Feature timing

.vscode/launch.json

@@ -0,0 +1,19 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "(Windows) Launch",
+            "type": "cppvsdbg",
+            "request": "launch",
+            "program": "enter program name, for example ${workspaceFolder}/a.exe",
+            "args": [],
+            "stopAtEntry": false,
+            "cwd": "${workspaceFolder}",
+            "environment": [],
+            "externalConsole": true
+        }
+    ]
+}

ConsoleAI/AINetClass.cpp

@@ -143,12 +143,11 @@ size_t AINetClass::Counter(bool bIncrease )
 size_t AINetClass::CurrentTrainingDataRow()
 {
 	// returns current Training Data Row
-	size_t tmpReturn;
+	size_t tmpReturn = 0;
 	size_t tmpMaxRows = this->getTrainingDataRowsMax();
 	if (tmpMaxRows == 0)
 	{
 		this->throwFailure("division by 0 iTrainingDataRowsUseMax", true);
-		tmpReturn= 0;
 	}
 	else
 	{
@@ -659,10 +658,10 @@ void AINetClass::calculateLine(size_t iTmpRow)
 
 void AINetClass::sortNetwork()
 {
-	// this function sorts the network
-	
+	/** This function is meant to sort the network, so multiple networks can be combined */
+
 	// begin sorting at the end of the network (of course we won't sort output)
-	for (size_t iSort = this->getNumberOfLayers(); iSort > 1; iSort--)
+	for (size_t iSort = this->getNumberOfLayers(); iSort > 1; --iSort)
 	{
 		// reload network variables to tmp variables each layer.
 		std::vector<double> vdTmpValues = this->vecValues;
@@ -726,15 +725,15 @@ void AINetClass::sortNetwork()
 				{
 					jNode = jBegin + j;
 					this->vecWeights.at(jNode).at(iNode) = vdTmpWeights[jNode][iBegin + viSortList.at(i)];
-					//todo continue
+					//todo continue, but what?
 				}
 				// sort weights from previous layer
 				jBegin = this->getLayerStart(iSort);
 				for (size_t j = 0; j < this->getNumberOfNodesInLayer(iSort); j++)
 				{
 					jNode = jBegin + j;
 					this->vecWeights.at(iNode).at(jNode) = vdTmpWeights[iBegin + viSortList.at(i)][jNode];
-					//todo continue
+					//todo continue, but what?
 				}
 			}
 		}
@@ -974,7 +973,8 @@ void AINetClass::saveResultingNetwork(size_t iNumber)
 	}
 	catch (...) {
 		fileResultingNetwork.close();
-		this->throwFailure("Error while saving data." + strerror_s(clocalerror, errno), false);
+		//TODO This is in conflict with Windows UWP App. 
+		//this->throwFailure("Error while saving data." + strerror_s(clocalerror, errno), false);
 	}
 }
 
@@ -1022,7 +1022,7 @@ void AINetClass::connectNodes(bool bFullyConnected, size_t iRandSeed, bool bDele
 	/** This function is used to create the initial connection of nodes.
 		\param bFullyConnected is used to link al nodes from one layer with all nodes from the previous layer.
 		\param iRandSeed is the seed for the random number generator.
-		\param deleteExisting (optional) delete existing connections and reset all values.
+		\param bDeleteExisting (optional) delete existing connections and reset all values.
 	*/
 
 	if (!this->initializationDone)
@@ -1035,13 +1035,13 @@ void AINetClass::connectNodes(bool bFullyConnected, size_t iRandSeed, bool bDele
 		this->bHasBeenConnected = true;
 		// first do the auto-generation if parameter is set
 		this->autoGenerateInternalNetwork();
+
 		// TODO allow smart connected network by removing next line of code
 		bFullyConnected = true;
 		//variables
 		size_t tmpTotalNumberNodes = 0;
 
-		// seeding random number generator
-		srand(iRandSeed);
+		srand((unsigned int)iRandSeed); // problems with conversion don't matter because it's random
 
 		//function
 		tmpTotalNumberNodes = this->NUMNODES();
@@ -1439,6 +1439,14 @@ double AINetClass::updateWeights()
 	return sumOfSquaredErrors;
 }
 
+double AINetClass::getVersion()
+{
+	/** this function returns the Version of the AINetClass
+		\return double Version
+	*/
+	return this->dVersion;
+}
+
 double AINetClass::getNodeValue(size_t tmpNode)
 {
 	// returns value of node
@@ -1519,7 +1527,10 @@ bool AINetClass::autoGenerateInternalNetwork()
 
 double AINetClass::getTrainingDataValue(size_t row, size_t column)
 {
-	// safeAccesstoTrainingData
+	/** This function is used to access the training data in a safe way 
+		\param row select this row
+		\param column select this column
+		*/
 	double tmpReturn=0;
 	if (row < this->ptrAINDataContainer->getTrainingDataRowsMax())
 	{

ConsoleAI/AINetClass.h

@@ -29,7 +29,7 @@ class AINetClass
 	size_t getNumberOfNodesInLayer(signed int iTmpLayer);
 	size_t getNumberOfNodesInLayer(size_t tmpLayer);
 	size_t getNumberOfLayers(bool bOnlyHidden = false);
-	size_t getLayerStart(int iTmpLayer, bool falseForLayerEnd = true);
+	size_t getLayerStart(int tmpLayer, bool falseForLayerEnd = true);
 
 	double LearningRate();
 	size_t TrainingDataColumns();
@@ -53,6 +53,7 @@ class AINetClass
 	bool IsNetworkReady();
 	bool autoGenerateInternalNetwork();
 	double updateWeights();
+	double getVersion();
 	double getNodeValue(size_t tmpNode);
 	void TrainingDataColumnPush_Back(std::string tmpString);
 	void activateNetwork();
@@ -107,6 +108,7 @@ class AINetClass
 	// Constants
 
 	double E = 2.71828;
+	double dVersion = 0.20190422;
 
 	// variables
 	std::vector<double> vecValues = { 0.0 }; // vector containing values including input
@@ -164,6 +166,7 @@ class AINetClass
 	// functions
 	std::string generateFileOutput(std::string& strFileContents);
 	std::string generateFileInput(std::string& strFileContents);
+
 	bool IsDoubleCritical(double dToBeClassified);
 	std::string IsDoubleCritical(double dToBeClassified, std::string sText);
 	bool recalculateInputDataPullList();