaH62nakj/TS9ZjzNYVlXuMTAb4uzkgwM0RfkoH+Cnt4e0ih9LB1rQV52vwcJeOeGvNTijdPS18VUREREREltWoE9JVq1Zh2bJl2LRpEzIyMjBq1Cj4+/tjwIABUodWLplMhvy444h+sT0uZAKrD13HiRuZ2Hw6EZtPJ8JTYYeo9r7oH+KDUH9XuDnZSR2yxdy9FIpRCBQbBAxCAACEAMRfZUIAjo4OcHRyMr0uLRT4uz7+3l1y/vx8FBQZodUboNUXQ6M3QFtoQLauGGptIdK0hUjVFCItrxhp2kIYjKVHm5PJgObujmjt7YxgHwUClbZoprBCC3f7Sno3i5Cm0cCYnwsrWcP94iEtLa3KdR0dHaFQKOowGiIiIiKqzxptQiqEwGeffYaZM2eib9++AIBff/0Vq1atqrcJaSkrmQz9HvBGvwd8cCY+G98ev4VfL6qRri3Edydu47sTtwGU9LZ18lci0N0J3s5y+LjYw93JDva2VrC3tYbcxgpASfJmMAIGo/jr55JEzmi882eg2Gg0q6vLz0d+QUHJayEgRMm5jH8leEaU/Cz+KrOxtYWtnRxClLxPSf2SY/TFRuiLDSgsNkJfbIQuXw+dvgj6YiOKDAJ6Q8mfhcVGFJr+NKLYKFBsFMjJ1UDACjJra8DKGjKZlYR/QyXsbawQ6GaPFu72CHSzR3N3e7R0d0Cgmz3sbUviy8vLw4PduiMrs3rPehYVVW94b32gL9ABMhk6dOhQ5WPcPTxx6+YNJqVERERETVSjTUgzMzNx/vx59O/f37QvMjISEyZMkDCq6usc4IrOAa5YbDDi2LUM7DyXjBM3MnEjPQ8JWflIyMqXOkSLkNk5WmRZHGsZYGctK9lsZLCztoLcWgYnWys42llB6LKw7ZN/wZCbDoM2E1dQfi/p3aat2AJXT59K66XGX8eKGc+jqKi4WnFXtVeyLnski/QFgBCY8slGePk2q7R+Xk4mPhw/CDqdrl4kpHf3wlekOj3ARERERHRvjTYhVavVAABfX1/TPpVKhdzcXOTn58PBwcG0X6/XQ6/Xm17n5OQAAHJz7285DY1GAwDITElAgS6v0vq63CwAwI0bN0zH3snfGhgX6oRxoU7QFBQjLk2Hq+n5SNUUIkNXhPS8IuQWFENfbCzpiTSUJEvWMsBKBmRlZcJoMABGI4QwAkYjIAymn4XRCIiSTfz1p2/z1rCz+Xu2X5kMpsRQdse+4sIC3Lhw6q9jxV9jaAUAY8mfhiIIQzFEcaHpz+6PDoGjkwLWsr9jLN2sZYAVSracjGT8snoJnp35Idy9vGEFGaz+iuPOoa2lP+akJ+Pzf40tuRbg73G5EH+P1cUd+/4y+d/fQenhXcHfjgBgQHpOBgqTruD5uZ/C3avyBDM95Tail8xEnjYX9o6VJ12FBSUJUVZqIqyqkOxmpyYBQJV7JV3d3HDwwAE4/TWc+V7S09MBVP33tzSWwgJdlerr80uutaLf9/uJpbJ/S3fLy8tDRJ8+yM7KqrRuKXX8jWrdl6r+nVY39urcm/oUS3XPX9NzazQa2NvXfGK40nZAlPnsoIqU3qv7bUOJiKhhq2obKhONtJU9evQowsPDkZWVBVdXVwDA6dOn0bVrVyQmJkKlUpnqvvPOO1iwYIFEkRIRUX0XHx8Pf39/qcNoEBISEhAQECB1GEREVE9U1oY22oT00qVLCAkJwe3bt00N48GDB9GnTx/k5+ebfWN+dw+p0WhEZmYmPDw8ILuPyWVyc3MREBCA+Ph4uLi41PxiGhnel4rx3pSP96VivDflq637IoSARqOBSqWClZX0z643BEajEUlJSXB2dmYbWgd4XyrGe1M+3peK8d6Uz9JtaKMdsls6VDc5OdmUkCYlJcHV1bXM8C25XA65XG62r7RXtTa4uLjwl7wcvC8V470pH+9LxXhvylcb90WpVNZSNE2DlZVVrfYm83e7fLwvFeO9KR/vS8V4b8pnqTa00X7d6+bmhs6dO2Pv3r2mffv27UNkZKSEUREREREREVGpRttDCgCTJ0/Gm2++iR49eiAzMxPr16/H9u3bpQ6LiIiIiIiI0MgT0nHjxkGtVuOFF16Ag4MDPvvsMzz66KMWe3+5XI758+eXGQ7c1PG+VIz3pny8LxXjvSkf70vDx7/D8vG+VIz3pny8LxXjvSmfpe9Lo53UiIiIiIiIiOq3RvsMKREREREREdVvTEiJiIiIiIhIEkxIiYiIiIiISBJMSO+TEALvvvsuAgIC0KZNG3z55ZcV1lWr1XjiiSfg6uqKiIgIxMXFWTBSy6rqfTEYDHjvvffQqVMnuLm54ZlnnkFycrKFo7Ws6vzOlDp06BBkMhneeeedug9QItW5L0ajEYsXL0ZwcDC8vLwwatQoZGRkWDBay6rOvTl16hTCw8Ph5OSEzp07Y/fu3RaM1LLS0tIwb948BAYGolu3bves25Q+fxsStqHlYxtaMbah5WMbWjG2oeWrV22ooPvy2WefCTc3N7Fv3z6xceNGYWdnJ3bu3FmmntFoFGFhYeLJJ58UZ86cES+//LIIDAwUer1egqjrXlXvy8yZM8UjjzwifvvtN3Hy5EnRrVs30b9/fwkitpyq3ptSxcXFIjQ0VCgUCjF//nzLBWph1bkvM2fOFM2bNxe7d+8W586dE5MmTRInTpywcMSWU9V7o9PphEqlEvPmzROxsbFi4cKFwtHRUSQnJ0sQdd373//+J0aOHCnat28vHnzwwQrrNbXP34aEbWj52IZWjG1o+diGVoxtaPnqUxvKhPQ+GI1G0aFDB7F48WLTvnHjxokhQ4aUqfvnn38KACIxMVEIIURBQYFQKBRiy5YtForWcqpzX1JSUoRWqzW9/u233wQAkZWVZYFILa8696bUF198IQIDA8Wzzz7baBvT6tyX1NRU4eDgIA4fPmzBCKVTnXtz6tQpoVQqhdFoFEKU/EfMx8dHbN682VLhSmL+/Pn3bEyb0udvQ8I2tHxsQyvGNrR8bEMrxja0cvWhDeWQ3fuQmZmJ8+fPo3///qZ9kZGR2L9/f5m6Bw4cQEhICFQqFYCS9X169uxZbt2Grjr3xcfHB05OTqbX7u7uAACNRlP3gUqgOvcGAHJzczF37ly8++67sLOzs1SYFled+7Jz5054enqiZ8+elgxRMtW5N0FBQSgoKIBarQYAWFtbQy6Xo23bthaLtz5qSp+/DQnb0PKxDa0Y29DysQ2tGNvQ+2eJz18mpPeh9BfW19fXtE+lUiE3Nxf5+fll6t5Zr7Ru6Tkak+rcl7udOnUKrq6u8Pf3r9MYpVLde/Pee++hXbt2ePHFFy0WoxSqc19u376N5s2b48cff0Tnzp3Rpk0bfPTRRxCNdEnl6twbFxcXTJs2DZGRkdi9ezd++OEHtGnTBg888IBFY65vmtLnb0PCNrR8bEMrxja0fGxDK8Y29P5Z4vPXptbO1ARlZWUBAJydnU37Sn/OysqCg4ODWd0765XWTUhIsECkllWd+3Ino9GITz/9FGPGjIFMJqv7QCVQnXtz9epVfPbZZzhx4kSjvR+lqnNfEhIScPnyZURHR2PFihW4cuUKJk2ahODgYAwdOtSicVtCdf899e/fH5s2bcKIESOg1Wpx+PDhRv/7U5mm9PnbkLANLR/b0IqxDS0f29CKsQ29f5b4/GUP6X0ob2hMbm6uWdmdde8eQpObmwsPD486jtLyqnNf7vTll1/i5s2bmDFjRt0GKKHq3JsZM2bg1VdfRfv27S0XoESqc1+cnZ3h6emJjRs3Ijw8HC+//DKeeuopbNu2zXIBW1B17s2xY8cwefJkHDlyBDdv3sTMmTMxcOBAxMTEWCze+qgpff42JGxDy8c2tGJsQ8vHNrRibEPvnyU+f5mQ3ofS7us7p1hPSkqCq6sr7O3ty9S9eyr2pKSkMl3gjUF17kupP//8E6+99hq++uor+Pn5WSROKVT13iQmJmLbtm1YuXIlPD094enpie+//x4ffvghunTpYvG461p1fmcCAgJgZWVl9jxQixYtkJKSYplgLaw692blypUYPnw4vL294ebmhiVLlqBfv35YtmyZJUOud5rS529Dwja0fGxDK8Y2tHxsQyvGNvT+WeLzlwnpfXBzc0Pnzp2xd+9e0759+/YhMjKyTN2+ffvi0qVLSExMBAAUFBTg6NGj5dZt6KpzXwDg2rVrGDp0KKZOnYphw4ZZKkxJVPXe+Pj4ID4+HufPn0dMTAxiYmLQtWtXTJw4ETt27LB02HWuOr8zkZGRiI2NNWs8Y2NjERQUZJFYLa069yYvLw+2trZm+/z8/JCTk1PncdZnTenztyFhG1o+tqEVYxtaPrahFWMbev8s8vlba/P1NlGff/65cHV1Ffv27RM//fSTsLOzE7/++qtITU0VgYGBYs2aNaa6PXr0EIMGDTKt4dOiRQtRWFgoXfB1qKr35fr16yIgIEC88MILIi0tTSQnJ4vk5GSRnZ0t7QXUoer8ztwpIiKi0U5ZL0T17sugQYNEVFSUOHPmjFizZo2wtbUVMTEx0gVfx6p6bzZs2CCcnZ3Ft99+K65duyY2bNggHB0dxfr166W9gDqSkZEhkpOTxfTp00WnTp1EcnKySE1NbfKfvw0J29DysQ2tGNvQ8rENrRjb0PLVpzaUCel9MhqN4t133xXNmjUTrVu3Fl9++aUQomRtsICAAPHVV1+Z6qrVajFw4EChVCpF7969RVxcnFRh17mq3pfHHntMACizvfTSSxJGX7eq8ztzp8bemFbnvmg0GvHiiy8KDw8P0aZNm0a/Rlh17s3XX38t2rdvLxwcHETbtm3FZ599ZlpTrbGJiIgo89nRvHnzJv/525CwDS0f29CKsQ0tH9vQirENLV99akNlQjTSeZ6JiIiIiIioXuMzpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakRE3EM888gxkzZtx3nfri5s2bkMlkOH/+fI3PkZ+fj1atWmHLli21GBkRETU2bEPLYhtKtYUJKVETERQUhMDAQNPrKVOmlGk4767T2Nna2qJdu3bw9PQ07evQoQO2b98uYVRERFTfsA0ti20o1RYbqQMgIsv44IMPaqVOY2JjY4MdO3ZIHQYREdVzbEPLYhtKtYU9pNTk9enTB+vWrcPs2bOhUqnQtWtXHDhwALGxsRg4cCCUSiWeeuopaDQaAMDJkycxfPhwBAQEwNvbGzNmzIDBYAAAzJgxA6GhoabXW7ZsgZeXF7KzsyuNY+3atejVqxd++OEHdO3aFW5ubhgxYgQyMzNNdYxGI5YtW4Z27drB1dUVTz75JK5fv24q1+l0eOmll+Du7o7AwEBMnz4deXl5AEqGEo0ePRoA0KJFC6xYsQJLly6FTCYz7S+tYzQa4e/vj1WrVpnFOGHCBIwaNQoAIITAf/7zH7Ru3RoeHh54/vnnzWK9lwMHDiAgIAC//fYbevbsCaVSiaioKNy+fdusXnR0NDp16gQXFxf07dsXp0+frvCcRUVF+Pzzz9GrVy+4uLggJCQEv/76q9n9HTBgAHbt2oWQkBA8//zzAABPT0+sXbvWNHzpwoULePLJJyGTyfD1118jKCgIn376qdl7TZ482XQ8EVFTxjaUbSjbULpvgqiJi4iIEEqlUixcuFBcvHhRDB06VPj5+Ynu3buLX3/9VRw+fFgoFArx8ccfCyGEmDFjhli2bJk4d+6c2L59u7C3txcbNmwQQgiRm5sr/Pz8xKpVq0RRUZFo166dWL16dZXiWLNmjZDJZGLgwIHi999/F8eOHROdOnUSQ4cONdVZvHix8Pb2Flu2bBHnzp0To0ePFj4+PiI7O1sIIcSiRYuEr6+v2L9/vzh58qSYPn260Gq1Qgghhg0bJl566SUhhBCpqamiR48eYuLEiSI5Odl0/J11pk2bJqKiokzvbTAYhI+Pj9iyZYsQQogPPvhAhIaGikOHDomLFy+KZ555Rjz33HNVutb9+/cLAOKhhx4Sv/32mzh16pTo16+fCA0NFUajUQghRHR0tHBychJr164VFy9eFLNnzxYODg7i+vXrQgghbty4IQCIc+fOCSGESE9PF0OGDBE///yzuHLlinj11VeFu7u76frXrFkjfH19xcMPPywOHz4sEhMThRBCeHh4iDVr1oji4mKRnJwsAIj169eL5ORkodPpxKJFi0T37t3N7oOvr6/4+eefq3StRESNGdtQtqFsQ+l+MSGlJi8iIkKMHz/e9Pqnn34SAMTZs2dN+/r37y9GjRpV7vF9+/YVEydONL2Ojo4WPj4+4rPPPhOhoaGiuLi4SnGsWbNGODs7C4PBYNq3Z88eAUCkp6cLvV4vHB0dxfr1603lRUVFolWrVmLx4sVCCCFmzZolevbsaXaOUnc2lKXXPX369ArrHD9+XNja2poa2iNHjgiFQiF0Op0oLCwUTk5O4sKFC6Zjk5KShK2trSgqKqr0Wksb0/T0dNO+uLg4AUCcOnVKCCFE69atxbvvvmt23J1/V3c3pne7deuWACCOHz8uhPj7PysnT540q1famJYCIH755RfT68TERGFtbS0uX74shBDi8OHDwtXVVRQUFFR6nUREjR3b0PLrsA0twTaUqoJDdokAODs7m34ODg4ud1/pkKGioiJs3LgRw4YNQ4cOHfC///0PaWlpprr/+Mc/EBwcjClTpuCTTz6BtbV1leOwsrKCldXf/yw7duwIALhx4wbi4uKg0+nQp08fU7mNjQ0iIiIQExMDAJg6dSqEEOjQoQO+/vprFBUVVfm97xYWFoZmzZph586dAEqGTg0ePBgODg6IjY1FXl4ewsLCoFAooFAo0Lp1axQVFZndi8rceW+CgoLg4OCAa9euQavV4urVq2bXCgCRkZGmay1PcnIyFi1ahD59+uCxxx4DALN43N3d0a1btyrHBwAqlQpPPPEEvvvuOwDApk2b8NRTT0Eul1frPEREjRXb0LLYhpZgG0pVwYSU6C53NmZ37zMajRg6dCj+7//+D9OmTUNMTAyeeuops7oajQbXr1+HQqEwPTNTUzqdDgCgUCgghKiwntFoBAD4+fnhyJEjWLZsGb7++mu0b98eKSkpNXpvmUyGESNGYOvWrRBCYOvWrRgxYgQAmGLZuXMnYmJiEBMTgzNnziAuLg7e3t41er/i4mLo9foqX+vdLl68iE6dOkGn02H9+vW4dOlSjeIoz9ixY/Hdd99BCIFt27bhH//4R62dm4ioMWEbWoJt6N/YhlJlmJASVcPly5exY8cOrFu3DuHh4bCxsSnz4b5gwQJERETggw8+wJQpU0wNYk0cOHAAjo6OaNWqFdq0aQN7e3scPHjQVG4wGHDo0CF06dLFtE8mk+Gxxx7DwYMHYTQa8eOPP5Z7bhsbm0pjGzlyJHbv3o0LFy4gNTUVUVFRAEq+7XZwcMCtW7fQunVrs60632bf6ciRI6Zvpp2dnREUFGR2rQCwf/9+s2u909q1axESEoJFixYhMDDwng3yvVhbW5e5LwMHDkRBQQGio6Oh0WgQGRlZo3MTETVlbEPZhrINpfJw2ReianB0dAQArFu3DiNGjMC2bduwefNmDBgwAABw/vx5rFq1CmfPnkXz5s2xatUqLFy4EO+//36Vzp+bm4vZs2fjxRdfxLVr1zB79mzMnDkTdnZ2AIB58+ZhxowZUCqVaNWqFT7++GNoNBpMmjQJQgg8/fTTiIyMRL9+/XDhwgXcvHmzwjXRWrdujb179+LMmTNwdnZGq1atytTp0qULPD09MXfuXAwePBj29vYAALlcjnnz5mHq1KmwsbFBt27dcP78eeTl5ZlmEKyK6dOn4/XXX0dmZibGjx+PF198Ef7+/gBK/lMyYcIENG/eHN27d8c333yD48eP46uvvgJQ8o03AMTHx6NDhw5QKBQ4ffo0jhw5AmtrayxatKhGDXvr1q2xYcMGdOnSBc7OzvD19YWNjQ3++c9/Ytq0aRg2bBhsbPjRSURUXWxD2YayDaVyWfqhVaL65u6JCc6dOycAiBs3bpj2vfLKK+KJJ54QQgjx4YcfCjc3NxEUFCQWLFgglixZIoYNGyaMRqOIiIgQr732mum4I0eOCFtbW3H+/PlK41izZo1wdXUVCxcuFCqVSjRr1kzMnz/fbEIHg8Egli5dKlq3bi1cXFzE448/LuLi4kzlx44dE48++qhQKpVCpVKJd955xzTj3t0TMly/fl10795dODk5iTFjxpRbRwgh5s6dKwCIrVu3mu03GAxi2bJlol27dsLJyUl0797dNHtgZUonZFi6dKlo2bKl8PLyEq+88orQ6XSmOkajUXzzzTeiffv2QqFQiN69e4s///zT7DwDBgwQbdu2FUIIkZmZKaKiooSTk5MIDw8X+/btEx07djRNrrBmzRrh4eFRJpa7J2TYuXOnCAwMFK6uruLzzz837b906ZIAIPbv31+layQiagrYhrINZRtK90smRA375ImoVq1duxYzZsxAenq61KHUuQMHDqBv377QaDSmb2nruw0bNuCtt97C5cuXazykioiI6gbb0PqNbSjdC/vMiSzg7NmzeOSRRyosP3bsmAWjqVvZ2dmmIUPl+f77781mX6zv0tPTcfv2bcyZMwcfffQRG1IiIgtjG/o3tqHUGDEhJbKAtm3b3nOq9YCAAJw6dcpyAdUhZ2fne16rr68v/vzzT8sFdJ/efvtt/PDDD3j55ZcxbNgwqcMhImpy2Ib+jW0oNUYcsktERERERESS4LIvREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCQJJqREREREREQkCSakREREREREJAkmpERERERERCSJ/wfSfjXDv6hEYgAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAGHCAYAAABS74GwAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAATZ5JREFUeJzt3Xt8jvXjx/H3PWNkJ8zMbM6n5BitMIfNwq8k5RQqEkuik9FXEZL4pn5JhCJR+SpR38hxthFlfB2aUOZbarENOWxOs8Pn94fHrp/bZibjXlev5+NxPR67r891fa7P57ruw3vX/bmu22GMMQIAAABsys3VDQAAAABuJAIvAAAAbI3ACwAAAFsj8AIAAMDWCLwAAACwNQIvAAAAbI3ACwAAAFsj8AIAAMDWCLwAAACwNQIvAPxFPPTQQxo/fryrm3FV1atX15YtW4q0zq+++kqbN28u0jr/ypYsWaL4+HhXNwP4yyDwAig0Pz8/ffjhh65uRr7i4uLkcDiUlZXl6qZcUW4bT58+nafs7Nmzio6Odpp36NAhbd++vcA6R48eLV9f3ytO1apVK9I+XEm3bt1u2HPDGKPx48fL19fXmnfhwgW9/PLLatq0qXx8fNShQ4c8+6+4GjBggHr06HFddbi7u+vNN98sohYB9kfgBXDDNGzYUCtWrHB1M/4SDh8+rLvvvttp3qpVqzRixIgC1zt37pwefvhhnTx5Mt/p119/LXB9Hx8feXp65juVLFlSgwYNspY9f/68U3mpUqU0YMCAq/YtMjJSvr6+KlmypEqXLm2F8TJlysjd3V2+vr7q16/fFdffvHmzPD09ddttt0mSMjMz1aFDB33xxReaOHGitmzZom7duun5559XRkbGVdtTVI4dO3bFf2CK2uWvpS5duig+Pl5JSUk3fNuAHbi7ugEAgP/n5+dn/X3+/Hm1aNHihm7v1KlTVywbNWqUTpw4YT0uXbq0U7h79dVXdeDAgatu47333tN7772nbt26KTQ0VFFRUZKkGTNmaMWKFVq9enWB67/77rt64oknrMdz585VYmKifvrpJ/n4+EiSbr31Vj355JNyd/97fKyVLFlSAwYM0OzZszVp0iRXNwco9jjDCyBf2dnZGjdunIKCghQUFKQJEyYoOzvbKs/JydGSJUvUoUMHlStXTrVq1dLHH38sSTp48KAcDof27Nmj++67Tw6Hw/q6Ozo6Wl26dJG/v7+Cg4M1derUa2rX7t271bFjR/n6+qpBgwaaN29envJOnTrJx8dHd999t44cOWKVbdu2TT179lRwcLD8/f0VFRXl1CeHw6GEhAQ9+uij8vPz02233aatW7da5ePHj1dkZKQWL16sFi1ayNfXV0OHDlVOTo61zOnTpzVkyBD5+/urWrVqefbb1Rw7dsyapk2bdk37pqhlZGTIw8PDpW0wxigmJkZt27a15s2cOVNPPfWUFXZzXRp2L1y4oDFjxqhGjRqqWLGi+vXr5/RcGD9+fJ5/Jlq0aOE0Rrqg58OHH36oihUrSpK8vLzkcDh08OBBxcXFKTg4WOvXr1fr1q3l4+OjTp066bfffrtiH3NycjRt2jTVr19fvr6+uu+++/Tzzz9LKvi11L59e8XGxl7D3gT+vgi8API1YcIEzZw5U9OnT9fatWuVkZHhdDYwKytLn376qSIjIxUfH6/BgwfrscceU1JSkoKDg5WcnCxJWrhwoZKTk9W7d28ZY/TJJ5/owQcf1MaNG/Xaa69p1KhRhb745uDBg7rrrrtUq1Ytbdq0SVOnTlVmZqbTMk888YSioqK0fv167d69W2+88YZV9tlnnyk0NFSrVq3S/PnzNXPmTH3++edO63fp0kURERHatGmT/P39NWzYMKfyxYsX6/PPP9ecOXP0/vvva9asWdYZSmOMevXqpbS0NMXGxmrx4sVasmSJPvjgg8Lv+GIkIyNDpUqVcpp36ZCGV155xanspZdeUsOGDXXo0KEia0NycrL++OMPBQYGSrr4j1hiYqKaN29e4HrDhw/Xp59+qnnz5mnt2rU6efKkOnTokOf5cjVXej707t1bS5culSQdOHBAycnJCg4OliT9/vvveumllzRx4kTFxcUpOztb999/v4wx+W7jn//8pyZPnqwpU6Zo06ZN8vPzU6tWrXTq1KkrvpakixcH7tix44r1AriEAYDLnD9/3pQuXdrMmzfPab6Pj4+ZP3/+Fdfx8PAwixcvtuZJMsuXLy9wWzVq1DBTpkwpVLsiIyNNy5YtTU5OTp6y2NhYI8kcPnzYmtenTx/ToUOHK9YXFhZmhgwZ4tTeRYsWWY/nzJlj3N3dTXZ2tjHGmHHjxpn69etbj3NyckxAQICZOHGiMcaYb7/91lSuXNlkZmZadSxatMiEhYU5tTE9PT1PWxITE83lb8nvv/++adeunfW4d+/eZty4cU7LPPPMM8bDw8NUqFDhitPJkyevuA8Kcu+995qpU6c6zZNkkpKS8ix7//33m6lTp5pffvnFVKlSxXz33XdXXCbXO++8Yzp16lRgG3bu3GmqV69uPU5JSTGSzLZt2664zm+//WYcDofZuHGjNe/kyZPG29vbOr7jxo0zzZs3d1qvefPmTvv3as+H/I5n7rxjx45Z83KP7Y4dO4wxxvTv3990797dGGNMRkaGueWWW8zChQut5TMzM03NmjXNa6+95tSWy19LFy5cMG5ububUqVNX3BcALuIML4A8EhMTdf78eUVERBS43IkTJzR9+nTdfffdatKkibKzs3X06NEC1zl79qzmz5+vLl26qEGDBkpJSbnqOrl27typTp06yeFwXHEZLy8v6+/atWvr5MmT1uPMzEwtWbJE3bt3V8OGDbV9+/Y82758/aysLJ09e9aaV7ZsWbm5XXzrdDgcqlOnjrWNHTt2KDU1Vb6+vtZZ0Mcee6xQZzzd3Nzk4+PjdIeFqKgoeXp6FrjetGnTdP78eWsYhCStWLHCaWjE5V/9F9Z///tfVa9evdDL+/n5qXr16nnG0Q4fPly+vr76+uuvNWbMGKt/I0eOVHR0tHx9fTVw4MB86zx9+rQqVarktI2SJUsqNTX1iu1ISEiQu7u7WrVqZc3z8fFR8+bNtWvXrkL3R7r68+FKSpQoYf1dq1YtlSlTRv/973/zLJeYmKizZ8+qffv21jx3d3e1a9fuqm0tWbKk/Pz8lJaWdvWOAH9zf4/R/QCuSe7XvgVdAJScnKzWrVsrIiJCM2bMUN26dVWjRo0C6z1z5ozat2+voKAgTZw4UU2aNFF4eHih25WdnV1g2L1cbjCVLo6T7Natm9LT0/Xaa6/prrvu0qBBgwq8wv7S9QuzjDFGVapUUUxMjNMylw8LyE/NmjWdwrmrGWN0+PDhKwZeY4zS09P1888/XzVQv/POO3rnnXf+VDs8PT2d/ikpUaKEateurR07dujee++9Ytuu5NLx1pf+XRiFeT7kJysrSxkZGfn+81LYtl6p3j/++EPe3t5/ql3A3wlneAHkUbNmTbm5uTmNrTXGOH0Af/HFF8rOztacOXNUr149ORyOPB/QJUqUcDobtmHDBu3atUuLFy9Ws2bN5Obmdk2ho1GjRlq/fv2fGrP4448/auXKlVqwYIFCQ0Pl7u5+zYHnapo0aaLDhw/L3d1dtWvXtqaqVasWuo6BAwdqypQp+Zb94x//0MMPP1xUzS2Qw+HQyZMn8x0rW6dOHd1yyy2qW7eunnjiiSL/kYlLVaxYUUlJSU7HasiQIXr33XfznNnMHevauHFjZWZm6rvvvrPK0tLStH37djVr1kzSxTO3P//8s1XvuXPn9Mcff1xT23L/IbzaGd9NmzbJGKOGDRvmKatbt65Kly6tDRs2WPOys7O1ceNGq61S3teSdPE+zSVKlHA6Cw0gfwReAHn4+Piob9++GjFihGJjY7Vz50499NBDTmdDPT09lZSUpK+//lo7duxQ//79lZKS4lRP7dq1tXjxYiUmJiolJUWenp7KysrSggULtHv3bkVFRV3Tr0W98MIL+u677zRixAjt3btXa9as0bBhwwoVgG+55RZJ0oIFC7R3715NnjxZy5YtK/S2CyM0NFTh4eF64IEHtGHDBu3fv1+zZs3Stm3bJP3/1+MF3Tv17NmzOn/+fL5lTZs2Ve3atYukrVu3br3i/XdzJy8vL3l5ecnT09O6n29mZqbOnj2rc+fOKSUlRfHx8erTp88Vt/PYY4+pdOnShZo6duyYZ/3AwED5+Pg4PbeeeOIJBQUFqV27dlq1apX27t2rGTNmqF69etq7d6+qVq2qQYMGaeDAgYqNjdX333+vvn37Kjg4WD179pQkNWvWTKdOndK8efO0YcMGPfDAA9d8D98aNWrI4XDo/fff108//aRz585ZZSNGjFBCQoLi4uIUGRmpRx99VEFBQZIuPg+OHTumc+fOqVSpUhozZoyioqK0fPly7dmzR5GRkUpPT9eTTz5p1Xf5a0m6eBFn06ZNr+lbD+DvisALIF/vvvuu2rRpox49eqhfv37q2LGjQkNDrfKHHnpIvXv31kMPPaTIyEh16dJFnTt3dqpj2rRp2r59u0JCQvTVV1+pTZs2evbZZxUVFaU+ffqoRo0aioyMLHSbbr31VsXFxSk+Pl533nmnRowY4XQWrCDVq1fX66+/runTp6tr167KzMzU2LFjC73twnA4HFq6dKlatWqlhx9+WHfeeadWrFih0qVLS7p4BrhRo0Z69tlnr3tbWVlZ+YbU3LHXl8/PvWVcrpCQEJ0+fbrQU+4vtrm7u19TwJo/f77Onz9fqGnt2rV51nc4HGrXrp127NhhzfPw8NA333yjiIgIjRgxQq1atdKyZcu0dOlSNWjQQNLFe/z26NFDAwYMUHh4uLy8vLR+/XqVLFlSkhQWFqbhw4dr5MiRevbZZzVkyBD9z//8zzUdgypVqujVV1/V//7v/6pDhw7at2+fVXbbbbepW7du6tWrlzp16qRZs2ZZZQ888IB27Nhh/cM1evRojRo1Ss8//7xatWqllJQUbd68WeXKlbPWufy1JF38QY527dpdU5uBvyuH+TPfDQIAboiHHnpIy5Ytu+K430aNGjl9VV9cvPXWW2revLnatm2r6tWra/HixbrrrruKpO6YmBh9/PHHf4nbu8XFxSksLEzp6elXveDwemRnZ6tevXpau3atatasecO2A9gFgRdAsZCQkOB0Vf3lvv32WzVu3PgmtgjFhTFGLVu21FdffSV/f39XN6dANyvwrlq1Su+9956++OKLG7YNwE64SwOAYqFevXoF3oYp96b++PtxOBx6/vnntW/fvmIfeG+WI0eOaMSIEa5uBvCXwRleAAAA2BoXrQEAAMDWCLwAAACwNQIvAAAAbI2L1vKRk5Ojw4cPy8vLixt6AwAAFEO5P3EeGBh41Z/+JvDm4/Dhw1wRDgAA8BeQlJRk/ZLhlRB483Hpz396e3u7uDUAAAC4XFpamoKDg63cVhACbz5yhzF4e3sTeAEAAIqxwgw/5aI1AAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtubu6AXbQfORCVzfBFrZPfdTVTQAAADbEGV4AAADYGoEXAAAAtkbgBQAAgK0ReAEAAGBrBF4AAADYGoEXAAAAtkbgBQAAgK0ReAEAAGBrBF4AAADYGoEXAAAAtkbgBQAAgK0ReAEAAGBrLg+8W7ZsUevWreXt7a0777xTsbGxkqQzZ86oX79+qlChgkJCQrRlyxan9fbv36+2bduqXLly6tKli44cOeJU/uWXX6px48aqVKmSnnnmGWVlZd20PgEAAKD4cGng3blzp8LDw9W1a1dt3bpVkZGR2rBhgyRp4MCBOnDggKKjo9WpUyd16tTJCrUZGRnq0KGD6tWrpw0bNsjhcOj++++36v3+++/Vq1cvDR06VMuXL9dXX32lsWPHuqSPAAAAcC2HMca4auM9e/aUj4+P5s6d6zQ/JSVFQUFB+uabb9SyZUsZY1SvXj0NHTpUzz77rJYuXarHHntMR48elYeHhw4dOqSgoCDt3LlTTZs21fDhw7V//36tWbNGkrRo0SI988wzOnTokEqVKnXVdqWlpcnHx0enTp2St7f3VZdvPnLhn9sBcLJ96qOubgIAAPiLuJa85rIzvNnZ2fryyy/Vt2/fPGWbN29WmTJlFBISIklyOBwKDw+3hjvExcWpTZs28vDwkCRVqVJF9erVcyqPiIiw6gsPD9exY8e0Z8+eG90tAAAAFDMuC7zJycnKysqSw+HQfffdpypVqqhnz55KTU1Vamqq/P39VaJECWv5wMBApaamSpJSU1MVEBDgVF9B5f7+/nJzc7PKL5eRkaG0tDSnCQAAAPbgssD7+++/S5KeeeYZPfLII/r000+1f/9+PfHEEzpx4oS8vLyclvfy8tLx48cl6ZrL3dzc5OnpaZVfbvLkyfLx8bGm4ODgIusnAAAAXMtlgTc3kM6cOVO9evVSaGiopkyZohUrVsjb21vp6elOy6elpalChQqSpPLly19TeU5OjtLT063yy40ePVqnTp2ypqSkpCLrJwAAAFzL3VUbzj2LWrp0aWte9erVlZ2dLX9/f6Wmpio7O9sa1nD48GFrmEJAQIASExOd6ru8PDk52SpLTU2VMSbPMIhcHh4e1nhgAAAA2IvLzvB6e3urRYsW1m3IpIv31vX09FR4eLgyMjIUHx8vSTLGKCYmRuHh4ZKksLAwffPNN8rIyJB0cXjE/v37ncqjo6OtemNiYuTv768GDRrcrO4BAACgmHDZGV5JeuGFFxQZGan69eurcuXKevHFF/XEE0+oYsWK6tmzp5577jnNmTNHS5cu1dGjR9WnTx9J0j333KPy5ctr2LBhGj58uF588UWFhoaqUaNGkqRBgwapefPmmj17tm6//XaNGTNGkZGRKlmypCu7CwAAABdwaeDt0aOH0tLSNHLkSB05ckR9+vTRq6++Kkl6//33NXjwYIWHh6tWrVpas2aN/Pz8JEmlSpVSdHS0Hn/8cbVt21ahoaFatmyZVW/Dhg21ZMkSvfTSS0pNTVWfPn00fvx4V3QRAAAALubSH54orvjhCdfghycAAEBh/SV+eAIAAAC4GQi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDWXBt4BAwbI4XA4TePHj5cknTlzRv369VOFChUUEhKiLVu2OK27f/9+tW3bVuXKlVOXLl105MgRp/Ivv/xSjRs3VqVKlfTMM88oKyvrZnULAAAAxYjLz/D27t1bycnJ1hQVFSVJGjhwoA4cOKDo6Gh16tRJnTp1skJtRkaGOnTooHr16mnDhg1yOBy6//77rTq///579erVS0OHDtXy5cv11VdfaezYsS7pHwAAAFzL5YE3ODhYAQEB1uTp6amUlBQtXbpU06ZNU7NmzfTKK6+oUqVKWrRokSRpxYoVOnXqlGbMmKHGjRtr9uzZ2rJli3bt2iVJmjt3rsLCwjRkyBCFhIRo0qRJmjt3ri5cuODCngIAAMAVXB54/fz88szbvHmzypQpo5CQEEmSw+FQeHi4YmNjJUlxcXFq06aNPDw8JElVqlRRvXr1nMojIiKs+sLDw3Xs2DHt2bPnRncHAAAAxYzLA++KFSvUsGFD1alTR2PHjtWFCxeUmpoqf39/lShRwlouMDBQqampkqTU1FQFBAQ41VNQub+/v9zc3Kzyy2VkZCgtLc1pAgAAgD24u3Ljd999t5o1a6a2bdtqx44devbZZ1WiRAmVLFlSXl5eTst6eXnp+PHjkqQTJ04oMDCwwPJL13dzc5Onp6dVfrnJkydrwoQJRdk1AAAAFBMuDbz9+vWz/m7WrJl+++03LVq0SM8995zS09Odlk1LS1OFChUkSeXLl8+3vEGDBvmW5+TkKD093Vr/cqNHj9bzzz/vVFdwcPD1dQ4AAADFgksD7+UaNGigQ4cOKSAgQKmpqcrOzraGNRw+fNgaphAQEKDExESndS8vT05OtspSU1NljMkzDCKXh4eHNR4YAAAA9uKyMbxZWVk6c+aM07xdu3apfv36Cg0NVUZGhuLj4yVJxhjFxMQoPDxckhQWFqZvvvlGGRkZkqTff/9d+/fvdyqPjo626o2JiZG/v791BhgAAAB/Hy4LvIsWLdKdd96pTz/9VPv379f8+fP19ttva9SoUapYsaJ69uyp5557Trt27dLLL7+so0ePqk+fPpKke+65R+XLl9ewYcOUkJCgIUOGKDQ0VI0aNZIkDRo0SBs2bNDs2bO1detWjRkzRpGRkSpZsqSrugsAAAAXcdmQhkceeURnzpzRrFmztGPHDlWpUkWzZs1Sr169JEnvv/++Bg8erPDwcNWqVUtr1qyxbmFWqlQpRUdH6/HHH1fbtm0VGhqqZcuWWXU3bNhQS5Ys0UsvvaTU1FT16dPH+gU3AAAA/L04jDHG1Y0obtLS0uTj46NTp07J29v7qss3H7nwJrTK/rZPfdTVTQAAAH8R15LXXH4fXgAAAOBGIvACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDV3VzcAuJGaj1zo6ibYxvapj7q6CQAA/Cmc4QUAAICtEXgBAABga8Ui8F64cEH16tVT9erVrXmpqam699575evrq3bt2ikxMdFpnW+//VYhISGqUKGCHn74YZ05c8ap/L333lOdOnUUHBysV199VcaYm9EVAAAAFDPFIvDOnDlThw8fth4bY9S1a1eVKFFCGzduVJ06dRQREaELFy5IklJSUtS5c2d17txZ0dHR2r9/vwYNGmStv3LlSg0fPlxTpkzRggUL9Oabb+q999676f0CAACA67k88B49elQTJkzQsGHDrHk7duzQ1q1bNXv2bDVu3FgzZ87U8ePHtXLlSknSJ598osDAQE2YMEHNmjXTtGnT9Pnnn+vIkSOSpFmzZmnAgAHq3r27wsPDNXLkSM2aNcsl/QMAAIBruTzwvvzyy2rWrJk6duxozYuLi1ODBg0UGBgoSfLw8FDr1q0VGxtrlXfo0EEOh0OSFBISolKlSmnz5s1WeUREhFVfeHi4vv/+e504ceJmdQsAAADFhEtvS7Z79259+OGH2rFjh1JTU635qampCggIcFo2MDDQWiY1NVUhISFWmbu7uypVqqTU1FSdOXNGp0+fdlo/NzinpqaqXLlyedqRkZGhjIwM63FaWlrRdBAAAAAu57IzvMYYPfvssxoxYoRuvfVWp7ITJ07Iy8vLaZ6Xl5eOHz9+1fKTJ09ajy8tk2Stf7nJkyfLx8fHmoKDg6+rbwAAACg+XBZ4//3vf+uXX37Riy++mKesfPnySk9Pd5qXlpamChUqXLW8fPnykuRUnnvGNnf9y40ePVqnTp2ypqSkpD/fMQAAABQrLhvSkHtnhqpVq0qSMjMzlZ6eLj8/Pz333HNKTk52Wv7w4cNq0KCBJCkgIMCpPCsrS0eOHFFAQIDKlCkjb29vp/LcO0BUqlQp37Z4eHjIw8OjSPsHAACA4sFlZ3gXLVqkAwcOaNeuXdq1a5fGjRunwMBA7dq1SxEREdq3b58OHTokSTp//rw2b96s8PBwSVJYWJiio6Ote+vGx8crMzNToaGhTuW5YmJidPvtt8vX1/fmdhIAAAAu57LAW7FiRQUFBVlT+fLl5e7urqCgIN15551q2bKlhgwZooSEBA0bNkwVK1ZU586dJUl9+/ZVamqqxo0bp127dum5555T7969rSELTz75pBYsWKBly5YpJiZGb7zxhoYOHeqqrgIAAMCFXH5bsiv58ssvlZ2drbZt2yoxMVHr1q1TyZIlJUn+/v5avXq1Vq1apfDwcNWtW1dz5syx1u3UqZNmzJihUaNGqX///oqKitLAgQNd1RUAAAC4kMPwm7t5pKWlycfHR6dOnZK3t/dVl28+cuFNaJX9bZ/6aJHXybEpOjfi+AAA8GddS14rtmd4AQAAgKJA4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALbm7uoGAPh74lfwig6/ggcABeMMLwAAAGyNwAsAAABbI/ACAADA1hjDCwDIgzHWRYPx1UDxUORneBs2bFjUVQIAAAB/WqED75w5c/LMGzt2bJ55v/766/W1CAAAAChChQ68o0ePzjMvvxDscDiur0UAAABAESp04DXGFGoeAAAAUJwUOvDmd+aWs7kAAAAo7q7rLg3GGN1zzz1F1RYAAACgyF33bcnuvfde629jjDZt2nS9VQIAAABF5roCr8Ph0FNPPeU078UXX7yuBgEAAABF6ZoC74kTJ5wuVMvJySnyBgEAAABFqdCBNycnR35+ftZjYwwXrQEAAKDYK3Tg/e233wp1GzJuVQYAAIDipNCB19vbu1DLjRo16k83BgAAAChqhb4Pb2GNHTtWX3/9dVFXCwAAAPwphQq8bm5uKlGiRIHT2rVrreX79OlzwxoMAAAAXItCDWnYt2/fVZcJCgqy/mYcLwAAAIqLQgXeevXqXVOl3L0BAIAbo/nIha5ugm1sn/qoq5uAm6TQF62dOnVK33//fd4K3N3VqlWrIm0UAAAAUFQKHXh3796tfv36KTk5WZUrV7bmnzlzRsePH78hjQMAAACuV6EDb2hoqJKSklSxYkUlJSVZ88uVK3dDGgYAAAAUhWu+Ldnl43MZrwsAAIDirMjuw1uuXDmVKFFCbm5uOnPmTKHWWbdundq3by8vLy/deuut+uSTT6yy1NRU3XvvvfL19VW7du2UmJjotO63336rkJAQVahQQQ8//HCebb733nuqU6eOgoOD9eqrr3LnCAAAgL+pQg9p+OWXX7Rw4UKdOXNGr7zySp7yrVu3Kicnp9AbPnHihAYMGKCXXnpJc+bM0Zo1a/TII4+odu3aCgkJUdeuXVWpUiVt3LhR06dPV0REhBITE1WqVCmlpKSoc+fOevbZZ/XAAw/oiSee0KBBg/Svf/1LkrRy5UoNHz5cixYtUrly5dS9e3dVrFhRTzzxRKHbBwAAAHsodOA9e/asDhw4oAcffNDpbGvPnj0lSXXq1LmmDZcrV04HDhxQmTJlJF289dn777+vdevWyd3dXVu3btWhQ4cUGBiomTNnys/PTytXrlS3bt30ySefKDAwUBMmTJDD4dC0adPUrl07vf322/L399esWbM0YMAAde/eXZI0cuRIzZo1i8ALAADwN1TowHvbbbfpo48+KtKN54ZdScrJydHp06fl6empuLg4NWjQQIGBgZIkDw8PtW7dWrGxserWrZvi4uLUoUMHa/xwSEiISpUqpc2bN+uBBx5QXFycPvjgA6vu8PBwvfTSSzpx4gQX2QEAAPzNFNkY3j/LGKPk5GQ9//zzOnfunPr27avU1FQFBAQ4LRcYGKjU1FRJylPu7u6uSpUqKTU1VWfOnNHp06edynODc+76l8vIyFBaWprTBAAAAHtweeAdMWKEAgMDNW/ePC1ZskT+/v46ceKEvLy8nJbz8vKy7vdbUPnJkyetx5eWSbri/YInT54sHx8fawoODi6q7gEAAMDFXB54R44cqbi4OA0fPlz33HOP1q9fr/Llyys9Pd1pubS0NFWoUEGSCiwvX768JDmV556xzV3/cqNHj9apU6es6dL7DAMAAOCvrdBjeG+UypUrq3LlymrXrp3S09P1yiuvqFu3bkpOTnZa7vDhw2rQoIEkKSAgwKk8KytLR44cUUBAgMqUKSNvb2+n8sOHD0uSKlWqlG8bPDw85OHhUdRdAwAAQDHgsjO8mZmZOnv2rNM8X19fnT17VmFhYdq3b58OHTokSTp//rw2b96s8PBwSVJYWJiio6Ote+vGx8crMzNToaGhTuW5YmJidPvtt8vX1/cm9AwAAADFicsC78cff6w777xTn332mRITE7V06VLNmDFDPXv2VNOmTdWyZUsNGTJECQkJGjZsmCpWrKjOnTtLknVh27hx47Rr1y4999xz6t27tzVk4cknn9SCBQu0bNkyxcTE6I033tDQoUNd1VUAAAC4kMuGNAwYMECnT5/WjBkztHPnTvn7++uFF15QVFSUJOnLL7/UgAED1LZtWzVp0kTr1q1TyZIlJUn+/v5avXq1nn76ac2YMUP33HOP5syZY9XdqVMnzZgxQ6NGjVJGRoaioqI0cOBAl/QTAAAAruWywOtwODR8+HANHz4833J/f3+tXLnyiuu3bNlS27Ztu2L54MGDNXjw4OtuJwAAAP7aXH6XBgAAAOBGIvACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1gi8AAAAsDUCLwAAAGyNwAsAAABbI/ACAADA1lwaeGNjY9WpUyf5+PioSZMmWrVqlVV25swZ9evXTxUqVFBISIi2bNnitO7+/fvVtm1blStXTl26dNGRI0ecyr/88ks1btxYlSpV0jPPPKOsrKyb0icAAAAULy4LvN9//7169OihHj16KD4+Xl26dFG3bt303//+V5I0cOBAHThwQNHR0erUqZM6depkhdqMjAx16NBB9erV04YNG+RwOHT//fc71d2rVy8NHTpUy5cv11dffaWxY8e6pJ8AAABwLZcF3saNG+s///mPBg8erPr16+vVV19VQECAVqxYoZSUFC1dulTTpk1Ts2bN9Morr6hSpUpatGiRJGnFihU6deqUZsyYocaNG2v27NnasmWLdu3aJUmaO3euwsLCNGTIEIWEhGjSpEmaO3euLly44KruAgAAwEVcFngdDodq1Kjh9LhcuXJKS0vT5s2bVaZMGYWEhFhl4eHhio2NlSTFxcWpTZs28vDwkCRVqVJF9erVcyqPiIiw6g4PD9exY8e0Z8+em9U9AAAAFBPurm5ArnPnzmnfvn1q1KiRDh8+LH9/f5UoUcIqDwwMtM7gpqamKiAgwGn9wMBApaam5lvu7+8vNzc3q/xyGRkZysjIsB6npaUVVbcAAADgYsXmLg2zZs1ShQoV1LlzZ504cUJeXl5O5V5eXjp+/LgkXXO5m5ubPD09rfLLTZ48WT4+PtYUHBxclF0DAACACxWLwHvo0CG99tprevnll1W6dGmVL19e6enpTsukpaWpQoUKknTN5Tk5OUpPT7fKLzd69GidOnXKmpKSkoqyewAAAHAhlw9puHDhgnr27KmWLVvqiSeekCQFBAQoNTVV2dnZ1rCGw4cPW8MUAgIClJiY6FTP5eXJyclWWWpqqowxeYZB5PLw8LDGAwMAAMBeXHqGNzs7WwMHDtTJkye1YMECORwOSVJoaKgyMjIUHx8vSTLGKCYmRuHh4ZKksLAwffPNN9a4299//1379+93Ko+Ojra2ExMTI39/fzVo0OBmdg8AAADFgMsCb27Y3bBhgz7//HNduHBBKSkpSklJUcWKFdWzZ08999xz2rVrl15++WUdPXpUffr0kSTdc889Kl++vIYNG6aEhAQNGTJEoaGhatSokSRp0KBB2rBhg2bPnq2tW7dqzJgxioyMVMmSJV3VXQAAALiIywLvZ599poULF+r333/XbbfdpsqVK1uTJL3//vuqVauWwsPDtXr1aq1Zs0Z+fn6SpFKlSik6Olo//fST2rZtK0latmyZVXfDhg21ZMkSzZw5U126dFHXrl01fvz4m95HAAAAuJ7LxvD26dPHOmObn7Jly1o/NJGfOnXqaOPGjVcs79q1q7p27XpdbQQAAMBfX7G4SwMAAABwoxB4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGsEXgAAANgagRcAAAC2RuAFAACArRF4AQAAYGvurm4AAACAHTQfudDVTbCN7VMfLdL6OMMLAAAAWyPwAgAAwNYIvAAAALA1lwbeo0ePasyYMapatapatGjhVHbmzBn169dPFSpUUEhIiLZs2eJUvn//frVt21blypVTly5ddOTIEafyL7/8Uo0bN1alSpX0zDPPKCsr64b3BwAAAMWPSwNvUlKSDhw4IG9v7zxlAwcO1IEDBxQdHa1OnTqpU6dOVqjNyMhQhw4dVK9ePW3YsEEOh0P333+/te7333+vXr16aejQoVq+fLm++uorjR079qb1CwAAAMWHSwPv7bffrsWLF6tHjx5O81NSUrR06VJNmzZNzZo10yuvvKJKlSpp0aJFkqQVK1bo1KlTmjFjhho3bqzZs2dry5Yt2rVrlyRp7ty5CgsL05AhQxQSEqJJkyZp7ty5unDhws3uIgAAAFysWI7h3bx5s8qUKaOQkBBJksPhUHh4uGJjYyVJcXFxatOmjTw8PCRJVapUUb169ZzKIyIirPrCw8N17Ngx7dmz5yb3BAAAAK5WLO/Dm5qaKn9/f5UoUcKaFxgYaJ3BTU1NVUBAgNM6gYGBSk1Nzbfc399fbm5uVvnlMjIylJGRYT1OS0srqq4AAADAxYrlGd4TJ07Iy8vLaZ6Xl5eOHz/+p8rd3Nzk6elplV9u8uTJ8vHxsabg4OCi7A4AAABcqFgG3vLlyys9Pd1pXlpamipUqPCnynNycpSenm6VX2706NE6deqUNSUlJRVldwAAAOBCxXJIQ0BAgFJTU5WdnW0Nazh8+LA1TCEgIECJiYlO61xenpycbJWlpqbKGJNnGEQuDw8PazwwAAAA7KVYnuENDQ1VRkaG4uPjJUnGGMXExCg8PFySFBYWpm+++cYad/v7779r//79TuXR0dFWfTExMfL391eDBg1uck8AAADgai4NvMePH1dKSopOnz6tzMxMpaSk6OjRo6pYsaJ69uyp5557Trt27dLLL7+so0ePqk+fPpKke+65R+XLl9ewYcOUkJCgIUOGKDQ0VI0aNZIkDRo0SBs2bNDs2bO1detWjRkzRpGRkSpZsqQruwsAAAAXcGngffDBB1W5cmW9+eabSkhIUOXKlXXHHXdIkt5//33VqlVL4eHhWr16tdasWSM/Pz9JUqlSpRQdHa2ffvpJbdu2lSQtW7bMqrdhw4ZasmSJZs6cqS5duqhr164aP378Te8fAAAAXM+lY3jj4uKuWFa2bFnrhybyU6dOHW3cuPGK5V27dlXXrl2vp3kAAACwgWI5hhcAAAAoKgReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2BqBFwAAALZG4AUAAICtEXgBAABgawReAAAA2JqtA68xRq+88oqCg4NVt25dzZ0719VNAgAAwE3m7uoG3EizZ8/WtGnTtHTpUv3xxx/q16+fgoKC1LlzZ1c3DQAAADeJbQOvMUbvvvuuRo4cqbCwMEnS2rVrNXv2bAIvAADA34hthzQcP35cP/zwgyIiIqx54eHhio2NdWGrAAAAcLPZ9gxvamqqJCkgIMCaFxgYqLS0NJ07d05lypSx5mdkZCgjI8N6fOrUKUlSWlpaobaVnXGuKJr8t1fY/X0tODZFp6iPD8em6PDaKb44NsUb72vFV2GOTe4yxpirV2hsatOmTUaSOXHihDVvx44dRpI5dOiQ07Ljxo0zkpiYmJiYmJiYmP5iU1JS0lVzocOYwsTiv559+/apQYMG+u233xQcHCxJ2rBhg9q3b69z586pdOnS1rKXn+HNycnR8ePHVaFCBTkcjpve9qKWlpam4OBgJSUlydvb29XNwSU4NsUbx6f44tgUXxyb4s1Ox8cYo/T0dAUGBsrNreBRurYd0pA7lCE5OdkKvIcPH5avr69T2JUkDw8PeXh4OM3z9fW9Ke28mby9vf/yT2674tgUbxyf4otjU3xxbIo3uxwfHx+fQi1n24vWypUrpyZNmig6OtqaFxMTo/DwcBe2CgAAADebbc/wStLQoUP1wgsvqGXLljp+/LgWLlyoFStWuLpZAAAAuIlsHXgHDx6s1NRUPfLIIypTpozeffdd3X333a5u1k3n4eGhcePG5Rm2Adfj2BRvHJ/ii2NTfHFsire/6/Gx7UVrAAAAgGTjMbwAAACAROAFAACAzRF4AQAAYGsE3mJs5MiRatmypdO84cOHq0GDBk7zxo8fr86dO//p7QwYMEA9evQocBk/Pz99+OGHf3obf1VX2jfx8fGqUqWKkpKSJF28+fWoUaNUrlw5BQUFWT9tfTPExcXJ4XDo9OnTN22bfyUDBgyQw+HId6pevfp11/3www8XTUNd6Er7KC4uztVNu6qoqKirvn/hokuPc8mSJVWjRg394x//cPrhpT+rR48eioqKKoJW2kN2drb++c9/qnbt2vL29labNm30+eefF1n9ISEhmj59eqGWLegz4uDBg3I4HBo6dGiesnnz5snhcFzXZ/+MGTOc3mdd+Xol8BZjt99+u3744Qfl5ORY8zZt2qSffvpJx44ds+Z9//33atasmSua+Lfl4+Oj+vXr65ZbbpF08Zf9pk6dqkmTJik6Olr+/v5/uu7PP/9cLVq0KKqm/u29/fbbSk5OVnJysoYMGaKWLVtaj7dt2+bq5hUb9957r7VfcqdWrVoVat0uXbpoxowZ192GP/PcDw4OVu3ata972zdaw4YNi8VtMXOP8/79+/Xmm2/qgw8+0Isvvnjd9daqVUtVq1Ytghbaw9SpU/Xuu+9q4sSJio+P1+OPP65//OMf+v3334uk/vr166ty5cpFUpeHh4e++OILZWdnO83//PPPr/rrZdfKla9XW9+W7K/u9ttv1+nTp/Xrr7+qRo0aSktL06+//qrQ0FBt2rRJ3bp1k3Qx8Pbp08e1jf2bqV+/vtavX289PnLkiCTp4YcftsUv19iJj4+P9Us8ZcuWValSpaxfYsT/K1269F9yvzzzzDOubsJfyqXHuUaNGtq3b58++ugjvfnmm9dV7z//+c+iaJ5tfPbZZ4qMjLQ+m2+99VY98sgjKlGiRJHUv3DhwiKpR7r4y7JVqlTRpk2b1K5dO0nS8ePHFR8fr4YNGxbZdiTXvl45w1uM1alTR2XLllVCQoIk6bvvvlPTpk3VokULffPNN5Iu/ib2L7/8Yp3hXbdunUJCQuTl5aWQkBCnUCZJDodD+/bt0/333y9fX1+dOnUqz3azs7M1btw4BQUFKSgoSBMmTMjzn9/f3Q8//CCHw6GDBw8qLi5OYWFhki6GqwEDBkiSTp8+rSFDhsjf31/VqlVz2o9nz55V//79Vb58eVWtWlUjRozQmTNnNGDAAPXs2VPbt2+3vnJfuHCh/P39lZmZaW0/JSVFbm5uSkxMzLd9O3bsUJs2beTl5eX0fEH+1q1bp+bNm8vLy0vt2rXTDz/8YJUZYzRnzhw1bNhQ3t7e6tixo/bt2+dUPmvWLDVs2FB+fn565ZVXXNGFG2bAgAGaNGmS3nnnHd12223y8/PTxIkTrXKHw6Gvv/5aw4cPl8Ph0Pjx4yVJmZmZeumllxQcHKzKlSvr6aef1rlz5yRd/Bq1dOnSOnjwoMLCwlStWjX1798/z3NfuvjB++KLL6pp06by8vJSWFiYfv75Z2v7UVFRat++vfW4evXqio6O1tNPP63AwEDVqFFDX3/9tVX+4YcfqmPHjlq5cqXuuOMOVa5cWWPGjFFmZqZGjRqlwMBA1atXT5s3b3baDwU9R9q3b6+FCxdq/PjxqlmzpgIDAzVv3jyrrw6HQ3v27NF999133V8RFzUPDw+VL19e0sV+XDos4fTp005DW670viVdHNKQ+94nXXxeJCQk6NFHH5Wfn59uu+02bd261WnbBe3TVatWqWHDhvL09FRoaKg2bNhw1TYUJ15eXoqOjtb58+eteZeH3at9Xp85c0bPP/+8qlevrooVK+rxxx/XyZMnJUktWrSwXms5OTlasmSJOnTooHLlyqlWrVr6+OOPC93Ws2fP6sEHH3QacvHVV1/p7rvvdvqGWSr4c02Sdu/erbZt28rb21vt27fX7t27nda//PW6bds29ezZU8HBwfL391dUVNQNyxsE3mLMzc1NzZo1s54wmzZtUkhIiFOA2b17tzw9PVWrVi1t3rxZ9957r/r06aNt27bpoYceUufOnfXdd9851fvAAw/onnvu0datW/P9DeoJEyZo5syZmj59utauXauMjIx8gzEuatWqlZYuXSpJOnDggN5++20ZY9SrVy+lpaUpNjZWixcv1pIlS/TBBx9IkqZNm6a1a9dq2bJlWrZsmRwOh6SLX7+PGDFCjRs3tr5y79GjhzIyMrRu3Tprm1988YWaNWumOnXq5GnPL7/8ooiICA0YMEA7d+7U0KFD1bVrV6dhMPh/W7ZsUd++fTV69Gjt3LlTnTt31n333WeNa3zzzTc1atQojRo1Slu3brX+ucn1xRdfKCEhQYsWLdKYMWM0btw47d271xVduWFef/117d27V//61780ZswYvfzyy1Yfk5OTFRQUpEmTJik5OdkKTM8884y2bt2qL7/8UqtWrdL333+vV1991aozIyNDDz74oIYOHaq4uDhNnz49z3NfkhISEnTkyBFNnz5dW7ZsUVZWlp566qkC29u3b1/VqlVLMTExatWqlQYNGqSsrCyrfPPmzZo2bZreffddTZkyRZMmTdIdd9whPz8/rV+/XrVq1XIa03i154gkPf3008rJydGKFSvUr18/PfXUUzp+/LiCg4OVnJws6eJZueTkZPXu3fs6j8j1y87O1rZt2/TOO+/owQcfLNQ6V3rfupIuXbooIiJCmzZtkr+/v4YNG2aVFbRPMzIy1LNnT3Xo0EHbtm3T448/bu3Da22Dq7z00kvasmWL6tevr7lz51r/7OW62ue1MUa9e/fW6tWrNW/ePK1fv17+/v4qVapUnm1lZWXp008/VWRkpOLj4zV48GA99thj1jUmV3P27Fn16NFDS5cutQLukiVL1KNHD6d2X+1z7dixY2rbtq2qV6+u7777Tv/4xz+uerLls88+U2hoqFatWqX58+dr5syZRTrW2YlBsfb000+bnj17GmOMad++vfn3v/9tfv31V1OiRAmTnp5uZs6caVq3bm2MMSYiIsIMHDjQaf3+/fubjh07Wo8lmaeffjrPMt27dzfGGHP+/HlTunRpM2/ePKdlfHx8zPz584u6e8XepfvmUrt37zaSzC+//GKMMSY2NtZIMunp6cYYY7799ltTuXJlk5mZaa2zaNEiExYWZowxZtSoUaZ169YmOzs7T93jxo0zzZs3d5r35JNPmj59+liPO3ToYF5//fV8tx0ZGWlGjRrltH7Hjh3NggULrrX7tjNixAjTrl07p3kdO3Y07777rvU4JyfH1K1b12zYsMFcuHDBlC1b1syePTvf+i5/fZ0+fdqUKFHCfPTRRzek/TdK//79jZubmylbtqw1jR071iq7Wh+rVatm3nnnHetxUlKSKV26tDlx4oQ179tvvzU1atQwxhjzyy+/GEnmf//3f53akd9z/3ILFiwwpUuXNjk5OcaYvMe0WrVq5rXXXrMer1mzxkgySUlJxhhj5s+fb7y8vMy5c+eMMcZkZ2cbb29v8/zzz1vrfPzxx0aSycjIMMYU/Bwxxph27dqZyMhIq/ynn34yksw333xjzZNkli9fXmDfbrRLj3OJEiWMJNO8eXNz5MgRY8zFfowYMcJaPj093UgysbGxxpiC37e6d+9u+vfvbz2WZBYtWmQ9njNnjnF3d7fWLWifpqWlGTc3N7NmzZo82ymoDcXNTz/9ZDp16mQkmcDAQKf+XO3z+ttvvzUOh8Ps3bs337qbN29uxo0bl2/Z+fPnjYeHh1m8eLExJu9nxKVyX4vGGNOkSROzceNGc+LECVOuXDlz5swZU61aNeuz/2qfaxMmTDA1atRwKn/rrbdMtWrVrMf5vQdfKiwszAwZMuSK5deDM7zFXPPmzfXDDz9Y/423atVKVatWVWBgoHbs2KE9e/ZYwxl27drl9FWBJIWHh2vXrl1O8wr6eeXExESdP39eERERRd2Vv5UdO3YoNTVVvr6+8vT0lKenpx577DEdOnRIkvTss8/KGKOGDRvqgw8+cBqukJ9Bgwbpyy+/VHp6uv744w9t2LBBvXr1uuK2p02bZm3X09NT69evt7YNZzt27NDzzz9v7SsvLy8lJibq0KFDSkxM1JkzZwq8C4qXl5f1d9myZRUYGGh97fhXcvfdd2vXrl3W9PTTT1tl19rH77//XufPn1dQUJC1Xzt06JDnOVjYn3pPSEjQ008/rZCQEI0ZM0bnz58v8GvsS9ube4HMpe0tVaqUSpcuLeniN2m1atVyWif3m5O0tDRJBT9HCrvN4iL3OP/www+Kjo5WjRo11LRpUx09evSq617r+9bl+yQrK0tnz56VVPA+9fLy0vTp09WvXz8NHDhQ+/fv/9NtcKW6detq9erV2rZtm+rUqaNOnTpp7dq1kq7+eb1z505Vq1ZNt956a6G2deLECU2fPl133323mjRpouzs7EId00v17NlTy5Yt08qVK9WxY0frouxcV/tcS0hIUPv27eXuXvjLwzIzM7VkyRJ1795dDRs21Pbt26+53YXFRWvF3O233679+/crISFBVapUkZ+fnyQpNDRU//nPf7Rv3z7rtkjmCr8SffkYnILkvnlcyxMWeRljVKVKFcXExDjNz/06qnLlytq0aZPWrVunV155RVOmTNHGjRuveNHQ7bffrltvvVVffPGFJOmOO+5QtWrVrrjtoUOH5vnaN/e5A2fGGE2cONG6CDRXQECAfvnlF0m6pq9Ni/qq5pvF09Oz0FdPX62Pxhi5u7trx44dTsv+mX3zr3/9S0899ZTeeOMNTZw4UTt37swzrOR62prfMpc/Lug58me36SqXHuf69esrLCxMVapU0aeffiqp4M+La33futS17tOnnnpKvXv31rRp03TnnXfqxRdf1MiRI6+rDa7SokULxcTEqHnz5vr888/VsWPHq35eZ2dnF/p9Jzk5Wa1bt1ZERIRmzJihunXrqkaNGtfczp49e6pjx446fPhwvsNurva5lpmZeU3ZIScnR926dVN6erpee+013XXXXRo0aNANu8Vm8X1VQtLFN6SSJUtq0aJFat26tTU/NDRU27dv148//qimTZtKkpo2bWoN7M8VGxt7Tbcsq1mzptzc3BQfH2/NM8ZcU2iG1KRJEx0+fFju7u6qXbu2NV162x6Hw6GOHTtqw4YNysnJ0WeffSbp4j8buWdBLjV48GB9+umnWr16dYFjAJs0aaLdu3c7bbd27dry9fUt8n7aQZMmTfTjjz/m2V+enp6qU6eOPDw88lxMAmeXP2cbN26srKwsHTlyxGmf1qxZ85rqkaS33npLw4cP18CBA+Xj4+OS96KCniOFVaJEiXxf165mjFFWVpbKlCkjLy8v/fe//7XK8ruF1pXet65VYfapn5+fXn31VU2bNk2TJ08u8jbcSJff8tDNzU2BgYEqW7aspKt/Xjdq1EgHDx7UgQMHrrqt3FuKzZkzR/Xq1ZPD4fhTr5O6devK19dXa9eu1f/8z//kKb/a51qdOnW0detWpzBf0AVoP/74o1auXKkFCxYoNDRU7u7uN/T1zWm8Ys7d3V1NmjTRwoULNWnSJGt+aGioJk6cqD/++EO33XabJGncuHHq0KGDGjVqpM6dO2vVqlX65JNPFBsbW+A2vLy8dPDgQZ07d04+Pj7q27evRowYIV9fX/n6+mrKlCl/6x81OH36dJ43nSv9d54rNDRU4eHheuCBBzRt2jRVrlxZ69evV4sWLdSiRQs9+OCDCg8PV4cOHbRnzx4dPHjQetOoXbu2fvrpJ61bt05BQUHWV1p9+vTRCy+8IHd3d73xxhvWtnK/NkxKStKtt96q0aNHq1GjRnruuec0ePBgnTlzRsuXL9e4ceOK7JY4djJhwgS1b99eNWvWVPfu3XX06FF98803Gj16tEqXLq2RI0cqKipKZcuWVZMmTbRq1Sr5+/urb9++rm56kTp//rxSUlKc5hX2Fnu1a9fWv//9b913330qW7asqlatqsGDB+uRRx7RjBkzVK9ePW3ZskUVKlTI94P00nouf+57enpqzZo16t69u3799VeNHj36uvr5ZxT0HCms2rVra/HixWrWrJm8vLxcdkYy9zhnZWUpKSlJb731lowx6ty5s3799Ve98847io2N1alTp/Tqq69aQz+MMQW+b12rgvbp+vXrNW3aND3//PMqX768li5dqqpVqxZ5G26U3377TWFhYXrggQf06KOPqkaNGlq5cqViYmKsO5xc7fO6Xbt2Cg0NVc+ePTV9+nT5+Pho7ty5Gjp0qOrXr++0PU9PTyUlJenrr79WYGCg3n77bafX8uWfEQXp37+/fvrpJ5UpUyZPWUGfa3fccYcGDx6st956S6NHj9aAAQO0fft2TZ48+Yr/GOYOmViwYIF69eqlf//731q2bNl1/ZBWgW7IyGAUqSeffNJIchq8npWVZXx8fEyTJk2cll29erVp3ry5KVu2rGnevHmeQf/K58KJ9evXGy8vL/Pxxx8bY4xJS0szjz76qClfvry59dZbzdy5c02bNm3+thetScozXe2iNWMu7sehQ4eaoKAg4+vra+655x6TkJBgjLk4+P/uu+82Pj4+JjAw0IwfP966COf8+fOmR48epmzZsqZhw4bmwoULVp29e/fOM+A/MzPTNGrUyOnCou3bt5v27dsbLy8vU716dRMVFWXOnj17g/bSX8eVLphYt26dCQkJMWXLljV169Y1kyZNsi6KycrKMpMnTza1atUy3t7epnPnzmb37t3GmPwvarz8Aq6/gis9z2fNmlWoPv7nP/8x9evXN15eXtbFbhkZGWbMmDGmZs2axsvLy7Rr185s3LjRGPP/F8rk7sdc+T33d+zYYW677Tbj6+trHnzwQfOf//zH6bWW30Vrl7bt8m3Nnz/fVKhQwWm7l18AtG3bNiPJHD161JpX0HPk8ou9jMn7Xrtq1SpTtWpV4+vra+bMmVPA0bhxLj3ODofDVK5c2XTv3t16X/rjjz9Mp06djJeXl4mIiDB79+41NWvWtC5aK+h9K7+L1i7tf37vkVfap2fOnDEvvviiqV+/vilbtqxp27atdfwKakNxkpiYaPr162dq1aplvLy8TGhoqLUfc13t8/r48eNm4MCBplKlSqZSpUpm8ODB5tixY8YY5+dsRkaGeeihh6x6PvvsM3PfffdZr4P8PiNyXXrRWn4uvWjNmII/14wxZsWKFaZhw4bGx8fHdO/e3Xz88ccFXrT2+uuvm3LlyplatWqZCRMmmClTpuR7oXhRcBhzlVNVAIqF7OxsNW7cWC+88IIeffRRVzcHAIC/DIY0AMWcMUa//PKLPvzwQ5UoUUL9+vVzdZMAAPhLIfACxdxvv/2mxo0bq169evroo48YhwsAwDViSAMAAABsjduSAQAAwNYIvAAAALA1Ai8AAABsjcALAAAAWyPwAgAAwNYIvABQxPz8/PThhx+6uhl5xMXFyeFwKCsry9VNuaLcNv6df84cQNEj8AKACzVs2FArVqxwdTMAwNYIvAAAALA1Ai8AXIfs7GyNGzdOQUFBCgoK0oQJE5SdnS1JysnJ0ZIlS9ShQweVK1dOtWrV0scffyxJOnjwoBwOh/bs2aP77rtPDofDGgYRHR2tLl26yN/fX8HBwZo6deo1tWn37t3q2LGjfH191aBBA82bNy9PeadOneTj46O7775bR44cscq2bdumnj17Kjg4WP7+/oqKirL6I0kOh0MJCQl69NFH5efnp9tuu01bt261ysePH6/IyEgtXrxYLVq0kK+vr4YOHaqcnBxrmdOnT2vIkCHy9/dXtWrVnPYZANwIBF4AuA4TJkzQzJkzNX36dK1du1YZGRk6deqUJCkrK0uffvqpIiMjFR8fr8GDB+uxxx5TUlKSgoODlZycLElauHChkpOT1bt3bxlj9Mknn+jBBx/Uxo0b9dprr2nUqFGKj48vVHsOHjyou+66S7Vq1dKmTZs0depUZWZmOi3zxBNPKCoqSuvXr9fu3bv1xhtvWGWfffaZQkNDtWrVKs2fP18zZ87U559/7rR+ly5dFBERoU2bNsnf31/Dhg1zKl+8eLE+//xzzZkzR++//75mzZql1atXS5KMMerVq5fS0tIUGxurxYsXa8mSJfrggw+ubccDwLUwAIA/5fz586Z06dJm3rx5TvN9fHzM/Pnz813ew8PDLF682JonySxfvrzA7dSoUcNMmTKlUG2KjIw0LVu2NDk5OXnKYmNjjSRz+PBha16fPn1Mhw4drlhfWFiYGTJkiFN7Fy1aZD2eM2eOcXd3N9nZ2cYYY8aNG2fq169vPc7JyTEBAQFm4sSJxhhjvv32W1O5cmWTmZlp1bFo0SITFhbm1Mb09PRC9RcACsPdtXEbAP66EhMTdf78eUVERFxxmRMnTuijjz7S8uXLlZSUpOzsbB09erTAes+ePatPP/1US5cu1c8//6yUlJSrrpNr586duvfee+VwOK64jJeXl/V37dq1tX//futxZmamvvzySy1evFg//fSTkpKSVL58+QLXz8rK0tmzZ+Xp6SlJKlu2rNzcLn6B6HA4VKdOHZ08eVKStGPHDqWmpsrX19eqIysrS9WqVStU/wDgzyDwAsCflDtUwN09/7fS5ORktW7dWhEREZoxY4bq1q2rGjVqFFjnmTNn1L59ewUFBWnixIlq0qSJwsPDC92m7OzsAsPu5XKDqXRxzHG3bt2Unp6u1157TXfddZcGDRpU4C3CLl2/MMsYY1SlShXFxMQ4LVOqVKlCtxkArhWBFwD+pJo1a8rNzU3x8fF64IEHJF0MdLkXaH3xxRfKzs7WnDlzrBB66cVbklSiRAmdPXvWerxhwwbt2rVLmzZtkoeHR77rFKRRo0Zav369xo4de03BV5J+/PFHrVy5Uj///LMVzK9l24XRpEkTHT58WO7u7qpevXqR1g0AV8JFawDwJ/n4+Khv374aMWKEYmNjtXPnTj300EPWGVFPT08lJSXp66+/1o4dO9S/f3+lpKQ41VG7dm0tXrxYiYmJSklJkaenp7KysrRgwQLt3r1bUVFRhb5gTZJeeOEFfffddxoxYoT27t2rNWvWaNiwYTLGXHXdW265RZK0YMEC7d27V5MnT9ayZcuuYY9cXWhoqMLDw/XAAw9ow4YN2r9/v2bNmqVt27ZJ+v/hEklJSUW6XQB/bwReALgO7777rtq0aaMePXqoX79+6tixo0JDQyVJDz30kHr37q2HHnpIkZGR6tKlizp37uy0/rRp07R9+3aFhIToq6++Ups2bfTss88qKipKffr0UY0aNRQZGVno9tx6662Ki4tTfHy87rzzTo0YMULNmjUr1LrVq1fX66+/runTp6tr167KzMzU2LFjC78zCsHhcGjp0qVq1aqVHn74Yd15551asWKFSpcuLeniGeBGjRrp2WefLdLtAvh7c5jC/NsPAAAA/EVxhhcA/iISEhLk6el5xSkhIcHVTQSAYokzvADwF5GRkVHg2Nbg4GDrQjcAwP8j8AIAAMDWGNIAAAAAWyPwAgAAwNYIvAAAALA1Ai8AAABsjcALAAAAWyPwAgAAwNYIvAAAALA1Ai8AAABs7f8ArPQwVahlh2sAAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 이상치 개수 및 비율 계산 (IQR 방식)\n", + "outlier_counts = {}\n", + "for col in num_cols:\n", + " Q1 = X[col].quantile(0.25)\n", + " Q3 = X[col].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR\n", + "\n", + " mask = (X[col] < lower) | (X[col] > upper)\n", + " outlier_counts[col] = mask.sum()\n", + "\n", + "out_df = pd.Series(outlier_counts, name='이상치 개수').sort_values(ascending=False).to_frame()\n", + "out_df['이상치 비율(%)'] = out_df['이상치 개수'] / len(X) * 100\n", + "out_df = out_df.sort_values('이상치 비율(%)', ascending=False)\n", + "out_df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "FBhPBxXYBsnR", + "outputId": "6e06bb98-cb54-4b0d-e51c-f56d4d5093ab" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 이상치 개수 이상치 비율(%)\n", + "kw_min_min 4601 20.725225\n", + "title_sentiment_polarity 4094 18.441441\n", + "num_imgs 3528 15.891892\n", + "LDA_00 3342 15.054054\n", + "n_non_stop_words 3091 13.923423\n", + "LDA_01 3082 13.882883\n", + "LDA_02 2626 11.828829\n", + "LDA_03 2512 11.315315\n", + "avg_negative_polarity 1878 8.459459\n", + "LDA_04 1861 8.382883\n", + "self_reference_avg_sharess 1659 7.472973\n", + "kw_min_max 1633 7.355856\n", + "kw_max_max 1621 7.301802\n", + "self_reference_min_shares 1605 7.229730\n", + "num_hrefs 1573 7.085586\n", + "self_reference_max_shares 1540 6.936937\n", + "min_positive_polarity 1533 6.905405\n", + "num_videos 1481 6.671171\n", + "kw_max_min 1454 6.549550\n", + "kw_max_avg 1424 6.414414\n", + "avg_positive_polarity 1340 6.036036\n", + "global_subjectivity 1299 5.851351\n", + "n_tokens_content 1277 5.752252\n", + "kw_avg_min 1259 5.671171\n", + "n_non_stop_unique_tokens 1252 5.639640\n", + "abs_title_sentiment_polarity 1221 5.500000\n", + "max_negative_polarity 1210 5.450450\n", + "kw_avg_avg 1196 5.387387\n", + "average_token_length 1074 4.837838\n", + "n_unique_tokens 1059 4.770270\n", + "num_self_hrefs 1047 4.716216\n", + "global_rate_positive_words 1018 4.585586\n", + "global_rate_negative_words 953 4.292793\n", + "rate_positive_words 953 4.292793\n", + "kw_avg_max 821 3.698198\n", + "global_sentiment_polarity 699 3.148649\n", + "rate_negative_words 467 2.103604\n", + "n_tokens_title 68 0.306306\n", + "num_keywords 21 0.094595\n", + "kw_min_avg 0 0.000000\n", + "min_negative_polarity 0 0.000000\n", + "max_positive_polarity 0 0.000000\n", + "title_subjectivity 0 0.000000\n", + "abs_title_subjectivity 0 0.000000" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
이상치 개수이상치 비율(%)
kw_min_min460120.725225
title_sentiment_polarity409418.441441
num_imgs352815.891892
LDA_00334215.054054
n_non_stop_words309113.923423
LDA_01308213.882883
LDA_02262611.828829
LDA_03251211.315315
avg_negative_polarity18788.459459
LDA_0418618.382883
self_reference_avg_sharess16597.472973
kw_min_max16337.355856
kw_max_max16217.301802
self_reference_min_shares16057.229730
num_hrefs15737.085586
self_reference_max_shares15406.936937
min_positive_polarity15336.905405
num_videos14816.671171
kw_max_min14546.549550
kw_max_avg14246.414414
avg_positive_polarity13406.036036
global_subjectivity12995.851351
n_tokens_content12775.752252
kw_avg_min12595.671171
n_non_stop_unique_tokens12525.639640
abs_title_sentiment_polarity12215.500000
max_negative_polarity12105.450450
kw_avg_avg11965.387387
average_token_length10744.837838
n_unique_tokens10594.770270
num_self_hrefs10474.716216
global_rate_positive_words10184.585586
global_rate_negative_words9534.292793
rate_positive_words9534.292793
kw_avg_max8213.698198
global_sentiment_polarity6993.148649
rate_negative_words4672.103604
n_tokens_title680.306306
num_keywords210.094595
kw_min_avg00.000000
min_negative_polarity00.000000
max_positive_polarity00.000000
title_subjectivity00.000000
abs_title_subjectivity00.000000
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "out_df", + "summary": "{\n \"name\": \"out_df\",\n \"rows\": 44,\n \"fields\": [\n {\n \"column\": \"\\uc774\\uc0c1\\uce58 \\uac1c\\uc218\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1089,\n \"min\": 0,\n \"max\": 4601,\n \"num_unique_values\": 39,\n \"samples\": [\n 821,\n 68,\n 3091\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\uc774\\uc0c1\\uce58 \\ube44\\uc728(%)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.907302909758159,\n \"min\": 0.0,\n \"max\": 20.725225225225223,\n \"num_unique_values\": 39,\n \"samples\": [\n 3.698198198198198,\n 0.3063063063063063,\n 13.923423423423422\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 이상치 비율 상위 8개 변수 재시각화\n", + "top8 = out_df.head(8).index.tolist()\n", + "for col in top8:\n", + " plt.figure(figsize=(11,4))\n", + " plt.subplot(1,2,1)\n", + " sns.histplot(X[col], bins=30, kde=True)\n", + " plt.title(f'{col} 히스토그램', fontproperties=fontprop)\n", + " plt.subplot(1,2,2)\n", + " sns.boxplot(x=X[col])\n", + " plt.title(f'{col} 박스플롯', fontproperties=fontprop)\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "CHCVPb9wV9fp", + "outputId": "e444c011-e7cd-49cf-82ba-8f1021e16b40" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 이상치 비율 상위 8개 변수 재시각화 분석\n", + "이상치 비율이 10% 이상인 상위 8개의 변수를 재시각화하였습니다.\n", + "\n", + "처리 기법:\n", + "- 로그 변환\n", + "- Winsorizing\n", + "\n", + "Skewness(왜도) 계산 후\n", + "- skew > 1.0 (극단적인 꼬리): 로그 변환\n", + "- swew 0.5~1.0 (적당한 꼬리, 소량의 이상치): Winsorizing\n", + "- skew 음수 값 포함(±범위): Winsorizing\n", + "\n", + "EDA 단계에서는 로그 변환/Winsorizing 시도하여 분포가 어떻게 변하는지만 시각화하였습니다.\n", + "\n", + "최종적으로 어떤 변환을 적용할지 결정하고 제거, 교체하는 단계는 이후 피처 엔지니어링 단계에서 진행합니다.\n" + ], + "metadata": { + "id": "3Yrul4qGXmuj" + } + }, + { + "cell_type": "code", + "source": [ + "# top8 로그 변환 및 Winsorizing\n", + "X_log = pd.DataFrame(index=X.index)\n", + "X_cap = pd.DataFrame(index=X.index)\n", + "\n", + "for col in top8:\n", + " # 로그 변환\n", + " # - 양수 변수: log1p\n", + " # - 음수 포함 변수: shift → log1p\n", + " if X[col].min() >= 0:\n", + " X_log[f'{col}_log'] = np.log1p(X[col])\n", + " else:\n", + " shift = -X[col].min() + 1e-6 # 최소값을 0 이상으로 올리기 위한 shift\n", + " X_log[f'{col}_log'] = np.log1p(X[col] + shift)\n", + " # Winsorizing (상·하위 1% 컷)\n", + " capped = winsorize(X[col], limits=(0.01, 0.01)).data\n", + " X_cap[col] = pd.Series(capped, index=X.index)\n", + "\n", + "X_cap" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "7rMCo3e6cu14", + "outputId": "ad0b39b1-f3fc-4da5-b13c-0bebf17a717d" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " kw_min_min title_sentiment_polarity num_imgs LDA_00 \\\n", + "0 217.0 0.500000 11.000000 0.139798 \n", + "1 217.0 0.000000 4.473431 0.028627 \n", + "2 -1.0 0.125000 1.000000 0.028573 \n", + "3 -1.0 0.070169 4.473431 0.025806 \n", + "4 -1.0 0.000000 4.473431 0.524843 \n", + "... ... ... ... ... \n", + "22195 -1.0 0.000000 1.000000 0.022224 \n", + "22196 4.0 0.000000 4.473431 0.034357 \n", + "22197 4.0 0.000000 1.000000 0.486985 \n", + "22198 -1.0 0.000000 1.000000 0.366135 \n", + "22199 217.0 0.070169 1.000000 0.033334 \n", + "\n", + " n_non_stop_words LDA_01 LDA_02 LDA_03 \n", + "0 1.000000 0.020023 0.020008 0.020092 \n", + "1 1.000000 0.028671 0.320833 0.593291 \n", + "2 1.000000 0.028573 0.885708 0.028573 \n", + "3 0.970175 0.025004 0.187663 0.436807 \n", + "4 1.000000 0.025733 0.149361 0.025220 \n", + "... ... ... ... ... \n", + "22195 1.000000 0.022549 0.266434 0.666563 \n", + "22196 1.000000 0.033676 0.218162 0.035036 \n", + "22197 1.000000 0.437312 0.025092 0.025395 \n", + "22198 1.000000 0.033334 0.533862 0.225225 \n", + "22199 0.970175 0.034012 0.433641 0.463871 \n", + "\n", + "[22200 rows x 8 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
kw_min_mintitle_sentiment_polaritynum_imgsLDA_00n_non_stop_wordsLDA_01LDA_02LDA_03
0217.00.50000011.0000000.1397981.0000000.0200230.0200080.020092
1217.00.0000004.4734310.0286271.0000000.0286710.3208330.593291
2-1.00.1250001.0000000.0285731.0000000.0285730.8857080.028573
3-1.00.0701694.4734310.0258060.9701750.0250040.1876630.436807
4-1.00.0000004.4734310.5248431.0000000.0257330.1493610.025220
...........................
22195-1.00.0000001.0000000.0222241.0000000.0225490.2664340.666563
221964.00.0000004.4734310.0343571.0000000.0336760.2181620.035036
221974.00.0000001.0000000.4869851.0000000.4373120.0250920.025395
22198-1.00.0000001.0000000.3661351.0000000.0333340.5338620.225225
22199217.00.0701691.0000000.0333340.9701750.0340120.4336410.463871
\n", + "

22200 rows × 8 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "X_cap", + "summary": "{\n \"name\": \"X_cap\",\n \"rows\": 22200,\n \"fields\": [\n {\n \"column\": \"kw_min_min\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 66.36376410202523,\n \"min\": -1.0,\n \"max\": 217.0,\n \"num_unique_values\": 16,\n \"samples\": [\n 217.0,\n -1.0,\n 47.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title_sentiment_polarity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.24461687484881456,\n \"min\": -0.666666666667,\n \"max\": 1.0,\n \"num_unique_values\": 561,\n \"samples\": [\n 0.0708333333333,\n -0.0769230769231,\n 0.075\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_imgs\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.659256469453794,\n \"min\": 0.0,\n \"max\": 35.0,\n \"num_unique_values\": 37,\n \"samples\": [\n 26.0,\n 18.0,\n 13.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LDA_00\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2506365810118212,\n \"min\": 0.0200005614692,\n \"max\": 0.899818754221,\n \"num_unique_values\": 19432,\n \"samples\": [\n 0.0200022346282,\n 0.0333337687393,\n 0.101470907549\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n_non_stop_words\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.16152716696004726,\n \"min\": 0.0,\n \"max\": 0.999999999227,\n \"num_unique_values\": 1141,\n \"samples\": [\n 0.999999998908,\n 0.999999998873,\n 0.999999985915\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LDA_01\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2074587010201049,\n \"min\": 0.0200010275104,\n \"max\": 0.885583137562,\n \"num_unique_values\": 19340,\n \"samples\": [\n 0.0250157393744,\n 0.0333528463579,\n 0.0334605785738\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LDA_02\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2689736401492148,\n \"min\": 0.0200004787852,\n \"max\": 0.910432718705,\n \"num_unique_values\": 19572,\n \"samples\": [\n 0.866664575353,\n 0.0222249606953,\n 0.86666493669\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LDA_03\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2810191680992508,\n \"min\": 0.0200003079565,\n \"max\": 0.910769526191,\n \"num_unique_values\": 19250,\n \"samples\": [\n 0.229478147597,\n 0.0400534665564,\n 0.266148493961\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"원본 X shape:\", X.shape)\n", + "print(\"X_log shape:\", X_log.shape)\n", + "print(\"X_cap shape:\", X_cap.shape)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zhjfrEzPcuzZ", + "outputId": "d4edc49f-46fe-4e56-85f8-3d07fb4634e7" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "원본 X shape: (22200, 46)\n", + "X_log shape: (22200, 8)\n", + "X_cap shape: (22200, 8)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 데이터 분석\n", + "\n", + "- 변수들 사이의 관계 분석, 인과관계의 확인, 많은 변수와 유사성을 확인 → 정보를 효과적으로 추출\n", + "1. 변수들간의 관계 확인법\n", + " 1. 상관분석\n", + " 2. 교차분석\n", + " 3. 분산분석 (ANOVA / MANOVA)\n", + " 4. 회귀모델 (다중 선형회귀 / 로지스틱 회귀)\n", + "2. 차원축소법\n", + " 1. PCA (주성분분석)\n", + " 2. FA (요인분석)\n", + " 3. CCA(정준상관분석)\n", + " \n", + " +) t-SNE, UMAP\n", + " \n", + "3. 개체 분류법\n", + " 1. 판별분석 (LDA 선형판별분석/ QDA 이차판별분석)\n", + " 2. 로지스틱 회귀분석\n", + " 3. 군집분석 (K-Means, Hierarchical)\n", + " 4. MDS(다차원척도법)" + ], + "metadata": { + "id": "Fik4DH5Y9Ej2" + } + }, + { + "cell_type": "code", + "source": [ + "# 상관계수 매트릭스 & 히트맵\n", + "\n", + "corr = X[num_cols].corr()\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "sns.heatmap(corr,\n", + " annot=False,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1,\n", + " center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"수치형 변수 상관계수 히트맵\", fontproperties=fontprop)\n", + "plt.show()\n", + "\n", + "'''\n", + "n_non_stop_unique_tokens 등은 0.8 이상의 매우 높은 상관\n", + "→ 본문 단어 수가 많아질수록 “불용어 제외 단어 수”도 당연히 함께 늘어남\n", + "\n", + "self_reference_min_shares, self_reference_max_shares, self_reference_avg_shares 간에 0.9 이상\n", + "→ 참조된 다른 기사들의 공유량(최소·최대·평균)이 거의 동일한 경향성\n", + "\n", + "rate_positive_words ↔ rate_negative_words\n", + "→ 본문에서 긍정 비율이 높으면 부정 비율은 낮아지는 단순한 반비례 관계\n", + "\n", + "y(바이럴 여부)와 모든 수치형 피처 간 상관계수는 –0.05~0.05 사이로 매우 낮음\n", + "→ 선형적인(직선적인) 관계로는 “바이럴 여부”를 잘 설명하기 어렵다는 뜻\n", + "\n", + "(1) 다중공선성 주의\n", + "→ 상관계수가 0.8 이상인 피처들은 하나로 묶거나, PCA/차원 축소, 혹은 불필요한 피처 제거 고려\n", + "\n", + "(2) 타과의 비선형 관계 탐색\n", + "→ y와 선형 상관이 낮으니, Mutual Information, Tree-based Importance, SHAP 같은 기법으로\n", + "비선형·상호작용 관계를 살펴보기\n", + "\n", + "(3) 모델링시 피처 선택\n", + "초기에 상관이 낮은 피처 위주(예: LDA 토픽 점수, metadata)로 시도해 보고,\n", + "성능에 따라 군집화된 피처 중 대표 하나만 선택해 추가\n", + "'''" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 784 + }, + "collapsed": true, + "id": "tRp4dJABA-WH", + "outputId": "a34930a3-5e49-4dc9-c412-8d6e0fa5115e" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nn_non_stop_unique_tokens 등은 0.8 이상의 매우 높은 상관\\n→ 본문 단어 수가 많아질수록 “불용어 제외 단어 수”도 당연히 함께 늘어남\\n\\nself_reference_min_shares, self_reference_max_shares, self_reference_avg_shares 간에 0.9 이상\\n→ 참조된 다른 기사들의 공유량(최소·최대·평균)이 거의 동일한 경향성\\n\\nrate_positive_words ↔ rate_negative_words\\n→ 본문에서 긍정 비율이 높으면 부정 비율은 낮아지는 단순한 반비례 관계\\n\\ny(바이럴 여부)와 모든 수치형 피처 간 상관계수는 –0.05~0.05 사이로 매우 낮음\\n→ 선형적인(직선적인) 관계로는 “바이럴 여부”를 잘 설명하기 어렵다는 뜻\\n\\n(1) 다중공선성 주의\\n→ 상관계수가 0.8 이상인 피처들은 하나로 묶거나, PCA/차원 축소, 혹은 불필요한 피처 제거 고려\\n\\n(2) 타과의 비선형 관계 탐색\\n→ y와 선형 상관이 낮으니, Mutual Information, Tree-based Importance, SHAP 같은 기법으로\\n비선형·상호작용 관계를 살펴보기\\n\\n(3) 모델링시 피처 선택\\n초기에 상관이 낮은 피처 위주(예: LDA 토픽 점수, metadata)로 시도해 보고,\\n성능에 따라 군집화된 피처 중 대표 하나만 선택해 추가\\n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 27 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 그룹별 분석" + ], + "metadata": { + "id": "8J8S3LsnkEro" + } + }, + { + "cell_type": "code", + "source": [ + "# 1. 기초 텍스트 통계\n", + "g1_cols = [\n", + " 'n_tokens_title',\n", + " 'n_tokens_content',\n", + " 'n_unique_tokens',\n", + " 'n_non_stop_words',\n", + " 'n_non_stop_unique_tokens',\n", + " 'average_token_length'\n", + "]\n", + "\n", + "corr_g1 = df[g1_cols].corr()\n", + "\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g1,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 1 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 557 + }, + "id": "pZ5FjEv7jN5h", + "outputId": "60a19d8c-3503-4d36-e5ff-688c4ffc249c" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 2. 하이퍼링크&미디어 개수\n", + "g2_cols = [\n", + " 'num_hrefs', 'num_self_hrefs',\n", + " 'num_imgs', 'num_videos'\n", + "]\n", + "\n", + "\n", + "corr_g2 = df[g2_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g2,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 2 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "AvLXjPDHrO0b", + "outputId": "428269cc-ebbc-4fa8-8d99-ea6c3f34d22a" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 3. 키워드 통계 (키워드 수 & 위치 정보)\n", + "g3_cols = [\n", + " 'num_keywords',\n", + " 'kw_min_min', 'kw_max_min', 'kw_avg_min',\n", + " 'kw_min_max', 'kw_max_max', 'kw_avg_max',\n", + " 'kw_min_avg', 'kw_max_avg', 'kw_avg_avg'\n", + "]\n", + "\n", + "corr_g3 = df[g3_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g3,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 3 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 474 + }, + "id": "nvIhl-lNjNzf", + "outputId": "47f3e485-db51-4761-fa7d-a00fff03f082" + }, + "execution_count": 40, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 4. 자체 인용 공유 횟수\n", + "g4_cols = [\n", + " 'self_reference_min_shares',\n", + " 'self_reference_max_shares',\n", + " 'self_reference_avg_sharess' ## 오타 수정?\n", + "]\n", + "\n", + "corr_g4 = df[g4_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g4,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 4 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 553 + }, + "id": "x2aLL78vjNxG", + "outputId": "f86bdd05-ce26-44c4-e584-c467a7e57fa2" + }, + "execution_count": 41, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 5. 토픽별 모델링 출력\n", + "g5_cols = ['LDA_00', 'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04']\n", + "\n", + "corr_g5 = df[g5_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g5,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 5 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "xBBCzVb8jNt_", + "outputId": "ba33b6ba-9512-4b01-9474-6a6d32079520" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 6. 글로벌 감성 지표\n", + "g6_cols = [\n", + " 'global_subjectivity',\n", + " 'global_sentiment_polarity',\n", + " 'global_rate_positive_words',\n", + " 'global_rate_negative_words'\n", + "]\n", + "\n", + "corr_g6 = df[g6_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g6,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 6 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 560 + }, + "id": "-vreSC8gjNra", + "outputId": "84c1cb26-c0b5-457a-88ef-6a96be881450" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 7. 본문(내용) 감성 지표\n", + "g7_cols = [\n", + " 'rate_positive_words', 'rate_negative_words',\n", + " 'avg_positive_polarity', 'min_positive_polarity', 'max_positive_polarity',\n", + " 'avg_negative_polarity', 'min_negative_polarity', 'max_negative_polarity'\n", + "]\n", + "\n", + "corr_g7 = df[g7_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g7,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 7 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 523 + }, + "id": "bF-Tg97XjNoD", + "outputId": "7fe33fbc-4ee4-41c1-e444-f001c05b58fd" + }, + "execution_count": 46, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAH6CAYAAABlDGr5AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXdYFFfbh++l914UxQ6W2EUUK6LG3nuNPWpssZvYayxJ7N3X3qLYe8WCiogiooC9I7132Pn+WFzYAIoIMeE793XNxe45z5z5zXB25pnnNJkkSRICgUAgEAgEgkKB2rcWIBAIBAKBQCDIP4RzJxAIBAKBQFCIEM6dQCAQCAQCQSFCOHcCgUAgEAgEhQjh3AkEAoFAIBAUIoRzJxAIBAKBQFCIEM6dQCAQCAQCQSFCOHcCgUAgEAgEhQjh3AkEAoFAIBAUIoRzJxAIspCYmMivv/5KjRo1MDIyon79+mzcuJF/24I2pUqVQiaTZdnmz5+frb1cLmf58uXUqVNHeV579+79h1UrcHZ2ZuLEiV9VRteuXb+6jE+Rk8b80C4QCAoOjW8tQCAQ/LuIjY2lYcOGAMyePZsKFSrw4MED5s+fT9OmTSlbtuw3VpiBp6cnaWlpyu++vr506dKFQYMGZbGVJInevXvj7u7OsmXLqFatGrdv32bWrFk0a9YMS0vLf1L6FzNq1Ch0dHRYtmyZMq1s2bIUK1bsG6rKHypXrsxvv/1G27Ztv7UUgaBQIJw7gUCgwuLFi4mPj+fevXvo6ekBUL58ebp06YJMJvvG6lTJ7JBJkkTfvn2ZP38+NjY2WWzPnDnDkSNHCAgIoGTJkgBUqFCB3r17o6Hx37wVLl68+FtLEAgE/0JEs6xAIFAiSRIrV65kypQpSsfuI5kdOzc3NypUqICXlxeOjo40aNAAgOTkZKZPn07p0qWxtLSkT58+BAcHK/ebPXs2Dg4OKuU6ODgwe/ZsleOcP3+ezp07Y2pqSvXq1bl8+fJntV+8eJGAgABGjhyZbf6aNWvo06eP0rH7SGbH7mOzbYUKFTAxMaFdu3Y8f/78s+ddqlQprly5woABAzA1NeXhw4cAnD9/nlq1amFoaEjjxo3x9fXNUb+npyfdunXD1tYWKysrJk6cqIxKlipVijVr1vD7778jk8kYMGAAoGiW/fgZICYmhpEjR2JjY4ONjQ0//fQTsbGxyvzZs2czbNgw9u3bh4ODAyYmJowcORK5XP7Z6/spUlJS+PXXX7G1taVo0aKMGTOGhIQE5TU9cOAATZs2xdTUlLJly7Jr1y4AXr58iUwm4+HDh7Rr1w6ZTMa2bdsART24d+8e3bt3x9zcnGbNmvHhwwdOnDhB7dq1MTc3Z/r06UoNnzoOKP53tra2XLx4kfr162NsbEyLFi14/fr1V527QPBvRDh3AoFASWBgINHR0Tg6On7W9s2bNwwfPpx58+axZ88eAEaPHs3+/fvZsmUL586dIzIykqZNm5KSkvJFOgYMGECXLl24ceMGLi4utGrVig8fPnxyny1bttC/f3/U1dWzzQ8ICKBWrVqfLGPx4sUsWrSI3377jevXr2NhYUG9evWIior65HkDDB06lDJlynDz5k3Kly/PrVu36N27N9OmTePevXu0bNmSdu3akZSUlO2x//rrLxo0aMDp06fZunUra9as4eDBg4DC8XNycmL48OEEBgayYsWKLPtLkkTXrl3x8vLi0KFDHDp0SOkwZu4ruW/fPg4ePMiGDRvYtGkT69at48yZM5+8Lp9j7Nix3L59myNHjnD69Gnu37+v7PeYmprK/v37GTZsGB4eHgwdOpSBAwfy5s0bbG1tCQwMBGDHjh0EBgbSo0cPZbkdO3akc+fOXL58mRcvXuDi4sL69etZv349CxcuZMGCBdy7d++zx/nI27dv+fXXX5k3bx5ubm6kpaXRoUOHf11fUoHgq5EEAoEgnVu3bkmAFBISokxzcHCQ9PX1JX19fWnBggWSJEnS5cuXJUA6dOiQ0u7169eSTCaTrl69qkyLjIyUjIyMpD179kiSJEmzZs2SatWqpXLMWrVqSbNmzVJ+B6SDBw8qv6elpUm2trbSH3/8kaPumJgYSUdHR/Lx8cnRRkdHRzpw4ECO+UlJSZKenp60Y8cOZVpKSopUpkwZaeHChTmetyRJUsmSJaX27dtLcrlcmfb9999La9euVX6Xy+WSvb29dOXKFUmSJKlx48bShAkTctTTpEkTafjw4crv2dl36dJF+uGHHyRJkqTr169Lampq0vPnz5X5z549k2QymXTjxg1JkhTXv0KFClJaWppSU5EiRaR58+Zlq6Fx48aShoaG8v//cVNTU1NqefPmjaSjoyNFREQo97tx44ZUunTpbMtMTEyUtLW1pX379inTAOn48eMqdoCy3kiSJI0aNUqytraWkpOTJUmSpOTkZElHR0fatGlTro7z8X8XGhqqtHny5IkESHfv3s22DIHgv8p/s6OJQCAoEIoXLw7Ahw8fsLCwAMDV1ZXk5GQmT55McnKyin3z5s2Vn318fNDQ0KBevXrKNGNjY2rVqoW3tze9evXKtY7M0Tc1NTW+++47nj17lqP9/fv3kSSJihUr5mhTrFgxgoKCcsx/8uQJ8fHxODs7K9M0NDRo3Lgx3t7eKraZzztzWuam67t373L16lUmTZqkTIuPj+fdu3fZHj8lJYUjR46wb98+AgICePPmDWZmZjnq/Tve3t6UKlWK0qVLK9PKlClDqVKl8Pb2xsnJCQB9fX3U1BSNNjKZDDs7OyIjI3Mst3///kybNk0lrU+fPsrP9+/fJzExUVl3QNFEmnmgS0REBDt37uT48eO8efOGtLQ0QkJCPntOhoaGys92dnbo6OigqakJgKamJqVLl1bRnpvjZK5bZcuWRVdXl2fPnlGjRo3P6hEI/isI504gECixsbHBwMCAO3fuULlyZQBKlCgBgJGR0Sf3lT7RtJW5T1de+nfFx8djYGCQY/69e/coX778JwdG2Nvbc/fu3Rzzc6s/t0iSxLx58+jYsaNKepEiRbItv2PHjsTExLBw4ULq1q3LkCFDVPrL5eZ4OfEp/R8dvZwwNTWlXLlyKmm6uroqx9XQ0ODu3bsqZX38HBgYSP369WnWrBmrV6/G3t5exQHNLdnpzJyWl+OkpqaSlJT0ybolEPwXEX3uBAKBEplMxsiRI1myZImyQ3xuqVq1KikpKdy8eVOZFh0djZeXlzIqYmhoyPPnz5XORkJCAmFhYZ8sNyoqinv37lG1atUcbZ4/f57tCNnMDB8+nD179qj0wQKFUyBJEvb29ujo6HDlyhVlXlpaGlevXs1TVKdatWr4+/tTrlw5lS07R8Lf359Tp06xfft2GjRogIaGRhaHTENDg/j4+ByPV716dV6+fMnLly+VaS9evODly5cFGpWqWrUqqampBAcHq5xnmTJlADh8+DBpaWls2LCB8uXLI5PJspyburr6J88tN+TmOH/n+vXrSJKkfJERCAoLwrkTCAQqTJs2DUmSaNSoEcePH+fp06ccO3YMNze3T0bGSpQowZAhQxg0aBCXL1/m/v379O7dG1tbW7p16wZAjRo1iIqKYsuWLVy5coVOnTplO8Bg6dKlXLlyBR8fH/r27Yu1tTVdu3bN8dixsbEq0aTsaNeuHS1atKBhw4YcOnQIf39/duzYQbVq1Th37hxaWlpMnz6diRMncvz4cR4+fMiwYcOIiYlhxIgRubx6GcyZM4dt27axcOFCAgICuH79OosWLVLmGxoa8uHDB1JSUpQjk7dv386jR49YtGgRhw4dUimvXLlyXLhwgfv376uM4P1I/fr1cXFxoVevXty+fRsPDw969uxJ8+bNlU2yBUGJEiUYOnQo/fr14+TJkzx9+pRdu3Zx+vRpAAwMDHjz5g0nT57k7t27/PDDD1kGx5QrV459+/bx5MmTzw6cyYncHAdgwoQJ+Pj44ObmxrBhw+jfv79Kk7JAUBgQzp1AIFDBxMQET09P6tevz9SpU6levTpz5szhxx9/ZPLkyZ/cd/Xq1crpOVxcXDA0NOTixYvKflJNmjRh9OjRTJo0iXHjxjF8+HBatWqVpZwGDRowadIkGjZsiFwu59KlS2hpaeV43Nw4dzKZDFdXV4YNG8a8efOoXbs2GzZsYPny5bRo0QJQOLaTJ09m/Pjx1KtXjw8fPuDu7o6pqennLlu253DmzBmOHj1KrVq1GDx4MJIkKaNJ3bp14/Dhw3h4eFCqVCmWLFnCypUrad++PSkpKcyYMUOlvClTpmBiYkL9+vVZsGBBjudXrVo1OnToQMeOHalZsyYHDx4s8PkJV69eTd++fRkzZgw1a9Zk8+bNyghlz5496dGjBz179mTYsGG0bduWli1bquy/fPly5fQyx44dy5OG3BwH4LvvvqNjx450796dFi1asG7dujwdTyD4NyOTPtVRQyAQCP5hZDIZx48fF6sVCPIVNzc3mjRpQkxMjOhjJyj0iMidQCAQCAQCQSFCOHcCgUAgEAgEhQjh3AkEgn8VkiSJJllBvuPs7IwkSaJJVpAvhISEMH36dEqUKJFlScW/ExQURJs2bTAxMaFx48Y8efJEJf/GjRs4Ojpibm5O3759iYuL+2p9wrkTCAQCgUAg+ALevHnD06dPczX/Z/v27VFXV+fq1avY2dnRrFkz5YTwHz58oGXLlrRs2ZILFy7w+PFjhgwZ8tX6xIAKgUAgEAgEgjwwe/ZsTpw4wZ07d7LN9/LywsHBgXfv3mFjY0NSUhIWFhbs3LmTjh078vvvv7Np0yb8/PyQyWTcuHGDxo0b8+7dO6ysrPKsS0TuBAKBQCAQCAoANzc3KlWqpJxkXVtbm/r163P58mVlftOmTZXTFTk6OqKlpYW7u/tXHVcsPyYQCAQCgeD/PUlJSVkmVdfW1kZbWzvPZQYFBWVZctDGxka5znVQUBCOjo7KPA0NDaytrT+5DnZuEM6d4F+PVo1B31pCnnna8r/Z62F/y1+/tYQ8M6jmp5ch+7fy5/VX31pCnplR3/pbS8gTEVLeH9rfGqvQh99aQp5QK+v4eaOv4GueF790KMGcOXNU0mbNmsXs2bPzXGZERASGhoYqaYaGhrx9+/aT+eHh4Xk+JgjnTiAQCAQCQSFBpqae532nTZvG+PHjVdK+JmoHYGZmlmW5wOjoaMzNzZX5MTExOebnFeHcCQQCgUAgKBR8jXP3tU2w2VGkSBECAwNV0t6/f0+lSpWyzU9NTSU4ODhLU+6XIgZUCAQCgUAgKBTI1NTzvBUETZo0wc/Pj3fv3gGQmJiIu7s7Li4uyvwLFy7wceISDw8PUlJSaNCgwVcdVzh3AoFAIBAICgX/lHMXHh7Ohw8fiI2NJSUlhQ8fPhASEkJISAglS5Zk27ZtAFSvXh0nJyeGDx+Oj48Po0aNwtLSkpYtWwLQu3dvgoKCmDVrFt7e3vz888/06NHjq5tlhXMnEAgEAoFA8AV07tyZokWL8vvvv+Pj40PRokWpXbs2crkcSZKQy+VK2yNHjpCWlkajRo148uQJ58+fR1NTEwArKyvOnDnD6dOncXFxwd7eng0bNny1PtHnTiAQCAQCQaFApl4wzat/x83NLce8169fq3y3srLi1KlTOdo7OTnh6emZX9IA4dwJBAKBQCAoJKgVUN+5/xrCuRMIBAKBQFAoKKiBEf81hHMnEAgEAoGgUCCcOwXCuRMIBAKBQFAokKmJcaIgRssKBAKBQCAQFCpE5E4gEAgEAkGhQDTLKhDOXT7z5s0b6taty+HDh3F0zH6B5NzY/NdxcHCgbdu2X7Xgcl6wMDVkdK9m9Glbj5CIGJz6zM3R1srMiI2zB1Kvuh0+j98wfO5Wnr4OVubXrVaW3yf2okxxK866P2Dk/O3EJyYX+DnINLUw6zwAHfsqpIYFE3F8N8lvnmdrq126PNbDpqqkpUaE8n7JJAA0bUpi0qIL2rZlkSfEEXPjAjHu5/JdsyRJ3Du5l4BrZ1HX1KJqi65UaNjik/sEPvbl5LIp1Gjbm1rt+wAQGxbMbdetvA+4j4aWDhUbt6Zqiy7IZLJ81/xR99bNGzl25DDa2tr06f8D7Tt2ztY2PCyMBXNn8+C+N+Xs7Zk2fSa2JUoq8+NiY/nz96XcdL+Orq4uLVq1ZuCQYWhoFMxtVpIk/M7+xfNb51DX1Ka8SyfKODX/5D4hzx7itupXKrXowXetegHw0uMinntXqdhZlv0O59ELCkT3R+ITEpi74Dfcb93CtlgxpkwcT7UqlXO0977vw5I/lvPm3TsaODkx45cp6OnqqthIksSQkaPxvOOFj+fNfNcsSRLbt2ziRHp96dXvB9p27JStbXhYGIvnz8H3vjdl7eyZ/OtMipcoocy/eO4sWzeuJzQ0hO+qVOXnyVMpblsi27Lyi/jERGat/B/XvB5QoqgVvwzvR/UK5XK0P3rxOq7nruD5wJ+jaxdiX8pWmRcdG8e8tdu5euc+xawsGd2vM03q1CxQ/blBOHcK/vXNsqGhochkMmJjY7+1lFyhq6tLhQoVMDY2VqYZGBjg6+v7SRtB/lDc2pSyJayIiUv4rO2h5WOQy+U0HfwbT18HcXr9RDQ1FDcGa3MjTqwez7kbvrQavgy7ktZsmDWwoOUDYNZ1MBrmVgRvXkriE1+sBk1ATd8wR3tJLufdwp95u2AsbxeM5cOadIdWTR2LHj+S9OIxH9YvIPKcKyatuqFb2SHfNftdOcXDC0dpPHA8tTv9wI29a3njeydHe7k8jVv7N6CpnfFwTk1O4sSyKRhYWNH654U4dOzH3WO7eHbbLd/1fuSw6wH2793DjDnz+HHkKH5f/Bu3brhnsZMkicnjx6GupsbaTVuwtS3BmJHDSUlJUeaP/WkEIcHBLFu+kllz5/Pu3TviCvC+9dz9DI+vHsexzziqtOnLvYMb+OB3N0d7SZ6G96HNaGjrZMnTNbGg3dytyq3eoKnZlJC/zJq7gNdv37JxzUrq1a3DiNHjCAsPz9Y2NDSMEWN/pr5TXTauWcmr16+ZPX9RFrtLbld4+MivwDQfdT3IwX17+GX2XIaOHMXypb/hcTP7+vLLxJ9RU1Nj5YbNFLctwfhRGfXlcYA/i+fNYfDwkWzYthNdXV3m/Frw1/zXPzfz+n0Q/1s4hfo1qzB0+hLCIqNytL/jG4C+btb6AjBr5f94HxzGzsXTGdilFRMXr8XrYUBBSc81/7blx74V/3rn7r+GhYUFFy9epHz58l9lI8gb3v6v6Tt1A4cueH3SrkbFkjhWKcNPC3by4MlbxizahZmRAa0aVAWgV2snAkMimbPuCN4Br5mwbC+dm9bC0jRnJys/UDMwQu+7WkSc2EtK4Guizh8mLTYa/ep1c9xHHh9HWkwk8thoxRYXk56RxodVs4h2O0Fq8Hvi73uQ+NgXXfsq+apZkiT83E5SpUUXbCpUo3StBtg5NcX/yukc93nsfoGkuBhKVMuIXGtoadNmwiIcOw/ErHgpytVpQmmHhrzwyvrwzC/dhw4coE+//tRyqI1Ls+a0atuOw64Hs9gG+Pvx6KEvk375lXJ29kyYMo2Y6GhuuF8H4Ib7dV69fMmipb9TsdJ3VKlWndnzFmBsYlJg2p+6n6Z8k45Y2VWhePV6lHRswjP3Mznu88LjEsnxsRT9rnaWPB1DE3SMTJWb1ideJvKD0NAwLlx2Y8r4cVQsX56fhg/D3NyMU2eyjyqfPHMWSwsLRv44lIrlyzN5/DguXLyk4gwmJyfz+4rV9OrWpUA0S5LEEdcD9Ozbn5oOtXFu2owWbdpyzNU1i+1jfz/8HvoyYeqvlLWzZ9zkqURHR3Mrvb54e92hdp26NGnWnJKlSjNk+E889vcnJjq6QLQDhIRHct7dk6k/9qVS2VKM6dcFcxNjTrjlHOGcN3Yw00f0z5IeER3Dmeu3+WV4P+xL29KuSX26t3Jh84ETBaY/t8jU1fO8FSb+cefO2dmZvXv3MnHiRCwsLDh16hSLFy/G0dERQ0NDHBwcuHtX8fa5bds2LC0tATA0NEQmk/Hy5UsAnj9/TqtWrTAyMqJy5cocPnz4izSsWLGCcePGYWNjQ5kyZVixYoVy4V6AoKAgevfujYWFBaVLl2bmzJkkJ2c0yd2+fZs6deqgr69PrVq1OHToEACxsbHIZDLc3Nxwc3NDJpMRFxdHlSpVlOmZbc6fP4+WlhZRURlvT0lJSRgZGXH+/HkAgoOD6dmzJ2ZmZtjZ2bFu3ToVrTmxfft2LC0tlbZxcXFoamqyaFHGG++KFStwcnJSfj9//rzyf+Ho6MjFixdVypTJZPj5+dGhQwdMTEyIiopCkiTWrFlDuXLlsLKyYsyYMcTHx6vst3TpUooXL46ZmRldu3ZV/h+/FY0dKvDo2TsCQyIBSE5J5Yb3ExrXrqDIr1WeS7czIgCevi9ITk2jXnW7AtWlXdIOKTVZpRk28Zkf2mUq5riPPD4mxzwpNUXlu0xbB3ly4tcLzURSXAwR719RrGJ1ZZpN+Wq8D/DJ1j45IZ47R3ZQs31f1NRVmywNLYqofNfWNyQlUbUu5RfRUVE8f/YUB8cMx7mWQ23u3skacbzrdYfSZcpgaWkFgJaWFlWqVefuHcWs8m4XL9KwcWP09PQKROvfSY6PITrwNdblqynTrOyqEvzUN1v7lMR4fE/u4rtWvbJccwBtfaMC05od93x80NbWpvJ3lQDFfcXRoRaeXtlHHj297lKntoOyeb7yd5XQ0NTE+35GHdu1dz/q6uq0b9umQDRHR0Xx4tlTajnWUabVdHDknlfW+uJ914tSpctgkf780tLSokrVakrbEqVK8/79W9LS0gDQ1tbGzMwcfQODAtEOcO/RE7S1tahqXxZQXPO61Spx2+fLI52v3wcBUK6EjTLNsWpFvB4+zh+xX4GI3Cn4JpG7WbNmER8fz9WrVylWrBheXl4sXLiQO3fuULp0afr27YskSfTo0QPX9Leip0+fEhgYiK2tLVFRUTRo0ABnZ2e8vLyYN28eAwcOxM8v95V06tSpWFlZcfHiRaZPn87kyZM5duwYACkpKTRt2pTo6GjOnz/P5s2b2bNnD2PHjlXu37dvX0qVKoWHhwfTpk0jMDAwyzHq1avH7du3Abh8+TKBgYHUq1dPxaZJkyaYmJhw+nRGlOPixYtoaWnh7OxMSkoKzZo1o2jRoty8eZN169Yxf/58Lly48NlzbNq0KaGhofj7+wNw/fp1bGxsuHz5stLm6tWrNGnSBAB3d3fatGlDr1698PT0pGfPnrRs2ZKbN1Xf7Dp16kTr1q25ffs2xsbGbN26lYkTJ/LLL79w9epVSpQowfPnGc7JjRs3mDx5MvPnz+f69etUqVKFuLi4z+ovSKzNjPgQqtocERgSibW54iFnZW5EUFhGflqanKCwaKzMC/YhqG5oRFpsDGRy3tOiI1E3yPm4ajp6WA74GZtpf2DRbwwa5tZZbXT1MWzUGq1ipYi9fSVfNSdERwKga2SqTNMzMSclMZ7U5KQs9vdO7sOkSHHsnJp+tuyw108xLVYqv6SqEB4eBqCyQLeFpSVxcbEkJao6wBFh4ZiZW6ikWVhYEh6mKCMoKBAr6yJsWr+WLu3bMKBPr2ybd/OLpBhF3dQxNFGm6RqbkZoYT1o219zv3F8YWhenZO0m2ZYXG/aBy6t+5cSswXjs/JPE6IgC0f2RsLBwzExNUc8ULbG0sMixWTY8PByLTP8nDQ0NzM3MlPahoWFs2rqNX6dMREtLq0A0R6QfS6W+WFhkW1/Cw8Iw+9vC7+aWlkSk17maDrUxNDTi14k/8/LFc/bs3E7n7j1QK8BpPEIjozA3NkJdPeMYluYmhEbk3CybE8aGCic0NCIj0qimJiMmLp6YuIJ5GRN8Gd9kQIWBgQErV65UdjT+66+/lHmjR4+mcePGBAcHY21tjZmZGQDW1tYYpL/VrF27loYNGzJlyhQA7OzsOHPmDAcOHGDmzJm50jBkyBB++eUXACpWrMilS5f43//+R4cOHfjrr794+/YtN27cwMhI8VDdvHkzLi4uTJ8+nWLFihEVFUXTpk2pXLkylStn3wlYS0tLGXm0sLCgSBFFVCJzBFBDQ4MuXbpw5MgRevbsCcDhw4fp3Lkzmpqa7N27F11dXf744w9kMhnly5dn1KhR7N27l+bNP915unjx4tjb23PlyhUqVqzI5cuXGTVqFHPnziUpKQktLS2uXbvGiBEjAJg9ezb9+vXj559/BqBChQr4+Pgwe/Zszp49qyy3RYsW/Pjjj8rv8+fPZ9KkSQwaNEi5365du5T5UVFR6Onp0aFDB0xNTZk1a1aOmpOSkkhKUn04SfK0fH+rMjHSIzZe9YYcE59IMWvT9Hx9YuJU82PjEzEz0s9XHX9HTUcfKUn1uFJSImp62R83NSyIeN87xPl4IFNTx6RlVyx/GEvgipmQlgqAQb1mmLXrg5SWSui+DaQGv89XzUnxin5lmjoZ/ec+fk6Kj0VDS1uZHhX8Hr8rJ+kw7Y/PDpIIffWED08f4dRzeL7q/UhMtCLiqaefcW310q9zdEw0ljoZfY2iY6KzROX09PUIDlZEMIKDgnn29DCt27Zn3qIlnD97mmmTJvLXkaPKaF9+kpx+zTUy9Vn8+Dk5IQ7dTNc8NiSQp9fP0PTnJdlecxPbshSr4kjxavVIiovB+/AWbu34ncY/zSuwgSzRMTHo/+166uvrEZ1Ds2R0TEyW66+vr0dUuv2qdetxbtiQuo61efc+64t2fhAToziWbqbfom563YmJiUE7U32JiYlR1qWP6OnpExKsGLClqalJjVoOXHW7xOA+PTE0MmLngdy3PuWF6Ng49PVU+8/p6+oQFfPlL9rFi1hSvIgla/ccZsbIH/B/8Zr563YAkJoejfxWFLYIXF75JpE7FxcXlRFkz549Y9q0adSrV4+BAxWd1kNCQnLc/+7duxw+fBgDAwPl9r///Y93797lWoP639rXq1SpwrNnzwDw9vamVq1aSscOoEGDBqirq+Pjo2gGWLt2LbNmzaJLly54eX26f9fn6NGjB6dOnSIpKYm0tDSOHj1Kt27dlOfq5eWFoaGh8lxnz56d63Nt2rQpV69eBRTRw+bNm1OuXDk8PDwICAggMjJSGU309vbG2dlZZX8XFxe8vb1V0jI7lTExMbx48YJmzZrlqKF58+b07NmTihUrMmvWLMJzeDsHWLRoEcbGxiqbPCj75r2vISIqDoO/3eiM9HUJj4pT5hvqq+Yb6usQFpW/HeT1qjtRfPY65Ya6OrK/dXiX6egij8/+uGnRkYrRtK+ekvQigNB9G9C0LIpWpmhXvPdNgtYvJOr8Ycw7D0SvulO2ZeUVbX3FS1dKYsYglpSE+PQ81b5btw9s4bsm7TC1KcmnkKelceuvTZSp1RBz2zL5qvcjRsaK33d8pihyfJziOhsZqQ52MjIyztLVIC4uTjkoSk9fj/oNGvLTmLFU+u47Ro39GQMDAzw9bhWIdi09xTVPTcq45qnpzdcf8z5y/+hW7Bq2wbho9iMxTWxKUbX9AMxK2lO0Ui1qdR9ByFPffI3enTh1hjqNXJRbWloacX+7nrFx8TkOMjMyMspy/WPj4jAxNuaRfwAX3a4wYezofNObHYbpz4OE+Mz1JU4lT1WvqtMUFxeLUfr5/bVnF48D/Nm0Yw/b9x2kQqXv+GnIgCzn+DUcu+ROrc5DlJvimqu+OMbFJ2Bi9OVNwRrq6iwa/yNX79ynZuchzF71P4Z2b4eOthYmhgXXtJwb1NTU87wVJr75VChubm507NiRGTNmcPToUeLi4ihduvQn95EkiQ4dOqj0HQO+avRpfHy8MjL4qf5scrkcgC5dutCiRQvWr19P69at6dGjBytXrszTsRs2bIi+vj6XL1/GwMAAuVyubCqVJAlHR0d27Nihso++fu4iSE2bNmXs2LHExMTw7NkzqlSpQuPGjbl06RLFihXDyclJ+Uac03l/POfsSE1VRIc+Nd2DhoYGW7Zswc/Pj0WLFlGmTBmOHDmSxZEEmDZtGuPHj1dJM2+Y/zftD2FRFLU0UUkrammC33NFVCsoLIoiFhn1SV1dDSsz1aba/CDB7x4f3jxTftcsWkLRBCuTKZtm1Q1NSIvJ3XHTIkKRpySjbmSiTJPHx5H06glJr54gpaVh8n1n4r3zb5oIvfTm2PiocAzMLNM/h6Glp4+GZkYTWVxEKK/u3yLw8QP8rykiwSlJ8chk6ry+f4tOMzKm4/A8tJWY0GCajZyRbzr/zsdm1rDQUKzTo+ohISEYGhqira2tYmtubk5YqOoLZ2hICKVLKxxPa+siaGXaR11dHesiRQgLDSsQ7Trp1zwxOgI9U8U1T4gKR1NXH/VM1zwhMoz3vrcJeerL81uKwQqpiQnI1NR473ub5pP+zFK2URHb9PLC0DU2yxe9zo0aUrXKd8rv/gGPCQsPJy0tTfmiHRISgoV59sezMDcnJNO1TE1NJTw8Agtzc/46eIj4+AQ691RMp5OWfr9q1KwlUyeNp3WL7/PlHMwz1Rcra0V9CQ0JwSCb+mJmbk5YWKhKWlhICKXS68uBvXuYOnM2GhoaFC9RgnmLl9GtXSuuXLpAq7bt80WvS92aVMs0zYnfs5eERUaRliZXNs0Gh0diYZq356ZD5fJc2bmSyJhYzIyNOHLxOjZWFgUW7c0tInKn4Js7d2vXrqVDhw5MmDABUESCMvPRacjsfFWrVo3du3dTqlSpfJtDys3NjapVFSMlq1evzpYtW4iJicHQUBF5cHd3JzU1lWrVMjowGxgYMHHiRGrXro2zszPz5s3LEhHMrD8n1NXV6datGydPnkRfX5/OnTsr96tWrRr/+9//sLKyUokk5pYmTZrw/v17jh49ipOTE+rq6jg7O7Ny5UrKli2rdCI/nveVK1fo16+fMu3y5cvUqFEjx/JNTU0xNzfHw8NDpT9hWjah+YoVK7Jjxw4GDhzIypUrs3XutLW1s9woC+LHesXTn2UTe2FjacL7kEi0tTSoV70c6/YrBpC4efozvHvGtXGsXAZNDXXc7z3JVx1SUiKpmZph5YkJyDQ00LItS/LrpwDolK1IzI3z2e4v09ZFyhS90bAogpqmFqkhHxT5WjpIyZnLj0emqZ2lnK9BW98Qs+Klee/njVVpxQjw9/4+2GTq7A+KPnm9Fm9XSbu4YRFWZSpQ9fuMEY5+V07jf/UMrScsRKcAR20aGRlhZ2+P520PKqV3rfC640kth6yjSWs61Gbln78TEhyMpZUVSUlJPLjvTdfuPQBwqO3IoYMHkCQJmUxGamoK79+9pVjx4gWiXUvPAGObUgQ9vo9ZSXsAgp88wMpOdSS0tqEJbWZvVkm7uXUp5qXKU96lIwApCXFo6ma8LEa+fQGAoVWxfNNrYKCPgYF+pu8GpCQn88D3IdWrVUWSJG7f8aJ3j+7Z7l+7Vk3+cj2kvL4PHj4kNTWVGtWqUbN6NYYPHay09XnwgInTpvPX7h0YGeVf/TE0MqKcnT13bntQ8TtFfbl7x5Oa2dSXGg61WbP8D5X64utzn07p9SUxMUHl2aWpqYmxsUm+Tp1joKeLgV5Gs72hvi7JKan4BDylRiV7JEni1v1H9Gufd+dXTU0Ns/QI+JmrHrRuVOczexQ8wrlT8M2nQjEwMODatWt4eXlx6dIl+vdXHXZdunRpZDIZmzZtIiAggISEBEaNGkV4eDj9+/fH29ub+/fvM3PmzCyO4adwdXVl//79BAQEMH36dG7evMnEiRMB6N69O0WLFqVPnz7cv3+fy5cvM2TIEIYNG0bx4sV59OgRLi4unD59mocPH7Jr1y7MzMyyjaZZWVlhYGDAtm3bePLkCRER2Td19OjRg5MnT3LixAm6d++ukl60aFE6d+7M7du38fPz47fffuPVq1e5Ok8zMzNq1KjBqlWrlM5Uo0aNuH37Njdu3MDFxUVpO2vWLHbs2MGKFSsICAhg+fLl7N69+5N95ABGjBjBggULOHbsGL6+vowePZqHDx8q8+fOncuECRPw8vLi6tWrXL9+nRIlsm8i+lpMjfSxNjdCX08bTQ11rM2NsDA1xMLUkCenltCvXX0A7j9+w837T1nza3+q2BVnxdS+hETEcPaGYrThvtO3sDIzYtaIjlSzt2XZxJ4cOHtb2WxbUMjjYoh/cAfTtj3RLGqLcfNOqOsbEn/fAwAdu++wmfoHmtbFkGnrYjN+IUbObdC0KYlWibKY9xhGQoAPKUFv0S5TkaLj5qFfsz4alkXQsauMsUt74n098113Jec2+Jxz5b3/fV54ufPk1kUqNG5NQkwU+6YO4PGN86ipq6NvaqGyqWlooqmjh56JImLjf+0sN/evx3nwBPTNLImPCic+Kpy0v436zS86de3Onp3b8brjyeWLFzhz8gQdu3QjIiKcTm1bcfK4YpCVffnyVK5alSWLFvD0yWP+WPIbJqam1E1/oWnVth1RUZGsXbWSly+es3rFcrS0tanXoEGB6AYo16AVAZeOEPzkAW/v3+CV52XK1m9JUmwUJ+cM5aXHRdTU1dEzsVDZ1DU00NTRRdfYjNjQQE7NH8HTayeJCnxFUMB97h5cT5l6LdHUKbiRv2ampjRv5sKSP1fgH/CYNes3EhERSasWii4fN2550Kx1O548VUS1W7f8nrCwcNZu2IR/wGOW/rGCls2bYWKi6LpRxNpKuZmaKqKaRaytskxy/LV06NqNfbt2cPeOJ1cuXeTcqRO079yVyIgIurdvzekTivpiZ1+e76pU5fffFvDsyWNWLF2MsYkpdZwU9aVJs+/5Y/FC7nre5u2b12zbtIH3799Rx6l+vurNjJmxES0aOPLbxt34PXvFyp2uRETF0KaxopuG+90HNO43hscv3wCQmJRMSHgk4VGK52pkdCwh4ZEkp6QiSRJLt+zl3HVPXrwNZPWuQ/gEPKNnm88PkhL8M3xz527mzJlYW1vTpEkTFi9ezOrVq5XRMoBixYoxf/58/vjjD5o2bYqfnx+mpqbcvHmTmJgYXFxcaNOmDYGBgSoDFT6Hk5MTe/fupXbt2pw5c4YLFy5gb694A9bS0uLSpUvo6enh4uLCgAED6N69O6tWKZqN7O3tadOmDVOnTsXR0ZF79+5x5MiRbKOIWlparFq1ikOHDlG/fn3c3bMfQefk5ERKSgpBQUEqEa2PWqytrWnXrh2NGzfm3r17uT5PUDTN3r59m8aNGwMKh69cuXI8f/6cOnUy3rQaNmzI8ePH2blzJ7Vq1WLXrl2cOHGC+vU/fcOZMWMG/fv358cff6R9+/YUK1aMPn36KPMHDRpEcHAw7du3p2PHjjg6OhbYyhV//f4Tby4sZ3z/llS1t+XNheXc2DUDNZkMGTLU1DKaDLr+vAo1dTUubplKuRLWtB7xO6mpiohjSEQMbUf9SYv6VTi7cRJPXgUxcsGOnA6br4Qf2kpqWAhWQyajY1+F4K2/Z/S5k8lApvgrJSUQvO0PtGxKYtlvDJb9xpDy/hWh+9YDkPTcj8izB9Gr7kSRETMw6zyQeJ/bRJ7cl++ayzdsSZVmnXD73+94Ht5G/d4jKV6pBpIkR5JAkn9+6p64yDCu71yJPDWV82vns2diX/ZM6seeSf0IelYwE9N26NSZnn36MnfmdNatXsnEKdNwrFtXoVdSTBD9kd+W/Yk8LY2RQwfz5s1rVqxZh4aGJgB6enqs27QFf79HDBnQH/9Hj/hz1Rp0dPLXuchMaafvsXduz+1dy3lwfCc1u/2IdfnqSJKk3D6HgUVRnH6YyAd/b66smYnnnpUUr16f6p0GFZjuj8z6dRq2xYsxZOQo3G96sG7VckzT5wWUy9PrTfo5mJuZsW7ln1y/cYshI0dRskQJZvwypcA1/p12HTvTvXdfFs6ewYY1K/l58jRq16mr1Ju5C8uCpX8gl8sZ8+MQ3r55ze+r1yrry6hx46nfsDG/zZvN4D49uX3rJkuWr8K25Kf7on4tc8cOwraoNQOnLeK6lw+b5k3C1FjxvJXLVevN6au3aNR3NN3HKV7uf5i6kEZ9R+Pt9wSZTEZluzKs33eUbmNn4vUwgD2/z8Tc5NtPzC+mQlEgk3JzByhkODs74+DgwLJly761FEEu0KpR8A+aguJpy//mz2t/y1+/tYQ8M6imzeeN/oX8eT130fh/IzPqZ51+579AhJS/XRT+SaxCH37e6F+IWtmCXXKzeJ8ted737e7Bnzf6j/DNI3f5zeTJk1VG0WbeMk/WWxhYs2ZNjudarFj+9ZcRCAQCgeC/gIjcKfjmAyrym0mTJjFs2LBs8wpqcstvRZ8+fWjRIvvF2QtyMkyBQCAQCP6NFLZlxPJKoXPuLC0tlRMH54Sbm9s/I6aAMTExwaSA1q4UCAQCgeC/RmGLwOUVEd4RCAQCgUAgKEQUusidQCAQCASC/5+IyJ0C4dwJBAKBQCAoFAjnToFw7gQCgUAgEBQKMs9l+v8Z4dwJBAKBQCAoFMiEcweIARUCgUAgEAgKCTKZLM/blyBJEnPnzsXW1hZ7e3s2b96crZ2zs3O2xxoyZAgA27Zty5KX3brrX4qI3AkEAoFAIBB8AevXr2f58uW4uroSFhZGnz59KF68OC1btlSxO3TokMrSqEFBQTRo0IBRo0Yp02xtbbl9+7bye37MySucO4FAIBAIBIWCf6LPnSRJrF27lkmTJtGkSRMAzp07x/r167M4d2ZmZirfp0+fzrBhw6hevboyzdramiJFiuSrRuHcCQQCgUAgKBT8E33uwsPD8fX1pVmzZso0FxcXfvzxx0/uFxAQwL59+3j37p1KuoWFRb5rFH3uBAKBQCAQFApkarI8b0lJSURHR6tsSUlJWY4RFBQEoBJts7GxITo6moSEhBy1bdu2jQ4dOmBsbKyS/uzZMxo3bkzx4sXp27cvHz58+OrrICJ3gn89T1tK31pCnil35r85cuvReOtvLSHPvI9N+dYS8sSkRqW+tYQ8I13Z9q0l5AntV6++tYQ8E//DrG8tIU8YFHD5al84MCIzixYtYs6cOSpps2bNYvbs2SppERERABgaGirTPn6OiIhAV1c3S9mSJLF7927WrFmjkl6zZk06duxI165dCQsLY9y4cfTq1YtLly598SCPzAjnTiAQCAQCQaHga5plp02bxvjx41XStLW1s9h97EcXExOjXN89OjpaJe/vvH//njdv3lCzZk2V9KpVq7JkyRLldx0dHVxcXAgMDMTGxibP5yKcO4FAIBAIBP/v0dbWztaZ+zsfm2MDAwOxtbUFFM6biYkJOjo62e5z7949jIyMPuuwVapUCYB37959lXMn+twJBAKBQCAoFHxNn7vcYmpqSrVq1bhw4YIy7dKlS7i4uOS4z/Pnz7GxscnS1BoVFaXy3dvbG4Dy5cvnWk92COdOIBAIBAJBoUBNTZbn7UsYOXIkS5cu5fLly7i6urJjxw6GDx9OSEgIJUuWZNu2bSr2sbGxWfriPXv2jHLlyrF69Wp8fX25cOECI0eOZPjw4RgZGX3VdRDNsgKBQCAQCAoFsn8oZDV06FCCgoLo168furq6rF27lubNmxMUFIQkScjlchX77Jy7smXLsn//fpYvX87cuXPR0tKib9++WQZw5AWZJEn/3aGIgv8XvJ428FtLyDP/2dGy51Z8awl5JiFV/nmjfyEljL5+VvpvhfZ/dLRswn94tKzmf3W0rF7WkaT5SY1fT+d533sLWuWjkm+LiNwJBAKBQCAoFPwTK1T8FxB97gQCgUAgEAgKESJyJxAIBAKBoFDwTyw/9l9AOHcCgUAgEAgKBcK5UyCcO4FAIBAIBIWCr1l+rDAhnDuBQCAQCASFAhG5UyAGVHwDVq5ciaOj47eWUWD4+voik8l4+fLlN9Mg09TCvMcwis1YhfXIGWjZlsnRVrt0eUos2qqy2UxeqszXtCmJ5cDxFJ+5BptJSzCs/32BaLYwNWTOyE48PbWUm7tnftLWysyIIyvHEnx1NRc2T6FcCSuV/LrVyuK+czqBl1eybf5Q9HQKdpoNSZLYs3UTP3Ruw9CenTl7/EiOtg/ve7N41jQ6utTjuOv+HO32bN1EmwYO+Ny9UwCKFUiSxIHtm/mxW1tG9+3ChRNHcrT18/Hmjzm/0qt5A04f+kslTy6Xc2D7ZoZ0bsXIXh1x3bWVgp5lSpIkNm/cQNtWLejSsT1HDh/K0TYsLIyfx4zCpVEDfhwymNevM6YACQ8PZ86sGbRp0ZyWzVxYNH8ecXFxBaodID4phWk7T9Po1/X0/nMvPi8Dc9YfE8+MPWdpNnsTTWZsZO5fF4hLTM5il5KaRvuF22k1d0uB6U5ISWXmaU++X3+CQXvd8A0Mz9V+W275U3f5YbzehGTJS0mT0337eTpuOZvfcgFFXdm4YQOtW7agU4f2HD706boyZvQoGjdswNDBg3mdabqY6Oho5s2dQ+uWLWj5fXNWLP+TlJSUAtGcV/6JFSr+C/yrnLvQ0FBkMhmxsbHfWkq+cfDgQRwcHFTSihYtSoUKFb6Rov8fmHUdjIa5FcGbl5L4xBerQRNQ0zfM0V6Sy3m38GfeLhjL2wVj+bBmriJDTR2LHj+S9OIxH9YvIPKcKyatuqFb2SHHsvJKcWtTypawIiYu4bO2h5aPQS6X03Twbzx9HcTp9RPR1FAHwNrciBOrx3Puhi+thi/DrqQ1G2YV7FyBp464cvSvvYyfPocffhzJ2j8Wc+fWjWxtH/s/BEBNLefbT2hwEIf27MzVOo9fw7ljrpw8uI/Rv8ym95CRbF6xlHseN7O1fRrwCAA19ay6zx07xNkjBxk3Yx6jps7k0qljHN6zvUC1ux48wL49u5k9dz4jR41m6W+LuOnunsVOkiQm/jwWNTV1Nmz+H7YlSjBq+HDlQ3nFn38gl0ss/eNPFi1Zxq2bN1izsuDnOZy17xyvQyPZMKIz9cqXZMSGw4TFxGdr+8exa8glWD6oHb8PaMNN/1esOHE9i92+6/cJiS7Y58f8c3d5GxnHqs4NqFPSirGH3QmPT/rkPsExCez2eoJ2+m/07xy8/5zQ2MSCkKso/8AB9u7ZzZx58/lp1GgW/7aIGznUlZ/HjUVdTZ3NWxR1ZUR6XZEkiVE/jUSSS/z+53J+mT6Do0ePsmP7tgLTLcg7oln2G9CtWze6dev2rWUUWtQMjND7rhZBGxeREviaqMDX6FV1RL96XWLcz2e7jzw+jrSYyGwy0viwahZSquJBmBr8Hv1qddG1r0KCb/5GlLz9X9N36gZm/NiB1o2q5WhXo2JJHKuUoeT34wkMiWTMol0EXl5JqwZVOeZ2j16tnQgMiWTOuiMATFi2l4ubpjDe1JCQiJh81QyKB8LJwwfo0rsf1WoqnN67tz04fdQVh7r1sth36tEHgIFd2+VY5tb1q6ldrwH+Dx/ku96PSJLE2SOutO/Zl8o1FLrv3/Hg3DFXatRxymLfrltvAEb06JAl79xRVzr06qcsZ+Co8axdPI82XXuirZ39QuJfq931wF/07f8DDrVrA+Bx8yaurgdwql9fxdbfz4+Hvr6cPHsOS0srJk+dxvcuzrhfv4ZzExem/TodbW1t5ZqX3Xv2wvXAgXzXnJnQ6Dgu+jxl6+huVCxuRYVilpz1fszpu/70bVwzi/2Mbk3R1lRXauzZsDoHbvio2ITHxrPh7C16NqjGmbsBBaI7LC4Rt6fvWd+tIeWtTLC3NObC47ec839Dz5rlctxvjftD6pcuwoNsonwR8UlsueVP12plOBfwNt81S5LEgQN/0a//D9ROryu3bt3k4MED1Muhrpw5ew5LKyumTptG0ybOXL9+jSZNXFj022Ll+qgVK1akW7fuXLxwgcFDhua77rwi5rlTUKCRO2dnZ/bu3cvEiROxsLDg1KlTLF68GEdHRwwNDXFwcODu3bsAbNu2DUtLSwAMDQ1VmvWeP39Oq1atMDIyonLlyhw+fPiLNOzYsYPZs2dTpkwZbGxs2LJFNWT/qfJTUlKYNGkSRYsWRSaTKbe6desC8O7dO0aPHk2lSpUwMjKiQ4cOhIaGAjBgwAC6deuGl5cXMpmMUqVKAbB69Wrl5z59+tCzZ08VPXv37qV48eLK5UvOnz9PrVq1MDQ0pHHjxvj6+ubq3Js0acKkSZOU3zdt2oRMJiMwMKP5o0aNGmzYsAGA5ORkpk+fTunSpbG0tKRPnz4EBwcrbbdt20bLli05c+YMlSpVom/fvoCiWad3796YmZnx3XffcehvIf/g4GA6duyIkZERdnZ2LFiwoEBD+dol7ZBSk0l+81yZlvjMD+0yFXPcRx6fs9Pz0bH7iExbB3lywb1lf47GDhV49OwdgSGRACSnpHLD+wmNayuiwY1rlefSbT+lvafvC5JT06hX3a5A9MRER/Hq+TOqO2R0NahWK+/Nqf6+D3B3u0jfwT/ml8RsiY2O4vWLZ1StlaG7cg0HHnp7fXFZH969pXjJjKb/76rXIjoqkvevX+eL1r8TFRXFs6dPcaxTR5nm4OiI152s1/yu1x1KlymDpaWi6V5LS4uq1arjdccTAB0dHZXFzOPi4tAt4FUEvF+8R1tTg8oligAgk8lwLGeL59PsnRsdLQ0VjfFJyehpaarYrD19k/LFrXAqX7LAdN9/H4a2hjqVipgpdTvYWuL1NmtT60d8A8O5/OQdQ52yb63ZeNMPeytj6pS0yjb/a/lYV+rUzagrtWs7ciebunLnzh3KlCmDpVVGXalWvTp3PBV1pVixYir/B2NjI2L/gSb8LyHzc/pLt8JEgTfLzpo1i/j4eK5evUqxYsXw8vJi4cKF3Llzh9KlS9O3b18kSaJHjx64uroC8PTpUwIDA7G1tSUqKooGDRrg7OyMl5cX8+bNY+DAgfj5+X3myBmMGaNowjpx4gR9+vThp59+Ijxc8Qb1ufKXLVvGjh07WLNmDe7u7jg7OzNixAhOnjwJgJubG9ra2mzdupUrV64QEBDAjBkzAFixYgUTJkygatWqBAYG4pn+A8lMjx49OHXqFElJGWH9w4cP07VrV9TU1Lh16xa9e/dm2rRp3Lt3j5YtW9KuXTsV+5xo2rQpV69eVX6/dOkSJUqU4PLlywBERkZy//59mjRpAsDo0aPZv38/W7Zs4dy5c0RGRtK0aVMVR+z+/fvMmTOHjRs3smTJEiRJomPHjgQEBHDq1Cm2b9+e5aYxffp0nj59yrlz5/jf//5HTEzMJ5vkvhZ1QyPSYmMgU5+ntOhI1A1yXohZTUcPywE/YzPtDyz6jUHD3Dqrja4+ho1ao1WsFLG3rxSI9txgbWbEh9AolbTAkEiszRXnZ2VuRFBYRn5ampygsGiszL9uIeqciEz/LZmamSvTzC0siY+LIynpy5xguVzOhhXL6NK7P8VKFNxDGiAyQqHbJJNuMwuLPOk2MDIiKiJM+V0ul6Ourk5IUM79yL6G8HDFsczNLZRpFhaWxMXGkpioqj0sLEzFDsDS0pLwMNUoUkJCAm6XL7F/7x569+1XILqVmmLiMTPQQz3TfcDSWD/HZtmPxCelcOnBU3Zf9aafc0aE78n7UI55PuLXri4U5DM6PD4JUz1t1DNFhyz0dXNslpVLEn+4+dDXwY4Splm7hTwNjeLUo1dMcsk5Uv/VmsOy1hVLy+zrSnh4GOYWn68rH/H386dcuZwjlt8CmVret8JEgTfLGhgYsHLlSjQ0FIf666+MjsijR4+mcePGBAcHY21tjZmZ4m3I2toaAwMDANauXUvDhg2ZMmUKAHZ2dpw5c4YDBw4wc+anO51/pEePHsydq+hDNXToUJYtW8ajR49o0KDBZ8v38PCgY8eOdO7cGYBx48Yxffp0zM0VD4Q+ffrQp08f5bF69+7N3r17ATA2NsbAwABNTU2KFCmSrbYWLVqgpqbG5cuXadmyJYmJiZw+fZqzZxUda2fNmsXcuXPp2rUrAFOnTmXbtm14eHjQqFGjT55306ZNmT17NjExMRgYGHDt2jXGjh3LpUuX6N27N+7u7tjY2GBnZ8ebN2/YtGkTV65coWHDhgDs2bOHEiVKcPDgQXr16gVAUFAQx48fV/YjvHr1KtevX+fx48fKH/ncuXM5fvy4UkdUVBRVq1alTp06yGQyZfnZkZSUlMVxTUpNy7GvSnao6egj/e3hLCUloqann619algQ8b53iPPxQKamjknLrlj+MJbAFTMhLRUAg3rNMGvXByktldB9G0gNfp9rPfmNiZEesfGq5xcTn0gxa9P0fH1i4lTzY+MTMTPK/vy/ltiYaAB0M11fXT299LyYL2qWdDt3mpjoKLr3G5CvGrMjLkYRrdXV1VOm6aSfQ9wX6q5SqzYnDu6juqMTKcnJrFu6QLF4eFpa/opOJyZacc319DO06+srtMfERKOjk6E9JiZaxe7jfsHBQcrvjx8H0LdnDwB69elL6zZtC0T3R6ITktDXVo286WtrERWfs1Md8C6E7st2A9C3cQ3aOigi8ZIkseTIFfo716KMtRlhMQUXSYpJSkFPU/WxqaelQXQ2gzsAzvq/IToxmf61y2fJkySJ5Vce0KuWHaXNjAiPyzn69zVEp/8+9TPVAb2PdSVata5ER0ejp/e3uqKnR1BQEH8nODiICxfO89viJQUhO8+IZlkFBe6ruri4KB07gGfPnjFt2jTq1avHwIGKTt4hITlX6rt373L48GEMDAyU2//+9z/evXuXaw2GhhlvTB8dkMjIyFyV36pVK86fP8/9+/cJDAxk165dVKpUSaV8d3d3hgwZQs2aNVm3bt0nz+fvaGtr07FjR44cOQLAhQsXMDY2Vjb73r17l/Hjxyu1GRoa8uTJk1ydf+3atdHT0+PmzZv4+/tjYWHB999/z6VLlwC4du0aTZo0QSaT4ePjg4aGBvXqZfSRMjY2platWnh7eyvTzMzMVAaI+Pj4UKpUqU++vc2aNQsfHx/q1q3LoUOHlM3N2bFo0SKMjY1VtrU3fXK0B9Cr7kTx2euUG+rqyP72YJbp6CKPz76jdVp0JBHHd5P86ilJLwII3bcBTcuiaBUrpbSJ975J0PqFRJ0/jHnngehVz9on658iIioOAz3V8zPS1yU8Kk6Zb6ivmm+or0NYVMF0NDcwMgYgIT7joRqf3lRjaJj7aGFCfDzb1q9mxPgpBdJP7e8YGCm0JSRkRIsS0nUbfIFugN5DRqKhqcmQzq2YNnIQLTp2AcDcKmsEOD8wSr/m8XEZ2uPiYlXyMttmtgOIi43D2NhE+b1kyVJs27mL6TNnc/3qFX5bMD9f9Z6440fdKWuUW1qanLgk1e4OcYnJmOjl/H8vZWXK7p97Mrtnc64+fMG8AxcBuOz7jHdhUQxuVjtfNQOc9ntNkzXHlFuaXE58Sqqq7uQUjLMZjR6fnMra6w+Z2KQaOtm8nF59Fsj7qDgG1LbPd92ZMU6vD3GZ60r6oEUjY9W6YmxsTHz83+pKXBwmmeoKpDumf/6Jnb09DT8TZPinEaNlFfyjAyrc3Nzo2LEjM2YoRtnExcVRunTpT+4jSRIdOnRg0aJFKunGf6uUueXvzYGfK3/AgAEsWrSIxo0bExUVhaOjIwcPHlTaLVmyhJUrV7J8+XJWrlzJX3/9xcSJE79IU48ePRg0aBBr167lyJEjdOvWTalTkiTmzZtHx44dVfbJKRKYGQ0NDRo3bqxsEm/UqBFVqlQhMjKSly9fcu3aNYYMGaI8Tk58yhlLSUlRcd6zo1KlSvj4+HDw4EFmz57NsmXLOH/+vDLSkJlp06Yxfvx4lbSgeaM+WX6C3z0+vHmm/K5ZtISiCVYmUzbNqhuakBYTlVMRKqRFhCJPSUbdyESZJo+PI+nVE5JePUFKS8Pk+87Ee2c/qrKg+RAWRVFLE5W0opYm+D1XRBODwqIoYpHx+1BXV8PKTLWpNj/52BwbHhaKpbWiXoaFhqBvYIjWF4x2vXX9CmGhISybO12ZFhMdzbxpE3Bp0ZoR46fkq+6PzbERYaFYpDthEWFfrhvA3NKK39ZtJToyEgMjI8JCgpHL5crrkd98bDoLDQ3BOv1eEBISgqGhYZYRxubm5oSl9wP+SEhICKXLZPQR1NbWptJ3lan0XWXs7O35oW9v+g8cRLFixfJFr3PlMlQtWVT53f9dMGEx8aTJ5cqm2eDoWMw/EV3+2EevcokilLexoNcfexnUtDb7r/sQEh1Hy/TpT1LT5MQlJdN4+nqWD2pPjTI2edbdqExRKhc1U35/HBxFeHwiaXJJ2TQbGpuIuX5Wp/Tq80BC4hKZdSajm0p0YjKTj9+iVcUSvIqIITQukQ7p05+kyuXEJ6fSYv1JlrSvSzUb8yxl5oXMdaVILupKaEjWulImU10B+Gv/fq5fv86evfsKXV+1wsI/6tytXbuWDh06MGHCBABiYlQ7sX90EuLj45XNstWqVWP37t2UKlXqs05EXvhc+Vu2bMHR0ZHdu3cTFRWFxd/6IyxbtozFixcrm03/7ghpaGhkeRP6O82aNSM5OZm7d+9y8uRJZd/Dj/r8/fPer6Fp06acOXOGly9f0qlTJ9TU1GjUqBGXLl3i7t27yv52VatWJSUlhZs3b9KgQQNAEaL38vJSOoDZYWdnx4sXLwgKCsLaWvGATMumKUpNTY3u3bvTtm1bLCwsuHjxIu3bt89ip62tneWGE/mZJlkpKZHUTM2w8sQEZBoaaNmWJfn1UwB0ylYk5kb2I2Vl2rpISRnTj2hYFEFNU4vUkA+KfC0dpOTM5ccj0yzYKTo+xRVPf5ZN7IWNpQnvQyLR1tKgXvVyrNuviGS4efozvHsTpb1j5TJoaqjjfu9JgegxNDKidDl7vO/cpnylygD43L1DtVpfNl2MU0Nnth86qZL2Q+c2jJkyg+oO+R+VMTA0olRZO3y8bmNX8TsAHty9Q+WaeZ/mxsjEBIAbly/wXfVaKv358hMjIyPs7O257eHBd5WrAHDH8zYOtbPOn+lQuzbL//id4OAgrKysSUpKwue+N917KAZyxcbGKu+3kNHSkZj4+Sl5couBjjYGOhm/GQNdLVJS03jw6gPVS9sgSRKeT97Sq2H1bPePTUxS2d9QV/E5MTmVRf1akpKacc856/2EnW532TWuB6YGXzcwRF9bU6X52EBLk+Q0OQ8/hFPVxhxJkrjzNoTu1ctm2bdx2aIcG9xSJa39ljP80qwmDiUskcslUtIynhcXnrxl792nbOnhjIlu/s1LaWRkhH16XamcXlc8PW9TO4e68sfvqnXlvrc3PXpkDPq7evUqK5b/yW+Ll1C8ePF805lfCGdTwT/ahfBjvy8vLy8uXbpE//79VfJLly6NTCZj06ZNBAQEkJCQwKhRowgPD6d///54e3tz//59Zs6cmcUxzCufKz88PJwnT57g6+tLamoqYWFhKg6cgYEBR44c4eHDh+zZs4c5c+aolF+uXDkCAgI4f/58joNANDU16dy5M/PmzUNDQ0PZJAswZ84ctm3bxsKFCwkICOD69etZooyfomnTpnh5eXHz5k1lHz1nZ2fWrVtH0aJFlaN2S5QowZAhQxg0aBCXL1/m/v379O7dG1tb209O29KiRQtsbW0ZMGAAPj4+XLp0ScUZTExMpGnTpmzfvh1/f3+2bdtGQkICtra2uT6HL0UeF0P8gzuYtu2JZlFbjJt3Ql3fkPj7HgDo2H2HzdQ/0LQuhkxbF5vxCzFyboOmTUm0SpTFvMcwEgJ8SAl6i3aZihQdNw/9mvXRsCyCjl1ljF3aE++bdXDM12JqpI+1uRH6etpoaqhjbW6EhakhFqaGPDm1hH7tFNMW3H/8hpv3n7Lm1/5UsSvOiql9CYmI4ewNxSjqfadvYWVmxKwRHalmb8uyiT05cPa2stm2IGjTqSuue3Zy/+4d3N0ucvHMSVp37EJURAQDurTl/ClFH8y0tDTCw0IJDwtFLpcTHxdHeFgoCfHx6OjqYmFlrbIBGJuYYGiUt0j952jRsSvH9u3C994dbl25xJVzp/i+fWeiIiMY3qM9l0+fUOqOCAslIiwUuTyN+Pg4ItJ1A1w+fYLjf+3m3etXXLtwBted/6Nz3wEFovkjXbt1Z9eO7dzx9OTSxQucOnGCzl27EhERTvvWrThx7CgA9uUrUKVqVX5bsIAnjx+zdPFvmJia4lSvHtHR0XTr3JFt/9tCgL8/fo8esXD+PMqULUupUp9uVfkazAz0aF7djqVHruD/Lpg1p28SERtPq5qKvmk3/F/RbPYmngSGEh2fSIeF29ly4TZ+b4N59CaIuX9dpGwRc0pbm2JmoIe1iaFyM9bTRkNNhrWJIVr5HBAw1dOmqV0xll95wOPgSDbe9CMyPpnvyyucHI9XQbTbdJpnodHoampgZairsgGY6GphrKOFqZ62Sp6RthbqMjWsDHXR+oI+xrmhW/fu7Ni+HU9PTy5euMDJEyfo0q0rEeHhtGnVimPpdaV8+QpUrVqVhel1ZfFvv2GaXlcArl+7xpRJE5kwcRKVq1QhNDSU0NDQLAMzviVqarI8b4WJf9S5mzlzJtbW1jRp0oTFixezevVqlf5wxYoVY/78+fzxxx80bdoUPz8/TE1NuXnzJjExMbi4uNCmTRsCAwNJTs6+A+uX8rny+/bti7+/P/Xr16do0aJYWFhgamrK2rVrAdiwYQM+Pj44Oztz/vx5Nm7cqFJ+p06d6Ny5M506daJ79+45TgHSo0cPjh07phwl+5EGDRpw5swZjh49Sq1atRg8eLCio/YnmkozU7lyZdTU1NDV1VVONePs7MydO3eUUbuPrF69mq5duzJgwABcXFwwNDTk4sWLaGpqZlc0oHBMz5w5Q1JSEg0aNGDGjBmsXLlS+fako6PDzz//zObNm6lduzbLli1j/fr11KhRI1f680r4oa2khoVgNWQyOvZVCN76e0afO5kMZIq/UlICwdv+QMumJJb9xmDZbwwp718Rum89AEnP/Yg8exC96k4UGTEDs84Dife5TeTJffmu+a/ff+LNheWM79+Sqva2vLmwnBu7ZqAmkyFD9ebT9edVqKmrcXHLVMqVsKb1iN9JTY9ehETE0HbUn7SoX4WzGyfx5FUQIxfsyHe9mWnZvhOdevbh93kz2bZ+NSMnTKFG7brIJTlIElJ6fQ0NDqJfh5b069CS0OAgdmxcS78OLTm0d2eB6suJZm070rZ7b1YumM2ujWsYOm4y1RzqKPRKkkI/EBYcxNAurRnapTVhIcHs3byOoV1ac2z/LgBK29njdcudaSMGcHTfLsb8OodqDnU+deivpmPnLvTu24/ZM35lzcoVTJ72C3XqOiGXS0hIyOUZXS2W/rEcuTyNH4cM4s3r16xeux4NTU2MjIz4Y/lK/B49YvzY0Yz+aQTGJsYsX7kadfX8dTD+zszuzbC1MGHoWldu+L9i7fBOykibJEkgKf4a6emwamgHHr4JZvTmowxffxgTPR3WDOugMtr2n2JasxoUN9FnlOt1br0KYnmnepikRxLlCtnIC3h1ki+lU+cu9Onbj5nTf2XVyhVMnfYLdes6IZcUdUXKVFd+/3M58rQ0hgxW1JW169YrnwETJ4wnOTmZhQvm07ypCy2aN6NF82acO1cwK2vkBdHnToFMKug1cv7jNGvWjGHDhtG9e3dAMfJz8uTJXLx4kadPn35jdf8/eD2tYFdXKEjKnflv3jAenSv4FQoKioTU3L34/NsoYVSwS8QVJNpXtn1rCXkiIdPSWv81NH+Y9a0l5AmDAp5DscXarCtv5JazI+t/3ug/wn96ZpfJkyerjHLNvDk55c9oxkePHnHlyhVu3LiBv78/J06c4Nq1a7Ru3Tpfyv8anJyccjz/yZMnf2t5AoFAIBD8o6iryfK8FSb+08uPTZo0iWHDhmWbp6WVP2/BBw4cYNq0abRs2RK5XE65cuUYMmQIY8aMyZfyv4b9+/fn2Dyd19HEAoFAIBAI/tv8p507S0tLZT+ygqJ+/foqqzz8myhRosS3liAQCAQCwb+GwhaByyv/aedOIBAIBAKB4CPCuVMgnDuBQCAQCASFAuHcKRDOnUAgEAgEgkKBhnDuAOHcCQQCgUAgKCSIyJ2C//RUKAKBQCAQCAQCVUTkTiAQCAQCQaFARO4UiMidQCAQCASCQoG6mlqety9BkiTmzp2Lra0t9vb2bN68OUdbmUyWZXv58qUy/8aNGzg6OmJubk7fvn2Ji/v6dcCFcycQCAQCgaBQ8E+tULF+/XqWL1/Ojh07WLhwIT/99BNnzpzJ0f7QoUMEBgYqN1tbWwA+fPhAy5YtadmyJRcuXODx48cMGTLkq64BiGZZgUAgEAgEhYR/ollWkiTWrl3LpEmTaNKkCQDnzp1j/fr1tGzZMtt97O3tKVKkSJb03bt3Y2Njw5w5c5DJZCxfvpzGjRuzYsUKrKys8qxRRO4EAoFAIBAUCv6JyF14eDi+vr40a9ZMmebi4sLly5dz3MfCwiLbdDc3N5o2bYpMpji+o6MjWlpauLu751pPdgjnTiAQCAQCwf97kpKSiI6OVtmSkpKy2AUFBQGoROJsbGyIjo4mISEh27LHjBlD8eLFcXJy4ty5cyplZS5HQ0MDa2tr5THyimiWFfzr2d/y128tIc88Gm/9rSXkiUrfj/3WEvLM5IWTvrWEPHH6xqtvLSHP3Gyd9+ajb4lRCftvLSHPSBEvv7WEvKFXsUCLV5flvVl20aJFzJkzRyVt1qxZzJ49WyUtIiICAENDQ2Xax88RERHo6uqq2I8aNYqWLVsyadIk1q1bR7t27fDx8aF8+fJERESolPOxrPDw8DyfBwjnTiAQCAQCQSHha/rcTZs2jfHjx6ukaWtrZ7EzMzMDICYmBhMTEwCio6NV8jKzatUq5eeNGzdy+fJlXF1d+eWXXzAzMyMmJkbFPjo6GnNz8zyfBwjnTiAQCAQCQSHha5w7bW3tbJ25v/OxGTXzqNf3799jYmKCjo7Op/Wpq1O+fHnevXunLCswMFCZn5qaSnBwcLaDL74E0edOIBAIBAJBoUBDTZbnLbeYmppSrVo1Lly4oEy7dOkSLi4uWWyjoqJUvqekpPDw4UMqVKgAQJMmTbhw4QKSJAHg4eFBSkoKDRo0yMvpKxHOnUAgEAgEgkLBPzXP3ciRI1m6dKmyiXXHjh0MHz6ckJAQSpYsybZt2wDo1KkTQ4YMwc3NjUePHjFkyBBSUlLo06cPAL179yYoKIhZs2bh7e3Nzz//TI8ePb66WVY4dwKBQCAQCARfwNChQxk/fjz9+vVj6tSprF27lubNmyOXy5EkCblcDsD+/fvR1NTkp59+om7dugQFBXH58mVl3zwrKyvOnDnD6dOncXFxwd7eng0bNny1PtHnTiAQCAQCQaHgn1pbViaTMWPGDGbMmKGSbm1tzevXr5XfLS0tWbdu3SfLcnJywtPTM1/1CedOIBAIBAJBoeCfcu7+7QjnTiAQCAQCQaFAOHcKhHMnEAgEAoGgUCCcOwXCuRMIBAKBQFAoEM6dAjFaViAQCAQCgaAQISJ3AoFAIBAICgUicqdAOHe55NChQ0ycOJGHDx9mWRT4S2z+TTg7O+Pg4MCyZcvyXMbEiRN5+fIlBw8ezEdlX4ckSdw7uZeAa2dR19SiaouuVGjY4pP7BD725eSyKdRo25ta7RWTS8aGBXPbdSvvA+6joaVDxcatqdqiC7KvWJj6c7r3btvM2eNH0NLSpmuf/rRo1zFb24f3vTlxaD83r11h8E9jadelR7Z2e7ZuYveWDSxauZ6qNR0KRLeFqSGjezWjT9t6hETE4NRnbo62VmZGbJw9kHrV7fB5/Ibhc7fy9HWwMr9utbL8PrEXZYpbcdb9ASPnbyc+MblAdIPimvud/Yvnt86hrqlNeZdOlHFq/sl9Qp49xG3Vr1Rq0YPvWvVSpidEhXPPdSMhTx+ibWBM2QYtKdewTYHVF4ChzmXp6FCcpBQ5O66/4IjX2yw2GwY54lA663qXR+68Yd7RhwA4V7RiuIsdZgZanHsQyJ9nAkiTSwWmGyA+KZk5Ww/j/uAxtlbmTO3TlmrlSmRrGxWXwJ/7T3Pd9wmSXKKNUzVGd2mOpobiEZYml7P60HmOXr9LSmoaTWtWYnLvNujpfH4pqS/WnZjErPV7uH7vESWKWPDL4O5Usy+dve7YeH7feZjr9x4ilyTaNqzNmF7t0dJU6H70/DXLdx/j/uMXGBno0be1M/3buhRYnYlPSGT2H2u57nkP26JF+GX0EKpVKp+j/bFzlzl0+gKe9x9yZMsK7EqXzGIjSRKDJszktvcDHl46UiC6vwTh3CkQzbK5xMLCggoVKqCpqQnAnTt3sLCw+KTN/wdsbW0pV66c8vuyZcvo2rXrN1QEfldO8fDCURoPHE/tTj9wY+9a3vjeydFeLk/j1v4NaGpnOOSpyUmcWDYFAwsrWv+8EIeO/bh7bBfPbrsVmO5TR1w5+tdexk+fww8/jmTtH4u5c+tGtraP/RUPZTW1nH/CocFBHNqzM1drJX4Nxa1NKVvCipi4hM/aHlo+BrlcTtPBv/H0dRCn109EU0MdAGtzI06sHs+5G760Gr4Mu5LWbJg1sEC1P3c/w+Orx3HsM44qbfpy7+AGPvjdzdFekqfhfWgzGtqq60emJidxafkU1DW1aTxqHjW6DCHizTPkqSkFpr1rbVt6OZVklusD1lx4zJS2lXAqZ5HFbtLee3y/+JJy67nGnbikVPZ7KObisitiyG/dq3Pg9mt+3nWXRhWsGNHUrsB0f2TmFlfeBIexafJg6lW2Y/jvWwmLjs1iJ0kSI37filySWDmmLzMHdOTwNS+2nrqmtNl38RZnPB7wx6g+rB3/A16PX7Lm8MUC0T19zU5efwhhy6wx1K9eiaHzVhEWFZPFTpIkhs1fjVwuZ/XU4cz+sTeHLt3kf0fPA5CSmsakP7dSs2JZdi+cyLje7fl952HO3rxXILoBZixdxet3gWxZOof6taszbMocwiIic7S/4/MIvc8EKi5e98A34Ek+K807/9QKFf92ROQulzRq1IhGjRp9tU1hY+zYsd9aggqSJOHndpIqLbpgU6EaAG8f3cX/ymlsK2cfuXrsfoGkuBhKVHNUpmloadNmwiIMLRSLN5sVL8Xbh3d54eVOuTpNCkT3ycMH6NK7H9XSI2x3b3tw+qgrDnXrZbHv1EMRXRzYtV2OZW5dv5ra9Rrg//BBvuvNjLf/a/pO3cCMHzvQulG1HO1qVCyJY5UylPx+PIEhkYxZtIvAyytp1aAqx9zu0au1E4EhkcxZdwSACcv2cnHTFMabGhISkfXh+bVIksRT99OUb9IRK7sqAHwIuMcz9zMUqVgz231eeFwiOT6Wot/VVkl/6XERNXUNHPuMRZbucFuXr57vmjPTrU4Jdrq/5M6LcADqln1HV0dbbj4NVbGLTlB1MEc2tePwnTc8/qC4ph1rFefOi3BcPd8AsPbCEya2rsj6S09ITSuY6F1oZAwX7jxk2y/DqFjShgolinL2tg+nbt6nX4v6KrYymYwlI3pSzMJUGdHq4VKH83d8GdZe8Vu89egZvZs5UT098teneT0OuN3Od90hEVGcv+XNzvnjqVTGloqli3Pa3YuT1zzp31Z1XVGZTMbvPw+imJU5MpmMSmWgV4tGnLt1j+FdW6GpoY7rsmnoaGsBUM62KCeveeLu/YiW9bKvf1+lPTyC81dvsmPFQiralaFCudKccXPn5MWr9O/aPtt95k78iXcfgrhyK/uX4+TkFJat30rvjm3YvNc13zXnhcLmpOWVAoncBQQEMGDAAMqWLYuZmRkDBgwgPj6e+vXrM3XqVBXbRYsWUb++4sccExPDkCFDsLCwQCaTKbeePXvm6rgymYzz58/TuXNnTE1NqV69OpcvX1ax8fLywtnZGSMjI6pWrcq+fftU8nfs2EG5cuUwMjKiRYsW+Pj4AHDixAnljWX27NnUrl2bsLAwpca/2/z666/UrVtXpeybN2+io6NDdHQ0AHfv3qVhw4YYGhri4ODAtWvXyC2lSpVi7969DBgwAAsLCypWrJjlXJ49e0bbtm0xMTGhQoUKrFy5UrkkSnZ4enrSrVs3bG1tsbKyYuLEiaSlpalcXz8/Pzp06ICJiQlRUVFMnDgRZ2dnQNHMO2nSJFxdXZHJZDg7OzN37lxq1lS9Ud2+fRtdXV3ldchPkuJiiHj/imIVqyvTbMpX432AT7b2yQnx3Dmyg5rt+6Kmrvqu89Gx+4i2viEpifH5rhkgJjqKV8+fUd0hw8GsVssBn7s5Rxw/hb/vA9zdLtJ38I/5JfGraexQgUfP3hEYEglAckoqN7yf0Li2YgHtxrXKc+m2n9Le0/cFyalp1KteMFGk5PgYogNfY10+wyG1sqtK8FPfbO1TEuPxPbmL71r1ylJX3vrcxLZmQ6VjV9AY62pSztoQj2cZjpzni7Bsm18zU9JCn++rFGXT5WfKNIfSZqrlPA/DVF+LslYG+S88nXtPXqGtpUGVMsUBxb3FsWJZbvs/z9a+uKWZSlOlsb4esQlJyu+li1ryKijjHLS1NClVJGsU86t1+z9HW0uTKuVKKXXXqWKPh+/j7HVbW6jqNtQnLj5R+f2jY/eRuMSkAmlKBrjn64e2thZVKih+TzKZjDo1qnDbO/v6nht2uB5HXV2dji1cPm/8D6Euk+V5K0wUyJ3o+PHjlCtXjoMHD3L06FFOnz7NqlWr6NGjB0eOHFGxPXz4MN26dQMU/beuX7/O7t27uXDhAlWqVGHhwoWsX78+18ceMGAAXbp04caNG7i4uNCqVSs+fPgAwPPnz2nQoAH169fHw8OD8ePHM3jwYPbv3w/AmzdvGDhwIIMHD8bDw4NWrVoRGhqa5RgTJ05k+fLlmJqaEhgYSGBgYBabHj164OHhwfv371XOtWXLlhgZGfHixQuaNWvGgAEDuHfvHiNHjqR9+/bZHi8nRowYQZ06dbh+/ToDBw6kV69eeHt7AxAREUG9evWwtrbm+vXrLFq0iPnz57N06dIcy/vrr79o0KABp0+fZuvWraxZsyZLX7pOnTrRunVrbt++jbGxsUreoUOH6N69O23atCEwMJBDhw4xcOBAvL29efTokdLO1dWV1q1bY2RklOtzzS0J0ZEA6BqZKtP0TMxJSYwnNTkpi/29k/swKVIcO6emny077PVTTIuVyi+pKkSGK6IvpmYZi0WbW1gSHxdHUlJiTrtli1wuZ8OKZXTp3Z9iJbL2kflWWJsZ8SE0SiUtMCQSa3NFPbAyNyIoLCM/LU1OUFg0Vub5X08AkmIUx9IxNFGm6RqbkZoYT1o2dcXv3F8YWhenZO2skdv4iBB0jEy4e3ADJ2YP5tKKaYS/LrimKjMDhVMQFpPRHzEkOgkDHU20NXK+rberUYwr/sHEJqVmlKWvRVhsRjnhccmkySXM9AuuOT8sOhYzQwPUMznDlqaGhEdlbZbNDr9X77Erbq383r2JI5fv+rFs3ymCIqL465IHPV3qfqKEPOqOisbc2BB19QzdVqYmhEXm7kX10fPX2JWwUUmTJInImDg2Hz6H77PXdGveIF81fyQsIhJzE2PU1dWVaZbmZp9slv0UIeERbNx9gBnjfkRLSzQC/tsoEOdu4sSJTJ8+nRo1atCwYUPatGmDm5sbXbt25fHjx/j7+wPw9u1b7ty5o+yj5eHhwQ8//ECLFi1o2rQpgwcP5saNG5iYmOT62CtXrqRPnz5UrFiRZcuWYWVlxd69ewFFlLBevXosWLCAihUrMmDAACZNmsTMmTMBiI2NRS6X06VLFypWrMi4ceNwccn6RmJgYICxsTFqamoUKVKEIkWKZLGpUqUKFSpU4NixY4DiB3z48GG6d+8OwG+//cbQoUMZPHgw5cqVY9CgQTg6OnLq1Klcn+uMGTMYMWIEFSpUYPLkydSvX5+tW7cCsHbtWoyMjNi4cSOVK1emU6dOLFmyhHnz5pGSkn0/oKVLlzJ27FgqV65MmzZtcHJyws3NTcWmRYsW/Pjjj9jb22fZ38zMDF1dXXR0dChSpAhmZmbY2trSsmVLdu3apbwOrq6uOUZjk5KSiI6OVtmyc8pyIile8XDQ1MnoJ/Lx88e8j0QFv8fvyknq9xn52Q7Moa+e8OHpI+zrNcu1li8hNkbxcNDV01em6erpped9WZOk27nTxERH0b3fgHzTlx+YGOkRG6/qqMbEJ2JqpJ+er09MnGp+bHwiZkb6FATJ6fVBI1Nfy4+fkxPiVHWEBPL0+hlqdv0x27qSEBmG3/mD6JtZUW/QVPTNLHHfvPCL6u6XYKSr6Ncbl5zhpMWnfzbUzbnPb6uqRTn7QPVl1EhXU7kvgCRBQnIqxnoF13c4Oi4B/b9FqPR1tImK+3xkPCgiinOeD+jcKKObhaWxIXa2Rbj18Bnfj1+CsYEetcqXym/ZRMXGo6er2t9SX1ebqNhc6A6L5OzNe3RpqtrNYtcpN+oNmMTKvcdYOKof5WyL5qvmj0THxKGnp9p/Tl9Xl6iY3DnUf2flll00qedI3Zo5d8X4FqjJZHneChMF4tzJ5XLOnDlDnz59qFq1KseOHSMkJAQbGxsaNWqkjN4dOXIEJycnihdXhOZbtWrFkSNHePr0KS9evMDV1ZVKlSp90bEzv5Woqanx3Xff8eyZognC29tb2YT4ERcXFx4/fkxcXBwVKlTgl19+oV69evz888+8e/cuz9dAJpOpRCofPnzImzdvaNu2LaBokl2+fDkGBgbK7eLFi190zMznCgqHMvO5NmrUSMXGxcWFuLg4nj59mm15KSkpHDhwgC5dulC5cmW8vLwICQlRsWne/NMjCbNj6NCh7N69G7lcjo+PDx8+fKBNmzbZ2i5atAhjY2OV7dLuDbk+lra+oikpJTGjc39KQnx6nqGK7e0DW/iuSTtMbT4d3ZKnpXHrr02UqdUQc9syudbyJRgYKaKgCfEZTkV8nOKzoWHuI1cJ8fFsW7+aEeOnoP23Tv/fmoioOAz0VDUZ6esSHhWnzDfUV8031NchLJfRnC9FS09RV1KTMupKanqz+8e8j9w/uhW7hm0wLpr9aE4NbV3K1mtBeZdOmJWwo2a34STFRhFRQNG7qPR+dPqZIib62orPf+9j9xFLQ22KmOji/141yhSVkIJepnJkMtDT0iAqPv8Ggxy/cQ/HH2crtzS5nLhEVcc3LiEJYwO9T5YjSRK/7z9NeduiNK5eQZk+Yc1e6lQsw4G5o9g8eTDvQyOYseXr+4Adu+JBrT4/K7c0uZz4hL+9gCQkYmL46RcQSZJYtuMQ5UsWw9mhikpeu0a12Tl/PKN7tmPm2t0cu+Lx1boBjp93w6F1T+WWlpZGfLzqoKe4+HhMjAxzKCFnHj1+xoXrHkwcPiBftOYn6rK8b4WJAomljho1iqtXr7Js2TKaNGnCokWLOHHiBKBorty+fTtTp07lyJEjykgWwPjx49m4cSPVqlUjPj6eFi1a8Msvv3yVlvj4eAwMFDdqScq5c7AkSchkMhYsWMDw4cNZsmQJFStWZMOGDfTq1SvH/T5Fjx49WLhwIdHR0Rw5coRWrVopmyIlSWLkyJH89NNPKvv8fQTul5Dbc82u351cLqdjx47ExMSwcOFC6taty5AhQ4iN/foHa9u2bRkxYgTXr1/Hzc2N9u3bo6eX/U182rRpjB8/XiVtjcebXB9LL705Nj4qHAMzy/TPYWjp6aOhmdG/JS4ilFf3bxH4+AH+184CkJIUj0ymzuv7t+g0Y5XS1vPQVmJCg2k2ckaudXwpH5tjw8NCsbRWRILDQkPQNzBE6wtGu966foWw0BCWzZ2uTIuJjmbetAm4tGjNiPFT8lf4F/AhLIqiliYqaUUtTfB7rui6EBQWRRGLjKZ+dXU1rMxUm2rzE530upIYHYGeqaKuJESFo6mrj3qmupIQGcZ739uEPPXl+a1zAKQmJiBTU+O9722aT/oTPVMLlX00dfTQ0jMkMb2bQH4TFqNwjCwMtQmKVjgbloY6RCekkJyafb/aCkWNiE1MISRG1akKi03CwjCjjpnra6OmJiM0Nv+ijk1qVKRa2QzH2O/Ve8KiY0mTy5VNs8GR0VgYf9rR2HvxFtfuB/DXnNHKCOqb4DBuPHzCkhE9kMlk1K5YhqUje9J1xipGdmxGMUvTT5b5KVxqV1WZ5sTvxVtCo2JIS5Mrm2aDw6OwMPn0C9ie01e4ctcX16W/ZIn8mhgaUKtiOWpVLIeGhjor9hynfeM6edb8kSb1HKmaaZoT/yfPCY2IIi0tTfnSHxwWjoWpyReXve/YGeITEuk4aAyQ8Uyp37Efv4weSpum325goZoYUAEUgHMXHR3Nhg0buHDhAk2aKPqmZHYmunTpwtixY3n27BnXrl1j27ZtyrylS5cyePBg5syZQ1JS0hc1x2ZHVFQU9+7d48cfFZ3Kq1evzpUrV5gxI+MhffnyZezs7JROESim91i1ahUWFhYsXbo0W+dOQ0ODxMRE5HJ5jtNRVKxYkQoVKnD+/HlOnDjBmDFjlHnVqlXjwYMHKtOIfA2pqalcv36dwYMHK891x44dKj/ky5cvo6enl22Tqr+/P6dOneL58+eULq24mX1q8EVOaGhoEB+v2kShqanJgAED2L9/P/fu3WPKlJwdDG1t7SxTd2ho5d650dY3xKx4ad77eWNVWnFje+/vg0151aYDXSNTei3erpJ2ccMirMpUoOr3XZRpfldO43/1DK0nLERH/8vfcHOLoZERpcvZ433nNuUrVQbA5+4dqtX6srnpnBo6s/3QSZW0Hzq3YcyUGVR3qJ3DXv8MVzz9WTaxFzaWJrwPiURbS4N61cuxbr9iygo3T3+Gd8/oz+ZYuQyaGuq43yuY6JeWngHGNqUIenwfs5KK30TwkwfKkbMf0TY0oc3szSppN7cuxbxUecq7dAQUAzFCnj6kvEsnAJLjYkiKi0bfImuXjfwgJjGVgMBoHMua8/CdwvmtXcYMz+dhOe5TzEwvi2MHcOd5OHXKmrPj+gtlOWGxSbwIzr+IqYGuDgaZmjMNdLVJSU3lwbM3VLcriSRJ3PZ7Tu9mTjmWccXbnz/2n2bZyF7YWmUMHIlPSlY0q2Xuv5fuJMYmfFl/1Sy69XQxyNSUaainS0pKCj5PXlCjQlkkScLjQQB92zjnWIbbnQcs23mYP8YPxjbTIA9JkohPTEI/03Ux0tMlMTl/5nU00NfDQD/jJdpQX0+h3e8JNSpXUGi/94C+nbJvRfkU44f1Z+QPGXNr3n8YwPi5S3Hd9CdGBgU3ECc3FLaBEXkl35tltbW10dTUZN++ffj5+bFu3TrWrl2rzLeysqJx48aMHz+e2rVrK5tkAcLDw7l//z4vXrwgMTGRiIiIT0agsmPp0qVcuXIFHx8f+vbti7W1tbJP39SpU3F3d2fGjBn4+/uzY8cOli5dypw5cwDYunUrAwYM4MaNG3h6enLmzBlKlMi+GaZcuXLExcWxY8cOHj58mKPOHj16sHXrVh48eEC7dhnTVkybNg13d3d+/vlnHj16hKenJzNnzlQZnfo5Nm3axKlTp3j06BHDhw8nIiKC4cOHAzBy5EgiIyMZPnw4jx494tixY0yePJnp06cr5+EzNDTkw4cPpKSkKCNp27dv59GjRyxatIhDhw7lWkvm6+Lh4cGtW7d4/DhjBNmgQYPYt28fAQEBtGjx6QmFv5ZKzm3wOefKe//7vPBy58mti1Ro3JqEmCj2TR3A4xvnUVNXR9/UQmVT09BEU0cPPRPFg8P/2llu7l+P8+AJ6JtZEh8VTnxUOGkFNHdZm05dcd2zk/t37+DudpGLZ07SumMXoiIiGNClLedPHQcgLS2N8LBQwsNCkcvlxMfFER4WSkJ8PDq6ulhYWatsAMYmJhgaGX/q8HnG1Egfa3Mj9PW00dRQx9rcCAtTQyxMDXlyagn92ilGw99//Iab95+y5tf+VLErzoqpfQmJiOHsDcVovX2nb2FlZsSsER2pZm/Lsok9OXD2trLZtiAo16AVAZeOEPzkAW/v3+CV52XK1m9JUmwUJ+cMTZ/iRB09EwuVTV1DA00dXXSNFXWlXMPWhDzz5bHbMaI/vMHrwHpMi5fFtHjBNOMDHLj9mv4NSuNQ2gyXSta0qV4MV883mOhpcmJCY9rVKKZir6ulTlJK1vvLEa+31CxlRpfatnxXzJgRzRRTpaQW4CTGZkYGNK9dmSV7T+L/6j2rD10gIjqO1nUVL2E3fJ/QdNxvPHmrGAx39X4A41fvYXLvNlQpa0toZAyhkTEkJqdQ1sYKWyszpqzfj/+r9zx+84HZWw9TqogFZWys8le3sSEtnGry2zZX/F68YdW+E0REx9KmgeLFyd37Ec5Dp/HktSIafcXLl3HLNjF1YFeq2pcmJCKKkIgoEpOS8fB9TPtx8zl86SbP337g+r1HrD1wmhZONfJVs1K7iTHfN67H4rVb8Hv6nFVb9xARGUXr9Cibu+c9mnQbxJMXrwBITEoiJDyC8PTBIhFR0YSER5CckoKJkSFFLC2Um2l65LKIpUWWPomCb0OBOHebN2/m2LFjNG/enMePH/PHH3+o2PTo0YNjx44pR8l+ZNCgQVy+fJkaNWpQtGhRzMzMsLa25vDhw7k+foMGDZg0aRINGzZELpdz6dIltLTS5xEqV45r165x5coVateuzZIlS9i4caMyMtehQwf09fXp27cvTZs2xdzcnFWrVmV7nDp16jBy5EjGjh1L+/btsx0x+/FcT548yffff4+hYUbkp1y5cri7u+Pt7U3dunXp3r07CQkJJH/BW1vDhg1ZtmwZdevW5cmTJ1y9ehUzM8XDxszMDHd3d969e4eTkxMTJ05k6tSpKlGzbt26cfjwYTw8PChVqhRLlixh5cqVtG/fnpSUFJUIZ24ZOnQoNWrUoGnTpowaNUqZbm9vj52dHe3atUNHp2B//OUbtqRKs064/e93PA9vo37vkRSvVANJkiNJIOXioRUXGcb1nSuRp6Zyfu189kzsy55J/dgzqR9Bz/w+u39eaNm+E5169uH3eTPZtn41IydMoUbtusglOUgSUnokNTQ4iH4dWtKvQ0tCg4PYsXEt/Tq05NDenQWi63P89ftPvLmwnPH9W1LV3pY3F5ZzY9cM1GQyZMhUmkm6/rwKNXU1Lm6ZSrkS1rQe8TupqQqHIyQihraj/qRF/Sqc3TiJJ6+CGLlgR4FqL+30PfbO7bm9azkPju+kZrcfsS5fHUmSlFtu0DezotGIuby5d52Lf04mNSmBeoOnFujUKIfvvGX3jZfM7VKV0d/b89vxh3g8C0u/7oq+c5nR01InMSVrNP5ZcCxT9t+je50SLO9bk6v+wWy4lH2/3Pxk9sDOFLcyY/CSLbj7Pmb9xIGYpvddk8slJCTk6dd/3KpdJKemMm/7UZzHLKTJuEU0GbeIMx4+aKirs278ALQ01Bm27H8M/G0Tamoy1k0YoJwgOz+ZM6IPJawtGDh7BdfvPWTjjFGYGimiVXJJQpIyWj3GLNlIckoqczbspeGgKTQeMo3GQ6Zx2t2LulXKM75vB05c86TXtKXMXLeLVvVrMvmHLp86/Ndpn/ATtjZFGDRhJtc977FxyWxMjY0ytCMhT78/nr58HeeuA+k5chIAA8fPwLnrQLwf+heYvvxADKhQIJO+NDRWQMjlcipUqMCuXbtwdFTM9RUaGsoPP/xAUlISFy5c+GwZMpmM48ePKwctFGZKlSrFxIkTVRyofzMxMTGUKVMGV1fXL57oeemVgn/QFBSdKll/3uhfSKXv/12TU38JkxdO+tYS8sTpG6++tYQ8c7N1wUVXCxL1TNPg/NeQTG0+b/QvRKNYxQItf8+9rEvw5ZbeNYp/3ug/wr9m+bH4+HhevnzJ6dOn8fT05MGDBxw9ehRvb29at26Nk5OTysjSzNvkyZO/tfx8pWfPnjmea24ndP63kJSUxPPnzxk3bhx16tT5f7eCh0AgEAj+OdTUZHneChP/mpkHDQwMOHjwIPPmzWPJkiVoampSvnx55s+fz4ABA+jatWuOTZZ/n0z3v86ff/7J/Pnzs83T1y+YOb8KCk9PT1q0aIGjo6PK4BmBQCAQCPKbwta8mlf+Nc4dQPv27WnfPvs17nIa2JCZf0kL81dTtOjnJ7F8+fJlwQvJBxo0aEBc3H+zyUYgEAgE/y0K23x1eeVf0ywrEAgEAoFAIPh6/lWRO4FAIBAIBIK8IpplFQjnTiAQCAQCQaFAvZANjMgrwrkTCAQCgUBQKBCROwXCuRMIBAKBQFAoEAMqFIgBFQKBQCAQCAoF/9QKFZIkMXfuXGxtbbG3t2fz5s3Z2qWlpTF//nyqVq2KqakpXbt2VVnRys3NDZlMprKVKlXqay4BICJ3AoFAIBAIBF/E+vXrWb58Oa6uroSFhdGnTx+KFy9Oy5YtVew+riO/fPlyjIyMGDFiBP379+f8+fNKG5lMxtu3b1FLX65QXf3rl80Tzp1AIBAIBIJCwT8xoEKSJNauXcukSZNo0qQJAOfOnWP9+vVZnLsJEyYwa9Ys5QIEixcvpmnTpkRGRmJiYgKAubk5Njb5u5ycaJYVCAQCgUBQKFCT5X3LLeHh4fj6+tKsWTNlmouLC5cvX85ia21trbKylJmZGaBYb/0jFhYWeTjTTyOcO4FAIBAIBIUCdZksz1tSUhLR0dEqW1JSUpZjBAUFAVCkSBFlmo2NDdHR0SQkJHxS3927dzExMaF48eLKtMjISFq1aoWNjQ0dOnTgyZMnX30dRLOs4F/PoJr5G67+J3kfm/KtJeSJyQsnfWsJeWbJL0u/tYQ8EXlj7beWkHcSI7+1gjwRLDP81hLyjHW437eW8K/ka6ZCWbRoEXPmzFFJmzVrFrNnz1ZJi4iIAMDQMKP+fPwcERGBrq5utuXL5XJWrlzJoEGDkKXrtLOzo2vXrvTs2ZOUlBSmTZtG27Zt8fHxQVtbO8/nIpw7gUAgEAgEhQL1r2iPnDZtGuPHj1dJy87Byty0+rHfXHR0tEpedmzevJmXL18yceJEZVqxYsVYtWqV8vuePXsoU6YMXl5e1KtXL8/nIpw7gUAgEAgE/+/R1tbOVbTsY3NsYGAgtra2ALx//x4TExN0dHSy3efOnTuMGTOG3bt3U7Ro0RzLLlWqFDo6Orx79y4PZ5CB6HMnEAgEAoGgUPBPzHNnampKtWrVuHDhgjLt0qVLuLi4ZGv/7NkzOnbsyLhx4+jSpYtKXlRUlMr3x48fk5iYSIUKFb7grLMiIncCgUAgEAgKBer/0PJjI0eOZMqUKTg5OREeHs6OHTs4ceIEISEhODg4MGfOHAYMGMCLFy9o0qQJLi4uTJw4kQ8fPgAo++VVqFCB0aNH07JlS5KSkpSfK1eu/FX6hHMnEAgEAoGgUPBPrS07dOhQgoKC6NevH7q6uqxdu5bmzZsTFBSEJEnI5XIAhg8fzps3b9i5cyc7d+5U7v/DDz+wbds2Tp8+zYIFC1i3bh1JSUl07NiRJUuWKAdc5BWZJEnSV5UgEBQwYTHx31pCnvmvjpbd7/3+W0vIM2K07D+Pxn90tGyYGC37j6NWxqFAy/d+F5nnfasXM8k3Hd8aEbkTCAQCgUBQKPinInf/dsSACoFAIBAIBIJChIjcCQQCgUAgKBSIwJ0C4dwJBAKBQCAoFKghvDsQzp1AIBAIBIJCgojcKRDOnUAgEAgEgkKBmnDugG84oMLR0ZGVK1d+q8N/ER4eHhQrVow3b958lc2/iQEDBtC1a9evKmPlypU4Ojrmk6L8Q5Ik/rdpAx3btKRH5w4cO3IoR9vwsDAmjB3N984NGTlsMG9ev1LJj4uNZf6cWbT5vildO7Rl0/q1pKamFpjuA9s382O3tozu24ULJ47kaOvn480fc36lV/MGnD70l0qeXC7nwPbNDOncipG9OuK6aysFPeORJEk8OrOfE7MHc3rBSJ7fPP/ZfUKePeTAuI48PL1XJT0hKpwb//uNo7/048zCUTy5eqLA9FuYGjJnZCeenlrKzd0zP2lrZWbEkZVjCb66mgubp1CuhJVKft1qZXHfOZ3AyyvZNn8oejpaBaL5I5IksWHDelq2+J4O7dtx6FDO9TwsLIzRo0bRsEEDBg8exKtXqvX8xInjDBk8mBrVq/H06ZMC1f2R+IQEpsycR4Pv29Fr0I/c9334SXtvH196DfqRBt+3Y+qs+cQnJADg6XWPKnUbZ7t9CA7OV82SJLF980a6tWtF364dOXH0cI624WFhTP15DG2aNmLs8CG8ff1amSeXy9m+eSOdW39Pr07t2LVtS4H/RgHiExOZtHgNdbv/SPexM/D2+/T/+ujFa/SfPJ+Krfrw+KXqsy06No5Ji9dQp9tQOv/0C5dv3S1I6blGJsv7Vpj4Zs5dhQoVPrm+2r8JY2NjKlSogJ6eHgChoaHIZDJiY2NztPn/QNGiRVWWSDl48CAODgU7h1FuOOx6gP179zBjzjx+HDmK3xf/xq0b7lnsJEli8vhxqKupsXbTFmxtSzBm5HBSUlKU+WN/GkFIcDDLlq9k1tz5vHv3jrhM//f85NwxV04e3MfoX2bTe8hINq9Yyj2Pm9naPg14BIBaNqtknzt2iLNHDjJuxjxGTZ3JpVPHOLxne4Fo/shz9zM8vnocxz7jqNKmL/cObuCDX843e0mehvehzWhoq67DmJqcxKXlU1DX1KbxqHnU6DKEiDfPkKcWzHyBxa1NKVvCipi4hM/aHlo+BrlcTtPBv/H0dRCn109EU0MdAGtzI06sHs+5G760Gr4Mu5LWbJg1sEA0f+TAgQPs2b2befPnM2r0GH5btBB39+zr+bixY1BTV2PL//5HiRIlGD78R2U9B7jrdRd9/X/23jVz/mLevH3HplW/U6+OI8PHTiIsPCJb29CwMIaPm0T9uo5sWvU7r968YfbCJQBUr1qZyycPqWxdOrSluUtjilhZZVteXjl26CAH9+/hl1lzGTJiFCuW/obHzeyv+a+TfkZNXY2V6zdTzLYEE0Zn3FuOHTrIEdcDzJi7gKkzZ3Pq2FH2bN+ar1qz49c/N/I6MIj/LZxG/VpVGTp9MWGRUTna3/H1R183+7VSZ63cwvvgUHYumcHALm2YuHg1Xr4BBSVd8IV8s2bZHTt2fKtDfzEVKlTg4sWLX21T2OjWrRvdunX71jJUkCSJQwcO0Kdff2o51AbgtsctDrsepG69+iq2Af5+PHroy9HTZ7G0tGLClGm0btaEG+7Xaeys+Pvq5UuOnj6rdNqrVKteYLrPHnGlfc++VK6hcJDv3/Hg3DFXatRxymLfrltvAEb06JAl79xRVzr06qcsZ+Co8axdPI82XXuirZ39jfprtT91P035Jh2xsqsCwIeAezxzP0ORijWz3eeFxyWS42Mp+l1tlfSXHhdRU9fAsc9YZGoKx9W6fPV81/wRb//X9J26gRk/dqB1o2o52tWoWBLHKmUo+f14AkMiGbNoF4GXV9KqQVWOud2jV2snAkMimbPuCAATlu3l4qYpjDc1JCQiJt91S5LEgb/20/+HAdSurYie37p5k4MHDlC/vmo99/Pzw9fXl7PnzmNlZcW0ab/QxLkx169do0n6WpgzZ83i/bt3XL16Nd+1ZkdoWBgX3K6ybf1KKpa3p4K9HWcvXOLUuQv065n1nnLyzAUsLS34aeggZDIZU8aNZuCIMYT9HIG5mSkW5uZK29dv3nLpyjUO7NiSr5olSeKI6wF69ulPjfR7yx2Pmxw75EodJ9Vr/tjfD7+Hvhw8cRYLS0vGTZpKhxYu3LpxnYaNm3D00EF69ftBWc6onyeweP4cuvbsjXYOC89/LSHhkZy/7snOZTOpVK4UFcuW5PTVW5y4fIMfOrXKdp95Y4fyLigEt9v3VNIjomM4c82Dg6vmY1+6BPalS/Do2Qs2HzhOrcrlC0R/bhEDKhTkW+TO2dmZ7du3M3XqVGxsbKhZsyZubm48fvyYVq1aYWxsTKdOnYiJUdzoHBwcmD17NgAvX75EW1ub58+f06lTJ0xNTalTpw5Pnz7N9fFLlSrF3r17GTBgABYWFlSsWJF9+/ap2Dx79oy2bdtiYmJChQoVWLlypXKJEIDTp09TuXJlDAwMaNCgAVeuXAHA19cXmUzGy5cv2bZtG5aWlgAYGhoq0zPbbNq0iaJFi6qU/e7dO2QyGY8fPwbg+fPntGrVCiMjIypXrszhwzmH97O71itWrGDcuHHY2NhQpkwZVqxYoRLWDwoKonfv3lhYWFC6dGlmzpxJcnJyjmUGBAQwYMAAypYti5mZGQMGDCA+PmNliFKlSnHlyhUGDBiAqakpDx8+ZPXq1ZQqVQpQNPN269YNLy8vZDIZpUqVYseOHVhZWalECD58+ICamhpPnhRM0090VBTPnz3FwbGuMq2WQ23u3rmTxfau1x1KlymDpaXi7V5LS4sq1apz944nAG4XL9KwceN/JBobGx3F6xfPqForo5m7cg0HHnp7fXFZH969pXjJMsrv31WvRXRUJO8zNQvlJ8nxMUQHvsa6fIZzZGVXleCnvtnapyTG43tyF9+16oWauur75Vufm9jWbKh07P4tNHaowKNn7wgMiQQgOSWVG95PaFxbEbluXKs8l25nrBjg6fuC5NQ06lW3KxA9UVFRPH36lLp16ijTajvW5k563c3MnTuelClTBiurjHpevXp1PD2z2v5T3Lvvi7a2FlUqVQRAJpPh6FCT2173srX3vHuPOg41lUsyVa5UAU1NTbx9HmSxXbt5G107tMPayjJfNUdHR/Hi2VNqOWZc8xoOjnh7Zb23eN/1olTpMlikPyu0tLSoXLUa3un3oXdv31CydGmlffVatYmKjOT137qF5Cf3Hj1GW1uLqvZlAcU1r1utErd9Hn1xWa/fBwFQrkQxZZpj1Up4Pfz2kTvRLKsgX++gY8eOxcDAgIsXL1KyZEl69+5N3759GT9+PCdPnuTChQts3rw5232Tk5Np164d/fv359q1a8TGxvLLL7980fFHjBhBnTp1uH79OgMHDqRXr154e3sDEBERQb169bC2tub69essWrSI+fPns3SpYqmipKQkunXrRtOmTfH09GTw4MEEBgZmOUaPHj1wdXUF4OnTpwQGBmJra6ti07lzZ0JDQ7l9+7Yy7ejRo1SrVg17e3uioqJo0KABzs7OeHl5MW/ePAYOHIifX+6Xk5k6dSpWVlZcvHiR6dOnM3nyZI4dOwZASkoKTZs2JTo6mvPnz7N582b27NnD2LFjcyzv+PHjlCtXjoMHD3L06FFOnz7NqlWrVGyGDh1KmTJluHnzJuXLq76drVixggkTJlC1alUCAwPx9PSka9euJCUlcf58Rv+rw4cPU6NGDezsCuahFx4eBoB5pjd5C0tL4uJiSUpMVLGNCAvHzNxCJc3CwpLwMEUZQUGBWFkXYdP6tfwfe+cdFtXxNeB36XXpoAiKgjXYFcXexdgrtqixRY0xauy9xh6Nxl6ixth7Qw0qKtiwYEewN0R677vfHwuLK4sKgvjjm/d55tGdOXfuucPce889Z0rndq3p16uH2vBuXhAZEQ6AqXmm3uaWlsTHxZGUlJjdYWoxkkqJighT/pbJZGhqahISnLU/5wVJMYqwjp6xqTJP38Sc1MR40pKTssg/OLUbYxs7StRsnKUsPiIEPakpN/au5eiMAZz5cyLhL77OGLCPYWMu5W2oavgqKCQSGwspANYWUoLDMsvT0mQEh0VjnV6e14Sl91ELy8z+a2VlTWxsLIkf9PPwsHAsLVX7uZWVFWHhYRQUYeHhmJuZoampmamTpQXh2YRlwyIisLQwV/7W0tLC3NwsSxg3OiYGT69ztGvtluc6R4Qp7lFzlWeLpfpnS3iYihykP1vS21wqlRIRHq4sk8nS0NTUIljNOyevCI2MwsJEiuZ7QzmszM0Ijcg+LJsdJsZGijrfO1ZDokFMXDwxcQW7XaSGJPepMJGnxp27uztTpkyhfPny9O7dm6CgIDZu3Ejz5s2pV68etWvX5vr17D0RGzZsoGPHjjg7O9O+fXtu3MjZAM2pU6cydOhQypUrx7hx46hbty5//60Yx7Bq1SqkUinr1q3D2dmZjh07snDhQmbPnk1KSgrJyckkJCTQunVrypcvz48//kj37t2znENfXx9zc8VDxsbGhiJFiqg8oEBhWDRr1oyDBw8q8w4cOEC3bt2UutSvX5/x48dTunRpOnbsiLu7O3v27Pnsax04cCCTJk2ifPny9O/fn65du7Jp0yYAdu/ezatXr9i+fTtVq1aladOmbNiwgbVr1/L69Wu19Y0ZM4YpU6ZQtWpV6tevT+vWrfHy8lKRKV++PFOnTqVcuXJoaal6XExMTDAyMkJbW5siRYpgZWWFgYEBvXr1Ytu2bUq5ffv2qW3XDJKSkoiOjlZJSUlZDYTsiIlWeIYNDA2VeQYGiv9Hx0SryEbHRGfxyhkYGhAdrZB7F/yOI4cOkJycwux5C6lavToTx44hJCRvB2kDxKV7tPX1M/XRS9c7o+xzqVi9Jkf37iQyPIyQt0EsmT5BsZF1WlreKfweyfGKMYhauvrKvIz/JyfEqcjGhgTxyPsE1br8pHZj7ITIMB78txdDc2vq9J+AobkVPht+J1WNkfg1MZUaEBuv+gKPiU/ETGqYXm5ITJxqeWx8IuZSQ/KDmPQ+avhe/80YM5fRfzOIjo5W3gMZGBgYEh2V85d6XhEdE6uiOyiuJeoD3ZXy0TGfJX/a6zylHBxwKK76wZ0XxKQ/P/QNsj5bYj64R2NiYlTkMmQz/m7Va9Zi787thIeF8TboDdMnjkMul5GWT/coQHRMHIYGqiFfQwN9omLjsjkie+yKWGFXxIpV/x4gMSkZvweBzFm9GYDUfLyGz0HyBakwkafGnbFx5ibMGZ6ZD/MiIyM/63gnJ6ePyqrjQyOrYsWKPH78GAA/Pz8aNGigItOkSRPi4uJ49OgRxsbGLF++nF69etG/f39l+DS3uLu7K427iIgIvLy8lOPTbty4wYEDBzAyMlKmTZs2ZWt45eZaq1evjlSa6TWoV68empqa3L59W219MpmMEydO0KtXLypVqsThw4cJCQlRkWnevLnaF/LHGDhwIAcPHiQmJoawsDDOnTunNHLVMW/ePExMTFTSsiWLP/t8UhPFNcfHZT6w4uMUxodUaqIqKzVRCT0DxMXFYWKikDMwNKBuvfr8POJXKnz3HcN/HYWRkRG+Vy5/tj6fi1H63yohIVOfhPRrMDLOmfen58BhaGlrM7BTKyYO60/LDp0BsLC2ySNtVdExUHzFpyZlTkpITYxXKcvg1qG/KV2/NSZFi6utS0tXH8c6LSnbpCPmxUtTresQkmKjiChg711EVBxGH7wYpYb6hEfFKcuNDVXLjQ31CIvKn8k30vQ+Gvde/41Nf0ln9N8MTEykxMervsDj4mIxMTXNF93UccTjFC6N3ZQpLS1NRXeFTvGYmKjv6yZSqRr5uCzXeuvOPSqUzZ+oQMbzNOG9toxLv0eNpdIssglq2jzj7zZw6HC0tbXp9H1zhg3oR4fOineDtU3e3aOHz3hTvWN/ZUqTpRH3wQdKXHw8psZG2dSQPVqamsz7bQjnr92iWsf+zFixiUHd2qGnq5Or+vISDYkk16kwkW8TKjTUjJlRl5eT43NKfHw8RkaKjvaxaeYZY+N+/vln3N3dWbZsGbVq1WLSpEmMHTs2V+fu0KEDgwcPxt/fn2vXruHs7Kw0eOVyOe3bt2fevHkqx3z4oMoJOb3WDxk+fDjnz59n8eLFNG7cmHnz5nH06NFc65NBtWrVKF++vHJMYc2aNSlRokS28hMnTmT06NEqebHJn/8lmBFmDQsNxaZIEQBCQkIwNjZGV1dXRdbCwoKwUFUDNjQkhJIlFePVbGyKoPPeMZqamtgUKUJYaN6HszLCsRFhoVimG2ERYSEYGhmr6PA5WFhZM3/130RHRmIklRIW8g6ZTIaVTZE81xtAT2oGQGJ0BAZmijFGCVHhaOsboqmduRxIQmQYb+5eJeTRXZ5cPgVAamICEg0N3ty9SvOxSzEws1Q5RlvPAB0DYxKjI/NF98/lbVgURa1MVfKKWpny4MkbAILDoihimXn/ampqYG2uGqrNSzLCrKEhIRT5ZD+3JCQ0VCUvJCSEUqUc80U3dTRuUJfKzhWUvx8EBBIWHkFaWpryQ/VdaKhK6PV9LCzMCX3vvktNTSU8IjKL/IOAR7Ru2SwfrkD12WKdfi+FhYRgpKbNzS0sCPugzcNCQyiR/myxsrZm9aatREZGIJWaEPIuGJlMhk2RvFtBokmtalQu56T8/eDxc8Iio0hLkylDs+/CIrE0M81V/TWcy3Fu2woio2MxN5Vy0PMCttaWOXYACPKHb2vUch6SmpqKt7c3lSpVAqBKlSpcuHBBxe199uxZDAwMKFOmjDLP0tKSOXPmsGzZsizGVwYZIckPvT7vY2pqipubG8eOHePo0aMqs0orV67MnTt3cHBwwMnJSZkyJmrkBi8vL5VrvXHjhkqowMfHh9TUVCpXzjojMDo6mrVr17JixQrc3NzQ1dXN1gj8GFpaWmrbZNCgQezatYsTJ07g7u7+0Tp0dXWRSqUq6cMH58eQSqWULlMG36tXlHnXr/kqZ86+T7UaNXn29Ckh6WthJSUlceeWH9VrKmRr1HTh5vXrSmM5NTWFN69fUczO7rP1+VyMjKU4OJbm9vXMcZp3blzDuVrul5aRmpqioaHBxbOefFelusp4vrxEx8AIE1sHggNuKfPeBd5RzpzNQNfYlNYzNtBiwnKaj11K87FLMbVzpFQdN+oNngooJmKEPMpc7yw5LoakuGgMLfPHMP1czvn6U76ULbbpBp6ujhZ1qjjh5asYJ+vl60/TWpnGi4tzKbS1NPG5mT8eR6lUSpkyZblyJbOf+169Sk01607WrFmTp0+e8C5YMQg+KSkJPz8/arpkvSfyCyNDQ4rb2ylTzWpVSUlO5s49RfvJ5XKuXruBS3X1s6tdqlflsm/mvXjn3gNSUlOpVlm1j7168wbrD8YX5hXGUimOpctw3TezzW9c96Va9aztWKV6TZ4/++DZcvsW1T54DpmamqGhocFZz/+oUq1GlnF6X4KRoQElbIsok0vF8iSnpHL7oWKiolwu5/Kte9SuXOETNWWPhoYG5qYKr+WJC5f5vmHtTxyR/4gJFQoKlXG3fv16jh8/zv379xkyZAgREREMGTIEgGHDhhEZGcmQIUO4f/8+hw8fZty4cUyZMgVtbW1Onz5N27ZtOXv2LLdu3WLfvn0UL64+dFSyZEkkEgnr16/n4cOHJCSoXyMrIzR78uRJFeNu+PDhhIeH06dPH/z8/Lh16xbTpk3LMm7jY+zbt49du3bx8OFDpkyZwqVLlxgzZgwA3bp1o2jRovTq1Ytbt25x9uxZBg4cyODBg7FLN0yMjY0JDQ0lISEBXV1dtLW12blzJw8ePGD16tWsWrXqs3XJwMnJiYcPH/Lff/+pTA7p0aMH3t7eWdohv+jYpRvb/9nC9Wu+nD3tyYljR+nQuSsREeF0bNOKY0cUE0/KlC2Lc6VKLJw3l0eBAfyxcD6mZmbUrlMHgFZt2hIVFcmqFct59vQJf/25DB1dXerUq5cverfs0IXDO7dx9+Y1Lp87w7lTx2nRrhNRkREMcW/HWQ+FJzUtLY2IsFAiwkKRydKIj48jIiyUhHTD+qzHUY7s/pfXL55zwfME+/7ZRKfe/fJF5wyc6rXi4ZmDvAu8w6tbF3nuexbHum4kxUZxbOag9CVONDEwtVRJmlpaaOvpo2+i8MA41f+ekMd3CfA6TPTbl1zfswYzO0fM7Ep9QoPcYSY1xMZCiqGBLtpamthYSLE0M8bSzJjA4wv5oa1iiYtbAS+5dOsRKyf3oWJpO/6c0JuQiBhOXlTMCN7pcRlrcynTh3agchl7Fo/pzp6TV5Vh2/ygW7dubNmyGV/fq3h6enL06BG6dulKeHg4rVq5cfjQIQDKlitHpUqVmTt3DgEBAcyfPw8zMzPqpC8NlJiYSGhoKBERiskJkRGRhIaGqsxyz2vMzUxp3rQRC5f9hX9AIH+t20REZBTft2gKwMUrvjRt25nAx08A+L5FM8LCI1i5fhP+AYEs/PMv3Jo1xvSDaEd8fEKOPgZzSofOXdm5bSs3r/ly7sxpTh0/SrtOXYiMiMC9/fd4HFU8W0qXKct3FSvxx4K5PA4M4M/FCzA1NcPFVfFs8Th6mN3bt/Hi+TM8T3rwz98b6N2vf77pDWBuKqVlfRfmr9vGg8fPWP7PXiKiYmjdSKGTz/XbNOw9XLlYcWJSMiHhkYRHKsYJRkbHEBIeSXJKKnK5nEUbtnPK+ypPX73hr237uO3/mO6t88drmhM0viAVJgrV9dSvX5/FixdTu3ZtAgMDOX/+vHLyg7m5OT4+Prx+/RpXV1fGjBnDhAkTGD9+PACurq5UqlSJYcOGUbduXaKiolQmArxPsWLFmDNnDn/88QdNmzbNdpZru3btuHbtGg4ODiqzQ83MzLh06RIxMTE0adKE1q1bExQU9NGlSj7E1dWVHTt2ULNmTU6cOIGnp6fSA6mjo8OZM2cwMDCgSZMm9OvXj27duqnMfu3YsSM3btxg//796OrqsmHDBg4fPkzz5s0JCAjgjz/++Gxd3q+zU6dOdOzYkW7duilfDiYmJrRq1YqKFStia2ub43pzSvuOnejeqzezpk1h9V/LGTN+Ii61ayOXyUEO8ve8kvMXL0WWlsawQQN4+fIFf65cjZaWNgAGBgasXr8R/wf3GdivD/7377N0xUr09PSzO/UX0axNB9p068nyuTPYtm4lg0aOo3KNWgp95XJkcoXeYe+CGdT5ewZ1/p6wkHfs2LCaQZ2/5/AuRX8tWboM1y/7MHFoPw7t3MaIyTOpXKPWx079xZR0bUGZRu24um0Zd478Q7WuP2FTtgpyuVyZPgdDc2saDJ3Fy5venF46jtSkBOoMmJBvS6PsXvIzLz2XMbqPG5XK2PPScxkXt01FQyJBggSN96bQdRm1Ag1NDU5vnIBTcRu+H7qE1FRFJCAkIoY2w5fSsm5FTq4bS+DzYIbNzd+1PDt17kzvH35gyuTJLP/zTyZOmkRtV1dFW7/XXwCWLltKWpqMAf378+LFC1avWYu2tqKfnzp5kubNmtK7dy8ABg0aSPNmTbmVvtJAfjFj4ljs7GwZMHwUPpevsGbZIszSxwHKZDLFJCCZot9YmJuxetlCvC9dYcDwUZSwt2fahDEq9aWkpJCamoqubv7tDNKmQye69ejN3BlTWbdyOSPHTaRGrdrp+qo+W+Ys/ANZmowRQwby+uULFq9YpXy2lC5Tjss+Fxj6Yx92btvK5JlzqFEr/71es34diH1Ra36c8Dve126zfs54zEwUY91lH9yrHucv0aDXz3Qbqdi5pe/4uTTo9TN+DwKQSCQ4lynFmh0H6TpiKtfvPmT7HzOwMM390KK8QiKR5DoVJiTyr7HnyVfAwcGBMWPGMHz48IJWJd9p1KgRNWrUYPHiz59oUJCkpaVRqVIlxo8fT58+fXJ8fFhMwU6t/xLexOaf9yM/2eX3pqBVyDULJy0qaBVyReTFnHvLvxW0EiMLWoVcESYx/rTQN4pN+OcvnfUtoVEqf3cxCorMvbe8qGn+zG4vCP4nPHfdu3dXmVn6fvrYshr/i4wbNy7ba3V1zbpTwbeMXC7nyZMnzJw5E01NTXr16lXQKgkEAoGgECPG3CkosO3HcsLSpUuZM2eO2jJDw8JjaQOMHTuWwYMHqy3T0cnfjcjzmhcvXlCpUiXKli3LP//8k2X5FoFAIBAIBHnP/4RxV7Top6eHP3v2LP8V+QpYWVl9ctbsh4sLf6uUKFGC2Nj8WedLIBAIBIIP+Z8IR34F/ieMO4FAIBAIBIJPUdgmRuQWYdwJBAKBQCAoFBS2PWJzizDuBAKBQCAQFAqEbadAGHcCgUAgEAgKBcJzp0CMPRQIBAKBQCAoRAjPnUAgEAgEgkKBmFChQHjuBAKBQCAQFAo0JLlPOUEulzNr1izs7e0pU6YMGzZsyFY2ODiY1q1bY2pqSsOGDQkMDFQpv3jxIi4uLlhYWNC7d2/i4r58T2ph3AkEAoFAICgUSL4g5YQ1a9awbNkytm7dyu+//87PP//MiRMnssjJ5XLatWuHpqYm58+fp3Tp0jRr1ky5l/zbt29xc3PDzc0NT09PAgICGDhwYK6u/X1EWFYgEAgEAkGhQOMrhGXlcjmrVq1i7NixNG7cGIBTp06xZs0a3NzcVGRv3LjB1atXef36Nba2tqxcuRJLS0uOHz9Ohw4d+Pfff7G1tWXmzJlIJBKWLVtGw4YN+fPPP7G2ts61jsJzJxAIBAKBoFDwNfaWDQ8P5+7duzRr1kyZ16RJE86ePZtF1svLiwoVKmBrawuArq4udevWVcp6eXnRtGlT5VhBFxcXdHR08PHx+YJWEJ47gUAgEAgEApKSkkhKSlLJ09XVRVdXVyUvODgYgCJFiijzbG1tiY6OJiEhAX19fRXZ9+UyZDPqCA4OxsXFRVmmpaWFjY2Nsjy3CONO8M2z1Pt5QauQa8Y2cChoFXKFx8X/3TaPvLiqoFXIFaZ1hhW0Crkmbk27glYhV5gGPS1oFXJNcsshBa1CrtDL5/olcnmuj503fz4zZ85UyZs+fTozZsxQyYuIiADA2NhYmZfx/4iICBXjLiIiQkUuQ/bVq1cfLQ8PD8/1dYAw7gQCgUAgEBQW5LJcHzpx4kRGjx6tkveh1w7A3NwcgJiYGExNTQGIjo5WKXtf9smTJyp50dHRWFhYKMtjYmKyLc8twrgTCAQCgUBQKJB8gXGnLgSrjowwa1BQEPb29gC8efMGU1NT9PT0ssgGBQWp5L1584YKFSqoLU9NTeXdu3dZQrk5RUyoEAgEAoFAUDiQy3KfPhMzMzMqV66Mp6enMu/MmTM0adIki2zjxo158OABr1+/BiAxMREfHx+lbOPGjfH09ESeHk6+cuUKKSkp1KtX70taQRh3AoFAIBAICglyee5TDhg2bBiLFi3i7Nmz7Nu3j61btzJkyBBCQkIoUaIEmzdvBqBKlSq4uroyZMgQbt++zfDhw7GyslIumdKzZ0+Cg4OZPn06fn5+jBo1Cnd39y8OywrjTiAQCAQCgSAHDBo0iNGjR/PDDz8wYcIEVq1aRfPmzZHJZMjlcmSyTE/gwYMHSUtLo0GDBgQGBvLff/+hra0NgLW1NSdOnMDDw4MmTZpQpkwZ1q5d+8X6iTF3AoFAIBAICgdfMOYuJ0gkEqZOncrUqVNV8m1sbHjx4oVKnrW1NcePH8+2LldXV3x9ffNUP2HcCQQCgUAgKBR8yYSKwoQw7gQCgUAgEBQOhHEHCONOIBAIBAJBYUEYd4Aw7gQCgUAgEBQWhHEHiNmyAoFAIBAIBIUK4bkTCAQCgUBQOJAJzx0I4+6z6dKlCw4ODixevPiLZL4Vnj17RsmSJblz5w7Ozs65qiMhIYHvvvuOJUuW0LFjxzzWMPfI5XIenNzNk8un0NTWpWyTjpRybf7RY0Ie38NrxWQqtHTnu1Y9AHh25TS+O1aoyFk5fkejX+bmm94b16/j4IH96Orq8kPffnTo2EmtbFhYGHNmTueWnx+ly5Rl8rRpFC9eAoDw8HBW/LmUq5cvk5aWRsNGjRkxajSGhob5oncGgxo50qGGHUkpMrZ6P+Xg9VdZZNb2d6FGSfMs+QevvWT2oXsANCpvzZAmpTE30uHUnSCWnnhImiz3m4F/DLlczrp1azmwX9Hmffv9SKdO2bf5jPSFRsuULcO0adMpUaKEsvzo0SMcPHCQ69evsWfvXpycSueLzgCWZsb80qMZvdrUISQiBtdes7KVtTaXsm7Gj9SpUprbAS8ZMutvHr14pyyvXdmRJWN6UMrOmpM+dxg2Zwvxicn5pnsG8UnJzNy0D+/bAdjbmDOxdzsqO5VQKxsVF88fO4/jfScAuVxOa9eqjOjSAm0tLa4+eEz/eevUHue5bCJFzE3zVO+E5BRm7TvHxYCX2JlLGd+uHpVK2KiVDY9NYOnxS1wOfEWaTE7jCg6MbuOKoa6OUub282CWHLvIo7fhlLQ249dWtajpWCxPdc4gPiGB2bNm4XPxInZ2dkwYP55KlSplK+/n58fCRYt49eoVdevWZerUqRjo6yvL9+7dy5YtW0hKTqZLly4MGjgQiUSSL7p/LmK2rAIRlv1MHB0dKV68uPL38OHDGTNmzEdlCjva2tqUK1cOS0tLZZ6zszNHjx4tQK3gic8JAs4fwaXXSCq27s3NvWt5++BGtvJyWRp++zegpauXpUzf1JK2s/5Wpjr9J+Sb3vv27mHn9n+ZMWsOw4b/wqL587jk45NVX7mcMaN+RUNDk7UbNmFfvDjDhwwhJSUFgD+X/oFMJmfRH0uZt3Axly9dZOXyP/NNb4AuNe3p4VqC6fvusNIzgPFtKuDqZJlFbuyOm7RYcEaZuq/0IS4plV1XFOtClS5izPxuVdhz9QWjtt2gQTlrhjbNPyNpz549bP/3X2bPmcPwX0Ywf97v+GTT5iN/HYGGpgYbN22iePHiDBnyk7LNAW5cv4GhoUG+6fo+djZmOBa3JiYu4ZOy+5eNQCaT0XTAfB69CMZjzRi0tTQBsLGQcvSv0Zy6eJdWQxZTuoQNa6f/mN/qAzB1/R5eBIexYfxA6jqX4adFGwmLjs0iJ5fLGbJoEzK5nBUj+zKtXycOnPdl0/HzAFQtXQKv5VNUUpdGLrSoWTHPDTuA6Xu8eBkWzdqBbalTxp5hG48RHqv+7/DHsUvI5bC0jxuLe7fgYuBL/vS4oiz3fx3K4PVHaFTBgW3DO9HZpTzXnrzJc52Vuk+fzouXL1m7di116tRh6LBhhIWHq5UNDQ1l2M8/U7duXdauXcvz58+ZOXOmsvzChQvMX7CAX3/9ldmzZ7N161b27tuXb7p/Nl9h+7H/BYTn7jNZsGBBnsgUJrS0tD66MGNBIJfLeeTjQdnGHbAuXRGAtw9v8tjnBEXKV1N7zNMrZ0iOj6XodzWzlOkZm6InNctXnUGh9749u+ndpy81air0uHLpEvv27cG1bl0VWf8HD7h39y7HTp7CysqacRMm0qJJI3y8L9CocRMmTp6Crq6u8gu6W/ce7NuzJ1/171qrOP/4POPaU8WLorbja7q42HPpUaiKXHRCisrvYU1Lc+DaSwLexgDQobod156Gs8/3JQCrPAMZ83151pwJJDUtb713crmcPbt30advP2rWdAHg8qVL7N2zh7oftPmDBw+4e/cuJ0/9h7W1NRMnTqJxo4Z4X7hA4/Q9IqdNn86b1685f/58nuqpDj//F/SesJapP7Xn+waVs5WrWr4ELhVLUaLFaIJCIhkxbxtBZ5fTql4lDnvdpMf3rgSFRDJz9UEAflu8g9PrxzPazJiQiJh80z80MgbPa3fZMnkI5R2KUa6ELSeu3Ob4xZv84FZfRVYikbDo554UszRDIpFQwaEY3Zu68t/VO/zUrgnaWlpYmhor5V8Eh3L6+j32zv417/WOief03SdsGtKecsUsKWtrwcnbjzl+M5De9bN6wKZ0aoCulqbyXuxRpyJ7Lt9Tlm84e4NWVZz4sVFVAEpa59+zJjQ0lNOnT/P3pk2UL1eOcmXLcvLkSTyOH6d3795Z5I8dP46VlRXDhg5FIpEwbuxYBgwcSFh4OBbm5uzes4d27drRrFkzAPr17cue3bvp2qVLvl3DZ5HDbcQKK1/suWvUqBFbtmxhwoQJ2NraUq1aNby8vAgICKBVq1aYmJjQsWNHYmIUDwpfX1+6du2Kvb091tbWjBkzhrS0NADGjBlDlSpVlL8PHDiAlZUVkZGRn9Rj8+bN1K9fn127dlGtWjXMzMzo1q0b4e99lchkMpYtW0a5cuUwNTWlbdu2PHnyRFkeHx9P3759MTc3p3jx4vz222/ExcUBipBrv379AHBwcGDlypUsWbIEiUSizM+Qkclk2NnZsWbNGhUdf/rpJ3r16gUoXixLly7FyckJCwsLevfuraLrx/Dy8sLe3p7Tp09Tt25dTExMaNmyZZZVsbdv306lSpWQSqU0btyYmzdvZltnSkoKa9eupX79+kilUipUqMCpU6dU2tfNzY0TJ05QoUIF5cPA0tKSzZs38+zZMyQSCffu3aNt27ZIJBI2bdqEo6Mjy5cvVznXsGHD1D5M8oLk+Biig15gUzbzhWdduhLvHt1VK5+SGM/dY9v4rlUPNDSzfuvoGkrzRc8PiYqK4vGjR7jUqqXMq+HiwvVr17LI3rh+jZKlSmFlZQ2Ajo4OlSpX4fo1xQrnenp6KqGRuLg49A30s9STV5joa+NkY8yVx5mGnO/TMLXh1/cpYWlIi4pFWX/2sTKvRklz1XqehGFmqIOjtVGe6x0VFcWjR4+o/V6b13SpybVrWVeKv3bNl1KlSmFtndnmVapUyfNV5fOahjXKcf/xa4JCIgFITknlol8gDWuWU5RXL8uZqw+U8r53n5KcmkadKvnnLQW4GfgMXR0tKjraAwoDzqWCI1cfPFErb2dlrtKnTQz1iU1MVCu78oAnXRq5YGNukud6+z17i66WFs721pl6OxbL1tump62lei8mJaOvo9h2Kk0mw+veM1rlc1tn4Ofnh66urnIYjkQiwcXFBV81zxiAa9euUcvFRam/s7MzWlpa+Pn5Kcvfv3dcXFx4GBBAdHR0/l7IpxCeOyCPwrK//vorRkZGnD59mhIlStCzZ0969+7N6NGjOXbsGJ6enmzYsAGA3bt3U69ePTw8PPj7779ZuXIle/fuBRQu43fv3rFhwwZSU1OZNGkSv//+O6ampp+lh4+PD1u2bGHVqlUcP36chw8fMmDAAGX5ggULmDdvHvPnz8fb2xtLS0vq1KlDVFQUAMuWLePUqVPs37+f/fv3Zzt2wNfXV7kRcFBQEH/+qRry0tDQoFu3bhw8eFCZJ5PJOHToEF27dgVg0aJFbN26lb///htvb2+SkpL45ZdfPus6AV69esXkyZOZPXs2Xl5epKWl0b59e+TpXy07duxg8ODB/Pbbb1y5coXatWtTt25dnj59qra+6OhoPDw8GDduHNeuXaNZs2b06NFDadwC3Lp1i5kzZ7Ju3ToWLlyocry9vT1BQUEAbN26laCgIHr06MGAAQPYtm2bSjscOHAAd3f3z77WnJAUo/hb6hmbKvP0TcxJTYwnLTkpi/yDU7sxtrGjRM3GauuLDXvL2RWTOTp9AFf+WUpidES+6B0eHgaAhUVmKNPS0oq42FgSP3iJhYWFqcgBWFlZER6m+nGQkJCA19kz7NqxnZ69f8gXvQHMjRTjh8JiMsdphUQnYaSnja5W9o+YtlWLcc7/HbFJqZl1GeoQFptZT3hcMmkyOeaGunmud1hYepu/N6zAysqaWDVtHh4WrjL8QCFrRVj63+1bxcZcytvQKJW8oJBIbCwUHy3WFlKCwzLL09JkBIdFY22Rvx81YVGxmEuN0NTI7B/WZlLCoj/PW3j/+RvK2BXJkh8dl4Cn7x3a16ueZ7q+T1hsPOZG+ip6W0kNCIuJ/+hxCckpnLn3lO0+d/gh3cMXGh1PqkyGRAIjNnvQfO5Wxmw79cm6cq17WBjm5uZoampm6m5lpbwP1Mm/f29oaWlhYWFBWFgY8QkJxMfHq9wTVlZWyuMEBU+ehGXd3d2ZMmUKAL179+bgwYOcPHmSihUVYbHatWtz/fp1QGHUZODs7IyrqyteXl64u7tjbGzMkiVLGDVqFDKZDD09Pfr37//ZehgZGXH06FE00m+8JUuW0Lx5c8LCwjA2NmbOnDmsWbOGDh06ALB+/XrKli3LqlWrmDhxIlFRUTg6OtKgQQM0NDSoUaOG2vNYWVmho6ODoaEhRYpkfcBktEn9+vWJiorCxMSES5cuERcXR8uWLUlJSWHWrFlcvXqVChUqALB8+XJKlCjBli1b0NL6vD/LsWPHsLCwAGDNmjWULl0aPz8/qlatyrRp0xg/fjx9+/YFYN68eVy6dIn58+er3ZTYwsJCxRgdM2YMK1as4O7du9RK/zoLDg7myJEjattFU1NT2RZmZmbK//fr149p06bx8OFDypYty8WLF0lMTKRFixZqrykpKYmkJFUjLDUlGS1tHbXyH5Icrxizo6Wb6anK+H9yQhz6OplGQmxIEI+8T9B01EK1hrypvSPFKrpgV7kOSXEx+B3YyOWtS2j48+w8HzQck/61a/DemK2MCRAxMdHo6WWOB4yJiVaRyzju3btg5e+AgIf07q4woHv06s33rdvkqb7vI9VXeCLikjONtPj0/xvra5MUk9WoBmhVqSjzj97PUlf8e/XI5ZCQnIqJgXZeq61sc0OD99tc8f/oaNU2j46OxsBAdUKKgYEhwcHBfMuYSg2IjVc1VGPiEylmY5ZebkhMnGp5bHwi5tL8nXwTFZ+AoZ6qwW6op0tUNmPX3ic4PIpTV2+zZHivLGWe1+7iWMwGh6JWeabr+0QnJGGgq9oXDXS1iUpQ38cBHr4Jxf1PhQOjd71KtKlWBoDgKMWH88LDPgxsUo0fG1Zh3iFvZu8/z7K+bnmve3S0Sl8HRd/PcG58SIw6eUNDoqOiMp9XBlmfV9nV97UQEyoU5Innztg4c7xD6dKl1eZlhFZTUlLYs2cPnTt3xtnZmevXrxMSEqKU7d69O6VLl2b48OH8+eefKl8Zn0JDQ0Np2AFK4/Lp06cEBgYSHx9Po0aNlOVaWlo0bNhQ6WYeOXIkcrkcZ2dnNm3apDJYOqe4uLhQrFgxPDw8AEWIuV27dujr6xMQEEBcXBwuLi4YGRlhZGSEk5MTKSkpKm3xKd5vG0dHR/T19Xn8+DGxsbE8evRI5VoBmjRporxWdQQFBTF37lwaNWqkNL7e18fc3Dxbgzc7bG1tad26Nf/++y8A+/bto2PHjujqqvfEzJs3DxMTE5V0cZf6mXDq0DFQhO9SkzJfEqmJ8SplGdw69Del67fGpKj6STCmtg5UatcP8xJlKFqhOtW7DSXk0d188d5JpYoQUnxc5ld7XFysStn7su/LAcTFxmFiYqr8XaKEA5v/2caUaTPwPn+O+XPn5LnOGUSlj6Mz1Mn8KDHUVfz/wzF2GVgZ61LEVB//N6ohnKiEFAzeq0ciAQMdLaLic38vZofURNGucfGZbRkbq3jhmpiotrmJiZT4+DiVvLi4WEw+M6pQUERExWFkoDpRSGqoT3i6YRERFYexoWq5saEeYVFZJzZ8CUd8blBz0FRlSktLIy5R1SCKTUjE1OjjE1LkcjlLdh6nbPGiNKxSPkv5rUfPKV/CNs/0PnYjANepG5QpLU1OfJJqX4xLTMHUIOtkrAwcrEzZNrwTM7o04tyDZ8zZrxiTmWEkTuxQn5aVnahasigj3Gpxwf85qWlfbqAcPXaM2q6uypSWlqbS10ExZCO7yJjUxCSLfGysos9n3B/xKveOos98bqQt3xBhWSAfJlS8b1x9mCeTyejQoQMxMTH8/vvv1K5dm4EDByo7BUBMTAxPnjzByMhIOU4vt2R0PCMjI1JTU7OVk6Wvi1O0aFG8vb3577//mDVrFvPnz+f8+fPZeuc+hkQiUYZm3d3dOXjwIEuWLAFQhk49PDwoWrSoynEZY3pySmpqKklJSRgZGSnrV4csmzWA7t+/T8OGDRk8eDBbt27F3t5e7d8yNwwcOJCRI0cyc+ZMDh06lGUs4vtMnDiR0aNHq+T97qU+lKyOjMkPidERGJgpvt4TosLR1jdE8z3vX0JkGG/uXiXk0V2eXFaMLUxNTECiocGbu1dpPnZplrqlRezT6wtD3+Tj48lySkb4IzQ0BJv0/hYSEoKxsXEWQ9jCwoKwUNWJCiEhIZQsVUr5W1dXlwrfOVPhO2dKlylD39496fNjf4oVy/slFsLSPXOWxroERyu8QFbGekQnpJCcqr6/lSsqJTYxhZAPvHphsUlYGmder4WhLhoaEkJjs/eM5JaMkFJoSIjyHs++zS0JUdPmpUo55rleecnbsCiKWpmq5BW1MuVB+hix4LAoilhmGrKamhpYm6uGavOCxtUqUNkp8yPqwfM3hEXFkiaTKUOcIZHRWLw3MUIdOzwvcf7WA/bM/lWt9/zB8ze0qVM1z/RuWMGBisUzlznxfxNKWGy8qt7RcVgYZz+mVVdbMUbP2d6aMkUt6LliH/0bV6WIqeJjU0cr8yPd1syYNJmcqPhELIy/bOZ1o4YNqZTu4ADw9/cnLCyMtLQ0pWPgXUiIMvrzIZaWloS+93GfmppKeHg4lhYW6OnpYWRkpPLxn/F/c/O8fTbmmEJmpOWWr7oUir+/P8ePH2fLli3Uq1cPLS2tLMbGzJkzadiwIQsWLGD48OEqXwY5xcvLCwMDA0qVKkWZMmXQ09Pj3LlzyvK0tDTOnz9P1aqZDwOJREKLFi04d+4cMpmM3bt3q61bS0vrk7q5u7tz8uRJ7t27x7t372jZsiWg8GTq6+vz/PlznJycVFJOPJXv4+3trfQ6Ghsb4+joqHKtAGfPnlW51vfZvHkzFSpUYO7cuRQvXvyjBuLH0NTUzNIurVq1IjExke3btxMTE0OT9NmF6tDV1UUqlaqkzw3JgsI7Z2LrQHDALWXeu8A7ypmzyvMYm9J6xgZaTFhO87FLaT52KaZ2jpSq40a9wVMBSElQ9dREvlIYmcbWeW8gSaVSSpcpw9UrmcskXPO9So30WZzvU6NmTZ4+faIMwyYlJXH7lp9yxuf7H0uQ6UVPTPx0yCs3xCSm8jAoGhfHzJdEzVLm+D7JfuxNMXODLIYdwLUn4dT6oJ6w2CSevstbTxIo2rxMmbJcea/Nfa9epaZL1javWbMmT5884V1wZpv7+flR0yXrDOtviXO+/pQvZYttuoGnq6NFnSpOePkqJlF4+frTtFYFpbyLcym0tTTxuRmYp3oY6etR3MZSmWqWK0VKaiq3Hysmgcnlcq7cf0yt8tkby143H7Bk5zHmD+mOvbV6g+RVSDhWpnk3XtBIT4filibKVKOULSmpMu6krxMol8u5+vg1LtmsSxf7wXqBxvqKZ1lCcipGejpUsLPi+nuTMZ6HRmKgo4250ZdPgDIyMqJ48eLKVKNGDVJSUrhz545Sd9+rV3FR098BataoweUrV5Tvgjt37pCamqp8h9SsUUPl3rl69Srly5dHKv06k9CyRXjugK9s3GXE57ds2cL9+/eZN28e+/fvV5bfvXuXNWvWMHv2bAYOHIiJiQmzZ8/+7Pqjo6OZMGEC9+/f58iRI0yYMIGxY8eio6ODjo4OU6ZMYcyYMRw5coR79+4xePBgYmJiGDp0KHK5nI4dO7JixQru37/P/v37efbsWbbr1jk5OeHp6cmtW7dUZty+T9WqVbG0tGTy5Mm0a9dOOYZHV1eXKVOmMHLkSHbu3MmjR484ePCgMnT5ufz222/cvn0bLy8vBg8eTJ8+fbCzswMURvL8+fP5559/8Pf3Z/LkyVy+fJkJExTrtBkZKb4aX758qfx98+ZNvL29uXTpEu3atcuVoenk5MTOnTsJDAzk7du3gMIQ/vHHHxk1ahSdO3f+7DGFucWpXisenjnIu8A7vLp1kee+Z3Gs60ZSbBTHZg7i2ZXTaGhqYmBqqZI0tbTQ1tNH38Sc2NAgjs8ZyqMLx4gKek7ww1vc2LuGUnXc0NbLn7XMunTtxratW7jm68uZ054cP3qUTl26EBERTrvvW3H08CEAypQtR8VKlZg/dy6BAQEsWjAfUzMzXOvUITo6mq6dOrB500Ye+vvz4P59fp8zm1KOjjg4lMwXvQH2XH1Bn3olqVHSnCYVbGhdpRj7fF9iaqDN0d8a0raq6stPX0eTpJS0LPUcvP6Kag7mdK5pz3fFTBjaTLFUSmo+LWLcrVs3tmzZjK/vVTw9PTl69Ahdu3QlPDycVq3cOHxI0eZly5WjUqXKzJ07h4CAAObPn4eZmRl16iiWTElMTCQ0NJSICEXIPjIiktDQ0C8a2vExzKSG2FhIMTTQRVtLExsLKZZmxliaGRN4fCE/tFXodSvgJZduPWLl5D5ULG3HnxN6ExIRw8mLitnjOz0uY20uZfrQDlQuY8/iMd3Zc/KqMmybX5hLjWhRsxIL/z2K//M3/LXvFBExcXzvWgUAnzsBNPl1LoGvFM+Q87f8Gf3XNsb3aktFx+KERsYQGhlDYrJq+8YnJqOnk/fjM5V6G+nTrFIpFh+9iP+bUFad8iUiLhG3Kk4AXAx4SfO5W3n0Npzo+CQ6LN7JxrM38H8dyv1XIczedx5HGzNKWpsC8GPDKmw8e5PzD55z/1UIK05cpUutCvmyELC5uTnNmzVj0eLF+Pv7s3LVKiIiImjlphjfd/HiRZo1b07go0cAtPr+e8LDw1m1ejX+/v4sWryYli1bKsOuXbt14/CRI3iePs2Vq1fZsnUr3bp1y3O9Bbnjq65z5+DgwMKFC5k3bx7btm2jT58+TJ06FV9fX+RyOcOHD2fgwIE4Oiq+3lauXEnjxo3p3bs333333SfrNzExwcjIiObNmyORSBg6dChTp05Vlk+cOBF9fX1Gjx7Nu3fvqFevHj4+PpiZKUJ548aNY/r06UydOhVDQ0OmTp1K+/bt1Z5r/Pjx3Lhxg7p16+Lu7s7GjRuzyEgkEtzd3Zk7d67KZAWACRMmYGhoyMyZM3n58iUVKlRg0qRJn9uUAHz33Xd06NCB2NhYunXrpjJZpWfPnsjlcubPn8/z58+pVq0a3t7elEoP3VlaWuLm5saoUaNo1aoVv/zyCxcvXsTNzY2qVasya9asLEurfA7Lli3jp59+wsXFhQULFjB48GBAMdFm7ty5dO/ePcd15pSSri1IjInk6rZlaGrrUK3rT9iUrUJiTCRyufyzvJJGlkVx7TuGgHNHuH9yNxqaWhSv0ZDv3PJP/w6dOhMeHs6MqZPR1dVl3MRJ1KrtSlhYGHLkyN4zcBb9sYxZ06fy08D+lC5Tlr9WrUFLWxuptjZ/LFvO5k0b2bNrJ0nJydR0cWHajJm59gp/DgeuvcLCSJdZnSuRlJrG/CP3uPI4DHNDHSQoxs69j4GOJokpWb+UH7+LZfyum/zcrAzmhjqcvBPE2jOP8k3vTp07ExYexpTJk9HV1WPipEnUdlW0OXI5sve+5pcuW8q0qdMY0L8/ZcqWYfWatWhrKwyJUydPMn36NKXsoEEDAVi/foNy3cK8ZPeSn2lYo5zy90vPZTx7E0q9H+YgQYKGRmaDdxm1gg2zBnB64wRuB7zk+6FLSE1VGNYhETG0Gb6UpeN6MtS9CSe87zBs7tY811cdMwZ0ZsbGffSfvw57awvWjh2AmbFiUH7GfZrR53/9cyspqWnM2nyAWZsPKOuYM6grHeorxgGnpKaSmpaGrk7+vtamd27IzH3nGLzuCHYWUlYPaI2Zob5Sb+Qgk8uRGuiyvF8rNp69wc6Ld0lOTcPFsRgzuzZShnSbV3IkLimFpccuER6XgFtlJ35umX/e4GnTpzNr5kwGDR6MvZ0dq1avVr7/Mp6L8vRomoW5OatWrmTBggXs3LmTevXqMTV94iRA3Tp1mDhhAsuWLSM5OZm+ffrQMX2yYkEiJlQokMhzG3/7xti8eTNjxowh9INxMYURLy8vGjduTExMjNID962zc+dOpk6dir+/f46NjCkeDz4t9I0ytoFDQauQK5rMP/dpoW+UC5MaFbQKucK0zrCCViHXxK1pV9Aq5IrUoM8fz/utIWk5pKBVyBV6+vm35iZA2vNbnxbKBs0S2S8I/r/G/8QOFbdv36ZOnTrZll+8ePErapO/REZGKkOr6tixY4fKTORvndDQUF68eMHEiRNZtGhRvnqPBAKBQPD/nMLhr/pi/ieMu7Jly350CQ97e3tu3Mh+79D/JYyNjT96rUWKFOFaNiuKf4tMmzaNXbt2MWDAADp37lzQ6ggEAoGgMCPCssD/iHGnq6uLk5PTR2X69eun3AbsfxlNTc1PXmujRo1yPZv1a7Nq1SpWrVpV0GoIBAKB4P8BYsydgq86W1YgEAgEAoFAkL/8T3juBAKBQCAQCD6J8NwBwrgTCAQCgUBQWBDGHSCMO4FAIBAIBIUFWdbF0f8/Iow7gUAgEAgEhQJ5Nvun/39DTKgQCAQCgUBQOJCl5T59AXFxcfTq1QsLCwtcXFy4fPlytrIREREMGjQIe3t7ihUrxrhx40hOztyHeMaMGUgkEpWU09VAhOdOIBAIBAKB4Avo378/z549w9PTk/3799OyZUsCAwOxtrZWkZPL5bi5ueHs7MyhQ4d48+YN/fr1w8TEhMmTJyvl6tSpw759+5S/9XO4s4cw7gQCgUAgEBQOCmDM3du3b9m3bx8XLlygatWqVKlShV27drF9+3ZGjhypIiuRSNi5cycODg5IJBKqVavGsGHD2Lt3r4pxZ2trS5EiRXKtkwjLCgQCgUAgKBTI09JynXKLj48P+vr6uLi4AAoDrkmTJpw9e1atfMmSJZFIJMrf5ubmREdHq8hYWlrmWh8Qxp1AIBAIBILCgkyW65SUlER0dLRKSkpK+uQpg4ODsba2Vtk73dbWluDg4M9S+caNG1SsWFEl7+rVq7i4uFCiRAmGDx+exfj7FCIsK/jmmVrXpqBVyDXyc5sLWoVccel7608LfaskRha0Brkibk27glYh1xgOOVzQKuSK0o07FrQKuabE25sFrUKuOD6kTv6e4AvCsvPmzWPmzJkqedOnT2fGjBkfPS4iIgJjY2OVPGNjY8LDwz95ztevX7Nnzx52796tzKtXrx4aGhq0bt2aJ0+eMGLECGJjY9m8efNnX4sw7gQCgUAgEBQK5F9g3E2cOJHRo0er5Onq6maR27ZtG0OGDFH+Hjt2LDExMSoy0dHRWFhYfFxXuZyxY8dSuXJl2rRpo8xv1qwZzZo1A6B69erExsYydOhQNm7cqOId/BjCuBMIBAKBQPD/Hl1dXbXG3Ie0a9eO2rVrK3/fvHmT4OBg0tLSlMbXmzdvPjkhYuXKlRw7doybN2+qjMH7kAoVKpCUlER4eDhWVlafdS3CuBMIBAKBQFA4+AqLGEulUqRSqfK3iYkJSUlJXLlyhTp16iCXyzlz5gwjRozIto6jR48yduxYdu/eTalSpZT5crmc6OhoTExMlHl+fn5YWFjkaJKFmFAhEAgEAoGgUCCXpeU65RYrKyu6du3KqFGj8PPzY9q0aYSEhNCjRw8ATp06RbFixbh79y4Ax48fp0uXLixbtoxatWrx9u1b3r59S0JCAufPn8fZ2ZnNmzfj7+/P/v37mTJlCmPHjv2od+9DhHEnEAgEAoGgcFBAO1SsX78eR0dHmjRpwokTJzh58qTS0yaTyZDL5cjSvYodO3YkKSmJIUOGYGNjQ9GiRSlatCi7du2iYcOG/PHHH+zYsQNXV1fGjBnD+PHjGTNmTI70EWFZgUAgEAgEhYMC2lvW0NCQ7du3qy1zc3PjzZs3yt+fWl6la9eudO3a9Yv0EcadQCAQCASCQsGXLEZcmBBhWYFAIBAIBIJChPDcCQQCgUAgKBwUwN6y3yLCuBMIBAKBQFA4EMYdIIw7gUAgEAgEhQR5AU2o+NYQxp1AIBAIBILCgfDcAcK4y3O6dOmCg4MDixcvLmhVPsmzZ88oWbIkd+7cwdnZOVd1JCQk8N1337FkyRI6dvx2NuGOT0hg1tz5+Fy+jH2xYowfM5rKFbO/Rr9bt1n4xzJevn5NPVdXpk4aj4G+voqMXC5n4LBf8L12ndu+l/JP96QUZu/2xMf/OXaWJkzo2IhKDkXVyobFxLPsyAUuBbwgLU1O44ql+K1dAwz1dFTkUlLT6LxwGympqXhMG5BPeicz8+8D+NwJwN7aggm92lDZqbha2ai4BJbu8sD7biBymZzWrpX5pXNztLUUj6Q0mYy/9v/HIe8bpKSm0bRaBcb1bI2B3qe3BsqV7gkJzJy3GJ/LV7G3s2XC6BFUdv4uW3m/23dZsGwFL1+9oZ5rLaZN+A0DfX18r9+k/88j1R7z3+E9FLG2zlu9k5KZuWkf3rcDsLcxZ2LvdlR2KqFWNiounj92Hsf7TgByuZzWrlUZ0aUF2lpaXH3wmP7z1qk9znPZRIqYm+aZzpZmxvzSoxm92tQhJCIG116zspW1NpeybsaP1KlSmtsBLxky628evXinLK9d2ZElY3pQys6akz53GDZnC/GJyXmmqzqGtihN51olSEpJY9PZx+y78iKLzN/DXHFxyrqbwN7Lz5m++zYdatoxt0dVlbKrj0L5cVX+PVd6VLfDrbwNyaky9vq95qT/O7Vyx4fUyZLX79/rvItRLN/RwNGC3jWLY26gg39wDCsvPCEoOjHf9M4xwrgDxGzZL2L48OFZFhZ0dHSkeHH1L7TCiLa2NuXKlVPZFsXZ2ZmjR48WoFYwfdZcXrx6xbqVy6lTuxZDfxlJWHi4WtnQ0DCG/jqKuq61WbdyOc9fvGDGnHlZ5M54nePe/Qf5rTrTd57iRWgka4d2ok7ZEgxde4CwmHi1sn8cvoBMDsv6t2VJv9Zc8n/On0e9s8jt9L5FSHRsvuo9beM+Xr4LY/24AdRxLs2QJX8TpuaccrmcoUv+RiaXs3xEb6b168CBC9f5+/iFTH1PX+bElTv8MbwXq0b35XrAM1YeOJ1/us9ZwMtXr1m/Ygl1arkw5NexhIVHqJUNDQtjyMix1K3twvoVS3j+8iUzfl8IQJVKzpw9tl8ldW7fhuZNGua5YQcwdf0eXgSHsWH8QOo6l+GnRRuzbfMhizYhk8tZMbIv0/p14sB5XzYdPw9A1dIl8Fo+RSV1aeRCi5oV89SwA7CzMcOxuDUxcQmflN2/bAQymYymA+bz6EUwHmvGoK2l2LvTxkLK0b9Gc+riXVoNWUzpEjasnf5jnur6Ie51SvBDg1JM2nGTP4/7M6WzM/XKZd3rc+TmazScfkqZOi0+R1xiKju8nyllgiISVGRGbr6Wb3p/X8GGDhWLsuRMIJuvvmBY/VJUtzfNVn72CX96bfFVptBYhWFXysKQkY2c2Hr1BSP33yYhJY0Jzcvkm96C3COMuzxmwYIFH91PrrChpaXF8ePHqV+/fkGroiQ0NAzPs16MHz2S8mXL8vOQwVhYmHP8xCm18sdOnMTK0pJhPw2ifNmyjBs9Es/TZ1SMweTkZJb8+Rc9unbOX92j4zh9+xHjOjakvJ01P7dyxdzYAI8b/mrlp3ZtypyeLXAuXoRqjsXoXr8KFx8+V5EJj41n7cnLdK9XOf/0jozB89o9xvVsQ/kStgzv1AwLqRHHL93KIiuRSFg4tDsz+3eigkMxGlYph3uTWvx37a5S5vL9x/Rs5koVp+JULGVPr+Z1uHz/Uf7oHhaGp9d5xo0aTvmyZRg+uD8W5mYcP+WpVv7YCU+srCz5eVB/ypctw/iRv/DfmXOEhUegra2NpYWFMsXHJ3Dm3AXGj/wl7/WOjMHz2l3G92pLeYdiDO/cAgupMccv3swiK5FIWPRzT2YN6EIFh2I0qlqe7k1d+e/qHQC0tbSwNDVWpvikJE5fv8f4Xm3zXG8//xf0nrCW/Z7XPypXtXwJXCqW4ue5/3An8BUj5m3DXGpEq3qVAOjxvStBIZHMXH0Qv4cv+G3xDjo1rY6VmXGe65xB97oObDr7mKuPwjh1O4hDvq/o5uqQRS4qPoXQmCRl6lnPgT2Xn+P/JlopE/ZeeWhMElHxKfmmd5vvirDv1htuv4nG50kYng/f8X0Fm2zlX0clEJGQokwyuSK/UjEpN19F4v0kjFeRCWz1fUFpKyOMdDTzTfecIpfJcp0KE3li3D18+JB+/frh6OiIubk5/fr1Iz4+nrp16zJhwgQV2Xnz5lG3bl0AYmJiGDhwIJaWlkgkEmXq3r37Z51XIpFw+/Zt+vTpg6WlJd999x1Xr15Vkfnvv/+oXr06xsbGNGzYULm3G0BQUBBdu3bFxMRE5fwZOvv6+tK1a1fs7e2xtrZmzJgxpKUvkOjg4MDKlStZsmQJEomEfv36AYqwbL9+/ZDJZNjZ2bFmzRoVfX766Sd69eoFKL6mly5dipOTExYWFvTu3ZvwbLxLH+Ll5YW9vT2nT5+mbt26mJiY0LJlS168UA0RbN++nUqVKiGVSmncuDE3b2Z98GeQkpLC2rVrqV+/PlKplAoVKnDqVKZBtHnzZtzc3Dhx4gQVKlSgd+/eAFhaWrJ582aePXuGRCLh3r17tG3bFolEwqZNm3B0dGT58uUq5xo2bJjy+Lzm5u3b6Orq4vxdBUDRT1xqVMf3+g218r7Xb1CrZg3lvn3O31VAS1sbv1u3lTLbduxCU1OTdm1a54vOGfg9fYOuthbOxYtk6u5kj++jV2rl9XS0VPYbjE9KxkBHW0VmlcclytpZ41pWfbguL7gZ+BxdHS0qlrLL1Lu8I1f9n6iVt7MyV9HbxNCA2ITMVdtLFrXieXCo8reujjYORT5/0+wc6X7rLrq6OlSsUD5T9xrVuHpd/b3ie+MmtWpUy+wvFcqhra2N3+07WWRXbdhMl/ZtsbHO6t35Yr0Dnyna3NE+U+8Kjlx98Lltrk9sovpw2soDnnRp5IKNuYna8q9BwxrluP/4NUEhkQAkp6Ry0S+QhjXLKcqrl+XM1UxPuu/dpySnplGnSul80cfEQJsyRaVcDghR5l0JDMXFyeKjxzlYGfJ91WKsPhWgkh8Rl7/h4wyMdbVwsDDk5qsoZd6t11FUss3+bxudmKo2/1VEAkWkemikd6PkVBnh8cnEJX9DodAC2n7sWyNPjLsjR47g5OTE3r17OXToEB4eHqxYsQJ3d3cOHjyoInvgwAHlthpjxozB29ubf//9F09PTypWrMjvv/+exSD6GG3atKFZs2Z4e3tjbW3N8OHDlWWXL1+mZ8+eTJw4kZs3b+Lm5kbbtm2VW3/069eP169fc/ToUQ4dOoSdnR3//PMPkydPBmD37t3Uq1cPDw8P/v77b1auXMnevXsBheHn6urKkCFDCAoK4s8//1TRS0NDg27duqlcv0wm49ChQ8rrX7RoEVu3buXvv//G29ubpKQkfvnl87/wX716xeTJk5k9ezZeXl6kpaXRvn175HLFZ9aOHTsYPHgwv/32G1euXKF27drUrVuXp0+fqq0vOjoaDw8Pxo0bx7Vr12jWrBk9evQgLi5OKXPr1i1mzpzJunXrWLhwocrx9vb2BAUFAbB161aCgoLo0aMHAwYMYNu2bSrtcODAAdzd3T/7WnNCWFg45mZmaGpmfk1aWVpmG5YNDw/H0iLzAa2lpYWFublSPjQ0jPV/b2by+DHo6OiorSPPdI+Jx9zIAE2NzFvTysQw27BsBvFJKZy584h/z/vxQ6NqyvzAN6Ec9r3P5C5NyMGe0zkmLDoWc2MjVb3NjAmP+rxQ8IPnbyhtl+lJ6NbYhbM3HrB453GCI6LYfeYK3ZvUznO9AcLC1fUXC8KzCcuGRURgaWGu/K2lpYW5uVmWMG50TAyeXudo19otf/SOisVcqtrm1mZSwqJjPuv4+8/fUMauSJb86LgEPH3v0L5e9TzTNTfYmEt5GxqlkhcUEomNhRQAawspwWGZ5WlpMoLDorFOL89rLI0V4z1DYzI/Qt5FJ2Ksr42udvav0g4u9py5+5bYDwwmewsDNv9ch9PTmjG/V1Vl/XmNqYHiYy8iPtOYDI9PwVBXCx1N9XoPqVuSrb2rs6RDRaraZRqBfq+jiE1KZZpbOexN9elSpRhH7gYhzxfNc4kw7oA8Mu7GjBnDlClTqFq1KvXr16d169Z4eXnRpUsXAgIC8PdXhJRevXrFtWvX6NKlCwBXrlyhb9++tGzZkqZNmzJgwAAuXryIqanpZ597wYIF9OnTh3LlytGjRw9u3ryp3Jx3+vTpzJo1iy5duuDk5MSECRPQ0dHhypUryvP/8ssv1K9fn3bt2tGlSxeuXLmCsbHCrb9o0SJ+/fVXnJ2dad26Na6urnh5eQFgZWWFjo4OhoaGFClSBBOTrF9B7u7unDlzhqgoxQPo0qVLxMXF0bJlS1JSUpg1axb//vsv9evXp3z58ixfvpw9e/aQmqr+q0kdx44do0mTJlStWpU1a9bg5+eHn58fANOmTWP8+PH07duX8uXLM2/ePFxcXJg/f77auiwsLDh48CBt27alTJkyjBkzhvDwcBVvZ3BwMCtWrKBevXrY2tqqHK+pqUmRIoqXhZmZGUWKFEFfX59+/fpx48YNHj58CMDFixdJTEykRYsWWXRISkoiOjpaJX1qH74PiY6JwdDAQCXP0NCA6OjobOUN1MhHpcuvWL2GRvXrU9ulZo70yA3RCUkY6qp63gx1dYiKz37A8sPXIbhOWMmoTUdp71KBNjUUHii5XM7Cg+fo06g6pWzMsz0+T/SOS8Dwg8kOhnq6RMV93CgFCI6I4pTvHTo1qKHMszIxprR9ES7fe0yL0QsxMTKgelmHvFYbgOiY2Kz9xSDz759FPlpN/1Ijf9rrPKUcHHAobp+3CqcTFZ9Nm8d+eixbcHgUp67eplPDrH3a89pdHIvZ4FA0772NOcFUakDsB/0+Jj4RM6lherkhMXGq5bHxiZinl+c1Un3FfRn3npEWl5SqUqaONtXs8Lj5RiXv/qsoztx9y5Ij95m++zbO9qYs+qFaNjV8Gca6iklKCSmZxkt8uqfNSDdrOPXwnSBOB4Qw+6Q/LyLimdGqPMVM9ABIlcm5/SYaKyNd/upamTolzTl6922+6J1b5GlpuU6FiTwx7mQyGSdOnKBXr15UqlSJw4cPExISgq2tLQ0aNFB6rw4ePIirqyt2dorQTatWrTh48CCPHj3i6dOn7Nu3jwoVKuTo3BmGGICTkxOpqanExyteKDdu3GD06NEYGRlhZGSEsbExgYGBvH79Wnn+HTt28Pr1a+7evYuHh4fK+VNSUtizZw+dO3fG2dmZ69evExISwufi4uJCsWLF8PDwABRey3bt2qGvr09AQABxcXG4uLgo9XNyciIlJSVH53jf2+Do6Ii+vj6PHz8mNjaWR48e0ahRIxX5Jk2aKI0/dQQFBTF37lwaNWqkNL7e18fc3JwaNWpkd7habG1tad26Nf/++y8A+/bto2PHjujqZv1SnTdvHiYmJipp4R/LPlr/0eMnqNWgiTKlpaURF69qVMTGxas1wAGkUqmyz2TKx2FqYsJ9/4ec9jrHb7/m/ZgpgKPXHlB7/EplSkuTEZekOvYmLjEZUwO9bOtwsDbj31HdmdG9OefvPWX2HsXEg7N3H/M6LIoBzfLeKD1y8SYuP81QpjSZjLhEVSM8LiEJEyODbGpQIJfLWbLLg7L2RWlYpZwy/7eVO6hVvhR7Zg1nw7gBvAmNYOrGfXmju8cpXBq7KZO6/hIXF4+JiXoPkIlUqkY+Lkv/unXnHhXK5l2I8IjPDWoOmqpMaWlpWdo8NiER089p853HKVu8KA2rlM9SfuvRc8qXsFVz5NclIioOow/6vdRQn/CoOGW5saFqubGhHmGf6S3OKRlj4gz1MheZMNLTVin7EGsTPYqa6XP/taoHMiAohiVHH3DnRSTe/u+Ytfc2Lk6WWEnz3nsXk26M6mtnvisM0sfIxSRldSSs8XmK74sIAkPiWHH+MaFxydQtpYhsdKxki6OlIb/svcXQ3X4EvItlSYeK6Gl9Q8P3ZbLcp0JEniyFMnz4cM6fP8/ixYtp3Lgx8+bNU86WdHd3Z8uWLUyYMIGDBw/SrVs35XGjR49m3bp1VK5cmfj4eFq2bMmkSZNyrYeGhmoHk8vlzJ49mw4dOqjkZ3iXpkyZQu3atSlVqhTJycn07t2bgQMHAgqDtUOHDsTExPD7779Tu3ZtBg4cSGzs5z84JBKJMjSbEaJesmSJUjcADw8PihZVXebCOpez6lJTU0lKSsLIyEhZvzpk2XTi+/fv07BhQwYPHszWrVuxt7fP0qa5ZeDAgYwcOZKZM2dy6NChbEPvEydOZPTo0aqZSXFqZTNo1KA+lSpmLlvh/zCAsPBw0tLSlMZvSEiISijtfSwtLAgJDVP+Tk1NJTw8AksLC3bv3U98fAKduivGSaalt12DZm5MGDua71tm9T7mhEbOpahUIvPv7//6HWEx8aTJZMpw27voWCw+4o3IGKPnXLwIZW0t6fHHDvo3rcku79uERMfhNmuj4rrSZMQlJdNwyhqW9W9H1VK5f4E3rlqeyo6Zs8IfPH9DWHSsqt6R0ViafHxw+47Tl7lw6yG7Z/6iHA/28l0YF+8FsnCoOxKJhJrlS7FoWHe6TF3BsA7NKGZllmu9ARo3qEtl58yPuAcBgYSFR6j0l3ehodn2FwsLc0I/7C8RkVnkHwQ8onXLZl+kq4re1SqoLC3z4PkbwqJU2zwkMhoL00+0ueclzt96wJ7Zv6qMwXu/3jZ1qqo58uvyNiyKolamKnlFrUx58EThBQsOi6KIZaZBrampgbW5aqg2L8kIx1pJ9XgbqfAYWkt1iYpPJjlV/TO1fDEpMQkpvIv6+FIhj98qQuk2JnqEROcsUvEpMsKx5oY6hKaP87Mw1CEmKZWUtI8HVGVyeB2ZgKWhYjhKh0pFWXr2ETI5vIlKZO6ph2zpXZ16jhZ4Pvx8p4Qg//li4y46Opq1a9fi6elJ48aNAVXjoXPnzvz66688fvyYCxcusHnzZmXZokWLGDBgADNnziQpKSlH4djPoXLlyvj7++Pk5KS2fObMmSxYsIAffvgBiUSCkZGRsszf35/jx4/z5MkTSpYsmeW6QDHW5kOPz4e4u7vTtGlT7t27x7t372jZsiUApUuXRl9fn+fPn+fZTFNvb2/kcjnOzs4YGxvj6OjIuXPnVOo/e/YsVauqf3Bv3ryZChUqMHfuXCB7I/BTaGpqZmmXVq1aMWTIELZv305MTAxNmjRRe6yurm4Wj15S9MfD1EZGhhgZGb7324iU5GTu3L1HlcqVkMvlXL12nZ7u3dQeX7N6NXbv249cLkcikXDn3j1SU1OpWrky1apUZsigzHXhbt+5w5iJU9j971ak0i+flWekp4vRe6E1I30dUlLTuPP8LVVK2iKXy/ENfEWP+lXUHh+bmKRyvLG+4v+JyanM+8GNlNTMUMNJv0D+8brBtpHumBnpZ6krR3rr62Gkr/feb11SUlO58/glVUqXULT5gyf0bOaabR3n/Pz5Y5cHi4f1wN460zCKT0pGQyJR+bCwSjcSYxO+fD0tI0NDjAzV9Jd7D6hSyTm9v9ygZzf1M6Ndqldl176D7/WXB6SkplKtckUVuVdv3mBtmXeTQLK2uR4pqancfvyCqqUdkMvlXLn/mF7N62Zbh9fNByzZeYwlw3thb61+IsCrkHCsTPNn3FpOOOfrz+IxPbC1MuVNSCS6OlrUqeLE6l0Kz7SXrz9DujVWyrs4l0JbSxOfm4H5ok90Qgr+r6OoXdqSOy8iAahV2pKrj8KyPcbOwpB3ataAM9LTUhmDV66Ywkh9+u7jH7K5ITY5jcehcVQpZkLAO4VzonIxE26/zmoEG+hoKkO2AJoaEoqbGeD7QjGeVE9Lg1RZpkGYKpMTnZiKoc43tGRuIRs7l1u+2C2jq6uLtrY2O3fu5MGDB6xevZpVq1Ypy62trWnYsCGjR4+mZs2aypAsKAay37p1i6dPn5KYmEhERMRHPU45ZebMmWzevJnff/+dhw8f4u3tzbx5meuXhYeHc+XKFd6+fUtsbCxRUVHK82eMwdqyZQv3799n3rx57N+/X6V+JycnPD09uXXrFk+eqJ+hVrVqVSwtLZk8eTLt2rVDT0/xcNbV1WXKlCmMHDmSnTt38ujRIw4ePKgMXX4uv/32G7dv38bLy4vBgwfTp08fZRvPnDmT+fPn888//+Dv78/kyZO5fPmycjZwhjH78uVL5e+bN2/i7e3NpUuXaNeunUrY93NxcnJi586dBAYG8vatYjyGlpYWP/74I6NGjaJz585oaeXfw8DczIzmzZqwcOmf+D8MYOWadURERNKqZXMALl6+QrPv2xL46DEA37u1ICwsnFVr1+P/MIBFf/yJW/NmmJoqwsJFbKyVycxM4TUqYmOdZZHjPNHdyIDmVUqz6OA5/F+/Y6XHJSJi42lVraxCd//nNJuxnsCgUKLjE2n/+xY2el7lwat33H8ZzKzdp3EsYkFJGzPMjQywMTVWJhMDXbQ0JNiYGqOTx+1vLjWieU1nFu44hv/zN/y135OI6Di+r61YfuXi3UCajpxP4CtFfzh/6yGj/9rOuJ6tqehoT2hkDKGRMSQmp+Boa429tTnj1+zC//kbAl6+ZcbfB3AoYkkp27xfK87czJTmTRuxcNlf+AcE8te6TURERvF9i6YK3a/40rRtZwIfK+7x71s0Iyw8gpXrN+EfEMjCP//CrVljTD8Iy8bHJ6gdepBnekuNaFGzEgv/Papo832niIiJ43vXKgD43Amgya9z32tzf0b/tY3xvdpS0bG4Spur6J2YjJ5O9mPIvhQzqSE2FlIMDXTR1tLExkKKpZkxlmbGBB5fyA9tFcbprYCXXLr1iJWT+1CxtB1/TuhNSEQMJy8qxgDv9LiMtbmU6UM7ULmMPYvHdGfPyavKsG1+sNPnGT82dsTFyYLmlYrSroYduy4+w8xQh/+mNKVDTTsVeQMdTZJSVD+S7S0M8JjUhJ71HHAqYkzt0pZM6VyRnT7PlGP48ppj997SuUoxKtlKqVvSnKZlrDh+/y1SPS0296pOs7KK8ZVTW5bj14aOVLSVYm+mz68NHdHSkHA2UDFz/fzjMIbXL0VlWylFpXr0rG5HEaku116on3xUEMhlablOhYk8Me42bNjA4cOHad68OQEBAfzxxx8qMu7u7hw+fFg5SzSD/v37Kz1JRYsWxdzcHBsbGw4cOPClagFQr149Tpw4waFDh6hevToDBgxALpcrPVL9+/dn9+7dVKhQgaJFi2JqaoqDgwMXL17EwcGBhQsXsnz5ctq1a0dKSgpTp05VqX/8+PGYmppSt25dpbfrQyQSSbbXP2HCBKZOncrMmTOpUqUKv//+O4aGORsM/N1339GhQwe6detGy5YtWb16tbKsZ8+erFmzhgULFlCzZk28vb3x9vamVKlSgGIJEzc3N0aNGgXAL7/8Qp06dXBzc2PcuHH89ttvOR4DCbBs2TKuX7+Oi4sLhw8fVub37t2bkJCQz17q5kuYPnki9nbFGDhsOD6XrrB6xTLM0j3DMpkMuTwzNG5hbs7q5UvxvniZgcOGU6J4caZOGp/vOmbHtG7NsLc0ZdCqfVz0f86qIR2Vnja5XA7puksN9FgxqD33Xr7jlw2HGLLmAKYGeqwc3F5lBuXXYsaPnbCzNmfAwo343A1gzZgfMTNW9GeZTI4cObL0Nh+5YhvJqanM3nKIRiN+p/HIeTQeOY8TV26jpanJ6tH90NHSZPDiTfw4fz0aGhJW/9ZPuYBtnus+cSx2drYMGD4Kn8tXWLNs0Qf9RY5MltFfzFi9bCHel64wYPgoStjbM22C6mLmKSkppKamoqubv7OrZwzojL21Bf3nr8P7TgBrxw5QtrlcLlfR+9c/t5KcksqszQdoOHw2jUbModGIOZy4krkWYUpqKqlpaejmoydm95Kfeem5jNF93KhUxp6Xnsu4uG0qGhIJEiRoaGSGiruMWoGGpganN07AqbgN3w9dQmq6NzokIoY2w5fSsm5FTq4bS+DzYIbN3ZpvegPsufyCreeeMK9nVUa1LsfsfXe4FBCKhgTlUlrvY6CrRVKKqtHwMiyeMVuvU6eMFZuGujK3RxVO3XrDgkP38k3vEw+COXjrDWOalKZf7RKsvPCEm6+i0EjXV4Li33n/PSRVJmdYvVIs7VgRU31tJhy5R2y60bn+4jMuPw9nVOPSrOxamWr2pkw7/oDXnwg7f03EOncKJPK8dJXlAJlMRrly5di2bRsuLi4AhIaG0rdvX5KSkvD0VL+AaF4RFRVFhQoVuHTpEsWLF0cul/P27VtatWpFjRo12LBhQ76e/0vx8vKicePGxMTEqISTv2V27tzJ1KlT8ff3z5FHMCn689b++xaRe+8qaBVyhYZZ3nvIvhrlsg9LfstIAi4XtAq5xnDI4U8LfYOUbvztbJmYU0qU+fj6et8q6rY3y0tits7I9bHGfXJ/7LdGgQXK4+PjefbsGR4eHkgkEvT09Lh69Sp+fn789ttvuLq6cudO1kVBQbEA7odrrOWU4OBg3r59y5EjR6hXrx6pqan4+Pjw9OnTLB66r01kZKRK+PpDduzYoTJL+FsnNDSUFy9eMHHiRBYtWpSrUK9AIBAIBJ9Cnla4PHC5pcCMOyMjI/bu3cvs2bNZuHAh2tralC1bljlz5tCvXz+6dOlCcrL6FbyzW9IiJ5QpU4Z169axdOlSfvvtN4yMjHB2duaff/6hXbt2X1z/l2BsbPzR5UqKFCnCtWv5tw9hXjNt2jR27drFgAED6Nw5f7fvEggEAoHg/zsFFpYVCD4XEZb9+oiw7NdHhGW/PiIs+/XJ77Bs1MYpuT7WZMCcPNSkYPmG5i8LBAKBQCAQ5B4RllUgjDuBQCAQCASFAmHcKRDGnUAgEAgEgkKBrJDtEZtbhHEnEAgEAoGgUFDY1qvLLd/Qbr8CgUAgEAgEgi9FeO4EAoFAIBAUCsSYOwXCcycQCAQCgaBQIE+T5Tp9CXFxcfTq1QsLCwtcXFy4fDn7pY28vLyU29VlJAcHBxWZdevWUbp0aezt7ZkzZw45XbVOeO4EAoFAIBAUCgpqzF3//v159uwZnp6e7N+/n5YtWxIYGIi1tfo1QyUSCa9evUIjfR/w93duOn78OL/88gvbt2/HzMyMzp07Y2VlxU8//fTZ+gjjTiAQCAQCQaFAVgBh2bdv37Jv3z4uXLhA1apVqVKlCrt27WL79u2MHDlS7TEWFhbY2tqqLVu9ejX9+vVT7ug0duxYVq9enSPjToRlBQKBQCAQFAoKIizr4+ODvr4+Li4ugMIr16RJE86ePZvtMZaWltmWeXl50axZM+XvJk2acOvWLSIiIj5bJ2HcCQQCgUAg+H9PUlIS0dHRKikpKemTxwUHB2Ntba0SWrW1tSU4ODjbYyIjI2nVqhW2tra0b9+ewMBAQDF2LzY2liJFiqjUlXGez0WEZQXfPBFy3YJWIdfoPn9e0CrkCmnxMgWtQq55JzEuaBVyhWnQ04JWIdf8r+7RGnj2QEGrkGtOT19R0Cp8k3yJB27evHnMnDlTJW/69OnMmDHjo8dFRERgbKz63DE2NiY8XP2+6KVLl6ZLly50796dlJQUJk6cSJs2bbh9+zaRkZHK49+vC8i2PnUIz51AIBAIBIJCgVwmy3WaOHEiUVFRKmnixIlZzrFt2zaMjIyUKTU1lZiYGBWZ6OhoLCws1OpYrFgxVqxYQd26dWnUqBHbt28nICCA69evY25uDqBSX3R0NEC29alDeO4EAoFAIBAUCr7Ec6erq4uu7qcjRe3ataN27drK3zdv3iQ4OJi0tDRlaPbNmzcqodWP4eDggJ6eHq9fv6ZOnTpIpVKCgoKU5W/evAHAxsbms69FeO4EAoFAIBAUCr7GhAqpVIqTk5MyNWrUiKSkJK5cuaLQQS7nzJkzNGnSRO3xUVFRKr8DAgJITEykXLlyADRu3BhPT09l+ZkzZ6hWrRqmpqafraMw7gQCgUAgEBQKZDJZrlNusbKyomvXrowaNQo/Pz+mTZtGSEgIPXr0AODUqVMUK1aMu3fvEhUVRbly5fj999+5ceMGly5dolevXri5ueHs7AzA0KFD2bJlC/v37+fMmTMsXryYYcOG5UgnYdwJBAKBQCAQfAHr16/H0dGRJk2acOLECU6ePKlc7kQmkyGXy5HJZJiYmODh4cHNmzdp37497du3p1q1auzYsQOJRAJAy5Yt+euvvxg3bhx9+/ZlzJgx9O/fP0f6iDF3AoFAIBAICgUFtbesoaEh27dvV1vm5uamHDcHUKVKFfbs2fPR+gYNGsSgQYNyrY8w7gQCgUAgEBQK5GlpBa3CN4Ew7gQCgUAgEBQKCmpv2W8NYdwJBAKBQCAoFBRUWPZbQxh3AoFAIBAICgXCuFMgZssKBAKBQCAQFCKE504gEAgEAkGhQCY8d8BXNO5cXFzo3bs3I0aM+Fqn/OqMGTOGZ8+esXfv3oJW5bOQSCQcOXKENm3a5LqOb/HvKpfL2bJxPUcPHkBXV5ceP/SlTQf1G5uHh4WxYM5M7t7yw7F0GcZNnoZd8eLK8tOnTvL3ujWEhobwXcVKjBo3ATv74mrryisSUlKZ53mTy8+DsTMxYnSjSjgXNf/kcRsv+7P+8gNWdq5HdXsrlbKUNBm9tp0mOVXGwQEt80Xv+MQkpq/ZjvfN+xQvYsmkAd2oXKakWtmo2HiW/HMA75v3kMnltKlfkxE92qGjrXgk3X/ygmX/HuZWwFOkRgb0/r4Rfdo0Ua4DlVfI5XK2blzP0UOKvtL9h760aZ99X1k4ZyZ3bvvhVLoMYydl9hWZTMY/mzZwaP9edHV1ad2+A7369s9zfT8kITmFWfvOcTHgJXbmUsa3q0elEuq3KAqPTWDp8UtcDnxFmkxO4woOjG7jiqGujlLm9vNglhy7yKO34ZS0NuPXVrWo6Vgsz/Ue2qI0nWuVICkljU1nH7PvyossMn8Pc8XFyTJL/t7Lz5m++zYdatoxt0dVlbKrj0L5cdWlPNcXwNLMmF96NKNXmzqERMTg2mtWtrLW5lLWzfiROlVKczvgJUNm/c2jF++U5bUrO7JkTA9K2Vlz0ucOw+ZsIT4xOV/0hrx7JspkMrZu2sChfZn9vHe//O/nOUFMqFDw1cKy5cqVo2jRol/rdPnO4sWL6dKli0qevb09Tk5OBaRRwfDh37VNmzb89ddfBagRHNq3l707tzNpxiwGDRvOskXzuXLJJ4ucXC5n0phRaGhosHztBuzsizN6+BBSUlIACHjoz4LZMxkwZBhrN/+Dvr4+MydPyHf955y6wavIOFZ0qketEtb8esCH8Pikjx7zLiaBf68HoqulqbZ8760nhMYm5oe6Sqas/IcXb0PYOH0EdatUYNDsFYRFxWSRk8vlDJ7zFzKZjL8mDGHGTz3Zf+YSmw79B0BKahpjl/5NtfKO/Pv7GEb2bMeSfw5w8tLNPNf58P697N21nUnTZzFw6HD+/EhfmTx2FBqaGixfs4Fi9sX57ZfMvnJ4/14O7tvD1FlzmTBtBscPH2L7lr/zXN8Pmb7Hi5dh0awd2JY6ZewZtvEY4bEJamX/OHYJuRyW9nFjce8WXAx8yZ8eV5Tl/q9DGbz+CI0qOLBteCc6u5Tn2pM3auv6EtzrlOCHBqWYtOMmfx73Z0pnZ+qVs8oiN3LzNRpOP6VMnRafIy4xlR3ez5QyQREJKjIjN1/Lc30zsLMxw7G4NTFx6tv3ffYvG4FMJqPpgPk8ehGMx5oxaKffmzYWUo7+NZpTF+/SashiSpewYe30H/NNb8i7Z+Kh/Xs5tHcPU2fPZWJ6P//3K/TznPA1th/7X+Cree62bt36tU5VYPz6668FrcJX51v7u8rlcg7u20P33n2oVqMmAL5XLnF43z5qudZVkQ3wf8CDe3fZd+wkllZWjBw3gXYtmnDZx5v6jRrjd/0aNWvVpnGz5gAMHPIzfbt3ISY6GmOpNF/0D4tLxOvRG9Z0rU9Za1PKWJngGfCKU/4v6V4t+w+HlT73qFuyCHeCwrOURcQnsfGyP10ql+LUw1f5ondIRBT/XfbjnzmjqVDKnvIl7fDwuc6xC770aaO6v6JEImHJqP4Us7ZAIpFQoRT0aNmAU5dvMqRLK7S1NNm3eCJ66R4lJ/uiHLvgi4/ffdzqVMsznZV9pVcfqqb3lWtXLnF4f/Z9Ze/R9L4ydgLtWzbh8kVv6jdszKH9e+nxQ19lPcNH/caCOTPp0r0nunp6eabz+4TGxHP67hM2DWlPuWKWlLW14OTtxxy/GUjv+pWyyE/p1ABdLU2ll6VHnYrsuXxPWb7h7A1aVXHix0YKb1hJa7N80bt7XQc2nX3M1UdhANTxtaKbqwPe/iEqclHxKSq/f2lVlj2Xn+P/JlqZFxaTRGjMxz988go//xf0nrCWqT+15/sGlbOVq1q+BC4VS1GixWiCQiIZMW8bQWeX06peJQ573aTH964EhUQyc/VBAH5bvIPT68cz2syYkIisH0NfSl4+Ew/t20v3H/oq6xk++jcWzJ5J13zs5zlFniYvaBW+CXLtuWvUqBFbtmxhwoQJ2NraUq1aNby8vAgICKBVq1aYmJjQsWNHYmIUnbVGjRrMmDEDgGfPnqGrq8uTJ0/o2LEjZmZm1KpVi0ePHn32+R0cHPD09GTEiBHY2tpSsmRJjh07piJz48YN6tevj7GxMTVq1ODChQvKspiYGAYOHIilpSUSiUSZunfvDsDDhw/p168fjo6OmJub069fP+Lj45XXPnbsWPbt24dEIqFRo0aAIiyb8f+6desyYYKql2fevHnUrZt5M/3777989913mJiY0LZtW16+fPlZ1/7s2TM0NDS4evUqLVq0wMTEhDp16nD79m0Vuf/++w8XFxeMjY1xcXHh9OnT2dYpk8nYs2cPTZs2xczMDEdHR7Zt26Ys9/Lyoly5cly/fh0XFxfq1asHqP5dJRIJx44d45dffkEikTBjxgyaNGnC6NGjVc61cOFC5fF5TXRUFE8fP6K6Sy1lXrUaLty8nvWL3u/GdRxKlsLSSuE10NHRoWKlykrZ4g4lefPmFWnpi2Lq6upibm6BoZFRvugOcOtNGLpamlQoogjDSiQSathbcf1VSLbH3A0K52zgawa5llNbvu7SA8pYm1CrhHW+6Axw0/8JujraVHRyABR616pYhit3A9TK29lYqoRyTIwNiYvP9CzqvRcqBIhLTMJATzdPdY6OztpXqtZwwe8z+4pzpcr4XVPIvn71khIlM0PQVarXJCoykhcvnuepzio6PXuLrpYWzvaKv6tEIsHFsVi23jY9bS2VNo9LSkZfRxuANJkMr3vPaFWldL7pC2BioE2ZolIuB2T25yuBobg4WXz0OAcrQ76vWozVp1T7U0Rc/oUyc0vDGuW4//g1QSGRACSnpHLRL5CGNRX3Z8PqZTlz9YFS3vfuU5JT06iTT22fl8/E169e4lAqs59X/Qr9PKfI0mS5ToWJLwrL/vrrrxgZGXH69GlKlChBz5496d27N6NHj+bYsWN4enqyYcMGtccmJyfTtm1b+vTpw4ULF4iNjWXSpEk5On/Pnj1xdHTkzJkz1KlTh4EDB5KamgrA06dPadasGf369ePmzZsMGzaMdu3aERoaCigMMW9vb/799188PT2pWLEiv//+O2vWrAHgyJEjODk5sXfvXg4dOoSHhwcrVqwAYP/+/XTr1o3WrVsTFBTE/v37s+jm7u7OwYMHVfIOHDhA165dAdi9ezdTpkzhjz/+wNfXl5IlS9K9e3fk8s/76pDL5fTt25dffvkFHx8fSpYsSYsWLUhMVLwgfXx8aN26NT169MDX15fu3bvj5ubGpUvqx6Okpqaya9cuBg8ezJUrVxg0aBA//vijisH58uVLhgwZwuzZs9VusxIUFISdnR1z584lKCiIMWPGMHDgQHbs2KH8uwDs27cPd3f3z7rOnBIRrvBcWVhkviwsLS2Ji4slKVE1LBkeFoa5hepLxcLKiohwhUehWo2aGBtLmTxmFM+ePmH7P1vo1M0dDY38G80QHp+EmYEumhqZL2FLQ/1sw7IyuZw/vG7Tu0ZpipsZZyl/FBrF8fvPGdske09DXhAWFY2FiTGampltY21mSlhk9EeOyuT+kxeULm6rkieXy4mMiWPDgVPcffyCrs3z9oMgIkzRV97vA5ZW6vtKRHjWvmJpaUV4el+RSqXKvgcgk6WhqalFcFBQnur8PmGx8Zgb6aP5Xn+0khoQFhP/0eMSklM4c+8p233u8EO6hy80Op5UmQyJBEZs9qD53K2M2Xbqk3XlFEtjhYH+vrftXXQixvra6Gpnf191cLHnzN23xCamquTbWxiw+ec6nJ7WjPm9qirrL0hszKW8DY1SyQsKicTGQuHtt7aQEhyWWZ6WJiM4LBpri/yJBuTlM1EqlSrvG/g6/VyQO77oLeXu7s6UKVMoX748vXv3JigoiI0bN9K8eXPq1atH7dq1uX79erbHb9iwgY4dO+Ls7Ez79u25ceNGjs4/atQofv31V8qVK0ffvn15+/Ytb9++BWD+/PkMGjSIAQMG4OTkRP/+/XFxceH48eMAXLlyhb59+9KyZUuaNm3KgAEDuHjxIqampoDC+JsyZQpVq1alfv36tG7dGi8vLwDMzc3R19dHT0+PIkWKYG6edbB7ly5dCAgIwN/fH4BXr15x7do15Ti9qVOnsmrVKlq2bEmZMmX4448/8PPz+2zvHcC2bdto27Ytzs7OrFmzhqioKDw8PACYMWMGP/zwA6NGjaJcuXKMHj2aXr16Kb1sH6Kjo8PevXtxd3enTJkyjBo1Ck1NTS5evKiUiY+PZ9KkSbRs2ZLixbNOKihSpAiamppIpVKKFCmCkZERnTp1IikpiTNnzgAKA/H69etZxitmkJSURHR0tEpKSvr8sEtMjMKY0DcwVObpGxqml8V8IBuDwXtyAAYGhkRHK+rQ1tamavUaBAe/ZUCv7lzwOkOHrvljlCp1SkrBQFt1tISBjhbR2Qy2Pun/kujEZPrULJulTC6Xs+zcHXpUL01J8/x5cWQQFRuPgb5qWMZQX5eo2E8bB8FhkZy8dJPOTeuo5G877kWdfmNZvuMwvw//ASf7vB2zq66vZPQHdX1FX01fiUnvK9Vr1mLvzu2Eh4XxNugN0yeOQy6XKb2++UF0QhIGutqqOulqE5WQ/f3y8E0orlM3MnrrSdpXL0ebamUACI6KA2DhYR9aVy3Nwp7NeREaxez95/NUZ6m+Qt+494y0uKRUlTJ1tKlmh8dNVY/k/VdRnLn7liVH7jN9922c7U1Z9EPehe1zi6nUgNh4VaMpJj4RM6lherkhMXGq5bHxiZhLVftXXpGXz8TqNWux571+Pm1C/vfznCKXyXOdChNfZNwZG2d6CkqXLq02LzIy8rOOd3Jy+qjs5xwPKOu4ceMGy5Ytw8jISJlOnz7N69evAWjVqhUHDx7k0aNHPH36lH379lGhQgVlfTKZjBMnTtCrVy8qVarE4cOHCQnJPjT2Iba2tjRo0EDpvTt48CCurq7Y2dkRGxtLQEAAnTt3VupmampKfHy8Ur/PQVMzc/C8sbExDg4OPH78GAA/Pz9liDiDJk2a4Ofnl219ERERLF++nObNm1O5cmXS0tKyXHPz5s0/Wz8APT09fvjhB2WId//+/TRo0CDbyTXz5s3DxMREJa34Y/Fnny9jLFxCfJwyLz4uTqUsA6lUSvx7cgBxcbFITUwA2L19GwEP/Vm/dTtbdu6lXIXv+HlgZng+L/B48ILGKw8rU5pMRnyKqnciLjkFEz2dLMfGJ6eyyvseYxpXRk/NRIrzj4N4ExVHv5pl8kzfDA6fu0L1XqOUKU0mIz7hgxdWQiKmxh9/YcnlchZv3U/ZEsVoVKOiSlnbBjX5Z85ofunelmmr/uXwuSvZ1JI7pGr6StxH+krCR/rKwKHD0dbWptP3zRk2oB8dOis89NY26meu5oZjNwJwnbpBmdLS5MQnqY5Li0tMwdQg+7FPDlambBveiRldGnHuwTPmpBtvGUbixA71aVnZiaolizLCrRYX/J+TmofhqoxxdIZ6mR8wRnraKmUfYm2iR1Ezfe6/VvWGBQTFsOToA+68iMTb/x2z9t7GxckSK2nBeu8iouIw+uBvIDXUJzzdgI6IisPYULXc2FCPsKjYfNEnL5+Jg4Yp+nnHVs0Z2j9/+vmXIkuT5zoVJvJsQoW6UFVOwldfGur68Hi5XM6wYcP4+eefVfItLRVT60ePHs26deuoXLky8fHxtGzZUiUsPHz4cM6fP8/ixYtp3Lgx8+bN4+jRoznSyd3dXTku8eDBg3Tr1k2pG8CmTZuoUaOGyjH29vY5Osf7xMfHY5Q+Hiy78K4sm2niQUFB1K1bl2bNmvHXX39RpkwZSpZUv4xFThk4cCCurq6sWbOGQ4cOKcc1qmPixIlZxuhFfBCK+RgWFoq/b1hoKNY2RQAIDQnByNgYXV3Vh765hQVhYaEqeWEhITiULAXAnh3bmTBtBlpaWtgVL87sBYvp2rYV58540qpNu8/W6WM0KFVUZZmTgHdRhMcnkiaTK0OzobGJWBhmfWGffxJESFwi009kjp2JTkxm3JHLtCpfnOcRMYTGJdJ+40kAUmUy4pNTabnmGAvb1aay7cfHOX2MJjUrqSxz8uDpK0KjYkhLkylDs+/Co7A0/bjHcLvHOc7duMu+RZOyLKdgamxE9fJOVC/vhJaWJn9uP0K7hrWyqSnnmKvpK2Ef6yuhH/SV0BBKpPcVK2trVm/aSmRkBFKpCSHvgpHJZNgUyTtvY8MKDlQsnvkS9X8TSlhsPGkymTI0GxIdh4WxfrZ16Gorxug521tTpqgFPVfso3/jqhQxVTw3dN77SLA1MyZNJicqPhELY4M8uYaMcKyVVI+3kYqPAWupLlHxySSnqn82lS8mJSYhhXdRH5/t/fitwgtlY6JHSPTXmWShjrdhURS1MlXJK2plyoP0sZDBYVEUsTRRlmlqamBtrhqqzUvy8ploZW3Nmr/zt59/KYVt1mtuKbQ7VFSuXJk7d+7g5OSkkjLCrosWLWLAgAGEhoYSERHBiRMnMEn/OomOjmbt2rWsWLECNzc3dHV1sxhFWlpan/TgdO7cmRs3bvD48WMuXLhA586dAYWXrVSpUjx69CiLfh/ebJ/L48ePefnyJZUqKcbQVKlShXPnzqnInD17lqpVq6o7nAMHDpCWlsbatWspW7YsEokkW0PwY6hrl4oVK+Ls7MyuXbu4cuUKnTp1yvZ4XV1dpFKpSspJmxhLpTiVLsO1q5lenhvXfJWzu96nao2aPH/6lJB3ivWnkpKSuHv7FlVrKmQTExPQ0sr8/tHW1sbExJS42Lz7wjbU1cbe1EiZqtlZkpwm495bxbgWuVzOtVchWdatA2joWJTDA9z4p1cTZZIDk5pVY5BreWa51WRvvxbKsgG1y2FppMc/vZpQ3tr0i/Q2MtCnRFFrZXL5rjQpKSncDnyq1PvKnYfUqpi919Dr2h0W/3OABSP6YV8kcz0zuVxO3AdeQKmBPonJeTt43lgqxbF0Ga77vtdXrvtSrXrWvlKlek2eP1PtK3du38rSr0xNzdDQ0OCs539UqVYjy/ilL8FIT4filibKVKOULSmpMu6kr58ml8u5+vg1LtmsSxf7QWjfWF/hDU5ITsVIT4cKdlZcf28yxvPQSAx0tDE3yt5YzCnRCSn4v46idunMv3et0pbKmbPqsLMw5F10VsPOSE/VN1GumOL5/fRdXBbZr8k5X3/Kl7LFNt3A09XRok4VJ7x8FZMovHz9aVorM0rk4lwKbS1NfG4G5os+eflMzECln1fP237+pcjT5LlOhYlCa9xNnDgRHx8fRo0axf379/H19WXatGnKsQHh4eHcunWLp0+fkpiYSEREhNLbpauri7a2Njt37uTBgwesXr2aVatWqdTv5OTElStXuHz5MgEB6mcEWltb07BhQ0aPHk3NmjWxs7NTls2ePVs5gePRo0f8999/rFy5MkfXOG3aNHx9ffH19aVnz540aNAAV1dXAKZPn87WrVv5888/efjwIcuWLePff/9l+vTpyuONjIyUY/wy/n/s2DFu3LihHMOYU5ycnDh06BAPHjzgxYvMhUkHDhzI+PHjqV+/vtJ7ml+079KVndu2cuOaL+fOnObU8aO069SFyIgIurX7Ho+jhwEoXaYs31WsxJL5c3kcGMCfixZgYmpGLVfF2K/GzVrwx4LfueF7lVcvX7B5/VrevHmdZfmAvMTMQJempYux7NwdAt5Fsu7SAyLjk2lRVtF3rjwPpu16Dx6HRqOvrYW1sb5KAjDV18FETwczA12VMqmuDpoSDayN9VU8NHmBuYkxLV2rMX/zPh48fcmKnUeJiI6ldT3FS8HH7z6NBk0k8IXCeDh3/S4jF69nwo9dqFSmJCERUYRERJGYlMyVuwG0GzmHA2cu8eTVW7xv3mfVHg9auqr/MPkSOnRW9JWbavqKe/usfeWPBel9ZfECTE3NcEnvKx5HD7N7+zZePH+G50kP/vl7A7379c9zfd/H3EifZpVKsfjoRfzfhLLqlC8RcYm4VVEMUbkY8JLmc7fy6G040fFJdFi8k41nb+D/OpT7r0KYve88jjZmlEw39H9sWIWNZ29y/sFz7r8KYcWJq3SpVSHPF6jd6fOMHxs74uJkQfNKRWlXw45dF59hZqjDf1Oa0qGmnYq8gY4mSSmqH5r2FgZ4TGpCz3oOOBUxpnZpS6Z0rshOn2fKMXx5jZnUEBsLKYYGumhraWJjIcXSzBhLM2MCjy/kh7aK58KtgJdcuvWIlZP7ULG0HX9O6E1IRAwnL95VXL/HZazNpUwf2oHKZexZPKY7e05eVYZt84O8eiZ6HD3Mrn8V/fy/Ex78syn/+7kgdxRa487JyQkfHx/8/PyoXbs23bp1IyEhgeT0r//+/fsrPVlFixbF3NwcGxsbDhxQrOC9YcMGDh8+TPPmzQkICOCPP/5QqX/QoEFUrVqVpk2bMnz48Gz1cHd35/Dhw8pZshn07NmTjRs3snr1aipXrsxvv/2Gjk7WcVUfw8XFhX79+tGiRQucnJw4cOCA8kFcv359jhw5wj///EP16tXZtm0bR48eVVmKpVevXowfP56UlBS6d++Ou7s73bt3Z/DgwbRp0wY3N7cc6QMwd+5cwsPDqVWrlspMaXd3d8LDwz8aks0r2nboRLeevfl9xlTWrlzOqHETqVmrNjKZDLlcNTQ9d9EfyGQyRvw0kFcvX7Dkr1VoaSnGAA0fOZq69Rsyf/YMBvTqztXLl1i4bAX2JUrkq/4Tm1XFztSQ4fu8ufw8mGUd62Cqr/BeyuQgRzFL9ltj5tBeFLex5McZf+J98x7rpg7HTKoI98nkcpW2H7FwHckpqcxcu4P6/cfTcOBEGg6ciIfPdWpXLMvo3u05esGXHhMXMW31NlrVrca4vp3zXOc2HTrRrUdv5s6YyrqVyxk5biI13usr7692P2fhH8jSZIwYMpDXL1+weEVmXyldphyXfS4w9Mc+7Ny2lckz51CjVu081/dDpnduiJ2FlMHrjuAT8JLVA1pjZqgw8uVyRWeRyeVIDXRZ3q8V91+F8Mvm4wzdeBQTA13++vF7ZUi3eSVHRrd2ZemxSwzdeJSajrb83DKrd+dL2XP5BVvPPWFez6qMal2O2fvucCkgFA0JyiWp3sdAV4ukFNUB+y/D4hmz9Tp1ylixaagrc3tU4dStNyw4dI/8YveSn3npuYzRfdyoVMael57LuLhtKhoSCRIkaLw3w73LqBVoaGpweuMEnIrb8P3QJaSmKq4hJCKGNsOX0rJuRU6uG0vg82CGzc3f9ULz6pmY0c+H9Mvs5zW/Qj/PCWLMnQKJ/HPX3ihEyGQyypUrx7Zt23BxcQEgNDSUvn37kpSUhKenZ9hs1MwAAHxXSURBVAFr+HGePXtGyZIluXPnDs7OzgWtzmdx+fJlWrVqxbNnz5Th78/lbT5+0eY3uttnF7QKuUJaP2cTZ74l3tl9Wy+bz8XUa11Bq5Brapz/39yZJ/DsgYJWIde88FpR0CrkiiIm+TMrOINr3zfN9bE1jme/Fuz/Gl9th4qc0L1792wnL7Rp04adO3d+Uf3x8fE8e/YMDw8PJBIJenp6XL16FT8/P3777bcvqjsvKFasGFFR6gfXLliwgNatW39ljXJPREQEb9++Zfjw4UyePDnHhp1AIBAIBJ+LrJAtaZJbvknjbunSpcyZM0dtmaHhl1v9RkZG7N27l9mzZ7Nw4UK0tbUpW7Ysc+bMoV+/fl9c/5dy4cKFbCczWFpa5njJmIJk3bp1zJ07l86dO380fC0QCAQCwZdS2CZG5JZv0rjLbg20vKRdu3a0a5c3y1nkNaVKlfpouamp6WfvZFHQjB8/nvHjxxe0GgKBQCD4f0Bh20YstxTaCRUCgUAgEAgE/x/5Jj13AoFAIBAIBDlFhGUVCONOIBAIBAJBoUAYdwqEcScQCAQCgaBQIMbcKRDGnUAgEAgEgkKBXCyFAogJFQKBQCAQCAoJBbVDRVxcHL169cLCwgIXFxcuX76crWzGTiwfpm3btgHQr1+/LGUzZszIkT7CcycQCAQCgUDwBfTv359nz57h6enJ/v37admyJYGBgVhbW2eRDQoKUvl94sQJpk+fTseOHZV57u7uLFu2TPnbyMgoR/oI404gEAgEAkGhQF4AY+7evn3Lvn37uHDhAlWrVqVKlSrs2rWL7du3M3LkyCzyRYoUUf4/JSWFBQsW8Ndff6ls0mBvb68il1NEWFYgEAgEAkGhQJ4mz3XKLT4+Pujr6yv3qpdIJDRp0oSzZ89+8tgtW7Zgbm5O27ZtVfItLS1zrQ8I404gEAgEAkEh4UvG3CUlJREdHa2SkpKSPnnO4OBgrK2t0dTUVObZ2toSHBz8yWM3btyodtvTo0eP4uzsTOnSpZk6dSrJyck5agcRlhV881iH3itoFXJNfN/pBa1CrpBHPCtoFXKNTfiDglYhVyS3HFLQKuSaEm9vFrQKueL09BUFrUKuKd7ol4JWIVck39yUr/XLs9mX/XOYN28eM2fOVMmbPn36JyczREREYGxsrJJnbGxMeHj4R497/PgxV69excPDQyW/efPmVK1alQYNGnDjxg1GjhyJpqZmjiZVCONOIBAIBAJBoeBLZr1OnDiR0aNHq+Tp6upmkdu2bRtDhmR+jI0dO5aYmBgVmejoaCwsLD56vsuXL1OqVClMTU1V8nv16qX8f9WqVXnx4gXbt28Xxp1AIBAIBAJBTtDV1VVrzH1Iu3btqF27tvL3zZs3CQ4OJi0tTRmaffPmzScnRNy8eZMKFSp88nwVKlTg9evXn5R7H2HcCQQCgUAgKBR8je3HpFIpUqlU+dvExISkpCSuXLlCnTp1kMvlnDlzhhEjRny0nidPnmBra6uSl5qaSlJSksrMWT8/P8qVK5cjHcWECoFAIBAIBIUCeZos1ym3WFlZ0bVrV0aNGoWfnx/Tpk0jJCSEHj16AHDq1CmKFSvG3bt3VY6LjY1FX19fJW/79u3UqlWLXbt2ERAQwN9//82ff/7JuHHjcqST8NwJBAKBQCAoFHzpThO5Zf369QwaNIgmTZrg6OjIyZMnlcuZyGQy5HI5sg8me6gz7n744Qfi4uJYvXo1N27coFixYqxevZpu3brlSB9h3AkEAoFAICgUfI2wrDoMDQ3Zvn272jI3NzfevHmTJf/ixYtZ8iQSCUOHDmXo0KFfpI8w7gQCgUAgEBQKZPKCMe6+NcSYO4FAIBAIBIJChPDcCQQCgUAgKBSkCc8dIIw7gUAgEAgEhYQCGnL3zSGMO4FAIBAIBIUC4blTIIw7gUAgEAgEhQLhuVMgJlTkES9fvqRYsWJcvXq1oFX5LGbMmEGNGjW+qI79+/dTqlQpEhIS8kirvCM+MZGxC1dR230o3UZOx8//0UflD532ps/4uZT//gcCnr1UKYuOjWPswlXU6vYTnYZP4eyVG/mis1wuZ93atXzv1pKO7dtxYP/+bGXDwsIY8ctwGtavx6ABA3jx/HmmvtHRzJ41k+/dWuLWojl/LltKSkpKvuj8PvEJiYyb+wd1OvyA+9Cx3Lr/8KPyh0+dpd+oyXzXpAOBT5+rlZHL5fw4eirfNemQDxpnEp+YyNgFK6nd7Se6/ToVvweBH5U/dPoCfcbNoXyrXur7y4KV1Oo6iE4/T+Ls5fzpLwDxCQlMnDiRBg0b0rNXL27fvv1ReT8/P3r26kWDhg2ZOGkS8R/cu3v37qVt27a0aNmSdevXI88nL0iP6nZs6V2d9d2r0rKcdbZyx4fUyZKsjTO3h2rgaMG67lXZ278Wc1pXoKhUL1/0zUAul7N5wzq6tGlFr84dOHrwQLay4WFhjB81gtZNGjDip4G8evFCWSaTydi8YR0dW7Wge4e2/PP3xnxrawBLM2NmDuvIo+OLuPTvtI/KWptLObj8V96d/wvPDeNxKq7696ld2RGff6YQdHY5m+cMwkBPJ9/0zg1pcnmuU2FCGHe5xMjISGW1aX19fcqVK4eJiUkBavV1sbS0pFy5cmhrawNw7do15aKNBc3kpRt48SaYTb+Pp261igyaspCwyKhs5a/dfYihvvoXw/Tlm3jzLox/Fkzhx86tGLNgFdfvfdxwyQ179+xhx/Z/mTl7Dj8P/4UF8+dx0ccni5xcLmfUyF/R1NBkw8ZN2BcvztAhQ0hJSUEulzP852HIZXKWLF3GpClTOXToEFu3bM5zfT9k6qIVvHgdxMZFM6lbswqDx88kLCIyW/lrt+9j8MECnh9y2vsKdx9+3NDKCyYvXceLoGA2/T6RutUrMWjKgk/0F/+P9JeNvHkXyj8Lp/Jj59aMWfAX1+/mfX8BmD59Oi9evmTt2rXUqVOHocOGERYerlY2NDSUYT//TN26dVm7di3Pnz9n5syZyvILFy4wf8ECfv31V2bPns3WrVvZu29fnuv8fQUbOlQsypIzgWy++oJh9UtR3d40W/nZJ/zptcVXmUJjkwAoZWHIyEZObL36gpH7b5OQksaE5mXyXN/3ObRvL3t3bmfSjFkMGjacZYvmc+WS+nt00phRaGhosHztBuzsizN6+BDlR9ah/Xs5tHcPU2fPZeK0GRw/fIh/t/ydb3rb2ZjhWNyamLhPf4jvXzYCmUxG0wHzefQiGI81Y9DWUuyXamMh5ehfozl18S6thiymdAkb1k7/Md/0FuQeYdzlEZaWlpw+fZqyZcsWtCpfjQYNGnD8+HG0tL6t6H5IeCT/+fgy4afeVHB0YMQPnbEwNeGo16Vsj5n96wCmDO2TJT8iOoYT3leZNOQHypS0p23junRr1YQNe47mqc5yuZw9e3bzQ5++1KxZk2bNm9OmbVv27t2TRdb/wQPu3b3LpMmTKV2mDBMmTiQ6Ogpv7wtIJBLmzV/A1OnTKV++PA0aNKBr126c9vTMU30/JCQ8gv/OX2LCzwP+r707j4s5/+MA/ppOarp1KpRCKdHSRXS5j5xFIj/rzLUIYd13WEdu1rWO3Amxi0oHW0SxupArR9J96JzP74+2WbPNTMfi+x19no9Hj+Uz3+pl9lvzns8JUxMjzPifJzTUVHHlZoTIz1npOw2LZ04U+XhZWTk27TkEz8H9v0ZkvszsXFyPugu/yWNgZtwKM8cMh4aaCi6H1dxgtNqqWRPxs493jfac/AJci4zBoqlj0cawBQY6d4V7P2ccOHPpi+f++PEjbt68ifnz5sG0XTtM8/GBuro6roaECL3+SkgINDU14TN1KkzbtcP8efNw48YNfjF4+swZDBo0CK6urrCxtsY4b2+cOX36i+ce0F4H5xLe4uHbfESnZeFGygf0M9MWef2bvE/I+VTO/+D93cHSobkyHqTnIiotC+m5n3D07iuYaHLBlZP+4pmBqp/RoHNnMNJrLKw6d4Gjiyt69x+AYCEFcGpyEpIe/4W5fovR2qQNfprvh/z8fPwZHQWgqkgcOcYbVp27wNLqB0yfMxenTxxDaUnJV8ken/wKXn57cf5GnNjrOpm2hLWFEaat+Q2PnqRj5rpjUFfmom+3DgCAUf3s8C4zFyt2ByE+5RXmbjqJoS4/QFNN6avkbohK0vCP70m9ijtHR0ccOXIEfn5+0NPTg5WVFcLDw5Gamoq+fftCRUUFQ4YMQUFBAQDg7t27GDFiBAwMDKClpQVfX19UVlYCAHx9fdGxY0f+3y9cuABNTU3k5ubWmuPw4cPo1asXbt68CQcHB6ioqMDd3R3FxcX8a8rLy7F48WIYGBhAV1cXM2fOFBg+DAkJQceOHSErKwsOh8P/SE5ORnl5Ofbu3QsHBwcoKyvDzMwMf/zxBwAgPDwcHA4HRUVFsLCwAIfDQXh4OAoLC/l/vn79OuTk5JCX9887/9LSUigrK+P69esAgA8fPmDkyJFQV1eHiYkJdu/eXedu+XHjxsHPzw8rVqyAkZER9PX1sXjxYv5zCQAFBQXw8fGBnp4e9PT0MG3aNBQWFor8mm/evMGMGTNgZmYGZWVluLm54ePHj/zHHR0dcfLkSfj6+qJZs2a4du0aLl++DA6HA6BqmLdLly7IysriP5cRERGQlZUV+DplZWVQVVXFja9YbDxIfAJ5eTl0aNMaQNWO37aWZoh9mFTvr/XqbQYAwLjFP4c7W3cwRdzj1C8T9m95eXl49vQpbGxt+G1duljj3r17Na69d+8ejIyMoKlVNVwiJycHy44dce/uXQBA8+bN+f9fAEBFRRmFRUVfNO+/PfgrCfLycrBoZwKg6jm36WSB2Pi/avlM0Y6euwRpaWkM7u38pWIK9SAxVcT9kljvr/XP/dKc32bdweyr9PTGx8dDXl4e5ubmAKpyW1tb466Qewaoum9srK3594a5uTlkZGQQHx/Pf9zW5p/7z9raGimpqcjPz/9imZXkZdBKQxEP0v/53ZjwJg8d9ESPeOSXVAhtT8/5BB3lJpD6+1Yvq+Ahu7gMRWWVQq//r/Lz8vD82VP8YP3Pc2TV2RoP4mo+3/H349DK0AjNNDUBVP2MWnSw5F/7Jv01WhkZ8q/v9EMX5OXm4tUr4dMTvpUendsh8dkbvMvMBQCUlVfgdvwT9OhSdWh9jx/aIjT2n9+jd/96jrKKSth3NGEirlB0WLZKvXvuZs2aBS6Xi5s3b6Jly5bw9PSEl5cX5syZgytXruDGjRs4cOAAAOD06dPo1q0brl69ikOHDmHnzp04e/YsgKrhhA8fPuDAgQOoqKjAokWLsHbtWqiqqtYpR3R0NNauXYv169cjKCgIwcHBOHz4sEDO2NhYBAUF4erVq0hISMDq1asBAM+fP8eQIUPQr18/3Lt3DytWrICenh6eP38OY2Nj5Ofn4+rVq5g/fz7u3bsHV1dXjBo1CkVFRbC3t+fPqwsLC8O7d+9gb28vkM3JyQmqqqq4evUqv+3mzZuQk5ODo6MjysvL4erqCl1dXdy5cwe7d+/G6tWr61XwbNq0Cfn5+bh48SK2b9+OnTt3YseOHQCq3mEOHz4ccXFxOH/+PM6fP88vtEUVkOHh4ZCXl8ehQ4dw69YtpKSkYMmSJQLXLFu2DMXFxYiIiICDg4PAY76+vti6dSvU1NTw7t07vHv3Dg4ODjA0NMTpz979f/48fC0fc/OgoaIMael/bm9NDVV8zBE9zCaKihK36mvm/PMCJyXFQUFRMQqKikV9Wr1lZ2UBADQ0/hnW1tTURFFhIUr+9W4+OzsLGv8a/tbU1ER2lvDhuOSkZBgbG3+xrMJk5eRCQ1UF0tL/9JpoaqiLHZYVJzM7B/uOn8GSnyZDTu7r9gwLvV/U1f7j/fLP50pxpL74/QJUzbtUV1cXfM41NZH1970k7PrP7xsZGRloaGggKysLxZ8+obi4WGBahebfhYmor9cQqgpVUzhyisv4bdnF5VCUl4GctPCXoyldDXHU6wdsHmyBTvr/FIHxb/JQWFqBpX3awUC1KYZ3bI5Lf73D13qJzvm7h1NDQ4Pf1qxZMxQVFdboccvOyoL6Z9cBgIamJnKyq55LZWVl5Hz288rjVUJaWgYZ7959pfR1o62ujPcfBe/7d5m50NZQBgBoaSgjI+ufxysrecjIyofW34+zAe25q1Lv35oeHh74+eefAQBeXl4ICgrC77//DgsLCwCAra0t4uKqun43btzI/zxzc3PY2dkhPDwcHh4eUFJSwubNmzF79mzweDw0adIE48ePr3MOaWlpXLlyBU2aVM17sbKywv37VROX09PTcejQIbx7945fLK5fvx6jR4/GmjVrkJCQAABYs2YNOBwOLC0tsWXLFmRlZaFVq1bQ0NBAUFAQ/3v5+voiICAAf/31F2xsbPi/9Jo1awYdHR0AVT1S1WRkZDBs2DAEBQVh5MiRAKp6JocOHQpZWVmcPHkSTZs2xS+//AIOh4O2bdti+vTpOHnyJHr27Fmnf3/fvn2xefNmAICFhQXu37+PgwcPYtasWbh9+zZu3LiBp0+fwtCw6t1hYGAgjI2N8eeff8LOzq7G1xs9ejRGjx7N/7unpydOnjwpcA2Xy8X27duFDsNyuVyoqKhASkqK/5wAwIQJE3Ds2DH4+PgAAM6dO4cRI0aIHMotLS1FaWmpQJtsaRnk5es+aTe/sAiKCoLzoRSbNkFeQf17r/R1NKGvo4ldJy5giY83kp+/wurdRwEAFZVfrocgv6CqeFRUVOC3KSgqAgAK8vP59zlQtWBCQUFB4PMVFBSQkZFR4+t++JCBGzeuY/0G/y+WVZj8giIoKAjOn1Ns2hR5BaJ7i8XZ/usxONlbw9bKEm/e1/x3fUn5BULuF4WmyCv8D/fL8QtYMm0cktNeYvXuwwC+7P0CVN0Hiv+6DxQVFARGDD5XIOx6RUXk5+Wh4O/euc/vK8W/7z9RX68hlOSrfu4/lf/zXBT/3dPGlZdGdrHgwerBj94h7nUuziW8QT8zHSzvawqf0/F4k1eCCh7Bw7f5sDdUx44RligsrcCkwAdfLOu/Ffz9M9pUQZHf1rT6Z7SgAPKf/YwWFBRA4bPrAEBBQRGZHz4AAH7oYoMzgSdgbWePsrJS+K9ZBUJ4AqMvTFBVVkBhsWChWlBcgubaan8/roiCIsHHC4tLoK4s+G9l0vdWpDVUvXvulJT+GVs3MTER2lY9tFpeXo4zZ85g2LBhMDc3R1xcHDIzM/nXjhw5EiYmJpg+fTq2bdsm8A60NnJycgIveMbGxvzvm5CQgJKSEujr64PL5YLL5cLFxQVv3rwBANjb20NeXh779+9HQUEBjh49ik+fPsHIyIj/9d69e4c1a9bA0dERvXr1AgCB7LXx8PBASEgISktLUVlZiYsXL2LEiBEAgPv37yMuLg5KSkr8fMuXL+fnq4t/P1cWFhZ49uwZgKrhmlatWvELOwAwMjJCq1at+EMwwkRHR2PChAmwsrLC7t27a/x7nZ2d6z2/ztvbG/fu3cOzZ89QUVGBoKAgeHh4iLx+3bp1UFFREfhYv+eI2O8RHBqNH4ZO4H9UVlai6F+/oIqKP0FVmVuv7AAgIy2NdXMmI+JeAqyGTsDygIOY6D4QTeTloKpU/68niopyVY9E0We9O0V/D6Mr/2uRjoqKisAUhKrPK4KqiqpAGyEEW7dsgUmbNnDo3v2LZQWAS9fD0bnfSP5HZWUliosFJ2sXFRdDVbn+c3ESU5/hRlQMfKeM+0JpBQWHRuGHIeP5H5U8YfdLcYP+/8pIS2Pd3ClV98uQ8X/fL4O+yP1y+coV2NrZ8T+q7nMh94GI0Q9lFZUa1xcWFkJFVZW/EOzz+6p6GkddR1PqouDvIdamsv/8/lL4e45cQWnN4dc90c9x91UOnmQWISDiGT4WlaGrUVWP2JAOemjdTBEzziZg6ul4pH4oxObBFmgi83WmkispV/VOfSr+p+gv/nu6Q/Vj1ZSVlVFcLPjmoKiokP+zPNFnOmRlZTGkb09MHT8Og4dVvTZoaYuee/gt5OQVgfuvNzrKik2RnVfEf1xJUfBxJcUmyMpr2Ju4r4EOy1b5T+MdUlI1f4iq23g8HgYPHoyCggKsXbsWtra2mDBhgsC8r4KCAqSlpYHL5fLn6X2JLIQQyMjI4P79+wLt1X/W0tLC1KlT4efnh8mTJ0NNTQ1HjhyBmlrVu5PExET06NEDkyZNwtGjR2FgYCD03yqOg4MDFBUVERYWBi6XCx6PBycnJ34+a2trHD16VOBzqt8pN0RxcTG4XC7/64vC4/GEtvv7+2P79u3YunUrtm/fjtOnT8PX17fBeappa2tj4MCBOH78OBwdHSEvL49u3bqJvH7hwoWYM2eOQJtsuvjtHZxtrWDZ7p9hx6RnL5CVm4fKSh5/qO1Ddi6aqTVsJXNn87a49dt25BYUQl1FGUE3o6Cn1UxgXtt/VT1c9vFjJr/nMzMzE0pKSpCXlxe8VkMDHzM/CrRlZmYKvDkBgNOnTiEqKgonTgZ+0awA4GRvjQ5m/yweSn6Sho85eaisrOS/8fiQlY1maqr1/tqBwddQ/KkEg8fPBPDPPdt18BgsmjER/V3+W6HqbPPv++VlzfslK7dB2QGgs3k73DoWgNz8QqirKiPoRuQXuV8ce/RAh79HSAAgOTkZWVlZgs95ZqbAsOHnmjVrho+fvWGrqKhAdnY2mmlooEmTJuByuQJv6Kr/rK6u/p9yf656OFZdUQ4fi6r+rKEoh4LSCpTX0uXCI8Cb3E9opljViz+4gy62hD0FjwBv80qw5o8UHPH6Ad1aa+BGSt3fiNdV9ZSJrI8foaVd9TP6MTMTXCE/o+oaGsjKEvwZzcrMRCvDqp9RTS0t7Dl0FLm5OVBWVkHmhwzweDxo6+h+8dz18T4rD7qaqgJtupqqSEp7CwDIyMqDTrN/fo9KS0tBS11wqJZih682mSU5ORkhISFIS0vj9yD9u7BYsWIFevToge7du2P69OlwcnKqMdzUEB06dEBFRQU+fPggtJDIzc1FQEAA3r17h9LSUmhoaAj0hB0+fBhmZmZYs2aN0NzVvVf/7j35nLS0NEaMGIErV65AUVERQ4cO5X+epaUlDh48CC0tLSgrf5m5CuHh4ejQoWpFU8eOHfHixQu8ePECrVq1AlA1z/DFixfo1KmT0M/ftGkTNmzYgOHDhwMQXQSKIyMjg5KSEvB4PIFieOLEiZg7dy5KSkrg7u4utlCWl5ev8YuSV8uQLFehKbifDQkqKTZFWXkFHqY8RSezNiCE4M+ERIwZ1Kve/6ZqUlJSUFep+n91LSIG/brb1PIZ9aOsrIw2bdogNiYG5uZVL+B378aiSxfrGtd27tIFv2zejA8fMqClpY3S0lIkxMfDw2Mk/5qIiAhs27oF6zf4Q19f/4tmBQCuogK4nw0hKykqoLy8HA+TnqCTeTsQQhDz4BG8htR/peucSWPh4/1P727C4xTMWbkR5/ZvgTL3v/eW1siuoCDkfnmMMYN6N/h7SElJQV317/sl8k/062H733P/3cv/+d/Ly8vx6NEjdOzYEYQQ3I2NxShPT6Gf36VzZ5w6fRqEEHA4HDx69AgVFRX83wldOndGTEwMeveu+nfHxsbC1NT0i/2OAoDCsko8+1iEjs1VkPqh6o2+ZXMVPHxTszhQkJPmD9kCgLQUBy3UFHD3VQ4AoImMFCp4/xSEFTyC/JIKKH6lOZpKysowNmmDe7ExMG1ftYjl/r27sOrcpca1nTp3wc6tvyDzwwdoammhtLQUfz1MwBB3wVELVdWqDoWwG9fR8YfONebpfWu37iZjk+8o6Gmq4m1mLuTlZGDf0Ri7T90EAITfTcYUdyf+9dbmRpCVkUb0g6+/XVFd0WHZKl9tK5TqIu3IkSNITEzEunXrcP6zTVn/+usv7NmzB6tWrcKECROgoqKCVatWfZHv3aJFC0ycOBFjxozBlStX8PTpUxw7doy/wKGwsBCfPn3C77//joqKCnz8+FFgnheXy8WDBw8QFRWFO3fuYNCgQQLFn5aWFrhcLg4fPownT54gJydHaA4PDw9cuXIFly9fhru7u0C7rq4uhg4ditjYWCQlJWH9+vV4+bLuK6Vu3bqF/fv3IzU1Fdu2bcPx48exaNEiAEDXrl3h7OyMUaNGITY2FjExMRg5ciR69uzJn2+npKSE3Nxcfo8pl8tFUFAQHj9+jBMnTgjsf1VXxsbGKCoqwtGjR/H48WN+D2LPnj1RUFCAAwcOiB2S/VLUVZTRu5s11u87jqRnL7H9t3PIyStA/x5V//bo+4/QY8xM/uazJaVlyMzORXZe1XORm1+IzOxclJVXgBCCjb+exB9Rd/E8/R12HDuPhynPMLK/yxfPPcLdHUePHMHdu3dx88YNXLl8GcNGDEdOdjb69+2L4OCLAIC2bduhQ4cOWLtmDZ6kpmLD+vVQU1OD3d8Le6IiI7Fgni/m+s6DuYUFPn78iI8fP9ZYmPElqauqoFcPe2zY9SuSnqYh4NAJ5OTmod/fvWzRdx/AacR4/mbFJaWlyMzOQXZu1TymnLx8ZGbnoKy8HKrKStDRbMb/UPu7SNLRbAYFEXvL/bfsyujtYI31+44h6dkLbP/tbNX94lj1fEbHPUQPr+k175e/s+fmFwjeLwdO4I+oWDxPf4sdx87hYfIzjOzv+uVzq6ujp6srNm7ahOTkZOzctQs5OTno26cPAOD27dtw7dkTT55WbeDdt18/ZGdnY9fu3UhOTsbGTZvQu3dv/rDrCHd3BF+6hBs3byImNhZHjh4V+L31pVx5/B7DOjZHBz1ldDVUh0sbTYQkvodyExkcHv0DXNtWzWle0rsdZvVoDQs9ZRioNcWsHq0hI8VB2JOqHrGIZ1mY7mAESz1l6Co3gecP+tBRlse9V8J/H38JbsNHIPDYUdy/dxe3Qm/ij5DLGDR0OHJzcuA+qB+uXg4GAJi0aYv2Fh2wef0aPHuSim0bN0BFVQ02dlX31NXLwTh1/BhevXyB69eu4reDB+A1ru5zzutLTVkR2hrKUFSQh6yMNLQ1lNFMTQnN1JTwJMQfYwZ2BQAkpL7GnYSn2Ll4LCxM9LHNzwuZOQX4/XbVqvfAq39CS10Zy6YOhmUbA2zyHYkzv8fyh23ZgA7LVvlqxV2rVq34Q32DBg1CeXk5f/UlIQTTp0/HhAkT0Lp1a8jIyGDnzp3YvHkzHj9+/EW+/44dO+Dl5YWZM2fCysoKBw4c4L/r1dfXR8+ePTFhwgQ0b94cOjo6aNKkCQYOHIhPnz5hxowZsLe3R58+fTB//nzMnTsXZmZm/K8tJyeHgIAAnD9/Hl27dkW0kI1mAcDOzg7l5eXIyMgQWB0qJyeH0NBQ/pBljx498OBB/SYCd+zYEVFRUbCxscG+fftw6tQp/vfgcDg4d+4cLC0t4ebmhsGDB8PKygpnz57lDw317duX34MJAHv37sXDhw/h6OiI69evY9++ffXKAwA2Njbw8fHBrFmzMGjQILz7e+WXtLQ0PD09oaCgABubL9vjJcrKWeNhoKuN/y1ch6i4h9i/ah7UVKrmf/F4BIQQfvF5NeJPdPeaAfeflgEAvP3WorvXDMQnPQGHw4G5iRH2BF7EiFlLEfc4BSc2L4WG6pffrHrI0GEY7TUGS39ejIDt2+C3cBFsbe3AIwQEBOSzXorNW7aCV1mJCT+Ox+tXr7Br9x7+ZtK+c+egrKwMa9esRk8XZ/Tu6YrePV3xxx+/f/HMn1sxdxoM9HQwfu5SRN19gH3+y6H2d29n9b+B9/e/4WpYFByH/w8jfeYBAP43Zwkch/8P8Y+Tv2pGUVbOmgADXS38z28tou49xP7VC/65X8i/75c76D56Gtx/qtrp33vBGnQfPQ3xSalV90sbI+w5GYQRM5cg7q8UnPhl+Ve5XwBg6bJlMNDXx8RJk3A7Ohq7du/mTy+pzkv+7oXXUFfHrp07ER0VhYmTJqFly5ZY8vfiOADoam+PhX5+2Lp1K5YsWQLvsWMxZPDgL575WlIGghLewtfZBONsW2JnZBoepOdB6u/fTRxU/Xfd9RRU8Ah8uhlhyxALqDaVhd+lxyj8e27e/tsv8OfLbMx2MsHOEZawMlDF0pAkvMn7em9iBg4eCndPL6xdvgR7d27H7PkL0cXGFjweD4QIjnis2fgLeDxe1ekUr19h845dkJGp+hk1adMOf0ZHYsq4sQg8dhSLV6xGF5v/3rsryunN0/D6xlbMGdsHHdoY4PWNrbh9bAmkOBxwwIGU1D9TBobPDoCUtBRu/uoH4xba6Dd1MyoqqnpQM3MKMGD6FvTuaoHf983Dk5cZ8FlzVNS3ZQRdLVuFQ77mmScsdezYMZw6dQpBQUGQlpZGeXk5IiIi4OrqisjISLFzwthg3LhxKCws5G8rIwn69OkDe3t7LF0q/ugbYXjPJONIN2GKdS1qv4iFmuS8YDpCg0mVsqcXoT7KdNszHaHBhh75eqtUv6aDoyyZjtBgLRxnMB2hQcoeHPyqX3+9YsP33PMrYs/w8n/FrqMFADx8+LDGvnGfu337Nn9uWUMlJibixYsXuHr1Ktq2bYvnz58jMDAQenp6//lr/1c7d+7EggULhD6moqJSrxW1bPDq1StcvXoVDx48wJkzNU9boCiKoqgv5XvrgWso1hV3bdu2Fbtdh4GBwX/+Hn5+fvjw4QMmTJiA7Oxs6OnpwcXFBVFRUV908nBDjB49mj+h+d/qu2KXaaWlpejUqRM0NTVx8uRJgS1zKIqiKIr6OlhX3MnLy3/13fSVlZX5p2iwjaqqaq37Sn1+EgebycvLf9Hd7SmKoihKnO9tYURDsa64oyiKoiiKaoj6b+L1faLFHUVRFEVR3wXac1eFFncURVEURX0X6IKKKrS4oyiKoijqu0B77qpI1vJLiqIoiqIoSizac0dRFEVR1HeBDstWoT13FEVRFEV9F5g6W/bRo0eYMGEClJWV4evrW+v1t2/fhrW1NTQ0NODl5YWiIsGTdfbt2wcTExMYGBhg9erVqO9hYrS4oyiKoijqu8DU2bIJCQkoLCxEkyZNar32/fv36NOnD/r06YMbN24gNTUVEyZM4D8eEhKCGTNmYP369Thy5Ag2b95c7/PeaXFHURRFUdR3gameOy8vLwQGBsLMzKzWa48fPw49PT2sWLECnTp1wtatW3H27Fl8+PABALB7926MGzcOw4YNg7OzM+bNm4fdu3fXKw8t7iiKoiiK+i4w1XNXH+Hh4XBxcQGHwwEAWFtbQ05ODtHR0fzHXV1d+dc7OzsjISEBOTk5df4etLijKIqiKKrRKy0tRX5+vsBHaWnpF/8+GRkZ0NHR4f9dRkYG2trayMjIQFFREQoLCwUe19PT439enRGKasRKSkrIsmXLSElJCdNR6kVScxMiudklNTchkptdUnMTIrnZJTX3l7Bs2TICQOBj2bJl9foaPXr0IHPnzhV7jbGxMdmyZYtAW4cOHciaNWtIeno6AUAePHjAfyw7O5sAINHR0XXOQXvuqEattLQUK1as+Crvzr4mSc0NSG52Sc0NSG52Sc0NSG52Sc39JSxcuBB5eXkCHwsXLqxx3bFjx8DlcvkfL1++rNf3UVdXR0FBgUBbfn4+NDQ0oK6uDgACj+fn5wMANDQ06vw96D53FEVRFEU1evLy8pCXl6/1ukGDBsHW1pb/9+ph07rS0dHBu3fv+H+vqKjAhw8foKOjg6ZNm0JZWVng8bdv3wIAtLW16/w9aM8dRVEURVFUHSkrK8PY2Jj/ISsrW6/Pd3Jywo0bN/h718XExKC8vBzdunUTeLxaaGgorKysoKqqWufvQYs7iqIoiqKo/+D9+/d4//49ysrKUFRUhPfv3yMvLw8A8Mcff6B58+b466+/AACenp7IyMjAsmXLEB8fj9mzZ8PDw4M/7Dp16lQcOXIE58+fR2hoKDZt2gQfH5965aHFHdWoycvLY9myZXXqimcTSc0NSG52Sc0NSG52Sc0NSG52Sc3NNF1dXejq6uLOnTvYs2cPdHV1MWvWLAAAj8cDIQQ8Hg8AoKWlhWvXruHq1atwdnZGmzZtsHfvXv7X6t27N3bs2IH58+fD29sbvr6+GD9+fL3ycAj5jzv3URRFURRFUaxBe+4oiqIoiqK+I7S4oyiKoiiK+o7Q4o6iKIqiKOo7Qos7iqIoiqKo7wgt7iiK+qoKCwuZjkBRFNWo0OKOoqivSktLCyNGjMC5c+dQUlLCdByK5caOHYuQkBCUl5czHYWiJBYt7qhG59WrV0hKSuL/PSIiAsOHD8fcuXP5Z/ixUWVlJT7fuejTp0+4fPkyEhISGExVu/DwcLRu3Rp+fn7Q0tKCl5cXLl26hLKyMqaj1erZs2dMR2gwSS2SuFwuJkyYAB0dHUyYMAE3btzg7w/Gdt27d8euXbuQkZHBdJR6CQ0NlZjnmKobWtxRjY6vry/Onj0LAMjJycHgwYOhrKyMS5cuYfr06QynE83f3x+LFy8GABBC4OrqCg8PD1hZWeHAgQMMpxPN2toa69evx5MnTxAZGYm2bdti/fr1aN68OcaPH4/o6GimI4rUpk0bdOnSBZs3b8br16+ZjlMvklok7dq1C2/evMHFixehrKyMyZMnQ1dXF9OmTUNkZCTYvDXrgAEDcPToURgYGMDV1RUHDhxAdnY207FqNWzYMOjp6WHGjBmIiopiOg71JRCKamTU1dVJUlISIYSQ5cuXk7FjxxJCCLl16xbR0NBgMppYbdq0IVFRUYQQQs6fP086dOhAiouLyc6dO0nr1q0ZTld3kZGRZNq0aURDQ4PY2toSTU1NMnDgQPLq1Sumo9Xw/v17snv3buLq6kpkZWVJ165dyfbt28m7d++YjlYnPB6PREZGktmzZxMjIyOipaVFfHx8SEREBOHxeEzHq7Pjx48TLS0twuFwSJs2bcj58+eZjiTWq1evyNatW4mzszPhcrmkb9++5OjRoyQ/P5/paEKVlZWRkJAQMmHCBNKsWTOir69P5syZQ2JiYpiORjUQLe6oRkdfX58cPXqU5ObmEh0dHZKcnEwIISQuLo7o6+sznE40ZWVlEhsbSwghxNbWlpw5c4YQQkhycjJRVlZmMlqtkpKSyOLFi0mrVq2Irq4uWbhwIUlNTSWEEJKfn0/Gjx9PXFxcGE4p3sePH8nRo0eJp6cnadasGXFyciLHjh2jRdJXkpqaSpYtW0aMjY2Jvr4++fnnn0lycjI5efIkMTQ0JH5+fkxHrNWnT5/IoUOHiJaWFpGRkSHq6urkl19+IaWlpUxHE6miooJERESQRYsWEW1tbWJoaEhWr15NCgsLmY5G1QMt7qhGx9/fn3A4HMLlcsmPP/7Ib587dy4ZM2YMg8nEc3NzIxYWFmT06NHE3Nyc337kyBHi4ODAYDLxrKysiLS0NOnXrx+5cOECKS8vr3HNs2fPiKKiIgPp6q6wsJD89ttvpH///kRFRYWMHDmStG/fnlhaWpKHDx8yHU8kSSuSAgICiI2NDZGVlSVubm7k0qVLpKKiQuCauLg40qxZM4YSildZWUmuX79OvL29iZKSErGzsyMHDhwgBQUF5M6dO6Rz584Cv3fYJi0tjaxatYq0b9+etGjRgvj5+ZE+ffoQHR0dEh4eznQ8qo5ocUc1StHR0eTq1asCLxorV64k6enpDKYS78OHD2TixInEw8ODJCYm8ttdXFzIlStXGEwm3sqVK8nr16/FXlNaWsofcmaTiooKcvXqVTJ69GiiqKhIOnfuTPbs2UPy8vIIIVUv5EuWLCGdO3dmOGlNkloktW7dmqxdu5a8fftW5DUfP34kPj4+3zBV3cyZM4fo6ekRDQ0NMmvWLPLo0aMa1yQmJhIVFZVvH06MrKwssnv3bmJvb0/k5eXJ8OHDybVr10hlZSX/mj179hBTU1MGU1L1QYs7ipJwJSUlTEcQy8nJieTk5NRoT01NJb179/72gepBS0uLqKqqEh8fH3L//n2h1zx79oxwudxvnKx2klokHTx4UOg9/f79e7J//34GEtWds7MzOXHihNifyZycHLJhw4ZvmKp2srKypG3btsTf359kZGQIvebVq1esnpNMCeIQwuKlRxT1haxcubLO1y5duvQrJqmfV69e1fnaFi1afMUkDSctLY33799DU1NToD0mJgaurq4oKChgKFntfvvtNwwfPhxNmzat8VhlZSWkpaUBVP1/Ytvzf+jQIXh6ekJeXl6gPSMjA5cuXcKECRMYSiaetLQ03r17By0tLYH2p0+fwsbGBllZWQwlq93KlSvh6+sLBQUFgfbXr1/j119/xfLly5kJVovIyEg4ODgIfez9+/fQ0dEBAJSVlUFOTu5bRqMaiBZ3VKPQpUsX/p+Li4uRlpaGdu3a8dsqKyuRmJiI0aNH48iRI0xEFEpKSgocDqfW6zgcDioqKr5Borpzd3cHAJw9exYDBgxAkyZN+I8VFxcjJiYGnTp1wh9//MFUxFoZGRnh7t270NDQEGhPTk6Gh4cHq/cYlLQiKSQkBEDVdiLHjh2Dqqoq/7Hi4mIEBgYiLi4Oz58/Zyhh7UQ958nJybC1tUVubi4zwWohKndiYiLc3Nzw5MkThpJRDSXDdACK+hbu3r3L//OsWbPQokULzJ07V+CamTNnQl9f/1tHE+vRo0f8P58/fx737t0T6IWsqKjAxIkTERAQwEQ8sWxsbABUFXcKCgoCvV8qKirw9fXFxIkTmYpXJy9evBC6N1x6ejpri4zqIokQghs3bggtkpSVlRlKJ9qCBQsAVL1R8fLyEnhMWloaxsbG2LNnDxPRarVr1y4AVc/5oUOHoKSkxH+suLgYFy9eRPPmzZmKVytCiNA3kXfv3sXHjx8ZSET9V7Tnjmp0dHR0EBERgTZt2gi0P378GG5ubnj69ClDycSzsLDAkSNHYGVlJdAeGhqKZcuWITIykqFk4jk5OeHChQsCRQbbdenSBRwOB3FxcejQoQNkZWX5jxUXFyMlJQVDhw7FqVOnGEwpnIWFBYCqXpd//3qvLpK2bt2K3r17MxGvVlJSUnj37h20tbWZjlJnAwcOBFBVWDdv3pw/XA8AMjIyMDY2xpo1a2r87DJNU1MTHA4HWVlZUFNTg5TUP+calJSUoKioCD4+Pqx880iJR3vuqEZHSUkJd+7cqVHcpaWl4dOnTwylqt3bt29RWVlZo11TU1Ogh49twsLCmI5Qb35+fiCEwN3dHa6uruByufzHql+shwwZwmBC0arvBUkskgDg+fPnNYYH2e7SpUsAqp7ze/fuSUz+a9eugRACa2trLF26FCoqKvzHqu/z6h54SrLQ4o5qdH766SdMnToVL168gIuLC2RkZBATE4M1a9Zg7NixTMcTqXfv3vDx8cHp06dhaGgIAMjMzISfnx86d+7McDpBI0aMgImJCdauXQtnZ2ex14aGhn6jVHU3bNgwAIC3tzeWLl0qMMwmKSSpSLp48SKaNWuGrl274tatW2KvZfPPqCQc7/a5H374AQCwbNkyTJgwocZCEEpy0WFZqlE6ePAg1qxZw583paGhgalTp2LJkiUCQ3BskpOTAzc3N9y+fRutWrWCjIwMnj9/DgMDA1y7dg3GxsZMR+SbN28eWrRogRkzZsDX11fsopCNGzd+w2Tft8+LpKNHj4q9lk1FUuvWrWFqaorLly+jWbNmIu8XDoeDDx8+fON04u3YsQO6uroYNmxYravy2bQSv7S0FLKysgJDsdT3gxZ3VKNTUFDA74kpLCxERUWFRM0Hi4iIwKNHj1BeXg4TExP07t0bMjLs7YT//PmWBDY2NjAzM8OhQ4dgaGgotjBNS0v7hslqJ6lFUklJCWRlZQXmqkkKGxsbmJqa4vDhw+jcubPY5zw2NvYbpxOtdevWMDMzw6VLl2pdlS9sOgjFbrS4oxoddXV1xMbGsqqnqy7Gjh0Lf39//p5TkkLSnu+AgADo6upi+PDh2LFjh9gXvWnTpn3DZLWT5CKpmqTe55LmwoULaNasGRwcHHDlyhWx1/bv3/8bpaK+FPa+3aeor8TBwQFhYWESU2xUi4uLQ1pamsS96Ena8z1jxgz+n11cXNC2bVuJGbr6fC9BSS2SJPU+B4Du3bvjxIkTrNtSSZjPFwTJysrCwcFB6GbdlGSiPXdUo5OcnIyhQ4eiT58+cHV1rfF4v379GEhVu99//x0rV67Ejh070KlTJ6bj1JmkPt9A1VxMYSurJUH79u2xf/9+2NvbMx2lXiT1PgeqhmhXr16Nnj17Mh2lXrS0tBAZGYm2bdsyHYX6QmhxRzU64nphOBwOa+eX9O3bFwkJCfjw4QPU1dVrPM6mOVSfk9TnGwDGjRsHY2Nj/Pzzz0xHqTdJLZIk9T4HgPv372Pq1KlYvHix0DcybF2NOm/ePHz69Anbt2+XmF5qSjxa3FGUhKjtWDRvb+9vlKTxiIiIwKRJk2BmZib0xdrHx4eBVHUjqUWSJN/npqameP36NYqLi4XO1WTrG5nAwEAsW7YMSkpKQrcu8vf3ZyAV9V/Q4o6iKEqE6v0EheFwOKxbLfs5SS6SJFVte/T16NHjGyWpHycnJ5GPcTgcVu5FSYlHizuqUYqJicHy5csRHx8PHo+HTp06Yfny5bC1tWU6mliVlZU4e/YsP7eVlRWGDx/O6tWRhBCcPXsW4eHhyMzMrPH46dOnGUhFsdmnT5/w119/Cb1f2DxHk6LYghZ3VKMTGhqKPn36YPTo0fwhiNDQUJw8eRJXrlyBi4sLwwmFy8zMhIuLC169esXfWT4uLg4tW7bEjRs3oKmpyXBC4ebNm4edO3fCzs4O9+/fx+DBg8Hj8XD+/HksXboU8+bNYzrid0sSi6Tr169j1KhRyM7OBofD4Z+PKyMjAzMzM8THxzMbsBZv3rzB7du3hT7nbB7Gp74vtLijGh1bW1t4eHhg9uzZAu1bt27FiRMnWLXR6Oe8vLyQm5uLEydOQFlZGQCQn58PT09PqKqq4tixYwwnFE5bWxuHDx9G3759YWFhgYSEBEhJSWH9+vWoqKhg9WKFkpISBAQEiOx1ZOu9AkhukdS+fXv07NkTc+bMQe/evZGUlAQejwc3Nzf4+vqydmgTAI4fP47//e9/kJaWBo/Hg56eHng8HjIyMtCnTx8EBQUxHVGo7OxsLFmyROR9ztb5mZQYhKIaGSUlJZKSklKjPSUlhSgpKTGQqG40NTVJfHx8jfb4+HjSrFkzBhLVjYGBAXn8+DEhhJBhw4aR27dvE0IISUxMJCYmJkxGq9W4ceOItrY2GT9+PGnWrBlZvnw5WbJkCWnWrBn57bffmI4nlpmZGZk1axZ5+fIladeuHSGEkMrKSjJgwAASHh7OcDrRlJWVyZMnTwghhDg6OpL09HRCCCF37twh3bt3ZzJarVq3bk02bNhASkpK+M85IYR4e3uTs2fPMphMPDc3N9KuXTuycuVKoqurSw4fPkx+/fVX0rJlS/LHH38wHY9qALrmmWp0TE1Nce3atRrtv//+O6v3eeJyucjNza3RnpOTA0VFxW8fqI5sbW1x8+ZNAICHhwcWLlyIly9f4vTp08jOzmY4nXjBwcE4d+4cfv31V+jp6WHRokVYuXIlVq1ahQcPHjAdT6z09HRMnz4dLVq0gI6ODt68eQMpKSksXryYVWec/pulpSVSU1MBAD179sSePXsAVM03Zftznp2djaFDh0JeXh4GBgZ4+vQpAGDWrFnYsGEDw+lECwsLQ2BgIJYsWQJdXV14eHhg/PjxWLduHQIDA5mORzUALe6oRmf16tWYN28eJk2ahOPHj+P48eOYNGkS5s2bh7Vr1zIdTyRPT09MmjQJt2/fBiEEhBDcvn0bU6ZMgZeXF9PxRJo/fz5ev34NoGpXfEVFRRgaGmLlypVYuHAhw+nEU1NT45+La2FhgfDwcACAo6Mjzpw5w2Cy2klqkeTl5YUDBw4AAMaPH4+9e/fCyckJ/fv3R7du3RhOJ56VlRV/uHvgwIHYtGkTAODt27dITk5mMJl4+vr6KC8vB1D1b7h69SqAqnvo8uXLTEajGorprkOKYkJUVBTp2bMn0dTUJM2aNSOurq4kMjKS6VhilZWVkUmTJhEpKSkiLy9P5OXliZSUFJk4cSIpKytjOl6d8Xg8kpiYSF68eMF0lFqNGzeOrFmzhhBCyNWrV4mFhQUJDw8nU6ZMIXp6egynE2/v3r1kyJAhhBBC3r17RzQ1NYmjoyNRUVEhffv2ZTidaOXl5SQrK4v/98TERDJ//nyycuVKkpOTw1ywOjhz5gwZMWIEIYSQnJwcYmRkRFq1akWkpaWJp6cnw+lEmzFjBpk3bx4hpOp3o4GBATl8+DAZPHgwMTIyYjgd1RB0QQVFSZjXr1/j0aNHIISgQ4cOMDAwYDpSg1RWViImJobVx2Olpqbi0qVLmDt3LgghmDZtGvbt2wdZWVkcOHAAo0ePZjqiSBUVFcjPz+dvYJyUlITDhw+Dy+VixowZUFVVZTbgd4gQgoqKCsjKygIA3r9/jwsXLoDL5WLkyJH8drZ5+/YtwsPD4enpCaBq0+LNmzeDy+Vi7969QjfwptiNFndUo2Nvbw9nZ2e4uLjA3t4e8vLyTEeqk/3798PZ2RmtW7dmOkqt5s+fX+s11S98BQUF3yDRl1NQUAB5eXnIyckxHeW7kZiYWOs1r1+/xuzZs+t0LRtVVlayej9K6vtCizuq0dm+fTsiIiIQGRmJvLw82NnZwcnJCc7OzrCxsWHtu2srKyskJCSgefPm/LxOTk5o0aIF09FqELfjfTUlJSWMGDECY8aM+QaJvqzS0lKcP38eo0aNYjqKAEktkqSkpAS2axGlT58+CAkJ+Uap6qYueV6/fo0FCxYIXRDFZkVFRQgICICfnx/TUah6osUd1ailpKQgMjISsbGxuHPnDp4/f47CwkKmY4mUl5eHqKgoREVFITIyEg8ePICuri6cnZ2xb98+puN9F9zd3Wu95uPHj7h//z7rXqwltUh6+fJlrddwuVxoaGh8gzT1IyVV+7pELpeL0aNHY/fu3d8gUd106dJF6Pm3n8vNzUVubi7d504CyTAdgKKYUl5ejszMTLx9+xbPnj3DixcvYGJiwnQssVRUVNC/f3/0798fL1++RGhoKHbv3o1ff/2V9cVdZWUlnj59Ch6PhzZt2rB2iKou28ro6OhgyZIl3yBN/Tx//rzWa9hYJLVs2ZLpCA3G4/GYjtAgAwYMqPUaJSUl9O3b9xukob402nNHNTobNmxAWFgY7ty5A2NjY3Tv3h2Ojo7o3r071NTUmI4nUkZGBsLCwhAaGoqbN28iLy8P3bt3R48ePeDo6AhLS0umIwpFCMGmTZuwcuVKFBcXgxACRUVFLFmypE5z86jG5+XLl1i/fr3A2c9+fn5o1aoV09EoSiLQ4o5qdOTk5NC8eXNMnz4dPXv2hIWFRa3DE2wgJSWFli1bYvr06ejVqxfMzc0lIndAQABWr14Nf39/gbN8FyxYgEWLFmHmzJkMJ6zdu3fv8PDhQ/B4PFhaWkJPT4/pSHUiiUVSfHw8unbtCmtra/79EhYWhpiYGERHR6Njx47MBqxFTEwMli9fLvCcL1++HLa2tkxHq1VMTAw/t5WVFWxsbJiORDUQLe6oRqe4uBhRUVEICwtDWFgY0tLSYGdnB0dHR/To0QNWVlZMRxRq//79CAsLQ2RkJDQ1Nfm9dt27d2fdMNvnzMzMsG7dOri5uQm0BwcHY/78+aze3LW8vByzZs3C3r17+cPIlZWVmDx5MrZv3w4ZGfbObJHUIsnFxQU2NjY1NhRfvHgxbt++jbCwMIaS1S40NBR9+vTB6NGjBd7InDx5EleuXIGLiwvDCYUrKirCiBEj8Pvvv/ML/xcvXqB37944c+YMq0/AoUT4prvqURQLFRQUkGPHjhEbGxsiJSXFdJw6SUpKIrt37yZeXl7E2NiYdOjQgelIIqmqqvLPlv1cYmIiUVVVZSBR3S1dupQYGxuT8PBwUlFRQSoqKkh4eDgxMTEhS5YsYTqeWM7OzmThwoU12hctWkQcHR0ZSFQ3ysrKJDk5uUZ7cnIyUVZWZiBR3dnY2JBffvmlRvuWLVtIly5dGEhUN9OmTSPW1tYkLS2N35aWlkZsbGzItGnTGExGNRQ9foxqlFJSUrBv3z54eXnBzMwMY8eORUVFBXx9fZmOVquioiKkp6fj7du3+PjxI/Ly8pCRkcF0LJG6deuGrVu3CqzgJIRg69atrN7AGACOHTuGffv2oUePHpCWloa0tDR69OiBvXv34rfffmM6nlj37t2Dt7d3jfaxY8fi/v37DCSqm1atWuHu3bs12u/evcvKbX8+l5iYiP79+9do79evH6t7qIOCghAQEABDQ0N+m6GhIbZt24YLFy4wmIxqKPaOKVDUV6KlpYWsrCyYmJjAxcUFW7duhZOTE6sXUwDAggULEBERgbi4ODRt2hQ9evRAnz59sHHjRpibmzMdT6Rt27bB3t4et27dQvfu3QEAkZGRyM3NRXR0NMPpxMvPzxc65K2hocHqLXOAf4qktm3bCrSzvUhasGABJk+ejFevXqFHjx4AgFu3bmH9+vX883HZytTUFNeuXUObNm0E2n///fca/x/YhBAidP5uXbZ5odiJzrmjGp2jR4/CxcUFzZs3ZzpKvVSfquHi4oIuXbqwdisRYXJzc7Fz504kJCSAEIKOHTvCx8eH9QW1p6cnCgsLcfLkSf68o6KiIowePRoKCgo4ceIEwwlFO3HiBCZPnoyFCxcKLZKqj5pio5MnT2LZsmV4+vQpAKB169ZYsWIFqzMDwPXr1zFgwAB4e3sLPOdHjx7FpUuX0LNnT4YTCufj44MHDx7g1KlT/ML/1atXGDVqFCwtLbFr1y6GE1L1RYs7ihLCwMAAkZGRrF5VKIyfnx/mzZvH6gUWkuTDhw9wdnbG27dv8cMPPwAA7t+/D11dXYSGhkJLS4vhhOJJapFUraioCIQQcLlcpqPUWXR0NFasWIH4+Hj+G5lly5ahW7duTEcTqbCwEMOGDUNoaKjAggpnZ2ecO3dOop5/qgot7ihKCCUlJSQkJMDIyIjpKPWirKyM+Ph41uV++PAhDh06hGfPnoHL5cLS0hKTJ0+WiMPrKyoqcOrUKYFeR3d3d9YeUyeMJBZJCQkJePbsGZSUlGBubg5dXV2mI333oqOjBe7zrl27Mh2JaiBa3FGUEJJa3LEx96lTpzBq1Ch07doVFhYW/K1ocnNzERkZCVNTU6YjftckrUh6/fo13NzcEB8fj6ZNm6K0tBQA4O3tjV27dkFeXp7hhOKVlZXh6tWrAm9k6H5x1Df3zdfnUpQE4HK55NmzZ0zHqDc25jY1NSX+/v4CbRUVFeTHH39k9ZYc1YKDg8ngwYOJhYUFsbOzI5MnTyZPnjxhOlatXr16RTp16kQ4HA5RUFAg0tLSRFpamowfP56UlJQwHU+k3r17E1dXV/L06VNCCCHl5eUkNDSUmJiYkJkzZzKcTryHDx8SHR0d0rRpU2JqakpatmxJpKSkiLOzM8nNzWU6nkg8Ho9s376ddOzYkSgpKRFdXV3Su3dvEhERwXQ0qoHoUhiKor6qzMzMGhsYS0tLY+7cuXjw4AFDqepm+/btGDFiBLhcLry8vNCrVy8kJyfD3Nyc9St9J06cCA0NDTx58gRFRUUoKSnB9evXERkZyepj3/78809s2rQJrVu3BgDIyMjAyckJ+/btw6lTpxhOJ96UKVPg4uKCDx8+IDExES9evEBqaiqKioowY8YMpuOJtGTJEixduhQDBgzAgQMHsHbtWmhra8PFxQVBQUFMx6MagunqkqLYiI09YHXBxtzjxo0je/bsqdEeEhJCnJ2dGUhUd0ZGRuTgwYM12hcvXszqTWkJIURFRYXEx8fXaA8LCyPa2toMJKqbrl27krCwsBrtDx8+JEZGRt8+UD2oqKgI3bA7NjaWqKurM5CobvT19cmFCxdqtG/dupWYm5t/+0DUf0b3uaMo6qvq3bs3fHx88OnTJ4F9s/bv3w8HBweBbRZ8fHyYiChSSUmJ0I2Wx44di4CAAAYS1Z25uTlycnJqtGtoaLD6OKmVK1di0aJFCAkJEbhftm/fjhkzZqC4uJjfpqCgwEREkWxsbPDixQuYmZkJtHM4HFbPdZSTk4OJiUmN9n79+mHFihUMJKL+K7qggqKE2LVrF7y8vKCsrMx0lHqxt7fH6dOnoa+vz3QUvs93vReHw+EgLS3tK6epn9mzZ0NbWxt+fn4C7adPn0ZgYCDOnz/PULLahYaG4ueff65RJM2dOxft27fHpEmT+G1sKpJatGiB9PT0Gpvqks822q3+c2VlJRMRRbp79y7GjRuH48ePCzznS5cuhbW1NQYPHsxv+3cByKSVK1eioKAAGzduFGj/9ddfERUVhUOHDjGUjGooWtxRjdLNmzdx8uRJvHnzBocPH4a2tjZev34NoGqPO7YqLy9HTEwM3rx5g4EDB7LqRfl7tH37dixcuBATJkwQ2DT63LlzsLOzEzjpwd/fn4mIIklqkXTr1q06X1u9UTBbSEtLQ9xLKofDYeVzvnDhQmzevBl9+/YVuM9v3bqFjh07Cuybefr0aSYiUvVEh2WpRmfPnj1Ys2YNpk2bhuPHj6OoqAhA1Y7+8fHxOHnyJMMJhXv8+DEGDhwIOTk5PH36FKmpqTAyMsL69esBoEbvkiTx9PTEli1boK2tzXQUARcuXIC1tTUePnwo0G5kZISMjAz+mb7Cjm5iGtvPvhWlLgXblStX4OTk9A3S1A/bep7r6v379xg9enSN9kGDBjGQhvoSaM8d1ei0bdsWu3fvhrOzs8C+cElJSXBycsL79++ZjiiUk5MT+vTpg/nz50NZWZmfOyoqChMnTkRSUhLTERuMjfvzNQbVRZKk9QCzdbPuuti9ezdGjx4tcVM+KMlCt0KhGp2PHz8KPTi9sLCQlT0w1eLi4jBixIgaGbW0tPDmzRuGUlFdu3ZFeno60zEaZNSoUax9MyOOJPdJLFiwAB8/fmQ6Rr0ZGBjgxYsXTMeg6ogWd1Sj06tXL+zZs4f/dw6Hg8LCQixatAiurq4MJhOvTZs2SEhIqNF+7tw5Vk3ObmwePnyIsrIypmM0iCQXSZJKUp/z3Nxc8Hg8pmNQdUTn3FGNzvbt2+Hq6go7OzuUlJRgzJgxSE5Ohq6uLk6cOMF0PJHWrVsHLy8vfPjwATweD0FBQUhOTsbhw4dx7do1puNRFEVRLEGLO6rR0dTURFxcHM6cOYMHDx6AEIIpU6bAw8OD1YfB9+zZExcvXsTy5cuhqKiIdevWwdLSEmFhYfSAb4qiKIqPFndUo3Pz5k04Ojpi1KhRGDVqFNNx6sXW1pb20lGNHpvnxlIUG9A5d1SjM3ToUOjq6mLKlCkIDQ2VmHkkBgYGmDt3LmJiYpiO8sUNGDCA1acmfK8ktUiS1HlrFPWt0OKOanQyMzNx+PBh8Hg8jBo1Cjo6OhJR6Pn7+yM9PR09e/ZEixYtMHfuXPz5559Mx6qT7OxsHDt2DBs2bEB+fj6Aqhfo0tJSAMDJkydZt8ddY8DmIonH4yEjI0NoxvT0dLRq1erbh/oCVFRUBE6voKivge5zRzVqPB4Pd+7cweXLl3HlyhV8+PCB9VtDlJaW4vr167h48SKuXLkCWVlZjBgxAps2bWI6mlBRUVFwc3NDly5dEBoaiuTkZBgZGWHZsmUoKSnBhg0bmI74nyxYsADz588X2MWfTXg8HjIzM6GlpVWjpy4vLw9KSkqsKjZyc3Mxffp0nDlzBpWVlfzNuk+cOAEulysRG+ump6fjzZs3sLKyYvU83vqQ1CMZGyv2/ERTFAMqKytRVFTE/5CTk2M6Uq3k5eUxYMAA7N+/H5cuXUKnTp2wZcsWpmOJNGfOHGzevBnXrl2DvLw8v33AgAE4e/Ysg8nqJiUlBatXr8bUqVORlZUFACgqKkJOTg4AYMOGDaws7HJzc+Hl5YWmTZuiefPmeP78OYCqk1iCg4MBsLMXaebMmSgvL8eLFy/QpEkTfruioiLWrl3LYLLapaeno1u3bmjXrh3s7e35Rxru2rULv/76K8PpxLt58yYmTJiAvn378k9eef36Nf/f4OPjQws7CcKun2qK+gYKCgpw+vRpeHp6QlNTE//73/8gJSWF3377DS9fvmQ6nljl5eX4448/4OPjg+bNm6NPnz7Q1tbGH3/8wXQ0kZKTk9G9e/ca7Vwul18ssVVQUBDs7OyQnp6OgwcPIi8vD0DVEDnbj3uT1CIpJCQEK1euhK6urkBPo7m5ORITExlMVrtp06ahc+fOyM3NFTj1o02bNggICGAwmXh79uzBuHHjYGxsjPDwcIEjGefPn89wOqpBCEU1MnJyckRbW5tMnTqVhIeHk8rKSqYj1cnIkSOJiooKUVNTI+PGjSNXr14l5eXlTMeqlYODAzl06BAhhBAul0vS0tIIIYTMnj2bODk5MZisdhYWFiQoKIgQUpX92bNnhBBC4uPjiYGBAZPRaqWhoUGSk5MJIYLZnz59SpSUlJiMJpaxsTGJjo4mhAjmDgwMJKampkxGq5WKigp58uQJIaTmc87lcpmMJlabNm3IzZs3CSGCuRMTE4m2tjaT0agGoluhUI1O9Xma0tLSTEepF2lpafz222/o3bu3RAwfV9u6dSt69uyJlJQUlJeXY8eOHXj06BGio6MRHR3NdDyxXr58CQsLC6GPFRcXf+M09aOmpia0Z/TevXvQ19dnIFHd+Pj4YMaMGTh+/Dg4HA7ev3+PP//8Ez/99BOWLFnCdDyx9PX18eLFCxgbGwu037hxA61bt2YoVe0k9UhGSjRa3FGNwr1796CiogITExPIycmJLSqEDSGywbFjx5iO0CBWVla4d+8eNmzYgA4dOiAiIgKWlpbYvXs3q1/wgKpzY8+fPw9fX18AVVuH8Hg8+Pv7s37jaEktkmbPno3y8nJ06dIFRUVF6NatGxQUFLB48WLMmDGD6XhiLVy4EJMnT8auXbtACEFcXBwCAwOxdu1aVs+5qz6SsXpRlqQcyUiJRlfLUo2Cvr4+TE1Ncf36dbETyDkcDiorK79hMvFsbGxgZmaGQ4cOwdDQUOy76LS0tG+YrO4KCwvB5XKZjtEgaWlpcHBwQPfu3XH+/HkMHjwYjx49QnZ2Nu7cuQNDQ0OmI4rl7++PVatW8edQVRdJCxcuZDhZ7SoqKpCamgpCCP9NmSQ4ffo0li5ditTUVACAkZERVq1axeoN0zMzM+Hq6goFBQXcu3cPNjY2/CMZQ0NDoampyXREqp5ocUc1Cunp6WjatCk0NDT4L3SisGkz3YCAAOjq6mL48OHYsWOH2OJu2rRp3zBZ3TVt2hR9+/aFh4cH+vfvL3GFXl5eHnbv3o379++DEAJLS0tMnz4dqqqqTEerE0krksaMGQN3d3f06tVLYHW1pCkqKgIhRGLu94qKCoEjGS0tLVl/JCMlGi3uqEYnIiIC9vb2kJERnJWQm5uL27dvo1+/fgwla7jKykrWziG8c+cOgoKCcOHCBaSnp6Nv374YMWIEBg4cyKpCWpinT5/WmD8lKSS1SJo8eTKCg4NRXFyMQYMGwd3dXWLmmTo4OMDd3R3Dhw+Hrq4u03HqrPpIRrb+DqHqj26FQjU6Tk5OyM7OrtH+8eNHjB8/noFEdWNkZCR0gnxycjKsrKwYSFQ3dnZ22LBhA1JTUxEbGwsrKyts27YNOjo6GD58ONPxxGrTpg2srKywfv161g57i6KgoIBJkyZBS0sLY8aMwaVLl1BWVsZ0rFrt3bsXb9++RUhICHR0dDBnzhxoaWlh7NixuHz5MtPxxOrXrx+OHTsGAwMDODg4ICAgAG/fvmU6Vq0k9UhGSjTac0c1GtV7ZJmbm+PWrVsCG88WFxfj8OHDOH/+PGt/GUtJSSEjI6PG/JcbN25g6NCh/GO92K6wsBB//PEHDh06hJCQEFbNcfy3t2/f4uLFi7hw4QLCw8NhYWGBESNGYMSIEaxfDAJUHS92+/ZtBAUFISgoCJmZmfzesAEDBjAdr84eP36MxYsX49KlS6y+X6qlp6cjKCgIFy9eRGRkJKytreHu7o7p06czHU2osrIy3Lhxg5+ZEIKhQ4fC3d0djo6OrNvomqodLe6oRkNJSQnAP1tY/PvW53K52LFjB8aOHfvNs4nTpUsXcDgcxMXFoUOHDgJzYIqLi5GSkoKhQ4fi1KlTDKYU7/379wgODkZQUBBCQ0PRrl07eHh4wN3dXSKKJADIyclBSEgILl++jD/++AOGhoa4d+8e07HqRZKKpIKCAly9ehVBQUH4/fffoaGhAQ8PD6xatYrpaPWSlpaGOXPmSMRzDkjmkYxUTXQrFKrRKCgoAFDVAxYbGyvQAyYjIwMtLS1WTh728/MDIQTu7u5wdXUVmKAtIyMDY2NjDBkyhMGE4tnZ2SE2NhZt27aFh4cHfvnlF7Rr147pWPWmpqaGLl26ICMjA2/evJGYwk5YkbRo0SKmY4m0b98+/psAPT09uLu748aNG+jUqRPT0eosNTWV31t6//59dO/eHfv27WM6Vp1I4pGMVE20545qdG7duoWuXbvWWFDBdv/73/+wfft2fg+kpPj555/h4eEhcjNgtouNjeW/UKelpaFPnz5wd3fHoEGDWL0SUliR5OHhwfoiycDAgJ/V2tqa6Tj1smjRIgQFBSE1NRUODg7w8PDAsGHDWL+VyOdvAEJCQqCoqIgRI0bA3d0ddnZ2dCNjCUSLO6pR+HwT44iICLHXsnUTY0lSWloKWVlZiZ+ro6enh6ysLLi4uMDDwwODBw+GiooK07HqRFKLJEKIxBYT3bp1g4eHh8StlpWXl4eamhqGDh0KDw8PODg4SPzPbmNHizuqUZDUTYw/l52djSVLliA8PByZmZk1Hv/w4QMDqYRr3bo1zMzMcOnSJUhJSYl9sWbr8w0ABw4cwNChQ6Gurs50lHqTpCJp/vz5aNGiBaZPn17rivWDBw9+o1SNx40bNyTySEZKNMkal6KoBvrzzz/RtGlTAP/MvZM048ePR0pKCjw9PbF7926sW7cOlZWVWLlyJfbv3890PAGbNm1Cs2bNAADBwcESU2QAwJs3b6CgoAA1NTX06tULhYWFKCwsFHqtsPM4mfR5kfTjjz+KvZZNRdLz58/5hUV+fr5E3S8jRoyAiYkJ1q5dC2dnZ7HXhoaGfqNUtfsejmSkRKPFHdUofH5QurCNcwsKCqCoqMjqoYiwsDD+uaxBQUHw8PBAkyZN0LRpUwQGBqJnz55MR+T7fIGHuC032LgKz8bGht/L26pVK6GFRnWvGNt6HSW1SDpz5gz/z2fPnmUwSf21atWKPwRrZWUlMc/54MGD+fe5o6OjyOvYeJ9TtaPDslSjExQUhHfv3mHq1KkAgFmzZiEgIAAaGhq4ePEi7O3tGU4oXPv27XHkyBF07twZEydORL9+/TBkyBAkJibCyckJGRkZTEcUSlpaGu/evYOWlpZAe2JiItzc3PDkyROGkgkXGxsLFRUVtG3bFo8fPxZ7bfv27b9RqsZj/Pjx2LZtW42FQ8+fP8fatWtZ10stqST1SEaqbtjbTUFRX8maNWugra0NALh//z5OnTqF8PBwDB48GD/99BOz4cRwcXHB6dOnAQDjxo3DrFmzcOTIESxevJjVqzZFzf26e/cuPn78yEAi8aytrdG2bVsAVXsjmpmZoX379gIfrVu3Rnl5OcNJxRs/frzQKQjPnz/HxIkTGUhUN0eOHEFJSUmN9oKCAlbv5QgAzs7OyM3NrdH+5MkT9OnT59sHEkNfX5+/kXtcXBzk5eWhqKgo8FFeXo5bt24xnJRqCNpzRzU6XC4Xd+/ehampKUaMGAFbW1vMnTsXjx8/hq2tLWvn5L19+xbh4eHw9PQEAPj7+2Pz5s3gcrnYu3cvXF1dGU4oSFNTExwOB1lZWVBTUxMY8i4pKUFRURF8fHwQEBDAYErxRPU6pqamwtXVFa9evWIoWe2kpaXx/v37GttwPHz4EN26dWPdiSbz588HUDVfc+rUqQK9RcXFxbhx4wZUVVXx559/MhWxVqJOkYmJiYGrqytrf7eIus+fPn2Kbt26sXL6BCUenXNHNTrm5uZYtWoVnJ2dER0djSNHjgAAXr9+zerTEvT09PiFHVD1Ylj9gshG165dAyEE1tbWWLp0qcAWItWbL9vY2DCYULTi4mIQQkAIQXFxMf9Uk+rHzp49i0+fPjGYULTqe4IQguXLlwstkszMzJiKJ9Lnz2d8fLzA5rkyMjJwcnLCggULmIhWK3d3d/6ff/zxRzRp0oT/9+LiYsTExMDOzo6JaGJVH8lICEFKSopAT3r1kYxsnodMiUaLO6rRCQgIgJeXF8LCwrBz504oKCgAADZu3Ahvb2+G04lmb2+PK1euQE1NrcZje/bswS+//IL+/ftj8+bNrPiF/MMPPwAAli1bhgkTJvCfZ0nA5XLB4XDA4XBEFvxr1qz5xqnqRlKLpOoe3MuXLyM4OFjg7Ge2q36TcvbsWSgoKPBX5gOAiooKfH19WTkUXp2bw+HA0dFR5JGMlOShw7IU9bekpCS0adOGtXs9SUlJwcfHB5GRkTAzM8OmTZvQvHlzEEKgpqaGkSNHIjIyEpMmTcKsWbMYzSrpmxhnZWWBEAItLS2cO3dOYJ87GRkZGBoaQk9Pj8GEtas++1aSiiRJ5+TkhAsXLkBVVZXpKPUiaUcyUrWjxR3VaL179w4PHz4Ej8eDpaUl61+spaSk4OTkhFmzZuHYsWNIT0/H7du3kZ2dDU1NTeTm5iI4OBibN2/G/fv3Gc36vWxifOTIEYwcORLy8vJMR2k0CCE4e/asyM26qxcVUV+OpB7JSIlG/09SjU55eTlmzZqFvXv38nvpKisrMXnyZGzfvp3Vv+DWrFkDW1tbdOnSBfr6+sjOzuav2lRSUkLnzp3x9OlThlN+P5sY17bFDJs3MZbUkx7mz5+PnTt3ws7ODvfv38fgwYPB4/Fw/vx5LF26lOl4YpWUlCAgIEBkYRobG8tAqtr16NEDnz59woMHD4Tm7tevHwOpqP+Cva9iFPWVrF69GtevX0doaCi6desGAIiKisLEiROxcuVKrFy5kuGEwuno6PALjczMTKioqEBVVRUPHz7kz/HJzs5mxVBoXTcxZqPvZRNjtq7MrM3Ro0dx7tw59O3bFxYWFvj1118hJSUFU1NTlJaWMh1PrKlTp+Lq1avo378/YmNjMX36dFRWVmL37t3YsmUL0/FEun79OkaNGoXs7GxwOBz+3DsZGRmYmZnR4k4SEYpqZIyMjEhoaGiN9tDQUNKqVSsGEtWNr68v0dHRIVOmTCFGRkakVatWxMPDg3Tq1Ik0bdqUnDlzhkyfPp306NGD6agCHj16RMLCwvh/DwwMJD/88AMZOnQoSU9PZy6YCDExMSQ5OZkQQshff/0l9oP68gwMDMjjx48JIYQMGzaM3L59mxBCSGJiIjExMWEyWq3U1dVJVFQUIYSQDh06kLKyMkIIIbt37yZz5sxhMppYZmZmZNasWeTly5ekXbt2hBBCKisryYABA0h4eDjD6aiGYP4tPkV9Y/n5+UInmWtoaIg8Q5QN1q1bh5kzZ+L9+/dYsGABUlJS0KNHDwwbNgxJSUnYu3cvzp8/j3Xr1jEdVcC8efPw8OFDAFV79U2aNAmDBg3Cu3fv4OPjw3C6mj7fxPjfmxf/+4PNPn36JLBy9sOHD9ixYwcuXbrEYKra2dra4ubNmwAADw8PLFy4EC9fvsTp06eRnZ3NcDrx1NTU+CdrWFhYIDw8HADg6OgocMQa26Snp2P69Olo0aIFdHR08ObNG0hJSWHx4sWsHwqnRGC6uqSob23UqFFk4MCBpLCwkN9WWFhI3NzcyKhRoxhM9n1SVlbm94TNmTOHzJgxgxBCSGxsLFFRUWEwWe0iIiLIqVOn+H/ftGkT0dDQIFZWVvzeJbby9fUl8+bNI4QQUl5eTtq0aUOMjY2JrKwsWbVqFcPpRLt7965A7n79+hEOh0OkpKTIpk2bGE4n3rhx48iaNWsIIYRcvXqVWFhYkPDwcDJlyhSip6fHcDrRHBwcyJUrVwghhKxZs4b8/PPPhBBCoqKiiJKSEpPRqAaixR3V6GRkZJD27dsTNTU14urqSlxdXYm6ujpp3749ycjIYDqeWMnJyWTVqlVkypQp5OPHj4SQqsI0Ozub4WSiGRsbkw0bNpAnT56QZs2a8Ydib9++TVq3bs1wOvEcHBzI4cOHCSGEpKamEjU1NXLkyBEyaNAg4ujoyHA68Vq2bEni4uIIIYQcPHiQdOvWjVRWVpLTp08TfX19htPVHY/HI4mJieTFixdMR6lVSkoKvwDl8Xhk6tSpRFpamjRp0oQcP36c4XSi7d27lwwZMoQQQsi7d++IpqYmcXR0JCoqKqRv374Mp6MaghZ3VKNUXl5Ojh07RubNm0d8fX3JsWPH+PNj2OrChQtETU2NTJ48mcjJyZFnz54RQghZunQpmTRpEsPpRDty5AiRlpYmUlJSZPHixfz2cePGkenTpzOYrHZcLpff6/jjjz/yezTi4+NZ36Ohrq5OIiIiCI/HI+3btyfXr18nhFTNXVNTU2M4nWgLFy4U6FX/3J07d8jEiRPJhQsXvm2o/yA/P5+UlpYyHUOs8vJykpWVxf97YmIimT9/Plm5ciXJyclhLhjVYLS4oygJYWFhQYKCggghVUVHdXEXHx9PDAwMmIxWq9evX5OkpCSBtkOHDrG6x5EQQszNzcns2bPJ9evXiaamJv8FMCwsjJiamjKcTryxY8cSPT094uDgQLp27cpvDwgIIL169WIwmXhSUlLk+PHjZObMmWTPnj2koqKC/1jLli1Jx44diaqqKrl8+TKDKYUbNWoUycvLE/pYUFAQ6dmzJ9m6des3TlW7lJQUUllZKfSxkpISEh0dTQoKCr5xKuq/oMUd1SgFBweTwYMHEwsLC2JnZ0cmT55Mnjx5wnQssZSVlfkF3b+LOw0NDSaj1VlxcTEpKipiOkadhYSEEC6XS2RkZMjOnTv57QMHDuT34rFVcXExWbNmDVmwYAF59+4dv33IkCHkzz//ZDCZeBwOh7Ro0YLMnj2btGvXjnh7exNCCMnLyyNSUlLk3bt3ZNu2bcTJyYnZoEJwOByyfv16MmjQIOLn50fy8/P5j2lpaZH+/fsTfX19cuTIEQZT1iQlJUVevXpF7t+/T3JzcwUeGzhwIOFwOERfX18ihsapKrS4oxqdbdu2EXl5eeLl5UU2bNhAli1bRnr06EHk5eX52xiwUd++fcnGjRsJIVXFXVpaGqmsrCSenp5k0KBBDKcT7/Tp06Rdu3ZESkqKSElJkbZt2wosVGCz0tLSGr0WN2/eJMXFxQwl+r5xOBxy6dIlQkjVNjpSUlKkqKiIvH//nkhJSRFCCImLiyOamppMxhSKw+EQS0tLsm3bNtK9e3fSr18/QgghOTk5REpKimRlZZEDBw4QW1tbhpMKqi7e1NTU+NsqEVJ170tJSZGbN2+S8ePHkwkTJjCclKorWtxRjY6RkRE5ePBgjfbFixeTLl26MJCobp49e0b09PTIyJEjiZycHHF3dyempqZEW1ubpKWlMR1PpMDAQKKoqEiWL19OIiIiSEREBFm+fDlRUlIigYGBTMerk4qKCpKcnEwSExMFhgnZLj8/n6xfv56MHDmSuLu7k/Xr19fomWEbFRUVEh0dTQgh5M2bN6RJkyaksLCQPHnyhMjJyRFCCElISCBNmzZlMqZQHA6Hv4dmWloakZKSInl5eQKF6aNHj1g355HD4fBXUB85coRoaGgQHo9HMjIyiJSUFKmoqCC///47MTQ0ZDgpVVd0nzuq0SkpKYG9vX2N9rFjxyIlJYWBRHVjZGSEpKQkWFpaws3NDTweD56enkhOToahoSHT8URat24ddu7ciWXLlsHBwQEODg5YtmwZduzYgTVr1jAdTyxCCDZu3Ag1NTWYmZmhffv2UFVVhb+/P9PRavX8+XO0adMG+/fvh4KCArhcLg4cOIB27dohLS2N6Xgi9ezZE/Pnz8epU6cwdepUyMrK4rfffsPq1ashIyODN2/eICoqCsbGxkxHrUFDQ4N/agmPx4OCggKaNGmC7OxsyMnJAaj6/VN9ZCBbyMjIwM3NDQAwdOhQZGdnIysri/9vkZaWhpGREd6+fctkTKoe6PFjVKPj7u6OCxcuwM/PT6A9Pj4eLi4uDKWqG2Vl5Rq52e758+ewtbWt0W5jY4OXL18ykKjuduzYgU2bNiEgIADOzs4AgNDQUCxYsABNmjTBzJkzGU4o2k8//YS+ffti//79/CPJeDweJk6ciFmzZrF2M+OAgAB4e3tjypQp6NevH/7880+sWLEC2dnZOHDgAGxsbJCRkYF9+/YxHbUGNzc3zJw5E97e3rhw4QLk5eWxdOlSxMfHg8fjIS4uDiEhITA1NWU6qoD27dvj9OnTsLCwwMmTJ6Guro4mTZogLCyMvylzSkoK1NXVGU5K1RWHkL8PkaOoRmL79u1YuHAhJkyYwH/RA4Bz587Bzs5O4DB4tvXQ5ObmIjExEbm5uTUeY+v5j05OTnBwcKhxZu+yZcsQHh6OW7duMZSsdmZmZli3bh2/V6NacHAw5s+fj+TkZIaS1U5NTQ3R0dEwMzMTaE9MTETXrl2Rk5PDULL/Jj8/HxkZGTAxMWE6Sg2FhYWYO3cuYmNj0bdvX8yZMwcbN25EdnY2xo8fj3HjxuHly5c4e/Ysq85cDgkJwdChQ8HhcCAvL48pU6YgICAAJSUlcHV1RZMmTfDixQvY2NiwsqimaqLFHdXoODk51ek6DoeD0NDQr5ym7g4ePIjp06ejpKSkxmNsPMS+WlxcHBwcHNC1a1c4OjoCAG7duoXo6GhERkbCysqK2YBiiCqQkpKSYG9vz+oCydTUFP7+/hg4cKBA++XLlzFnzhykpqYylKx25eXliImJwZs3bzBw4EAoKCgwHemLIISguLgYioqKTEep4cWLF0hISIC9vT00NTWRl5eHwsJCNG/eHMePH0diYiIWLlwILpfLdFSqDmhxR1ESonnz5hg9ejQmTpwIY2NjcDgcpiPVWVpaGtatW4eEhAQQQtCxY0f4+fmhdevWTEcTa+DAgdDV1cXevXv5zzchBFOmTEF6ejquXLnCcELRtm7dig0bNmDz5s3o0aMHgKqi2tfXF/Pnz8dPP/3EbEARHj9+jIEDB0JOTg5Pnz5FamoqjIyMsH79egBg/bSE7OxshISE4M2bN5g6dSqUlZVBCEFZWRnk5eWZjkc1Fgwt5KAoVrO3tyevX79mOoYAIyOjGhsBU1/Xs2fPiLa2NmnTpg2ZMGECmTBhAmnbti3R1tYmT58+ZTqeWDwej6xdu5YoKiryt6BRUFDgn33KVo6OjmT9+vWEx+MJ7OcYGRlJ2rVrx3A68SIjI4m6ujrp3bs3kZWVFThFZv78+QynE62kpIT8/PPPxMjIiDRt2pQ8f/6cEELItWvXWL09FCUaXS1LUUI8fPgQZWVlTMcQMHbsWAQGBjIdo0E+fPiAbdu2YebMmVi0aBFOnTqFiooKpmPVysjICMnJyRg7dizy8vKQm5uLMWPGICkpifW9jhwOBwsXLkROTg4ePXqEhw8fIjc3F4sWLWI6mlhxcXEYMWJEjZ5pLS0tvHnzhqFUdTNnzhxs3rwZ165dE+ilGzBgAM6ePctgMvHmz5+PqKgoHD9+HBwOBzweDwDw8eNHLFmyhOF0VEPQ1bIUJSGMjIwwa9YsZGRk8FewfY5tiz+qhYeHo2/fvtDS0kL79u1RXFzM3xrl5s2baN68OdMRxVJVVcXixYuZjtFghYWFKCkpAZfLlYih/DZt2iAhIQFGRkYC7efOnasx95FtkpOT0b179xrtXC4XWVlZDCSqm7Nnz+Lq1avo0KEDpKT+6fOxtbXFtGnTGExGNRQt7ihKQhw8eBCWlpZCV2iy+UV79uzZ8PHxwaZNm/g58/Ly4OXlhalTpyI4OJjhhOI9fPgQhw4dwrNnz8DlcmFpaYnJkydDVVWV6Whi5eTkYMKECQgKCgL5e2q1oqIi5s+fj59//pm198y6devg5eWFDx8+gMfjISgoCMnJyTh8+DCuXbvGdDyxOnbsiIiICH5hWv0c79+/n9ULh6Slpfm9dZ9LSUmBpqYmA4mo/4zpcWGKYqPP5/pQ/42amhpJTk6u0Z6QkEBUVVUZSFR3gYGBhMPhkG7dupGpU6cSb29v0rp1a6KhoUESExOZjieWu7s7sbS0JDdu3CAZGRnk+fPn5ODBg0RbW5usXLmS6Xhi3blzh/Tu3ZtoamqSZs2aERcXF4mY+xUXF0fU1dWJn58fkZeXJ3PmzCE9e/YkCgoK5MGDB0zHE2nRokWkV69eJC8vj3C5XPL06VMSHR1NjIyMyNKlS5mORzUALe4oSghJLe7YuBBk6NCh5OTJkzXab926RaytrRlIVHempqbE399foK2iooL8+OOPxNHRkaFUdaOmpkb+/PPPGu3BwcHEwMCAgUSNQ1paGpk8eTLp0qUL6dy5M/nxxx9Zv/imvLycTJ06lTRt2pRwOBwiJydHpKSkyKRJk0h5eTnT8agGoMOyFPUdYeNCkKlTp+J///sfmjRpIjCfZ+PGjXB2dkZISAi/jW0bMWdmZtbYwFhaWhpz586FnZ0dQ6nqxtDQUOjQq7GxsdAhODYqLi6u0cb2Pe8MDQ2xZ88epmPUi4yMDHbt2oVFixbh4cOHIISgQ4cOMDAwYDoa1UC0uKMoIXx8fKCiosJ0jO9Cr169AFSdWflv0dHR2LBhAwB2bsQ8YMAAhIWFoU2bNgLtL168wA8//MBQqrpZsGABVq5cicuXLwu0//bbb/Dx8WEoVe3u3buH6dOn48GDBwIrqgkhrLxH/o3H4+H9+/dCT5Fh+4IQfX196OvrMx2D+gLoJsZUo5SSkoIzZ87gzZs3WL16NTQ0NFBUVISysjKoqakxHa/BlJSUhK40pBomMDAQPj4+WLp0qUCv4/79++Hg4ABzc3N+G9sKpu7duyM6OhpKSkoC2fPy8qCqqipw9N6HDx+YiCiUubk5tLS0MGrUKBgbGwtkB8DfkJmNQkJCMHHiRLx//x7/fmllc2FaUlKCw4cP4/79+0KL0tOnT3/7UNR/Qos7qtEJCgrC+PHj4e7ujkOHDiEpKQlGRkZYtmwZ3r9/j7179zIdscEktbjz8/PDvHnzoKGhwXQUAYaGhnW6jsPhIC0t7SunqZ8jR47U+Vpvb++vmKR+NDU1ER0dXaO3VBIYGRnB3t4e3t7eQgvTli1bMpRMPA8PDwQHB8PKykpo7kOHDjGUjGooWtxRjU6HDh2watUquLm5CRRDCQkJGDhwIF69esV0xAaT1OJOWVkZ8fHxEpdb0v31119o27YtZGVlmY7C5+3tjV69emH06NFMR6m35s2bCx3GZztVVVVcvHiR1b2iVP3QOXdUo/Py5UtYWFgIfUzYBG7q65Pk95ienp7YsmULtLW1mY5Sb/b29qwrqn19fTFw4EDIysoKPaSebYtuPjd8+HD8/vvvElfcdejQAXJyckzHoL4gWtxRjU7Xrl1x/vx5+Pr6AgD/uB1/f3907dqV4XT/DV0I8u1dunQJq1evZjpGg7CxqLa0tAQAjBw5ssZjbJ63BgCDBg3C6NGj8enTJ6GFKdvmZVZbvnw5li5dipCQEFb14lINR4dlqUYnLS0NDg4O6N69O86fP4/Bgwfj0aNHyM7Oxp07d+o8z4oJdCEI+9DsVDVxvzvYOC+zWu/evREdHQ0ZGRmhPXhsWnBD1Q3tuaMaHSMjIyQlJWHXrl2orKwEj8eDp6cnpk+fzuojpf69EKR6AYK/v7/ELwShqPpg6wKc58+fMx2hQTw9PeHp6cl0DOoLoj13VKPz6tUrGBgY1NjgtbS0FElJSejYsSMzwWpBF4KwE83+7UnyAhxJnaPJxsU3lGhStV9CUd8XQ0NDZGZm1mh/+fIlBg0axECiuvmeF4JYWFjQCd0MEHaChSSQ5D6JS5cuoaioiOkY9WZvb4/Xr18zHYOqI1rcUY1GcXExioqKQAhBcXGxwMfHjx9x9uxZfPr0iemYIlUvBKkmKQtBPD09kZ+fL/SxixcvolevXnB3d6c74zNAkosk6tui94pkoXPuqEaDy+WCw+GAw+GgdevWQq9Zs2bNN05Vdzt27ICDgwPi4uJQVlYGPz8/gYUgbBUYGAhLS0vcvn0bZmZmWLRoEZSUlAAAkydPRufOnbF582aoq6tj7NixDKetvwEDBkBRUZHpGDXk5+dDWVlZ6GNJSUkwNTXF7du36fmhFPUdonPuqEYjKysLhBBoaWnh3LlzUFdX5z8mIyMDQ0ND6OnpMZiwdvn5+di1axfu378PQggsLS1ZvxBESkoKHTp0wPjx43Hu3DlwuVxcuXIFubm50NDQQGZmJi5cuIADBw6wukiVNFpaWli/fj3+97//8YdfS0pKsHLlSvzyyy8oKSlhOGHDSepcQUBys0tq7saK9txRjUb1yrpDhw6hX79+kJeXZzhR/VQvBPHz8xNoLy0tRXx8PGsXggDAli1b4OTkhIEDB8LY2Bj5+fkoLS0FAKirq8PGxgbz5s1jOGVNaWlpWLRokcgzN9m8RcTevXuxePFi7Ny5E9u3b0d+fj6mTZsGVVVVhIaGMh2PoqiviBZ3VKNTfY5mZmam0IUVZmZm3zpSnRgaGuLdu3fQ0tISaK9eCMLW1bIaGhr8jWd5PB4UFBTQpEkTvHnzhr+IoqSkBOXl5UzGFMrT0xOFhYUYMWKE0DM32WzIkCEYNGgQ9u/fD0dHR3C5XGzYsAE//vgjpKWlmY73n9AFON+epC6+aaxocUc1OvHx8fDy8kJSUhKAqonC1b+4lJSUhPbQMKm4uBiEEIGFIJ8/xvaFIG5ubpg5cya8vb1x4cIFyMvLY+nSpYiPjwePx0NcXBxCQkJgamrKdNQaUlNTERUVxdqCXxxCCA4ePIgVK1bAzs4OaWlpiIiIQK9evdCqVSum44nk6emJPXv2CJ0vePHiRezcuVOiF+CwdY5mbegMLskiOW9DKeoLmThxItq3b4+wsDAYGhri+fPnePLkCezt7REREcF0vBq4XC6UlZX5C0GUlJT4H9ra2liyZAnmzp3LdEyRtm7dCgcHBwQGBsLZ2RnJyckghMDAwADh4eHw9PTE2rVrsXTpUqaj1uDo6IinT58yHaNBrKyssGLFCmzZsgURERFISkqCtrY2zM3NMWfOHKbjiRQYGIjdu3fDzc0NCxcuREFBAf+xyZMnQ05ODps3b8bRo0cZTNlwJ0+eZN0ed6JWswPgvwmmi28kC11QQTU6ysrKiIuLg4mJCbp27Yrg4GBoaGggLCwM/v7+uHr1KtMRBXwPC0HEqe6RZGNvxvPnzzFo0CD8+uuvQs8KZXOP3rx587Bs2bIauf/66y/MmDEDYWFhDCUTT5IX4EjqHM3vefFNY0WLO6rRsbGxwfr16+Hk5IRFixZBS0sLP/30E+7fvw9nZ2fWDctWO3LkCEaOHClxC0EcHBzg7u6O4cOHQ1dXl+k49dKkSROUlZUJtHE4HP5QPpsPsZdUUlJSuHnzJpycnPD8+XMYGxsjJycHnz59gp6eHiorK/HXX3+he/fuyM7OZjquAFtbWxQWFsLNzU3oHM3q+b5sc+HCBSxevBhNmzatsfhmx44dsLe3ZzoiVU90zh3V6AwbNgx79+6Fk5MTfvzxR3Tu3BkJCQmIjIxE+/btmY4nkqQuBOnXrx+OHTuG2bNnw87ODu7u7hg2bJhE9DampKQwHaHBCCE4e/YswsPDhd4vp0+fZiBV7SR5AY6kztH8nhffNFZ0zh3V6HTo0AGLFi0CALRu3RoXLlxAcXExevTogRMnTjCcTrT4+HiYm5tDR0cHFhYWMDc3h4WFBSwsLFj9znrhwoWIiYnBixcv4OHhgeDgYBgZGaF79+7YsWMH0/HEatmypdgPNps/fz68vb2RnJyM69evQ1FREU2bNsXVq1fRpUsXpuOJVL0AZ8OGDRg9ejR/Ac7s2bNZvwBHUudo/nvxjaKiIiIiIuhxYxKMDstSjY6WlhYiIiLQrl07pqPUS5cuXWBkZIRp06Zh/PjxuHnzJiorK+Ht7Y2dO3eiQ4cOTEess7S0NMyZMweXLl1i3dDmjh07oKuri2HDhmHlypVir2XjIpBq2traOHz4MPr27QsLCwskJCRASkoK69evR0VFBX7++WemIwpVWFiIuXPnIjY2Fn379sWcOXOwceNGZGdnY/z48Rg3bhxevnyJs2fPYsCAAUzHFSCpczQ7deqEzMxMbNq0CSNHjkRBQQGWL1+OvXv3YtKkSfjll1+YjkjVEy3uqEZn3rx5+PTpE7Zv3y5R+5ZJ2kKQf0tNTUVQUBCCgoJw//59dO/eHR4eHvjxxx+ZjibAxsYGpqamOHz4sNgeLg6Hg9jY2G+YrH5atGiBa9euwczMDMOHD8fcuXNhZ2eHpKQkuLm5ITU1lemIDcLmBTiSOkdTUhffUKLROXdUo/PDDz9g2bJlsLa2hrOzc43H/f39GUhVO1NTU6Snp8PExAQ9evTAb7/9hp9++gkqKiqsWzX4uUWLFiEoKAgpKSno3r07xo4di4sXL0JTU5PpaELFxMTw/3z37l0Gk/w3tra2uHnzJszMzODh4YGFCxfiyJEjOH36NOsWInyutgU4HA6HlYUdILlzNDdu3Ci03dzcnBZ2Eor23FGNjpOTk8jHOBwOa49m8vf3x/379xEYGIhnz56hc+fOGDx4MCIjI6GtrY3o6GimIwrVrVs3jBw5EsOGDZO41bJFRUU4cOAA4uLiaqyi5nA4uHjxIjPB6uDevXs4ffo0/P39UVFRATc3N37vrr+/P3x9fRlOKNy6desQFBSEuLg4iVuAI6kkdfENJRot7ihKQly7dg16enr8uXXh4eHYvXs3uFwuli5dytoJ/p8+fcKOHTtEvnCweWhz6NChuHHjBmxtbWFiYlJjGD8gIIChZPVHCEFycjIUFBRYe698Lj09HUFBQbh48SIiIyNhbW0Nd3d3TJ8+neloAr6HOZrz5s3Dzp07YWdnh/v372Pw4MHg8Xg4f/48li5dyspznynxaHFHURJCUheCjB8/HlevXkW/fv0QHByM6dOno7KyErt378aWLVvg5eXFdESRVFVVERISwurVyKKI63UEgODg4G8fqoHYvADne5ijKamLbyjR6Jw7ipIQ3t7e2LFjh8QtBLl48SKCg4PRtWtXWFpaYtGiRZCVlYWenh4ePHjA6uLO3t6e1ef2ijNmzBixvY5sJ2wBzr59+5iOVcP3MEdTXl6e35vbtm1bxMTEwM7ODm5ubnBzc6PFnQSixR1FSQhJXQiipqYGJSUlAICFhQXCw8PRs2dPODo6Yu3atdi8eTPDCUXbtm0bxowZA11dXaFbW7Ro0YKBVHUTGhqKa9euSVyvo6QtwPmcpM7RlNTFN5RotLijKAmxd+9e/qTyf/cQVJ8HyUYODg64fPkyOnToAC8vL8ydOxcBAQEIDAxk3RDbv719+xavX7+GhYWFQDvbt7YAJLfXMSIiAj4+PhK5AEdSe0vnz5/PXzQxZMgQHD58GIaGhgDY+6aREo/OuaMo6qtKTU3FpUuXMHfuXBBCMG3aNOzbtw+ysrL49ddf4enpyXREkX744Qe0bNkSXl5eQl+s2Xxc3ZMnTzBmzBgcPHhQonodJXkBjiTP0fycpC2+oWqixR1FUd9cQUEB5OXl+WeFslXz5s0RGhqKtm3bMh2l3m7dugVPT0+8f/9eoJ3tvY6SvACnX79+mDt3LlxcXJiOUi/f0+Ibqgot7iiKokRYuHAhlJSU+GcRSxJJ7XXU0NAQWIBz7949yMrKYs+ePXjy5Amr52hKam/p97TlD1WFzrmjKIoSwdTUFD/99BNev37NXxTyOTbPR3r//j1OnDghcb2OkrwAR1LnaErq4htKNFrcURRFiXDo0CFYWloiOTm5xmNsXsQCAGPHjsW5c+ckrtdRkhfgzJkzBzY2NiJ7S9lKUhffUKLR4o6iKEoEST5XU1J7HRcuXIhLly4BAHr37o3g4GC4uLjwF+CwmaT2lkrylj+UcHTOHUVR1HdIUs9QFkZSFuBI6hxNSV18Q4lGizuKoiiK+gKOHj2Kn376CR4eHhLVWyqpi28o0WhxR1EURVFfgKT2lkrylj+UcLS4oyiKoqhGTFKHkynR6IIKiqIoimrEJHXxDSUa7bmjKIqiqEZMUoeTKdFocUdRFEVRFPUdkYwdFimKoiiKoqg6ocUdRVEURVHUd4QWdxRFURRFUd8RWtxRFEVRFEV9R2hxR1EURVEU9R2hxR1FURRFUdR3hBZ3FEVRFEVR35H/A/YnYgzxH5XoAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 8. 감성 지표 분석\n", + "g8_cols = [\n", + " 'title_subjectivity',\n", + " 'title_sentiment_polarity',\n", + " 'abs_title_subjectivity',\n", + " 'abs_title_sentiment_polarity'\n", + "]\n", + "\n", + "corr_g8 = df[g8_cols].corr()\n", + "plt.figure(figsize=(6,4))\n", + "sns.heatmap(corr_g8,\n", + " annot=True,\n", + " cmap='RdBu',\n", + " vmin=-1, vmax=1, center=0,\n", + " fmt=\".2f\")\n", + "plt.title(\"Group 8 Correlation Heatmap\", fontproperties=fontprop)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 562 + }, + "id": "Einl89ZTjNk5", + "outputId": "2fc4d62c-a9de-4310-fd3a-6925e1e61aa9" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MxqX9PjNjNUi" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 0acf601b911da26db893ce47a5fed81655bf2719 Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:04:18 +0900 Subject: [PATCH 03/10] Delete model/Voting.ipynb --- model/Voting.ipynb | 209 --------------------------------------------- 1 file changed, 209 deletions(-) delete mode 100644 model/Voting.ipynb diff --git a/model/Voting.ipynb b/model/Voting.ipynb deleted file mode 100644 index 3a9aa0d..0000000 --- a/model/Voting.ipynb +++ /dev/null @@ -1,209 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.ensemble import VotingClassifier\n", - "\n", - "from lightgbm import LGBMClassifier\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import GaussianNB" - ], - "metadata": { - "id": "Jem6JkqczaCG" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tcK1pICWzfac", - "outputId": "a708c3eb-c6cc-466a-d303-5181fbba0dcb" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "f2IgC8bEzf0p" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# LightGBM + LogisticRegression(L1) + GaussianNB\n", - "model_lgb = LGBMClassifier(\n", - " random_state=42,\n", - " verbose=-1\n", - ")\n", - "\n", - "model_lr = LogisticRegression(\n", - " penalty='l1',\n", - " solver='liblinear',\n", - " max_iter=1000,\n", - " random_state=42\n", - ")\n", - "\n", - "model_nb = GaussianNB()\n", - "\n", - "# VotingClassifier\n", - "voting_model = VotingClassifier(\n", - " estimators=[\n", - " ('lgb', model_lgb),\n", - " ('lr_l1', model_lr),\n", - " ('nb', model_nb)\n", - " ],\n", - " voting='soft'\n", - ")\n", - "\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', voting_model)\n", - "])" - ], - "metadata": { - "id": "wNSYAeVM-wl-" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O8C5pyWx--Fa", - "outputId": "fdcb4af6-2388-40c8-d8c5-1fa5eee4d5b3" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6230, F1: 0.5562, AUC: 0.7038, Composite: 0.6277\n", - "[Fold 2] Accuracy: 0.6213, F1: 0.5476, AUC: 0.6915, Composite: 0.6202\n", - "[Fold 3] Accuracy: 0.6208, F1: 0.5602, AUC: 0.6899, Composite: 0.6236\n", - "[Fold 4] Accuracy: 0.6044, F1: 0.5103, AUC: 0.6927, Composite: 0.6025\n", - "[Fold 5] Accuracy: 0.6070, F1: 0.5219, AUC: 0.6811, Composite: 0.6033\n", - "\n", - "평균 Composite Score: 0.6154517960736017\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6286\n", - "F1 Score : 0.5594\n", - "ROC AUC : 0.7027\n", - "Composite: 0.6303\n" - ] - } - ] - } - ] -} \ No newline at end of file From 7b4d92e8af73fa388a92b0ba2ba4f54f001b85c6 Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:04:29 +0900 Subject: [PATCH 04/10] Delete model/SVM.ipynb --- model/SVM.ipynb | 183 ------------------------------------------------ 1 file changed, 183 deletions(-) delete mode 100644 model/SVM.ipynb diff --git a/model/SVM.ipynb b/model/SVM.ipynb deleted file mode 100644 index 778171c..0000000 --- a/model/SVM.ipynb +++ /dev/null @@ -1,183 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.svm import SVC" - ], - "metadata": { - "id": "9tkh-Fxuxd8J" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KVZoOCTgxhFw", - "outputId": "bf020b16-fe39-4748-c6dc-4bc4f0267c2d" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "tOlh4FZ3BAb1" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# SVM\n", - "svm_model = SVC(kernel='rbf', probability=True, random_state=42)\n", - "\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', svm_model)\n", - "])" - ], - "metadata": { - "id": "iFeSnLuqBDlb" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UhYcIbzQBKBe", - "outputId": "c541b379-d004-4317-f57c-c84aa9330611" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6551, F1: 0.6425, AUC: 0.7084, Composite: 0.6687\n", - "[Fold 2] Accuracy: 0.6436, F1: 0.6381, AUC: 0.6914, Composite: 0.6577\n", - "[Fold 3] Accuracy: 0.6396, F1: 0.6311, AUC: 0.6832, Composite: 0.6513\n", - "[Fold 4] Accuracy: 0.6481, F1: 0.6400, AUC: 0.6995, Composite: 0.6625\n", - "[Fold 5] Accuracy: 0.6374, F1: 0.6245, AUC: 0.6874, Composite: 0.6497\n", - "\n", - "평균 Composite Score: 0.6579987788139963\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6574\n", - "F1 Score : 0.6478\n", - "ROC AUC : 0.7124\n", - "Composite: 0.6726\n" - ] - } - ] - } - ] -} \ No newline at end of file From 8c7b9b42ae0afab27a77b2052bd817d84ba5f567 Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:04:38 +0900 Subject: [PATCH 05/10] =?UTF-8?q?Delete=20model/Logistic=E2=80=AFRegressio?= =?UTF-8?q?n=E2=80=AF+=E2=80=AFL1.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...gression\342\200\257+\342\200\257L1.ipynb" | 186 ------------------ 1 file changed, 186 deletions(-) delete mode 100644 "model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" diff --git "a/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" "b/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" deleted file mode 100644 index 633a88c..0000000 --- "a/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" +++ /dev/null @@ -1,186 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.linear_model import LogisticRegression" - ], - "metadata": { - "id": "QKFFZw-ix8W_" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o0AVIBdayCG5", - "outputId": "021ecc89-de67-4079-b6d6-1f928a903cb7" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "MfrRfJal8omV" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# LogisticRegression + L1\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', LogisticRegression(\n", - " penalty='l1',\n", - " solver='liblinear',\n", - " max_iter=1000,\n", - " random_state=42\n", - " ))\n", - "])" - ], - "metadata": { - "id": "H16W0Bdp8_Nt" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W5fw4lgj9JAO", - "outputId": "4450096f-7bce-4be1-831a-8289e22fb476" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6489, F1: 0.6342, AUC: 0.6994, Composite: 0.6609\n", - "[Fold 2] Accuracy: 0.6320, F1: 0.6206, AUC: 0.6822, Composite: 0.6450\n", - "[Fold 3] Accuracy: 0.6343, F1: 0.6264, AUC: 0.6792, Composite: 0.6466\n", - "[Fold 4] Accuracy: 0.6320, F1: 0.6202, AUC: 0.6831, Composite: 0.6451\n", - "[Fold 5] Accuracy: 0.6174, F1: 0.6044, AUC: 0.6702, Composite: 0.6307\n", - "\n", - "평균 Composite Score: 0.6456456039745565\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6563\n", - "F1 Score : 0.6454\n", - "ROC AUC : 0.6995\n", - "Composite: 0.6671\n" - ] - } - ] - } - ] -} \ No newline at end of file From 46bd0c93537c60bdcb7918e7c1f8c06064ffd2ec Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:04:46 +0900 Subject: [PATCH 06/10] Delete model/LightGBM.ipynb --- model/LightGBM.ipynb | 181 ------------------------------------------- 1 file changed, 181 deletions(-) delete mode 100644 model/LightGBM.ipynb diff --git a/model/LightGBM.ipynb b/model/LightGBM.ipynb deleted file mode 100644 index 7483e03..0000000 --- a/model/LightGBM.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from lightgbm import LGBMClassifier" - ], - "metadata": { - "id": "OQ6rixkbvjlI" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4NK5sHaNvyVp", - "outputId": "6b9b29fc-99f1-453b-9f39-365e90684c3e" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "ZUlr43O04kKZ" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 모델 정의 <- 모델 테스트할 때 여기만 수정하시면 됩니다\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', LGBMClassifier(random_state=42, verbose=-1))\n", - "])" - ], - "metadata": { - "id": "244ogFkt41m7" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_OtYZTeC47-C", - "outputId": "4440a009-7c76-484a-8218-011e6931b827" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6562, F1: 0.6494, AUC: 0.7139, Composite: 0.6732\n", - "[Fold 2] Accuracy: 0.6484, F1: 0.6447, AUC: 0.7059, Composite: 0.6663\n", - "[Fold 3] Accuracy: 0.6458, F1: 0.6408, AUC: 0.7046, Composite: 0.6638\n", - "[Fold 4] Accuracy: 0.6624, F1: 0.6606, AUC: 0.7139, Composite: 0.6790\n", - "[Fold 5] Accuracy: 0.6422, F1: 0.6411, AUC: 0.6990, Composite: 0.6608\n", - "\n", - "평균 Composite Score: 0.6686024719734138\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6588\n", - "F1 Score : 0.6539\n", - "ROC AUC : 0.7206\n", - "Composite: 0.6778\n" - ] - } - ] - } - ] -} \ No newline at end of file From c048712b80908fe5a0c512a6ae3f2571e29d2ed1 Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:04:54 +0900 Subject: [PATCH 07/10] Delete model/GaussianNB.ipynb --- model/GaussianNB.ipynb | 181 ----------------------------------------- 1 file changed, 181 deletions(-) delete mode 100644 model/GaussianNB.ipynb diff --git a/model/GaussianNB.ipynb b/model/GaussianNB.ipynb deleted file mode 100644 index d9395cc..0000000 --- a/model/GaussianNB.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_vikoWxE93iK" - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.naive_bayes import GaussianNB\n" - ] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "id": "5eK5b_NK9-Vg", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "92bbc4fb-da91-41a1-c525-3b3c2a863cb3" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "W1wQfRxAzCOH" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# GaussianNB\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', GaussianNB())\n", - "])" - ], - "metadata": { - "id": "z9xW2V6d-Ax9" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "miBLt2S8-Gpx", - "outputId": "16fa3b3c-f1f0-4dd7-e331-96320ac5364a" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6002, F1: 0.4968, AUC: 0.6707, Composite: 0.5892\n", - "[Fold 2] Accuracy: 0.6025, F1: 0.4964, AUC: 0.6562, Composite: 0.5850\n", - "[Fold 3] Accuracy: 0.5999, F1: 0.5054, AUC: 0.6564, Composite: 0.5873\n", - "[Fold 4] Accuracy: 0.5861, F1: 0.4600, AUC: 0.6563, Composite: 0.5675\n", - "[Fold 5] Accuracy: 0.5859, F1: 0.4649, AUC: 0.6489, Composite: 0.5666\n", - "\n", - "평균 Composite Score: 0.5791141760983816\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6036\n", - "F1 Score : 0.4977\n", - "ROC AUC : 0.6656\n", - "Composite: 0.5890\n" - ] - } - ] - } - ] -} \ No newline at end of file From 4e3c49d7f86c665daf080c7cc4a5fd7fb384fd64 Mon Sep 17 00:00:00 2001 From: xeoxaxeo Date: Fri, 16 May 2025 21:06:49 +0900 Subject: [PATCH 08/10] =?UTF-8?q?=EB=94=94=EB=A0=89=ED=86=A0=EB=A6=AC=20?= =?UTF-8?q?=EC=9D=B4=EB=8F=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .idea/.gitignore | 3 + .idea/misc.xml | 6 + .idea/modules.xml | 8 + .idea/pattern-recognition.iml | 9 + .idea/vcs.xml | 6 + model/GaussianNB.ipynb | 181 --------------- model/LightGBM.ipynb | 181 --------------- ...gression\342\200\257+\342\200\257L1.ipynb" | 186 ---------------- model/SVM.ipynb | 183 --------------- model/Voting.ipynb | 209 ------------------ 10 files changed, 32 insertions(+), 940 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/pattern-recognition.iml create mode 100644 .idea/vcs.xml delete mode 100644 model/GaussianNB.ipynb delete mode 100644 model/LightGBM.ipynb delete mode 100644 "model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" delete mode 100644 model/SVM.ipynb delete mode 100644 model/Voting.ipynb diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..952b1db --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..e459b48 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/pattern-recognition.iml b/.idea/pattern-recognition.iml new file mode 100644 index 0000000..d6ebd48 --- /dev/null +++ b/.idea/pattern-recognition.iml @@ -0,0 +1,9 @@ + + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/model/GaussianNB.ipynb b/model/GaussianNB.ipynb deleted file mode 100644 index d9395cc..0000000 --- a/model/GaussianNB.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_vikoWxE93iK" - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.naive_bayes import GaussianNB\n" - ] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "id": "5eK5b_NK9-Vg", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "92bbc4fb-da91-41a1-c525-3b3c2a863cb3" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "W1wQfRxAzCOH" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# GaussianNB\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', GaussianNB())\n", - "])" - ], - "metadata": { - "id": "z9xW2V6d-Ax9" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "miBLt2S8-Gpx", - "outputId": "16fa3b3c-f1f0-4dd7-e331-96320ac5364a" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6002, F1: 0.4968, AUC: 0.6707, Composite: 0.5892\n", - "[Fold 2] Accuracy: 0.6025, F1: 0.4964, AUC: 0.6562, Composite: 0.5850\n", - "[Fold 3] Accuracy: 0.5999, F1: 0.5054, AUC: 0.6564, Composite: 0.5873\n", - "[Fold 4] Accuracy: 0.5861, F1: 0.4600, AUC: 0.6563, Composite: 0.5675\n", - "[Fold 5] Accuracy: 0.5859, F1: 0.4649, AUC: 0.6489, Composite: 0.5666\n", - "\n", - "평균 Composite Score: 0.5791141760983816\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6036\n", - "F1 Score : 0.4977\n", - "ROC AUC : 0.6656\n", - "Composite: 0.5890\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/model/LightGBM.ipynb b/model/LightGBM.ipynb deleted file mode 100644 index 7483e03..0000000 --- a/model/LightGBM.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from lightgbm import LGBMClassifier" - ], - "metadata": { - "id": "OQ6rixkbvjlI" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4NK5sHaNvyVp", - "outputId": "6b9b29fc-99f1-453b-9f39-365e90684c3e" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "ZUlr43O04kKZ" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 모델 정의 <- 모델 테스트할 때 여기만 수정하시면 됩니다\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', LGBMClassifier(random_state=42, verbose=-1))\n", - "])" - ], - "metadata": { - "id": "244ogFkt41m7" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_OtYZTeC47-C", - "outputId": "4440a009-7c76-484a-8218-011e6931b827" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6562, F1: 0.6494, AUC: 0.7139, Composite: 0.6732\n", - "[Fold 2] Accuracy: 0.6484, F1: 0.6447, AUC: 0.7059, Composite: 0.6663\n", - "[Fold 3] Accuracy: 0.6458, F1: 0.6408, AUC: 0.7046, Composite: 0.6638\n", - "[Fold 4] Accuracy: 0.6624, F1: 0.6606, AUC: 0.7139, Composite: 0.6790\n", - "[Fold 5] Accuracy: 0.6422, F1: 0.6411, AUC: 0.6990, Composite: 0.6608\n", - "\n", - "평균 Composite Score: 0.6686024719734138\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6588\n", - "F1 Score : 0.6539\n", - "ROC AUC : 0.7206\n", - "Composite: 0.6778\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git "a/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" "b/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" deleted file mode 100644 index 633a88c..0000000 --- "a/model/Logistic\342\200\257Regression\342\200\257+\342\200\257L1.ipynb" +++ /dev/null @@ -1,186 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.linear_model import LogisticRegression" - ], - "metadata": { - "id": "QKFFZw-ix8W_" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o0AVIBdayCG5", - "outputId": "021ecc89-de67-4079-b6d6-1f928a903cb7" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "MfrRfJal8omV" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# LogisticRegression + L1\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', LogisticRegression(\n", - " penalty='l1',\n", - " solver='liblinear',\n", - " max_iter=1000,\n", - " random_state=42\n", - " ))\n", - "])" - ], - "metadata": { - "id": "H16W0Bdp8_Nt" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W5fw4lgj9JAO", - "outputId": "4450096f-7bce-4be1-831a-8289e22fb476" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6489, F1: 0.6342, AUC: 0.6994, Composite: 0.6609\n", - "[Fold 2] Accuracy: 0.6320, F1: 0.6206, AUC: 0.6822, Composite: 0.6450\n", - "[Fold 3] Accuracy: 0.6343, F1: 0.6264, AUC: 0.6792, Composite: 0.6466\n", - "[Fold 4] Accuracy: 0.6320, F1: 0.6202, AUC: 0.6831, Composite: 0.6451\n", - "[Fold 5] Accuracy: 0.6174, F1: 0.6044, AUC: 0.6702, Composite: 0.6307\n", - "\n", - "평균 Composite Score: 0.6456456039745565\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6563\n", - "F1 Score : 0.6454\n", - "ROC AUC : 0.6995\n", - "Composite: 0.6671\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/model/SVM.ipynb b/model/SVM.ipynb deleted file mode 100644 index 778171c..0000000 --- a/model/SVM.ipynb +++ /dev/null @@ -1,183 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.svm import SVC" - ], - "metadata": { - "id": "9tkh-Fxuxd8J" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KVZoOCTgxhFw", - "outputId": "bf020b16-fe39-4748-c6dc-4bc4f0267c2d" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "tOlh4FZ3BAb1" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# SVM\n", - "svm_model = SVC(kernel='rbf', probability=True, random_state=42)\n", - "\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', svm_model)\n", - "])" - ], - "metadata": { - "id": "iFeSnLuqBDlb" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UhYcIbzQBKBe", - "outputId": "c541b379-d004-4317-f57c-c84aa9330611" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6551, F1: 0.6425, AUC: 0.7084, Composite: 0.6687\n", - "[Fold 2] Accuracy: 0.6436, F1: 0.6381, AUC: 0.6914, Composite: 0.6577\n", - "[Fold 3] Accuracy: 0.6396, F1: 0.6311, AUC: 0.6832, Composite: 0.6513\n", - "[Fold 4] Accuracy: 0.6481, F1: 0.6400, AUC: 0.6995, Composite: 0.6625\n", - "[Fold 5] Accuracy: 0.6374, F1: 0.6245, AUC: 0.6874, Composite: 0.6497\n", - "\n", - "평균 Composite Score: 0.6579987788139963\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6574\n", - "F1 Score : 0.6478\n", - "ROC AUC : 0.7124\n", - "Composite: 0.6726\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/model/Voting.ipynb b/model/Voting.ipynb deleted file mode 100644 index 3a9aa0d..0000000 --- a/model/Voting.ipynb +++ /dev/null @@ -1,209 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n", - "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n", - "\n", - "from sklearn.ensemble import VotingClassifier\n", - "\n", - "from lightgbm import LGBMClassifier\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import GaussianNB" - ], - "metadata": { - "id": "Jem6JkqczaCG" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 데이터 불러오기\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'\n", - "\n", - "df = pd.read_csv(train_src)\n", - "\n", - "X = df.drop(['id', 'shares', 'y'], axis=1)\n", - "y = df['y']\n", - "\n", - "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tcK1pICWzfac", - "outputId": "a708c3eb-c6cc-466a-d303-5181fbba0dcb" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", - "cat_cols = ['data_channel', 'weekday']\n", - "\n", - "# 전처리 파이프라인\n", - "numeric_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='mean')),\n", - " ('scaler', StandardScaler())\n", - "])\n", - "categorical_pipe = Pipeline([\n", - " ('imputer', SimpleImputer(strategy='most_frequent')),\n", - " ('ohe', OneHotEncoder(drop='first', sparse_output=False))\n", - "])\n", - "preprocessor = ColumnTransformer([\n", - " ('num', numeric_pipe, num_cols),\n", - " ('cat', categorical_pipe, cat_cols)\n", - "])" - ], - "metadata": { - "id": "f2IgC8bEzf0p" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# LightGBM + LogisticRegression(L1) + GaussianNB\n", - "model_lgb = LGBMClassifier(\n", - " random_state=42,\n", - " verbose=-1\n", - ")\n", - "\n", - "model_lr = LogisticRegression(\n", - " penalty='l1',\n", - " solver='liblinear',\n", - " max_iter=1000,\n", - " random_state=42\n", - ")\n", - "\n", - "model_nb = GaussianNB()\n", - "\n", - "# VotingClassifier\n", - "voting_model = VotingClassifier(\n", - " estimators=[\n", - " ('lgb', model_lgb),\n", - " ('lr_l1', model_lr),\n", - " ('nb', model_nb)\n", - " ],\n", - " voting='soft'\n", - ")\n", - "\n", - "pipe = Pipeline([\n", - " ('pre', preprocessor),\n", - " ('clf', voting_model)\n", - "])" - ], - "metadata": { - "id": "wNSYAeVM-wl-" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# 5-Fold CV (only on train_val)\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", - "scoring = ['accuracy', 'f1', 'roc_auc']\n", - "\n", - "cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring)\n", - "\n", - "acc = cv_results['test_accuracy']\n", - "f1 = cv_results['test_f1']\n", - "auc = cv_results['test_roc_auc']\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"5-Fold CV (only on train_val)\")\n", - "for i in range(len(acc)):\n", - " print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n", - "print(\"\\n평균 Composite Score:\", comp.mean())\n", - "\n", - "# 전체 train_val로 학습 후 test로 최종 성능 평가\n", - "pipe.fit(X_trainval, y_trainval)\n", - "y_pred = pipe.predict(X_test)\n", - "y_prob = pipe.predict_proba(X_test)[:, 1]\n", - "\n", - "acc = accuracy_score(y_test, y_pred)\n", - "f1 = f1_score(y_test, y_pred)\n", - "auc = roc_auc_score(y_test, y_prob)\n", - "comp = (acc + f1 + auc) / 3\n", - "\n", - "print(\"\\n최종 Holdout Test 성능\")\n", - "print(f\"Accuracy : {acc:.4f}\")\n", - "print(f\"F1 Score : {f1:.4f}\")\n", - "print(f\"ROC AUC : {auc:.4f}\")\n", - "print(f\"Composite: {comp:.4f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O8C5pyWx--Fa", - "outputId": "fdcb4af6-2388-40c8-d8c5-1fa5eee4d5b3" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5-Fold CV (only on train_val)\n", - "[Fold 1] Accuracy: 0.6230, F1: 0.5562, AUC: 0.7038, Composite: 0.6277\n", - "[Fold 2] Accuracy: 0.6213, F1: 0.5476, AUC: 0.6915, Composite: 0.6202\n", - "[Fold 3] Accuracy: 0.6208, F1: 0.5602, AUC: 0.6899, Composite: 0.6236\n", - "[Fold 4] Accuracy: 0.6044, F1: 0.5103, AUC: 0.6927, Composite: 0.6025\n", - "[Fold 5] Accuracy: 0.6070, F1: 0.5219, AUC: 0.6811, Composite: 0.6033\n", - "\n", - "평균 Composite Score: 0.6154517960736017\n", - "\n", - "최종 Holdout Test 성능\n", - "Accuracy : 0.6286\n", - "F1 Score : 0.5594\n", - "ROC AUC : 0.7027\n", - "Composite: 0.6303\n" - ] - } - ] - } - ] -} \ No newline at end of file From be855ee4d2ffc5ecee996fcf6f7a6fb9c8dfd7d9 Mon Sep 17 00:00:00 2001 From: Iris-1004 Date: Thu, 22 May 2025 22:06:46 +0900 Subject: [PATCH 09/10] Create .gitignore --- model/seohyoun/.gitignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 model/seohyoun/.gitignore diff --git a/model/seohyoun/.gitignore b/model/seohyoun/.gitignore new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/model/seohyoun/.gitignore @@ -0,0 +1 @@ + From cd7c9e4b4a75913c54301f08fac7fd5d581bca87 Mon Sep 17 00:00:00 2001 From: Iris-1004 Date: Thu, 22 May 2025 22:09:09 +0900 Subject: [PATCH 10/10] Add models --- "model/seohyoun/(1) \354\240\234\352\261\260.ipynb" | 1 + "model/seohyoun/(1), (3) \354\240\234\352\261\260.ipynb" | 1 + "model/seohyoun/(1), (3), (4) \354\240\234\352\261\260.ipynb" | 1 + .../seohyoun/(1), (3), (4), (7) \354\240\234\352\261\260.ipynb" | 1 + model/seohyoun/(1), (3), (4), (7), (8).ipynb | 1 + model/seohyoun/Best_model_50.ipynb | 1 + model/seohyoun/SelectFromModel_1.ipynb | 1 + model/seohyoun/SelectFromModel_2.ipynb | 1 + model/seohyoun/feature importance_30.ipynb | 1 + ...5\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" | 1 + ...5\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" | 1 + ...5\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" | 1 + ...5\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" | 1 + 13 files changed, 13 insertions(+) create mode 100644 "model/seohyoun/(1) \354\240\234\352\261\260.ipynb" create mode 100644 "model/seohyoun/(1), (3) \354\240\234\352\261\260.ipynb" create mode 100644 "model/seohyoun/(1), (3), (4) \354\240\234\352\261\260.ipynb" create mode 100644 "model/seohyoun/(1), (3), (4), (7) \354\240\234\352\261\260.ipynb" create mode 100644 model/seohyoun/(1), (3), (4), (7), (8).ipynb create mode 100644 model/seohyoun/Best_model_50.ipynb create mode 100644 model/seohyoun/SelectFromModel_1.ipynb create mode 100644 model/seohyoun/SelectFromModel_2.ipynb create mode 100644 model/seohyoun/feature importance_30.ipynb create mode 100644 "model/seohyoun/mean + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" create mode 100644 "model/seohyoun/mean + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" create mode 100644 "model/seohyoun/median + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" create mode 100644 "model/seohyoun/median + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" diff --git "a/model/seohyoun/(1) \354\240\234\352\261\260.ipynb" "b/model/seohyoun/(1) \354\240\234\352\261\260.ipynb" new file mode 100644 index 0000000..7a80ec6 --- /dev/null +++ "b/model/seohyoun/(1) \354\240\234\352\261\260.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNkzld8EyYMdtLCSopbhxQQ"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747406426694,"user_tz":-540,"elapsed":39979,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"76782787-141f-4d48-8e60-b2bef02b1c1b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747406432030,"user_tz":-540,"elapsed":5326,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747406463137,"user_tz":-540,"elapsed":31103,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"86b02d56-79fc-4f4a-d1dd-7e49fcacf289"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_unique_tokens',], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747406464940,"user_tz":-540,"elapsed":1783,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747406465004,"user_tz":-540,"elapsed":29,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747406465128,"user_tz":-540,"elapsed":97,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747406465156,"user_tz":-540,"elapsed":16,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747406701074,"user_tz":-540,"elapsed":235902,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"5492a606-6c08-4d17-fce7-d40a9894415a"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6681, F1: 0.6619, AUC: 0.7235, Composite: 0.6845\n","[Fold 2] Accuracy: 0.6565, F1: 0.6556, AUC: 0.7172, Composite: 0.6764\n","[Fold 3] Accuracy: 0.6472, F1: 0.6469, AUC: 0.7057, Composite: 0.6666\n","[Fold 4] Accuracy: 0.6624, F1: 0.6614, AUC: 0.7211, Composite: 0.6816\n","[Fold 5] Accuracy: 0.6425, F1: 0.6408, AUC: 0.7087, Composite: 0.6640\n","\n","평균 Composite Score: 0.674643664179878\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6588\n","F1 Score : 0.6569\n","ROC AUC : 0.7244\n","Composite: 0.6800\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/(1), (3) \354\240\234\352\261\260.ipynb" "b/model/seohyoun/(1), (3) \354\240\234\352\261\260.ipynb" new file mode 100644 index 0000000..64dd680 --- /dev/null +++ "b/model/seohyoun/(1), (3) \354\240\234\352\261\260.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNTbsmEz5KrheaDFvQ7klAA"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747406488911,"user_tz":-540,"elapsed":33604,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"6b26e36e-f8cb-471a-9517-f2e6d2157fd7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.26.4)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.13.1)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.4.2)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.5.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.26.4)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.13.1)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.56.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.1.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.1)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.0.0)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747406493703,"user_tz":-540,"elapsed":4762,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747406521327,"user_tz":-540,"elapsed":27589,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"d3b490c9-46a9-4b21-cd5e-d646f8189136"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_unique_tokens','kw_max_min', 'kw_avg_min', 'kw_min_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg',], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747406521355,"user_tz":-540,"elapsed":98,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747406521432,"user_tz":-540,"elapsed":39,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747406521561,"user_tz":-540,"elapsed":147,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747406522039,"user_tz":-540,"elapsed":385,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747406715098,"user_tz":-540,"elapsed":193119,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"64602da3-4602-4125-f6fd-c7509dae5c62"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6574, F1: 0.6536, AUC: 0.7196, Composite: 0.6769\n","[Fold 2] Accuracy: 0.6441, F1: 0.6423, AUC: 0.7054, Composite: 0.6640\n","[Fold 3] Accuracy: 0.6450, F1: 0.6447, AUC: 0.6983, Composite: 0.6627\n","[Fold 4] Accuracy: 0.6540, F1: 0.6531, AUC: 0.7092, Composite: 0.6721\n","[Fold 5] Accuracy: 0.6506, F1: 0.6447, AUC: 0.7074, Composite: 0.6676\n","\n","평균 Composite Score: 0.6686366152114408\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6565\n","F1 Score : 0.6547\n","ROC AUC : 0.7177\n","Composite: 0.6763\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/(1), (3), (4) \354\240\234\352\261\260.ipynb" "b/model/seohyoun/(1), (3), (4) \354\240\234\352\261\260.ipynb" new file mode 100644 index 0000000..fed0ae3 --- /dev/null +++ "b/model/seohyoun/(1), (3), (4) \354\240\234\352\261\260.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMzJNlrD1P3N9jrqHsbqqfJ"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747406543507,"user_tz":-540,"elapsed":36313,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"2c8d5a3d-cad9-432f-af8d-552ef1c404d8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.26.4)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.13.1)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.4.2)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.5.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.26.4)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.13.1)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.56.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.1.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.1)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.0.0)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747406548564,"user_tz":-540,"elapsed":5034,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747406586880,"user_tz":-540,"elapsed":38309,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"22cf283c-414c-4486-ce10-8ff12bb2a773"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_unique_tokens','kw_max_min', 'kw_avg_min', 'kw_min_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg','self_reference_min_shares', 'self_reference_max_shares',], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747406587943,"user_tz":-540,"elapsed":1077,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747406587992,"user_tz":-540,"elapsed":17,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747406588110,"user_tz":-540,"elapsed":111,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747406588228,"user_tz":-540,"elapsed":83,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747406792014,"user_tz":-540,"elapsed":203838,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"62b3a55f-3eee-4f91-93ee-154f1dc50294"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6624, F1: 0.6601, AUC: 0.7193, Composite: 0.6806\n","[Fold 2] Accuracy: 0.6461, F1: 0.6434, AUC: 0.7057, Composite: 0.6651\n","[Fold 3] Accuracy: 0.6458, F1: 0.6434, AUC: 0.7009, Composite: 0.6634\n","[Fold 4] Accuracy: 0.6534, F1: 0.6492, AUC: 0.7111, Composite: 0.6712\n","[Fold 5] Accuracy: 0.6450, F1: 0.6398, AUC: 0.7022, Composite: 0.6623\n","\n","평균 Composite Score: 0.6685217343712756\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6545\n","F1 Score : 0.6533\n","ROC AUC : 0.7132\n","Composite: 0.6736\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/(1), (3), (4), (7) \354\240\234\352\261\260.ipynb" "b/model/seohyoun/(1), (3), (4), (7) \354\240\234\352\261\260.ipynb" new file mode 100644 index 0000000..c34ba85 --- /dev/null +++ "b/model/seohyoun/(1), (3), (4), (7) \354\240\234\352\261\260.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPrjn2GIxlSFRVm9GN7Z7uS"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747406823428,"user_tz":-540,"elapsed":32810,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"242b3c14-6673-45b6-e636-726be39d32a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747406826203,"user_tz":-540,"elapsed":2779,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747406855219,"user_tz":-540,"elapsed":29033,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"fdc80af7-1089-4528-d1c4-10133aa1e0e7"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_unique_tokens','kw_max_min', 'kw_avg_min', 'kw_min_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg','self_reference_min_shares', 'self_reference_max_shares', 'min_positive_polarity', 'max_positive_polarity', 'min_negative_polarity', 'max_negative_polarity',], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747406856435,"user_tz":-540,"elapsed":1230,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747406856483,"user_tz":-540,"elapsed":35,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747406856570,"user_tz":-540,"elapsed":76,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747406856600,"user_tz":-540,"elapsed":11,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747407054414,"user_tz":-540,"elapsed":197808,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"54dc17f7-58e4-44e1-fc40-4eb440a6e152"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6588, F1: 0.6563, AUC: 0.7235, Composite: 0.6795\n","[Fold 2] Accuracy: 0.6509, F1: 0.6495, AUC: 0.7071, Composite: 0.6692\n","[Fold 3] Accuracy: 0.6391, F1: 0.6372, AUC: 0.6981, Composite: 0.6582\n","[Fold 4] Accuracy: 0.6599, F1: 0.6574, AUC: 0.7129, Composite: 0.6767\n","[Fold 5] Accuracy: 0.6433, F1: 0.6371, AUC: 0.7006, Composite: 0.6603\n","\n","평균 Composite Score: 0.6687863636125364\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6552\n","F1 Score : 0.6521\n","ROC AUC : 0.7136\n","Composite: 0.6736\n"]}]}]} \ No newline at end of file diff --git a/model/seohyoun/(1), (3), (4), (7), (8).ipynb b/model/seohyoun/(1), (3), (4), (7), (8).ipynb new file mode 100644 index 0000000..8fc90df --- /dev/null +++ b/model/seohyoun/(1), (3), (4), (7), (8).ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMI4Ccjzauhv/2nZkSZOJyP"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747406818326,"user_tz":-540,"elapsed":18028,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"6e74d52d-b35f-4503-cd79-26f3d997d867"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747406821103,"user_tz":-540,"elapsed":2781,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747406882767,"user_tz":-540,"elapsed":61660,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"539051e9-6e20-4884-c996-3e9598fb671f"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_unique_tokens','kw_max_min', 'kw_avg_min', 'kw_min_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg','self_reference_min_shares', 'self_reference_max_shares', 'min_positive_polarity', 'max_positive_polarity', 'min_negative_polarity', 'max_negative_polarity','abs_title_subjectivity',], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747406885567,"user_tz":-540,"elapsed":2784,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747406885584,"user_tz":-540,"elapsed":7,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747406885777,"user_tz":-540,"elapsed":189,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747406885792,"user_tz":-540,"elapsed":7,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747407063581,"user_tz":-540,"elapsed":177782,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"f3fe8d13-748e-4a11-86c0-65152a41e015"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6548, F1: 0.6548, AUC: 0.7202, Composite: 0.6766\n","[Fold 2] Accuracy: 0.6456, F1: 0.6412, AUC: 0.7037, Composite: 0.6635\n","[Fold 3] Accuracy: 0.6413, F1: 0.6397, AUC: 0.6994, Composite: 0.6602\n","[Fold 4] Accuracy: 0.6599, F1: 0.6589, AUC: 0.7101, Composite: 0.6763\n","[Fold 5] Accuracy: 0.6399, F1: 0.6324, AUC: 0.7005, Composite: 0.6576\n","\n","평균 Composite Score: 0.6668438726384458\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6577\n","F1 Score : 0.6552\n","ROC AUC : 0.7114\n","Composite: 0.6748\n"]}]}]} \ No newline at end of file diff --git a/model/seohyoun/Best_model_50.ipynb b/model/seohyoun/Best_model_50.ipynb new file mode 100644 index 0000000..3fc4d6f --- /dev/null +++ b/model/seohyoun/Best_model_50.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNJv/AEBzSxKmw2okRXtyfT"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["! pip install feature_engine\n","! pip install CatBoost\n","! pip install optuna"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BaTFriqa5oqh","executionInfo":{"status":"ok","timestamp":1747447097127,"user_tz":-540,"elapsed":38622,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"42ba8ea1-9fa9-484f-8f88-84a9b49913b1"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n","Collecting optuna\n"," Downloading optuna-4.3.0-py3-none-any.whl.metadata (17 kB)\n","Collecting alembic>=1.5.0 (from optuna)\n"," Downloading alembic-1.15.2-py3-none-any.whl.metadata (7.3 kB)\n","Collecting colorlog (from optuna)\n"," Downloading colorlog-6.9.0-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from optuna) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from optuna) (24.2)\n","Requirement already satisfied: sqlalchemy>=1.4.2 in /usr/local/lib/python3.11/dist-packages (from optuna) (2.0.40)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from optuna) (4.67.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.11/dist-packages (from optuna) (6.0.2)\n","Requirement already satisfied: Mako in /usr/lib/python3/dist-packages (from alembic>=1.5.0->optuna) (1.1.3)\n","Requirement already satisfied: typing-extensions>=4.12 in /usr/local/lib/python3.11/dist-packages (from alembic>=1.5.0->optuna) (4.13.2)\n","Requirement already satisfied: greenlet>=1 in /usr/local/lib/python3.11/dist-packages (from sqlalchemy>=1.4.2->optuna) (3.2.2)\n","Downloading optuna-4.3.0-py3-none-any.whl (386 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m386.6/386.6 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading alembic-1.15.2-py3-none-any.whl (231 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m231.9/231.9 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading colorlog-6.9.0-py3-none-any.whl (11 kB)\n","Installing collected packages: colorlog, alembic, optuna\n","Successfully installed alembic-1.15.2 colorlog-6.9.0 optuna-4.3.0\n"]}]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S-r5IGrP5s3-","executionInfo":{"status":"ok","timestamp":1747447191638,"user_tz":-540,"elapsed":29561,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"70b1b739-8b36-4a60-e7d0-8545902ae20b"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["# ── ② 파생 변수 생성 ── # df 통계형 변수를 쓸 때는 데이터 누수 주의\n","\n","def create_features(df):\n"," # 1) 제목 길이 대비 본문 길이 비율\n"," df['title_content_ratio'] = df['n_tokens_title'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 2) 키워드 밀도: 전체 토큰 대비 키워드 개수 비율\n"," df['keyword_density'] = df['num_keywords'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 3) 비중어(non-stop) 단어 비율: 본문 대비\n"," df['nonstop_ratio'] = df['n_non_stop_words'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 4) 본문 내 링크 대비 자기링크 비율\n"," df['self_href_ratio'] = df['num_self_hrefs'] / df['num_hrefs'].replace(0, np.nan)\n","\n"," # 5) 이미지/동영상 비율\n"," df['img_video_ratio'] = df['num_imgs'] / (df['num_videos'] + 1)\n","\n"," # 6) 키워드 분포 폭: (최댓값 키워드 빈도 – 최솟값 키워드 빈도)\n"," df['kw_spread'] = df['kw_max_max'] - df['kw_min_min']\n","\n"," # 7) 감성 범위: (최대 양성 편향 – 최소 음성 편향)\n"," df['sentiment_range'] = df['max_positive_polarity'] - df['min_negative_polarity']\n","\n"," # 8) 제목 감성 상호작용: 주관성 × 편향 절대값\n"," df['title_sent_interact']= df['abs_title_subjectivity'] * df['abs_title_sentiment_polarity']\n","\n"," # 9) 주말 여부 플래그\n"," df['is_weekend'] = df['weekday'].isin(['Saturday','Sunday']).astype(int)\n","\n"," # 10) 채널×주말 교차 카테고리 (필요시 one-hot 인코딩)\n"," df['channel_weekend'] = df['data_channel'] + '_' + df['is_weekend'].astype(str)\n","\n"," return df\n"],"metadata":{"id":"IzrifFPNpLTl","executionInfo":{"status":"ok","timestamp":1747447191663,"user_tz":-540,"elapsed":6,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","execution_count":6,"metadata":{"id":"0QXfJ62f5fnV","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1747452432005,"user_tz":-540,"elapsed":4722295,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"f1f4df5b-78af-49d3-cc24-0cd8e511a3f6"},"outputs":[{"output_type":"stream","name":"stderr","text":["[I 2025-05-17 02:09:20,215] A new study created in memory with name: no-name-0575cab3-19b0-4d94-a3ed-84d9856fae86\n"]},{"output_type":"stream","name":"stdout","text":["▶ 선택된 피처: ['n_unique_tokens', 'n_non_stop_unique_tokens', 'average_token_length', 'kw_max_min', 'kw_avg_min', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg', 'kw_avg_avg', 'self_reference_min_shares', 'self_reference_max_shares', 'self_reference_avg_sharess', 'LDA_00', 'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04', 'global_subjectivity', 'global_rate_positive_words', 'global_rate_negative_words', 'avg_positive_polarity', 'title_content_ratio', 'keyword_density', 'self_href_ratio', 'img_video_ratio', 'data_channel', 'weekday', 'channel_weekend']\n"]},{"output_type":"stream","name":"stderr","text":["[I 2025-05-17 02:13:46,634] Trial 0 finished with value: 0.7099063220431535 and parameters: {'depth': 10, 'learning_rate': 0.010482260789173897, 'l2_leaf_reg': 0.1455509331160738, 'iterations': 619}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:13:57,189] Trial 1 finished with value: 0.6826745487358922 and parameters: {'depth': 4, 'learning_rate': 0.005378646586870534, 'l2_leaf_reg': 0.35295055322652047, 'iterations': 230}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:17:13,223] Trial 2 finished with value: 0.6990700109520475 and parameters: {'depth': 10, 'learning_rate': 0.07243259768948074, 'l2_leaf_reg': 0.7106464878254433, 'iterations': 449}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:18:17,323] Trial 3 finished with value: 0.6933368723158658 and parameters: {'depth': 7, 'learning_rate': 0.001959105214477111, 'l2_leaf_reg': 2.528578799269137, 'iterations': 590}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:20:14,449] Trial 4 finished with value: 0.7051776459512862 and parameters: {'depth': 8, 'learning_rate': 0.004280125570961514, 'l2_leaf_reg': 3.495820355297714, 'iterations': 699}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:23:19,325] Trial 5 finished with value: 0.7029545974063985 and parameters: {'depth': 9, 'learning_rate': 0.002690892553032814, 'l2_leaf_reg': 0.23421169934846123, 'iterations': 663}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:27:12,851] Trial 6 finished with value: 0.701069945017344 and parameters: {'depth': 9, 'learning_rate': 0.001865828074690242, 'l2_leaf_reg': 0.5633115331728187, 'iterations': 804}. Best is trial 0 with value: 0.7099063220431535.\n","[I 2025-05-17 02:28:05,503] Trial 7 finished with value: 0.7113348988403052 and parameters: {'depth': 6, 'learning_rate': 0.015308306638376906, 'l2_leaf_reg': 1.3418541124280778, 'iterations': 641}. Best is trial 7 with value: 0.7113348988403052.\n","[I 2025-05-17 02:28:31,498] Trial 8 finished with value: 0.6926318107390604 and parameters: {'depth': 5, 'learning_rate': 0.004389055806462067, 'l2_leaf_reg': 0.4032797888500006, 'iterations': 450}. Best is trial 7 with value: 0.7113348988403052.\n","[I 2025-05-17 02:29:57,870] Trial 9 finished with value: 0.713445058917238 and parameters: {'depth': 7, 'learning_rate': 0.013175557036821436, 'l2_leaf_reg': 0.4540825019349995, 'iterations': 778}. Best is trial 9 with value: 0.713445058917238.\n","[I 2025-05-17 02:31:48,355] Trial 10 finished with value: 0.709909938151175 and parameters: {'depth': 7, 'learning_rate': 0.032437948110239014, 'l2_leaf_reg': 9.015288342359854, 'iterations': 984}. Best is trial 9 with value: 0.713445058917238.\n","[I 2025-05-17 02:32:59,842] Trial 11 finished with value: 0.713758610349902 and parameters: {'depth': 6, 'learning_rate': 0.016421829735607976, 'l2_leaf_reg': 1.5836989831075927, 'iterations': 875}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:34:15,906] Trial 12 finished with value: 0.7125692401019794 and parameters: {'depth': 6, 'learning_rate': 0.02239553657580818, 'l2_leaf_reg': 1.5103007605101904, 'iterations': 931}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:35:36,970] Trial 13 finished with value: 0.708491406938251 and parameters: {'depth': 6, 'learning_rate': 0.04770680273049132, 'l2_leaf_reg': 4.100400131157065, 'iterations': 849}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:36:15,772] Trial 14 finished with value: 0.7048699544344865 and parameters: {'depth': 4, 'learning_rate': 0.009278936131672627, 'l2_leaf_reg': 0.988170538646985, 'iterations': 823}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:38:21,390] Trial 15 finished with value: 0.7128253002315196 and parameters: {'depth': 8, 'learning_rate': 0.019121149846670266, 'l2_leaf_reg': 1.9339187708632544, 'iterations': 749}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:39:16,890] Trial 16 finished with value: 0.7080027991333067 and parameters: {'depth': 5, 'learning_rate': 0.009052505885746825, 'l2_leaf_reg': 0.19585752169732754, 'iterations': 908}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:40:11,201] Trial 17 finished with value: 0.7057466103854555 and parameters: {'depth': 7, 'learning_rate': 0.037320165731095596, 'l2_leaf_reg': 0.10049107167856225, 'iterations': 487}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:41:15,326] Trial 18 finished with value: 0.701214068694514 and parameters: {'depth': 5, 'learning_rate': 0.09350588230810253, 'l2_leaf_reg': 6.485815210617422, 'iterations': 990}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:43:17,739] Trial 19 finished with value: 0.7131760604141965 and parameters: {'depth': 8, 'learning_rate': 0.01320846338705268, 'l2_leaf_reg': 0.869959142935816, 'iterations': 748}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:43:33,784] Trial 20 finished with value: 0.6826180060425605 and parameters: {'depth': 6, 'learning_rate': 0.0011611180236886706, 'l2_leaf_reg': 0.4411100122649556, 'iterations': 201}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:45:42,064] Trial 21 finished with value: 0.713104188681959 and parameters: {'depth': 8, 'learning_rate': 0.011041984454317071, 'l2_leaf_reg': 0.89724726409656, 'iterations': 779}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:48:08,034] Trial 22 finished with value: 0.7099300606042883 and parameters: {'depth': 8, 'learning_rate': 0.025570087278062037, 'l2_leaf_reg': 0.6425387281234312, 'iterations': 882}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:51:27,852] Trial 23 finished with value: 0.7118837702357896 and parameters: {'depth': 9, 'learning_rate': 0.01502191356707765, 'l2_leaf_reg': 1.132822383100968, 'iterations': 708}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:52:26,057] Trial 24 finished with value: 0.7054017093817584 and parameters: {'depth': 7, 'learning_rate': 0.0067908427247850445, 'l2_leaf_reg': 0.26927456701061836, 'iterations': 527}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:54:31,490] Trial 25 finished with value: 0.712890055763812 and parameters: {'depth': 8, 'learning_rate': 0.01471779596308872, 'l2_leaf_reg': 2.1221631047082132, 'iterations': 763}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:55:40,390] Trial 26 finished with value: 0.7079994880009863 and parameters: {'depth': 6, 'learning_rate': 0.006727267843006306, 'l2_leaf_reg': 0.8377609341551769, 'iterations': 857}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:56:18,945] Trial 27 finished with value: 0.7080707513251364 and parameters: {'depth': 7, 'learning_rate': 0.048359004352196464, 'l2_leaf_reg': 0.5221723487165844, 'iterations': 340}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:58:03,346] Trial 28 finished with value: 0.7131761735609418 and parameters: {'depth': 7, 'learning_rate': 0.02583856846336625, 'l2_leaf_reg': 3.094353426875538, 'iterations': 936}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 02:59:03,661] Trial 29 finished with value: 0.7126066825143269 and parameters: {'depth': 5, 'learning_rate': 0.029815324004448744, 'l2_leaf_reg': 3.4541134181297544, 'iterations': 943}. Best is trial 11 with value: 0.713758610349902.\n","[I 2025-05-17 03:00:21,699] Trial 30 finished with value: 0.7139316863917715 and parameters: {'depth': 6, 'learning_rate': 0.019604014878239595, 'l2_leaf_reg': 5.172061804107547, 'iterations': 940}. Best is trial 30 with value: 0.7139316863917715.\n","[I 2025-05-17 03:01:38,275] Trial 31 finished with value: 0.7137694540196566 and parameters: {'depth': 6, 'learning_rate': 0.018139601986308596, 'l2_leaf_reg': 5.330590729138109, 'iterations': 946}. Best is trial 30 with value: 0.7139316863917715.\n","[I 2025-05-17 03:02:51,470] Trial 32 finished with value: 0.7149144214772792 and parameters: {'depth': 6, 'learning_rate': 0.01887143254640496, 'l2_leaf_reg': 5.612912910084233, 'iterations': 897}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:04:03,848] Trial 33 finished with value: 0.7127113838219428 and parameters: {'depth': 6, 'learning_rate': 0.019286164896713292, 'l2_leaf_reg': 4.883400303984895, 'iterations': 884}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:04:54,076] Trial 34 finished with value: 0.709495256824602 and parameters: {'depth': 4, 'learning_rate': 0.04436134984694682, 'l2_leaf_reg': 6.158861442681265, 'iterations': 988}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:05:47,317] Trial 35 finished with value: 0.7122324040920746 and parameters: {'depth': 5, 'learning_rate': 0.019363537013005444, 'l2_leaf_reg': 9.555069075458718, 'iterations': 837}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:06:35,206] Trial 36 finished with value: 0.7076991780295773 and parameters: {'depth': 6, 'learning_rate': 0.06929419862251157, 'l2_leaf_reg': 6.446038357429165, 'iterations': 586}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:06:57,576] Trial 37 finished with value: 0.7005375454058491 and parameters: {'depth': 5, 'learning_rate': 0.010535430117050375, 'l2_leaf_reg': 2.6545688276022408, 'iterations': 344}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:08:14,121] Trial 38 finished with value: 0.7133837247448236 and parameters: {'depth': 6, 'learning_rate': 0.01889411239200195, 'l2_leaf_reg': 4.80708871341292, 'iterations': 940}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:09:27,419] Trial 39 finished with value: 0.7070351710738771 and parameters: {'depth': 6, 'learning_rate': 0.0062566508628435075, 'l2_leaf_reg': 7.658399116624044, 'iterations': 892}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:10:04,691] Trial 40 finished with value: 0.7090663051212777 and parameters: {'depth': 4, 'learning_rate': 0.05995510148435852, 'l2_leaf_reg': 5.070665792884728, 'iterations': 706}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:11:33,101] Trial 41 finished with value: 0.712162700368554 and parameters: {'depth': 7, 'learning_rate': 0.012661787415596036, 'l2_leaf_reg': 0.31791367435984447, 'iterations': 800}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:12:50,345] Trial 42 finished with value: 0.7107289145517166 and parameters: {'depth': 6, 'learning_rate': 0.02477935011693573, 'l2_leaf_reg': 2.0919693518031184, 'iterations': 952}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:14:29,338] Trial 43 finished with value: 0.7101949913306091 and parameters: {'depth': 7, 'learning_rate': 0.03428064574173828, 'l2_leaf_reg': 1.5236530852913832, 'iterations': 876}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:21:41,569] Trial 44 finished with value: 0.7114910793776764 and parameters: {'depth': 10, 'learning_rate': 0.00788182009282988, 'l2_leaf_reg': 4.139883094637172, 'iterations': 999}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:23:13,152] Trial 45 finished with value: 0.7061970952235891 and parameters: {'depth': 7, 'learning_rate': 0.004740196837841724, 'l2_leaf_reg': 7.917695527583087, 'iterations': 833}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:24:27,074] Trial 46 finished with value: 0.7133355204021056 and parameters: {'depth': 6, 'learning_rate': 0.015539188990521086, 'l2_leaf_reg': 3.170088211745366, 'iterations': 910}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:25:28,484] Trial 47 finished with value: 0.71217171886964 and parameters: {'depth': 5, 'learning_rate': 0.017148664006929604, 'l2_leaf_reg': 3.981420659031642, 'iterations': 963}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:26:23,591] Trial 48 finished with value: 0.7107801248333327 and parameters: {'depth': 6, 'learning_rate': 0.011910809326386992, 'l2_leaf_reg': 2.43793499889359, 'iterations': 673}. Best is trial 32 with value: 0.7149144214772792.\n","[I 2025-05-17 03:27:11,845] Trial 49 finished with value: 0.6972998414363144 and parameters: {'depth': 5, 'learning_rate': 0.0036216070781349497, 'l2_leaf_reg': 0.1732918432517504, 'iterations': 797}. Best is trial 32 with value: 0.7149144214772792.\n"]},{"output_type":"stream","name":"stdout","text":["▶ Best params: {'depth': 6, 'learning_rate': 0.01887143254640496, 'l2_leaf_reg': 5.612912910084233, 'iterations': 897}\n","▶ Best 3-fold CV ROC AUC: 0.7149144214772792\n"]},{"output_type":"error","ename":"SyntaxError","evalue":"keyword argument repeated: thread_count (, line 129)","traceback":["\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m129\u001b[0m\n\u001b[0;31m thread_count=1\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m keyword argument repeated: thread_count\n"]}],"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_validate\n","from sklearn.impute import SimpleImputer\n","from feature_engine.outliers import Winsorizer\n","from sklearn.feature_selection import SelectFromModel\n","from catboost import CatBoostClassifier\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","import optuna\n","from sklearn.model_selection import cross_val_score\n","\n","# 1) 데이터 로드 & train/holdout 분리\n","df = pd.read_csv(train_src)\n","\n","df = create_features(df)\n","\n","X = df.drop(['id','shares','y'], axis=1)\n","y = df['y']\n","X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n","\n","# 2) 컬럼 나누기\n","num_cols = X.select_dtypes(include=['int64','float64']).columns.tolist()\n","cat_cols = ['data_channel','weekday','channel_weekend'] # ← 실제 범주형명 사용\n","\n","# 3) 수치형 전처리: median → Winsorizer\n","num_imputer = SimpleImputer(strategy='median')\n","winsorizer = Winsorizer(capping_method='gaussian', tail='both', fold=3) # ← fold 조절 가능\n","\n","X_tr_num = num_imputer.fit_transform( X_tr[num_cols] )\n","X_tr_num = winsorizer.fit_transform(pd.DataFrame(X_tr_num, columns=num_cols)).values\n","\n","X_te_num = num_imputer.transform( X_te[num_cols] )\n","X_te_num = winsorizer.transform(pd.DataFrame(X_te_num, columns=num_cols)).values\n","\n","# 4) 범주형 전처리: most_frequent → category\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","X_tr_cat = pd.DataFrame(\n"," cat_imputer.fit_transform(X_tr[cat_cols]),\n"," columns=cat_cols, index=X_tr.index\n",").astype('category')\n","\n","X_te_cat = pd.DataFrame(\n"," cat_imputer.transform(X_te[cat_cols]),\n"," columns=cat_cols, index=X_te.index\n",").astype('category')\n","\n","# 5) 최종 학습용 DataFrame 합치기\n","X_tr_final = pd.concat([\n"," pd.DataFrame(X_tr_num, columns=num_cols, index=X_tr.index),\n"," X_tr_cat\n","], axis=1)\n","\n","X_te_final = pd.concat([\n"," pd.DataFrame(X_te_num, columns=num_cols, index=X_te.index),\n"," X_te_cat\n","], axis=1)\n","\n","# 6) SelectFromModel로 피처 선택\n","base_model = CatBoostClassifier(\n"," iterations=500, # ← 이후 그리드/베이지안 탐색할 파라미터\n"," learning_rate=0.05, # ← 여기부터 튜닝\n"," depth=6,\n"," eval_metric='AUC',\n"," random_seed=42,\n"," thread_count=1,\n"," verbose=False,\n"," cat_features=cat_cols\n",")\n","base_model.fit(X_tr_final, y_tr)\n","\n","selector = SelectFromModel(\n"," estimator=base_model,\n"," threshold='median' # ← 'mean','median' 또는 float 값으로 바꿔가며 실험\n",")\n","selector.fit(X_tr_final, y_tr)\n","\n","selected_feats = X_tr_final.columns[ selector.get_support() ].tolist()\n","print(\"▶ 선택된 피처:\", selected_feats)\n","\n","X_tr_sel = X_tr_final[selected_feats]\n","X_te_sel = X_te_final[selected_feats]\n","\n","\n","# 7) Optuna로 하이퍼파라미터 최적화\n","inner_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n","\n","def objective(trial):\n"," # 7-1) 하이퍼파라미터 제안\n"," params = {\n"," 'depth': trial.suggest_int('depth', 4, 10),\n"," 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 1e-1, log=True), # ← 변경\n"," 'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 0.1, 10, log=True), # ← 변경\n"," 'iterations': trial.suggest_int('iterations', 200, 1000),\n"," 'random_seed': 42,\n"," 'eval_metric': 'AUC',\n"," 'verbose': False,\n"," }\n","\n"," # 7-2) 모델 생성 & CV 평가\n"," model = CatBoostClassifier(**params, cat_features=cat_cols, thread_count=1)\n"," aucs = cross_val_score(\n"," model,\n"," X_tr_sel, y_tr,\n"," cv=inner_cv,\n"," scoring='roc_auc',\n"," n_jobs=-1\n"," )\n"," # 7-3) 평균 AUC 반환\n"," return aucs.mean()\n","\n","# 7-4) 스터디 생성 및 최적화 실행\n","study = optuna.create_study(direction='maximize')\n","study.optimize(objective, n_trials=50)\n","\n","# 7-5) 결과 출력\n","print(\"▶ Best params:\", study.best_trial.params)\n","print(\"▶ Best 3-fold CV ROC AUC:\", study.best_value)\n","\n","# 8) 최적 파라미터로 최종 모델 학습 & Holdout 평가\n","best_params = study.best_trial.params\n","best_model = CatBoostClassifier(\n"," **best_params,\n"," random_seed=42,\n"," thread_count=1,\n"," early_stopping_rounds = 30,\n"," verbose=False,\n"," cat_features=cat_cols,\n",")\n","best_model.fit(X_tr_sel, y_tr)\n","y_pred = best_model.predict(X_te_sel)\n","y_prob = best_model.predict_proba(X_te_sel)[:,1]\n","\n","acc = accuracy_score(y_te, y_pred)\n","f1 = f1_score(y_te, y_pred)\n","auc = roc_auc_score(y_te, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","# 7-5) 결과 출력\n","best_params = study.best_trial.params\n","print(\"▶ Best params:\", best_params)\n","print(\" - depth :\", best_params['depth'])\n","print(\" - learning_rate:\", best_params['learning_rate'])\n","print(\" - l2_leaf_reg :\", best_params['l2_leaf_reg'])\n","print(\" - iterations :\", best_params['iterations'])\n","print(\"▶ Best 3-fold CV ROC AUC:\", study.best_value)\n","\n","\n","print(\"\\n▶ Holdout Test Performance (best model)\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")\n","\n","\n","# # 7) 최종 CatBoost 모델 학습 & 평가\n","# model = CatBoostClassifier(\n","# iterations=1000, # ← 최종 탐색 범위\n","# learning_rate=0.05,\n","# depth=4,\n","# eval_metric='AUC',\n","# random_seed=42,\n","# thread_count=1,\n","# early_stopping_rounds = 30,\n","# verbose=False,\n","# cat_features=[c for c in selected_feats if c in cat_cols]\n","# )\n","\n","# # 7-1) 5-Fold CV (train_val)\n","# cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","# scoring = ['accuracy','f1','roc_auc']\n","# cv_res = cross_validate(model, X_tr_sel, y_tr, cv=cv, scoring=scoring, error_score='raise')\n","\n","# acc = cv_res['test_accuracy']\n","# f1 = cv_res['test_f1']\n","# auc = cv_res['test_roc_auc']\n","# comp = (acc + f1 + auc) / 3\n","\n","# print(\"\\n5-Fold CV (train_val)\")\n","# for i,(a,f,u,c) in enumerate(zip(acc,f1,auc,comp), 1):\n","# print(f\"[Fold {i}] Acc:{a:.4f}, F1:{f:.4f}, AUC:{u:.4f}, Comp:{c:.4f}\")\n","# print(\"평균 Composite:\", comp.mean())\n","\n","# # 7-2) Holdout Test\n","# model.fit(X_tr_sel, y_tr)\n","# y_pred = model.predict(X_te_sel)\n","# y_prob = model.predict_proba(X_te_sel)[:,1]\n","\n","# acc = accuracy_score(y_te, y_pred)\n","# f1 = f1_score(y_te, y_pred)\n","# auc = roc_auc_score(y_te, y_prob)\n","# comp = (acc + f1 + auc) / 3\n","\n","# print(\"\\nHoldout Test\")\n","# print(f\"Accuracy : {acc:.4f}\")\n","# print(f\"F1 Score : {f1:.4f}\")\n","# print(f\"ROC AUC : {auc:.4f}\")\n","# print(f\"Composite: {comp:.4f}\")\n"]}]} \ No newline at end of file diff --git a/model/seohyoun/SelectFromModel_1.ipynb b/model/seohyoun/SelectFromModel_1.ipynb new file mode 100644 index 0000000..cf225d6 --- /dev/null +++ b/model/seohyoun/SelectFromModel_1.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOFd8u2y4Ei6TymZ9PulIaV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["! pip install feature_engine\n","! pip install CatBoost"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BaTFriqa5oqh","executionInfo":{"status":"ok","timestamp":1747447472528,"user_tz":-540,"elapsed":26892,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"6c7fc661-fcfc-45ef-9e90-70ff71253adb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S-r5IGrP5s3-","executionInfo":{"status":"ok","timestamp":1747447631908,"user_tz":-540,"elapsed":26417,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"cece7366-7e33-4335-d868-a4d5cac22fcb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer\n","from catboost import CatBoostClassifier\n","from sklearn.feature_selection import SelectFromModel"],"metadata":{"id":"1T5rHhYpEqmn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 파생 변수 생성 # df 통계형 변수를 쓸 때는 데이터 누수 주의\n","\n","def create_features(df):\n"," # 1) 제목 길이 대비 본문 길이 비율\n"," df['title_content_ratio'] = df['n_tokens_title'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 2) 키워드 밀도: 전체 토큰 대비 키워드 개수 비율\n"," df['keyword_density'] = df['num_keywords'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 3) 비중어(non-stop) 단어 비율: 본문 대비\n"," df['nonstop_ratio'] = df['n_non_stop_words'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 4) 본문 내 링크 대비 자기링크 비율\n"," df['self_href_ratio'] = df['num_self_hrefs'] / df['num_hrefs'].replace(0, np.nan)\n","\n"," # 5) 이미지/동영상 비율\n"," df['img_video_ratio'] = df['num_imgs'] / (df['num_videos'] + 1)\n","\n"," # 6) 키워드 분포 폭: (최댓값 키워드 빈도 – 최솟값 키워드 빈도)\n"," df['kw_spread'] = df['kw_max_max'] - df['kw_min_min']\n","\n"," # 7) 감성 범위: (최대 양성 편향 – 최소 음성 편향)\n"," df['sentiment_range'] = df['max_positive_polarity'] - df['min_negative_polarity']\n","\n"," # 8) 제목 감성 상호작용: 주관성 × 편향 절대값\n"," df['title_sent_interact']= df['abs_title_subjectivity'] * df['abs_title_sentiment_polarity']\n","\n"," # 9) 주말 여부 플래그\n"," df['is_weekend'] = df['weekday'].isin(['Saturday','Sunday']).astype(int)\n","\n"," # 10) 채널×주말 교차 카테고리 (필요시 one-hot 인코딩)\n"," df['channel_weekend'] = df['data_channel'] + '_' + df['is_weekend'].astype(str)\n","\n"," return df"],"metadata":{"id":"IzrifFPNpLTl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","df = create_features(df)\n","\n","X = df.drop(['id','shares','y'], axis=1)\n","y = df['y']\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n","\n","num_cols = X_trainval.select_dtypes(include=['int64','float64']).columns.tolist()\n","cat_cols = ['data_channel','weekday','channel_weekend']"],"metadata":{"id":"UqP5_HLfE7Ps"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y"],"metadata":{"id":"6WUVrnZHFT7Y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 수치형 전처리: median → Winsorizer\n","num_imputer = SimpleImputer(strategy='median')\n","winsorizer = Winsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","X_trainval_num = num_imputer.fit_transform(X_trainval[num_cols])\n","X_trainval_num = winsorizer.fit_transform(pd.DataFrame(X_trainval_num, columns=num_cols)).values\n","\n","X_test_num = num_imputer.transform(X_test[num_cols])\n","X_test_num = winsorizer.transform(pd.DataFrame(X_test_num, columns=num_cols)).values\n","\n","# 범주형 전처리: most_frequent → category\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","X_trainval_cat = pd.DataFrame(\n"," cat_imputer.fit_transform(X_trainval[cat_cols]),\n"," columns=cat_cols, index=X_trainval.index\n",").astype('category')\n","\n","X_test_cat = pd.DataFrame(\n"," cat_imputer.transform(X_test[cat_cols]),\n"," columns=cat_cols, index=X_test.index\n",").astype('category')\n","\n","# 최종 학습용 DataFrame 합치기\n","X_trainval_final = pd.concat([\n"," pd.DataFrame(X_trainval_num, columns=num_cols, index=X_trainval.index), X_trainval_cat], axis=1)\n","\n","X_test_final = pd.concat([pd.DataFrame(X_test_num, columns=num_cols, index=X_test.index), X_test_cat], axis=1)\n"],"metadata":{"id":"sPuAApBSFfAS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# SelectFromModel로 피처 선택\n","base_model = CatBoostClassifier(\n"," iterations=500,\n"," learning_rate=0.05,\n"," depth=6,\n"," eval_metric='AUC',\n"," random_seed=42,\n"," thread_count=1,\n"," verbose=False,\n"," cat_features=cat_cols\n",")\n","\n","base_model.fit(X_trainval_final, y_trainval)\n","\n","selector = SelectFromModel(estimator=base_model,threshold='median')\n","selector.fit(X_trainval_final, y_trainval)\n","\n","selected_feats = X_trainval_final.columns[selector.get_support()].tolist()\n","print(\"▶ 선택된 피처:\", selected_feats)\n","\n","X_trainval_sel = X_trainval_final[selected_feats]\n","X_test_sel = X_test_final[selected_feats]"],"metadata":{"id":"1wDY0KciFsUa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 최종 CatBoost 모델 학습 & 평가\n","model = CatBoostClassifier(\n"," iterations=1000,\n"," learning_rate=0.05,\n"," depth=4,\n"," eval_metric='AUC',\n"," random_seed=42,\n"," thread_count=1,\n"," early_stopping_rounds=30,\n"," verbose=False,\n"," cat_features=[c for c in selected_feats if c in cat_cols]\n",")"],"metadata":{"id":"BmJfk5TeFvmp"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0QXfJ62f5fnV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1747447846932,"user_tz":-540,"elapsed":183345,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"b2f54611-964a-4ad9-cbf2-757064e1187d"},"outputs":[{"output_type":"stream","name":"stdout","text":["▶ 선택된 피처: ['n_unique_tokens', 'n_non_stop_unique_tokens', 'average_token_length', 'kw_max_min', 'kw_avg_min', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg', 'kw_avg_avg', 'self_reference_min_shares', 'self_reference_max_shares', 'self_reference_avg_sharess', 'LDA_00', 'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04', 'global_subjectivity', 'global_rate_positive_words', 'global_rate_negative_words', 'avg_positive_polarity', 'title_content_ratio', 'keyword_density', 'self_href_ratio', 'img_video_ratio', 'data_channel', 'weekday', 'channel_weekend']\n","\n","5-Fold CV (train_val)\n","[Fold 1] Acc:0.6695, F1:0.6599, AUC:0.7263, Comp:0.6852\n","[Fold 2] Acc:0.6470, F1:0.6472, AUC:0.7085, Comp:0.6675\n","[Fold 3] Acc:0.6534, F1:0.6518, AUC:0.7074, Comp:0.6709\n","[Fold 4] Acc:0.6554, F1:0.6570, AUC:0.7157, Comp:0.6760\n","[Fold 5] Acc:0.6394, F1:0.6378, AUC:0.6976, Comp:0.6582\n","평균 Composite: 0.6715705749852122\n","\n","Holdout Test\n","Accuracy : 0.6682\n","F1 Score : 0.6656\n","ROC AUC : 0.7256\n","Composite: 0.6865\n"]}],"source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","cv_res = cross_validate(model, X_trainval_sel, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_res['test_accuracy']\n","f1 = cv_res['test_f1']\n","auc = cv_res['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n5-Fold CV (train_val)\")\n","for i, (a, f, u, c) in enumerate(zip(acc, f1, auc, comp), 1):\n"," print(f\"[Fold {i}] Acc:{a:.4f}, F1:{f:.4f}, AUC:{u:.4f}, Comp:{c:.4f}\")\n","print(\"평균 Composite:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","model.fit(X_trainval_sel, y_trainval)\n","y_pred = model.predict(X_test_sel)\n","y_prob = model.predict_proba(X_test_sel)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\nHoldout Test\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"]}]} \ No newline at end of file diff --git a/model/seohyoun/SelectFromModel_2.ipynb b/model/seohyoun/SelectFromModel_2.ipynb new file mode 100644 index 0000000..2065858 --- /dev/null +++ b/model/seohyoun/SelectFromModel_2.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNhs2PVvTjOexEyOLS6Fc9M"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["! pip install feature_engine\n","! pip install CatBoost"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BaTFriqa5oqh","executionInfo":{"status":"ok","timestamp":1747454429537,"user_tz":-540,"elapsed":7707,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"701fda6c-004e-48f0-89e0-92980876e567"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: feature_engine in /usr/local/lib/python3.11/dist-packages (1.8.3)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Requirement already satisfied: CatBoost in /usr/local/lib/python3.11/dist-packages (1.2.8)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n"]}]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S-r5IGrP5s3-","executionInfo":{"status":"ok","timestamp":1747454432097,"user_tz":-540,"elapsed":2552,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"198d0829-78b8-46c9-fb4b-36735cc09569"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer\n","from catboost import CatBoostClassifier\n","from sklearn.feature_selection import SelectFromModel"],"metadata":{"id":"h9ZgUyplUs-U","executionInfo":{"status":"ok","timestamp":1747454433180,"user_tz":-540,"elapsed":1072,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# 파생 변수 생성 # df 통계형 변수를 쓸 때는 데이터 누수 주의\n","def create_features(df):\n"," # 1) 제목 길이 대비 본문 길이 비율\n"," df['title_content_ratio'] = df['n_tokens_title'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 2) 키워드 밀도: 전체 토큰 대비 키워드 개수 비율\n"," df['keyword_density'] = df['num_keywords'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 3) 비중어(non-stop) 단어 비율: 본문 대비\n"," df['nonstop_ratio'] = df['n_non_stop_words'] / df['n_tokens_content'].replace(0, np.nan)\n","\n"," # 4) 본문 내 링크 대비 자기링크 비율\n"," df['self_href_ratio'] = df['num_self_hrefs'] / df['num_hrefs'].replace(0, np.nan)\n","\n"," # 5) 이미지/동영상 비율\n"," df['img_video_ratio'] = df['num_imgs'] / (df['num_videos'] + 1)\n","\n"," # 6) 키워드 분포 폭: (최댓값 키워드 빈도 – 최솟값 키워드 빈도)\n"," df['kw_spread'] = df['kw_max_max'] - df['kw_min_min']\n","\n"," # 7) 감성 범위: (최대 양성 편향 – 최소 음성 편향)\n"," df['sentiment_range'] = df['max_positive_polarity'] - df['min_negative_polarity']\n","\n"," # 8) 제목 감성 상호작용: 주관성 × 편향 절대값\n"," df['title_sent_interact']= df['abs_title_subjectivity'] * df['abs_title_sentiment_polarity']\n","\n"," # 9) 주말 여부 플래그\n"," df['is_weekend'] = df['weekday'].isin(['Saturday','Sunday']).astype(int)\n","\n"," # 10) 채널×주말 교차 카테고리\n"," df['channel_weekend'] = df['data_channel'] + '_' + df['is_weekend'].astype(str)\n","\n"," return df"],"metadata":{"id":"IzrifFPNpLTl","executionInfo":{"status":"ok","timestamp":1747454433211,"user_tz":-540,"elapsed":14,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","df = create_features(df)\n","\n","X = df.drop(['id','shares','y'], axis=1)\n","y = df['y']\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n","\n","num_cols = X_trainval.select_dtypes(include=['int64','float64']).columns.tolist()\n","cat_cols = ['data_channel','weekday','channel_weekend']"],"metadata":{"id":"UElJS_FmUzLr","executionInfo":{"status":"ok","timestamp":1747454435537,"user_tz":-540,"elapsed":2316,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y"],"metadata":{"id":"931Z4VbyW_9f","executionInfo":{"status":"ok","timestamp":1747454435589,"user_tz":-540,"elapsed":32,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# 수치형 전처리 (median + 이상치 처리)\n","\n","# 왜도·이상치 리스트 뽑기 (train_val 기준)\n","# skew_k, outlier_k = 7, 8 ## 아 이거 8,7이었나... EDA 확인\n","# skew_feats = get_skew_feats(X_trainval, skew_k)\n","# outlier_feats = get_outlier_feats(X_trainval, outlier_k)\n","# both_feats = list(set(skew_feats) & set(outlier_feats))\n","# log_only = [c for c in skew_feats if c not in both_feats]\n","# winsor_only = [c for c in outlier_feats if c not in both_feats]\n","# base_num_feats= [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","# num_imputer = SimpleImputer(strategy='median')\n","# win_tf = Winsorizer(capping_method='gaussian', tail='both', fold=3)\n","# safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","\n","# both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf), ('log', safe_log_tf)])\n","# log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","# winsor_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf)])\n","# base_num_pipeline = Pipeline([('imputer', num_imputer)])\n","\n","# numeric_transformer = ColumnTransformer([\n","# ('both', both_pipeline, both_feats),\n","# ('log', log_pipeline, log_only),\n","# ('winsor', winsor_pipeline, winsor_only),\n","# ('base', base_num_pipeline, base_num_feats),\n","# ])\n","\n","# X_trainval_num = numeric_transformer.fit_transform(X_trainval)\n","# X_test_num = numeric_transformer.transform(X_test)\n","\n","# 수치형 전처리 (median → Winsorizer)\n","num_imputer = SimpleImputer(strategy='median')\n","winsorizer = Winsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","X_trainval_num = num_imputer.fit_transform(X_trainval[num_cols])\n","X_trainval_num = winsorizer.fit_transform(pd.DataFrame(X_trainval_num, columns=num_cols)).values\n","\n","X_test_num = num_imputer.transform(X_test[num_cols])\n","X_test_num = winsorizer.transform(pd.DataFrame(X_test_num, columns=num_cols)).values\n","\n","# 범주형 전처리 (최빈값)\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","X_trainval_cat = pd.DataFrame(cat_imputer.fit_transform(X_trainval[cat_cols]), columns=cat_cols, index=X_trainval.index).astype('category')\n","X_test_cat = pd.DataFrame(cat_imputer.transform(X_test[cat_cols]), columns=cat_cols, index=X_test.index).astype('category')\n","\n","# 합치기\n","X_trainval_final = pd.concat([pd.DataFrame(X_trainval_num, columns=num_cols, index=X_trainval.index),X_trainval_cat], axis=1)\n","X_test_final = pd.concat([pd.DataFrame(X_test_num, columns=num_cols, index=X_test.index),X_test_cat], axis=1)"],"metadata":{"id":"7S70AAq4WhU6","executionInfo":{"status":"ok","timestamp":1747454974786,"user_tz":-540,"elapsed":1399,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# SelectFromModel로 피처 선택\n","base_model = CatBoostClassifier(\n"," iterations=500,\n"," learning_rate=0.05,\n"," depth=6,\n"," eval_metric='AUC',\n"," random_seed=42,\n"," thread_count=1,\n"," verbose=False,\n"," cat_features=cat_cols\n",")\n","base_model.fit(X_trainval_final, y_trainval)\n","\n","selector = SelectFromModel(estimator=base_model, threshold=1.0) ## threshold = 'mean', 'median', 0.5, 1.0으로 바꿔보기\n","selector.fit(X_trainval_final, y_trainval)\n","\n","selected_feats = X_trainval_final.columns[selector.get_support()].tolist()\n","print(\"▶ 선택된 피처:\", selected_feats)\n","\n","X_trainval_sel = X_trainval_final[selected_feats]\n","X_test_sel = X_test_final[selected_feats]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UW682m-UY23m","executionInfo":{"status":"ok","timestamp":1747455036945,"user_tz":-540,"elapsed":62141,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"a7f213b3-6d6b-4aa0-93de-57d351170b23"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["▶ 선택된 피처: ['n_tokens_title', 'n_unique_tokens', 'n_non_stop_unique_tokens', 'num_hrefs', 'num_imgs', 'average_token_length', 'kw_min_min', 'kw_max_min', 'kw_avg_min', 'kw_min_max', 'kw_max_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg', 'kw_avg_avg', 'self_reference_min_shares', 'self_reference_max_shares', 'self_reference_avg_sharess', 'LDA_00', 'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04', 'global_subjectivity', 'global_sentiment_polarity', 'global_rate_positive_words', 'global_rate_negative_words', 'rate_positive_words', 'avg_positive_polarity', 'min_positive_polarity', 'avg_negative_polarity', 'title_subjectivity', 'title_sentiment_polarity', 'abs_title_subjectivity', 'title_content_ratio', 'keyword_density', 'self_href_ratio', 'img_video_ratio', 'is_weekend', 'data_channel', 'weekday', 'channel_weekend']\n"]}]},{"cell_type":"code","execution_count":18,"metadata":{"id":"0QXfJ62f5fnV","executionInfo":{"status":"ok","timestamp":1747455036952,"user_tz":-540,"elapsed":20,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"outputs":[],"source":["# 최종 CatBoost 모델 학습 & 평가 ## 여기 조정해보면서 결과 확인 (iterations, learnong_rate, l2_leaf_reg, depth)\n","model = CatBoostClassifier(\n"," iterations=1000,\n"," learning_rate=0.02,\n"," l2_leaf_reg=6,\n"," depth=6,\n"," eval_metric='AUC',\n"," random_seed=42,\n"," thread_count=1,\n"," early_stopping_rounds=30,\n"," verbose=False,\n"," cat_features=[c for c in selected_feats if c in cat_cols]\n",")"]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy','f1','roc_auc']\n","cv_res = cross_validate(model, X_trainval_sel, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_res['test_accuracy']\n","f1 = cv_res['test_f1']\n","auc = cv_res['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","model.fit(X_trainval_sel, y_trainval)\n","y_pred = model.predict(X_test_sel)\n","y_prob = model.predict_proba(X_test_sel)[:,1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FyhQv-zAZFuW","executionInfo":{"status":"ok","timestamp":1747455305934,"user_tz":-540,"elapsed":268992,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"b0f2e5ec-d47d-4935-eaa8-e1ac1ee1e46e"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6715, F1: 0.6674, AUC: 0.7308, Composite: 0.6899\n","[Fold 2] Accuracy: 0.6540, F1: 0.6535, AUC: 0.7182, Composite: 0.6752\n","[Fold 3] Accuracy: 0.6554, F1: 0.6554, AUC: 0.7117, Composite: 0.6742\n","[Fold 4] Accuracy: 0.6602, F1: 0.6614, AUC: 0.7198, Composite: 0.6805\n","[Fold 5] Accuracy: 0.6486, F1: 0.6467, AUC: 0.7125, Composite: 0.6693\n","\n","평균 Composite Score: 0.6778131821717504\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6689\n","F1 Score : 0.6656\n","ROC AUC : 0.7303\n","Composite: 0.6883\n"]}]}]} \ No newline at end of file diff --git a/model/seohyoun/feature importance_30.ipynb b/model/seohyoun/feature importance_30.ipynb new file mode 100644 index 0000000..caec203 --- /dev/null +++ b/model/seohyoun/feature importance_30.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPDuKR9SABThYpHFMUy7ZlV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747408073805,"user_tz":-540,"elapsed":8643,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"6b1fdc7b-4f42-47b1-e5d2-d0ee08ca80c6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: feature_engine in /usr/local/lib/python3.11/dist-packages (1.8.3)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.26.4)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.13.1)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.1)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.4.2)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.5.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Requirement already satisfied: CatBoost in /usr/local/lib/python3.11/dist-packages (1.2.8)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.26.4)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.13.1)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.56.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.1.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.1)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.0.0)\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from scipy.stats import skew\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from catboost import CatBoostClassifier\n","from feature_engine.outliers import Winsorizer\n","from sklearn.base import TransformerMixin"],"metadata":{"id":"n1XtzI-ivu-Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," sk = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return sk.head(k).index.tolist()\n","\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","class DFTransformer(TransformerMixin):\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)\n"],"metadata":{"id":"uMaXZFslvxS9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","X = df.drop(['id','shares','y'], axis=1)\n","y = df['y']"],"metadata":{"id":"mFnocCzuv0Ia"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["num_cols = X.select_dtypes(['int64','float64']).columns.tolist()\n","cat_cols = ['data_channel','weekday']\n","\n","skew_feats = get_skew_feats(X, k=7)\n","outlier_feats = get_outlier_feats(X, k=8)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [c for c in skew_feats if c not in both_feats]\n","winsor_only = [c for c in outlier_feats if c not in both_feats]\n","base_num_feats= [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]"],"metadata":{"id":"aUSS-SCiv2VO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","win_tf = Winsorizer(capping_method='gaussian', tail='both', fold=3)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","\n","both_pipe = Pipeline([('imputer', num_imputer), ('winsor', win_tf), ('log', safe_log_tf)])\n","log_pipe = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipe = Pipeline([('imputer', num_imputer), ('winsor', win_tf)])\n","base_pipe = Pipeline([('imputer', num_imputer)])\n","cat_pipe = Pipeline([('imputer', cat_imputer)])\n","\n","preprocessor_orig = ColumnTransformer([\n"," ('both', both_pipe, both_feats),\n"," ('log', log_pipe, log_only),\n"," ('winsor', winsor_pipe, winsor_only),\n"," ('base', base_pipe, base_num_feats),\n"," ('cat', cat_pipe, cat_cols),\n","])\n","all_cols = both_feats + log_only + winsor_only + base_num_feats + cat_cols"],"metadata":{"id":"PqJOIWiPv67Q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pipe_orig = Pipeline([\n"," ('pre', preprocessor_orig),\n"," ('to_df', DFTransformer(all_cols)),\n"," ('clf', CatBoostClassifier(\n"," cat_features=cat_cols,\n"," verbose=False,\n"," thread_count=1,\n"," early_stopping_rounds=30\n"," )),\n","])"],"metadata":{"id":"5jIG3kw2wCD2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_trainval, X_test, y_trainval, y_test = train_test_split(\n"," X, y, test_size=0.2, random_state=42, stratify=y\n",")"],"metadata":{"id":"4dHNLxSQwEdg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 1차 학습 → Feature importance 추출\n","pipe_orig.fit(X_trainval, y_trainval)\n","importances = pipe_orig.named_steps['clf'].get_feature_importance()\n","feat_imp = pd.DataFrame({\n"," 'feature': all_cols,\n"," 'importance': importances\n","}).sort_values('importance', ascending=False).reset_index(drop=True)\n","\n","# 하위 30% 제거 목록\n","threshold = feat_imp['importance'].quantile(0.3)\n","low_feats = feat_imp.loc[feat_imp['importance'] <= threshold, 'feature'].tolist()\n","\n","# 선택 피처 리스트 및 preprocessor 재정의\n","both_sel = [c for c in both_feats if c not in low_feats]\n","log_sel = [c for c in log_only if c not in low_feats]\n","winsor_sel = [c for c in winsor_only if c not in low_feats]\n","base_sel = [c for c in base_num_feats if c not in low_feats]\n","cat_sel = [c for c in cat_cols if c not in low_feats]\n","\n","preprocessor_sel = ColumnTransformer([\n"," ('both', both_pipe, both_sel),\n"," ('log', log_pipe, log_sel),\n"," ('winsor', winsor_pipe, winsor_sel),\n"," ('base', base_pipe, base_sel),\n"," ('cat', cat_pipe, cat_sel),\n","])\n","all_cols_sel = both_sel + log_sel + winsor_sel + base_sel + cat_sel"],"metadata":{"id":"6P2WiU8QwQrb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pipe_sel = Pipeline([\n"," ('pre', preprocessor_sel),\n"," ('to_df', DFTransformer(all_cols_sel)),\n"," ('clf', CatBoostClassifier(\n"," cat_features=cat_sel,\n"," verbose=False,\n"," thread_count=1,\n"," early_stopping_rounds=30\n"," )),\n","])"],"metadata":{"id":"XDNbD4WYwSHL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def evaluate(name, pipe, X_tr, X_te):\n"," print(f\"\\n===== {name} =====\")\n"," cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n"," scoring = ['accuracy','f1','roc_auc']\n"," cvr = cross_validate(pipe, X_tr, y_trainval,\n"," cv=cv, scoring=scoring, error_score='raise')\n"," acc, f1, auc = cvr['test_accuracy'], cvr['test_f1'], cvr['test_roc_auc']\n"," comp = (acc + f1 + auc) / 3\n","\n"," print(\"5-Fold CV (train_val):\")\n"," for i,(a,f,u,c) in enumerate(zip(acc,f1,auc,comp), start=1):\n"," print(f\"[Fold {i:>2}] Accuracy: {a:.4f}, F1: {f:.4f}, AUC: {u:.4f}, Composite: {c:.4f}\")\n"," print(f\"평균 Composite: {comp.mean():.4f}\")\n","\n"," pipe.fit(X_tr, y_trainval)\n"," y_p = pipe.predict(X_te)\n"," y_pr = pipe.predict_proba(X_te)[:,1]\n"," a = accuracy_score(y_test, y_p)\n"," f = f1_score(y_test, y_p)\n"," u = roc_auc_score(y_test, y_pr)\n"," c = (a + f + u) / 3\n","\n"," print(\"\\nHoldout Test:\")\n"," print(f\"Accuracy : {a:.4f}\")\n"," print(f\"F1 Score : {f:.4f}\")\n"," print(f\"ROC AUC : {u:.4f}\")\n"," print(f\"Composite: {c:.4f}\")\n","\n","# 실행\n","evaluate(\"원본 피처\", pipe_orig, X_trainval, X_test)\n","evaluate(\"선택 피처\", pipe_sel, X_trainval, X_test)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uUdRi3RVqN2H","executionInfo":{"status":"ok","timestamp":1747409590403,"user_tz":-540,"elapsed":480788,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"932d9e51-d213-42be-967b-1ef3bec6bf3c"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","===== 원본 피처 =====\n","5-Fold CV (train_val):\n","[Fold 1] Accuracy: 0.6619, F1: 0.6570, AUC: 0.7287, Composite: 0.6825\n","[Fold 2] Accuracy: 0.6529, F1: 0.6506, AUC: 0.7181, Composite: 0.6739\n","[Fold 3] Accuracy: 0.6515, F1: 0.6501, AUC: 0.7064, Composite: 0.6693\n","[Fold 4] Accuracy: 0.6622, F1: 0.6610, AUC: 0.7211, Composite: 0.6814\n","[Fold 5] Accuracy: 0.6512, F1: 0.6483, AUC: 0.7070, Composite: 0.6688\n","평균 Composite: 0.6752\n","\n","Holdout Test:\n","Accuracy : 0.6633\n","F1 Score : 0.6591\n","ROC AUC : 0.7280\n","Composite: 0.6834\n","\n","===== 선택 피처 =====\n","5-Fold CV (train_val):\n","[Fold 1] Accuracy: 0.6667, F1: 0.6609, AUC: 0.7304, Composite: 0.6860\n","[Fold 2] Accuracy: 0.6478, F1: 0.6453, AUC: 0.7138, Composite: 0.6690\n","[Fold 3] Accuracy: 0.6456, F1: 0.6455, AUC: 0.7072, Composite: 0.6661\n","[Fold 4] Accuracy: 0.6548, F1: 0.6525, AUC: 0.7184, Composite: 0.6753\n","[Fold 5] Accuracy: 0.6416, F1: 0.6393, AUC: 0.7043, Composite: 0.6617\n","평균 Composite: 0.6716\n","\n","Holdout Test:\n","Accuracy : 0.6633\n","F1 Score : 0.6595\n","ROC AUC : 0.7249\n","Composite: 0.6826\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/mean + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" "b/model/seohyoun/mean + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" new file mode 100644 index 0000000..d2729cb --- /dev/null +++ "b/model/seohyoun/mean + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMeLsZ/C1BkhD8WNwqy5sKS"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747404454173,"user_tz":-540,"elapsed":33925,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"3767e5c0-1cf0-41c7-cd20-5672482b5e81"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747404497180,"user_tz":-540,"elapsed":38177,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"7dbe7181-c894-4d88-f845-9dca5275b86f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y'], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='mean')\n","cat_imputer = SimpleImputer(strategy='constant', fill_value='missing')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vvBmsqmh_6hK","executionInfo":{"status":"ok","timestamp":1747404726234,"user_tz":-540,"elapsed":227865,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"0e8bcd84-b36f-41c8-cf8a-1b0496fa9faf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6608, F1: 0.6558, AUC: 0.7273, Composite: 0.6813\n","[Fold 2] Accuracy: 0.6515, F1: 0.6485, AUC: 0.7159, Composite: 0.6720\n","[Fold 3] Accuracy: 0.6478, F1: 0.6461, AUC: 0.7072, Composite: 0.6670\n","[Fold 4] Accuracy: 0.6667, F1: 0.6634, AUC: 0.7248, Composite: 0.6850\n","[Fold 5] Accuracy: 0.6489, F1: 0.6462, AUC: 0.7076, Composite: 0.6676\n","\n","평균 Composite Score: 0.6745717408895427\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6624\n","F1 Score : 0.6583\n","ROC AUC : 0.7248\n","Composite: 0.6818\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/mean + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" "b/model/seohyoun/mean + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" new file mode 100644 index 0000000..d977809 --- /dev/null +++ "b/model/seohyoun/mean + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMbs0ZCA0NGFj27CNVu0sWo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747404386452,"user_tz":-540,"elapsed":34573,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"4305dc83-23c7-4226-ef7f-98bbfa327386"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747404391626,"user_tz":-540,"elapsed":5154,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747404449396,"user_tz":-540,"elapsed":57755,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"6d65f6d2-a197-4038-f62f-bb798e41bddf"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y'], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747404451492,"user_tz":-540,"elapsed":2144,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747404451508,"user_tz":-540,"elapsed":11,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='mean')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747404451803,"user_tz":-540,"elapsed":289,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747404451832,"user_tz":-540,"elapsed":5,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747404672808,"user_tz":-540,"elapsed":220918,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"f8f2e12c-9a6f-44a0-e5d4-84b7a4406b3b"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6667, F1: 0.6607, AUC: 0.7308, Composite: 0.6861\n","[Fold 2] Accuracy: 0.6486, F1: 0.6451, AUC: 0.7174, Composite: 0.6704\n","[Fold 3] Accuracy: 0.6523, F1: 0.6494, AUC: 0.7079, Composite: 0.6699\n","[Fold 4] Accuracy: 0.6610, F1: 0.6593, AUC: 0.7232, Composite: 0.6812\n","[Fold 5] Accuracy: 0.6537, F1: 0.6480, AUC: 0.7105, Composite: 0.6707\n","\n","평균 Composite Score: 0.6756486737833474\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6617\n","F1 Score : 0.6575\n","ROC AUC : 0.7250\n","Composite: 0.6814\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/median + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" "b/model/seohyoun/median + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" new file mode 100644 index 0000000..6ef6132 --- /dev/null +++ "b/model/seohyoun/median + missing label + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyM1yvSqyKSHpV13T6Vd5na2"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747404812346,"user_tz":-540,"elapsed":40616,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"55c8a8b9-b046-4253-c32b-24fe5be7de72"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV","executionInfo":{"status":"ok","timestamp":1747404816889,"user_tz":-540,"elapsed":4528,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747404878807,"user_tz":-540,"elapsed":61928,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"1bb49b5c-899d-46cd-8c49-22fe490b8715"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y'], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_","executionInfo":{"status":"ok","timestamp":1747404880860,"user_tz":-540,"elapsed":2065,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z","executionInfo":{"status":"ok","timestamp":1747404880877,"user_tz":-540,"elapsed":12,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='constant', fill_value='missing')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb","executionInfo":{"status":"ok","timestamp":1747404881094,"user_tz":-540,"elapsed":203,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2","executionInfo":{"status":"ok","timestamp":1747404881107,"user_tz":-540,"elapsed":47,"user":{"displayName":"김서현","userId":"08818282308943516715"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747405101826,"user_tz":-540,"elapsed":220748,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"8fe95664-34d0-42fb-b7bf-9a3bea91dbb2"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6616, F1: 0.6574, AUC: 0.7293, Composite: 0.6828\n","[Fold 2] Accuracy: 0.6520, F1: 0.6499, AUC: 0.7174, Composite: 0.6731\n","[Fold 3] Accuracy: 0.6481, F1: 0.6453, AUC: 0.7053, Composite: 0.6662\n","[Fold 4] Accuracy: 0.6658, F1: 0.6638, AUC: 0.7238, Composite: 0.6845\n","[Fold 5] Accuracy: 0.6447, F1: 0.6405, AUC: 0.7079, Composite: 0.6643\n","\n","평균 Composite Score: 0.6741782759246452\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6559\n","F1 Score : 0.6516\n","ROC AUC : 0.7254\n","Composite: 0.6776\n"]}]}]} \ No newline at end of file diff --git "a/model/seohyoun/median + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" "b/model/seohyoun/median + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" new file mode 100644 index 0000000..47031e8 --- /dev/null +++ "b/model/seohyoun/median + \354\265\234\353\271\210\352\260\222 + \354\235\264\354\203\201\354\271\230 \354\262\230\353\246\254.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO/4Lx+pyL+CAtYJv2keIww"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7eA2cju_yBS","executionInfo":{"status":"ok","timestamp":1747405390424,"user_tz":-540,"elapsed":13721,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"bd8653db-2db8-4ce0-8d93-293f3904fd0b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting feature_engine\n"," Downloading feature_engine-1.8.3-py2.py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.0.2)\n","Requirement already satisfied: pandas>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (2.2.2)\n","Requirement already satisfied: scikit-learn>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.6.1)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (1.15.3)\n","Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.11/dist-packages (from feature_engine) (0.14.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=2.2.0->feature_engine) (2025.2)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (1.5.0)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=1.4.0->feature_engine) (3.6.0)\n","Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (1.0.1)\n","Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from statsmodels>=0.11.1->feature_engine) (24.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=2.2.0->feature_engine) (1.17.0)\n","Downloading feature_engine-1.8.3-py2.py3-none-any.whl (378 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.6/378.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: feature_engine\n","Successfully installed feature_engine-1.8.3\n","Collecting CatBoost\n"," Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from CatBoost) (0.20.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from CatBoost) (3.10.0)\n","Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.0.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.11/dist-packages (from CatBoost) (2.2.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.15.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (from CatBoost) (5.24.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from CatBoost) (1.17.0)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24->CatBoost) (2025.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (4.58.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (24.2)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (11.2.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->CatBoost) (3.2.3)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly->CatBoost) (9.1.2)\n","Downloading catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl (99.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CatBoost\n","Successfully installed CatBoost-1.2.8\n"]}],"source":["! pip install feature_engine\n","! pip install CatBoost"]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=FutureWarning) # 경고문 무시\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler, RobustScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, cross_validate\n","from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n","from sklearn.base import BaseEstimator, TransformerMixin\n","from xgboost import XGBClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.decomposition import PCA\n","from scipy.stats import skew\n","from feature_engine.outliers import Winsorizer as FEWinsorizer\n","from catboost import CatBoostClassifier"],"metadata":{"id":"BhdfagQ__3dV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","from google.colab import drive\n","drive.mount('/content/drive')\n","train_src = '/content/drive/MyDrive/Colab Notebooks/패턴인식/train.csv'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GANDaF4C_3aj","executionInfo":{"status":"ok","timestamp":1747405392339,"user_tz":-540,"elapsed":1663,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"3a78b1c8-68cc-49df-c21a-c452f6870a09"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["df = pd.read_csv(train_src)\n","\n","X = df.drop(['id', 'shares', 'y'], axis=1)\n","y = df['y']\n","\n","X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()\n","cat_cols = ['data_channel', 'weekday']"],"metadata":{"id":"fXO1o_dP_6o_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DFTransformer(TransformerMixin):\n"," \"\"\"NumPy → pandas.DataFrame으로 바꿔주는 Transformer.\"\"\"\n"," def __init__(self, columns):\n"," self.columns = columns\n"," def fit(self, X, y=None):\n"," return self\n"," def transform(self, X):\n"," return pd.DataFrame(X, columns=self.columns)"],"metadata":{"id":"HqjcN9agAR_Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 1) 왜도 상위 k개\n","def get_skew_feats(df, k=7):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," skews = num.apply(lambda x: abs(skew(x.dropna()))).sort_values(ascending=False)\n"," return skews.head(k).index.tolist()\n","\n","# 2) 이상치 비율 상위 k개 (IQR 기준)\n","def get_outlier_feats(df, k=8):\n"," num = df.select_dtypes(include=['int64','float64'])\n"," def outlier_rate(col):\n"," q1, q3 = col.quantile([.25, .75])\n"," iqr = q3 - q1\n"," return (~col.between(q1 - 1.5*iqr, q3 + 1.5*iqr)).mean()\n"," rates = num.apply(outlier_rate).sort_values(ascending=False)\n"," return rates.head(k).index.tolist()\n","\n","def safe_log1p(X):\n"," X_clipped = np.clip(X, a_min=0, a_max=None)\n"," with np.errstate(divide='ignore'):\n"," Y = np.log1p(X_clipped)\n"," Y[np.isneginf(Y)] = 0\n"," return Y\n","\n","# 3) 대상 피처 리스트 뽑기\n","skew_k, outlier_k = 7, 8\n","skew_feats = get_skew_feats(X, skew_k)\n","outlier_feats = get_outlier_feats(X, outlier_k)\n","both_feats = list(set(skew_feats) & set(outlier_feats))\n","log_only = [f for f in skew_feats if f not in both_feats]\n","winsor_only = [f for f in outlier_feats if f not in both_feats]\n","base_num_feats = [c for c in num_cols if c not in (both_feats + log_only + winsor_only)]\n","\n","\n","# 결측치\n","num_imputer = SimpleImputer(strategy='median')\n","cat_imputer = SimpleImputer(strategy='most_frequent')\n","\n","# 변환기\n","log_tf = FunctionTransformer(np.log1p, validate=False)\n","safe_log_tf = FunctionTransformer(safe_log1p, validate=False)\n","win_tf = FEWinsorizer(capping_method='gaussian', tail='both', fold=3)\n","\n","\n","# 수치형 변수 파이프라인\n","both_pipeline = Pipeline([('imputer', num_imputer), ('winsor', win_tf),('log', safe_log_tf)])\n","log_pipeline = Pipeline([('imputer', num_imputer), ('log', safe_log_tf)])\n","winsor_pipeline = Pipeline([('imputer', num_imputer),('winsor', win_tf)])\n","base_num_pipeline = Pipeline([('imputer', num_imputer)]) # ('scaler', StandardScaler()), # 필요 시 추가\n","\n","# 범주형 변수 파이프라인\n","cat_pipeline = Pipeline([('imputer', cat_imputer)]) # ('onehot', OneHotEncoder(handle_unknown='ignore'))\n","\n","# 전체 전처리 파이프라인\n","preprocessor = ColumnTransformer([\n"," ('both', both_pipeline, both_feats),\n"," ('log', log_pipeline, log_only),\n"," ('winsor', winsor_pipeline, winsor_only),\n"," ('num', base_num_pipeline, base_num_feats),\n"," ('cat', cat_pipeline, cat_cols),\n","])\n","\n","all_cols = (\n"," both_feats + log_only + winsor_only +\n"," base_num_feats + cat_cols\n",")\n","\n"],"metadata":{"id":"jJj52bkR_6mb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 최종 모델 파이프라인\n","pipe = Pipeline([\n"," ('pre', preprocessor),\n"," ('to_df', DFTransformer(columns=all_cols)),\n"," ('model', CatBoostClassifier(cat_features=cat_cols, verbose=False,thread_count=1, early_stopping_rounds=30)),\n","]) # thread_count=1 지우면 속도가 빨라지지만 매번 결과가 약간 다름"],"metadata":{"id":"mrmRpZpp_6j2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","# 5-Fold CV (only on train_val)\n","cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","scoring = ['accuracy', 'f1', 'roc_auc']\n","\n","cv_results = cross_validate(pipe, X_trainval, y_trainval, cv=cv, scoring=scoring, error_score='raise')\n","\n","acc = cv_results['test_accuracy']\n","f1 = cv_results['test_f1']\n","auc = cv_results['test_roc_auc']\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"5-Fold CV (only on train_val)\")\n","for i in range(len(acc)):\n"," print(f\"[Fold {i+1}] Accuracy: {acc[i]:.4f}, F1: {f1[i]:.4f}, AUC: {auc[i]:.4f}, Composite: {comp[i]:.4f}\")\n","print(\"\\n평균 Composite Score:\", comp.mean())\n","\n","# 전체 train_val로 학습 후 test로 최종 성능 평가\n","pipe.fit(X_trainval, y_trainval)\n","y_pred = pipe.predict(X_test)\n","y_prob = pipe.predict_proba(X_test)[:, 1]\n","\n","acc = accuracy_score(y_test, y_pred)\n","f1 = f1_score(y_test, y_pred)\n","auc = roc_auc_score(y_test, y_prob)\n","comp = (acc + f1 + auc) / 3\n","\n","print(\"\\n최종 Holdout Test 성능\")\n","print(f\"Accuracy : {acc:.4f}\")\n","print(f\"F1 Score : {f1:.4f}\")\n","print(f\"ROC AUC : {auc:.4f}\")\n","print(f\"Composite: {comp:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07vK997H_3Xy","executionInfo":{"status":"ok","timestamp":1747405639834,"user_tz":-540,"elapsed":246212,"user":{"displayName":"김서현","userId":"08818282308943516715"}},"outputId":"71f1b3e0-9556-4c99-8a1c-94fbc94a8abf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["5-Fold CV (only on train_val)\n","[Fold 1] Accuracy: 0.6619, F1: 0.6570, AUC: 0.7287, Composite: 0.6825\n","[Fold 2] Accuracy: 0.6529, F1: 0.6506, AUC: 0.7181, Composite: 0.6739\n","[Fold 3] Accuracy: 0.6515, F1: 0.6501, AUC: 0.7064, Composite: 0.6693\n","[Fold 4] Accuracy: 0.6622, F1: 0.6610, AUC: 0.7211, Composite: 0.6814\n","[Fold 5] Accuracy: 0.6512, F1: 0.6483, AUC: 0.7070, Composite: 0.6688\n","\n","평균 Composite Score: 0.6751952089568417\n","\n","최종 Holdout Test 성능\n","Accuracy : 0.6633\n","F1 Score : 0.6591\n","ROC AUC : 0.7280\n","Composite: 0.6834\n"]}]}]} \ No newline at end of file