Feature/add makefile

Makefile

@@ -0,0 +1,16 @@
+all: examples
+
+venv: requirements.txt
+	test -d venv || virtualenv -p python3 venv
+	. venv/bin/activate && pip install -r requirements.txt
+
+examples: venv
+	./venv/bin/python ./test_input_csv.py
+	./venv/bin/python ./test_input_array.py
+	./venv/bin/python ./train_csv.py
+	./venv/bin/python ./train_array.py
+	./venv/bin/python ./train_lstm.py
+	./venv/bin/python ./train_lstm_multivariate.py
+
+clean:
+	rm -rf venv *.jpg

requirements.txt

@@ -0,0 +1,4 @@
+matplotlib==2.2.2
+numpy==1.14.3
+pillow==5.1.0
+tensorflow==1.8.0

test_input_array.py

@@ -12,7 +12,7 @@
 noise = np.random.uniform(-0.2, 0.2, 1000)
 y = np.sin(np.pi * x / 100) + x / 200. + noise
 plt.plot(x, y)
-plt.savefig('timeseries_y.jpg')
+plt.savefig('test_input_timeseries_y.jpg')
 
 data = {
     tf.contrib.timeseries.TrainEvalFeatures.TIMES: x,

train_array.py

@@ -13,7 +13,7 @@ def main(_):
     noise = np.random.uniform(-0.2, 0.2, 1000)
     y = np.sin(np.pi * x / 100) + x / 200. + noise
     plt.plot(x, y)
-    plt.savefig('timeseries_y.jpg')
+    plt.savefig('array_timeseries_y.jpg')
 
     data = {
         tf.contrib.timeseries.TrainEvalFeatures.TIMES: x,
@@ -47,7 +47,7 @@ def main(_):
     plt.xlabel('time_step')
     plt.ylabel('values')
     plt.legend(loc=4)
-    plt.savefig('predict_result.jpg')
+    plt.savefig('array.jpg')
 
 
 if __name__ == '__main__':

train_csv.py

@@ -40,7 +40,7 @@ def main(_):
     plt.xlabel('time_step')
     plt.ylabel('values')
     plt.legend(loc=4)
-    plt.savefig('predict_result.jpg')
+    plt.savefig('csv.jpg')
 
 
 if __name__ == '__main__':

train_lstm.py

@@ -196,4 +196,4 @@ def _exogenous_input_step(
   predicted_lines = plt.plot(predicted_times, predicted, label="prediction", color="r")
   plt.legend(handles=[observed_lines[0], evaluated_lines[0], predicted_lines[0]],
              loc="upper left")
-  plt.savefig('predict_result.jpg')
+  plt.savefig('lstm.jpg')

train_lstm_multivariate.py

@@ -187,4 +187,4 @@ def _exogenous_input_step(
   predicted_lines = plt.plot(predicted_times, predicted, label="prediction", color="r")
   plt.legend(handles=[observed_lines[0], evaluated_lines[0], predicted_lines[0]],
              loc="upper left")
-  plt.savefig('predict_result.jpg')
+  plt.savefig('lstm_multivariate.jpg')