Added a (hacky) bias neuron

Layer.cpp

@@ -10,24 +10,25 @@ Layer::Layer(unsigned int numNeurons)
 
 void Layer::setOutputValues(const std::vector<double> & outputValues)
 {
-	if (size() != outputValues.size())
+	if (size() - 1 != outputValues.size())
 	{
 		throw std::exception("The number of output values has to match the layer size");
 	}
 
-	auto valueIt = outputValues.begin();
+	auto neuronIt = begin();
-	for (Neuron &neuron : *this)
+	for (const double &value : outputValues)
 	{
-		neuron.setOutputValue(*valueIt++);
+		neuronIt->setOutputValue(value);
+		neuronIt++;
 	}
 }
 
 void Layer::feedForward(const Layer &inputLayer)
 {
 	int neuronNumber = 0;
-	for (Neuron &neuron : *this)
+	for (auto neuronIt = begin(); neuronIt != end(); ++neuronIt)
 	{
-		neuron.feedForward(inputLayer.getWeightedSum(neuronNumber));
+		neuronIt->feedForward(inputLayer.getWeightedSum(neuronNumber++));
 	}
 }
 

Net.cpp

@@ -17,6 +17,8 @@ Net::Net(std::initializer_list<unsigned int> layerSizes)
 		Layer &currentLayer = *layerIt;
 		const Layer &nextLayer = *(layerIt + 1);
 
+		currentLayer.push_back(Neuron(1.0));
+
 		currentLayer.connectTo(nextLayer);
 	}
 }
@@ -25,7 +27,7 @@ void Net::feedForward(const std::vector<double> &inputValues)
 {
 	Layer &inputLayer = front();
 
-	if (inputLayer.size() != inputValues.size())
+	if (inputLayer.size() - 1 != inputValues.size())
 	{
 		throw std::exception("The number of input values has to match the input layer size");
 	}

Neuro.cpp

@@ -9,9 +9,9 @@ int main()
 	{
 		std::cout << "Neuro running" << std::endl;
 
-		Net myNet({ 2, 3, 1 });
+		Net myNet({ 3, 4, 2 });
 
-		myNet.feedForward({ 1.0, 0.0 });
+		myNet.feedForward({ 1.0, 2.0, 3.0 });
 
 		std::vector<double> result = myNet.getResult();
 

Neuron.cpp

@@ -2,6 +2,12 @@
 
 #include "Neuron.h"
 
+Neuron::Neuron(double value)
+	: outputValue(value)
+{
+
+}
+
 void Neuron::setOutputValue(double value)
 {
 	outputValue = value;
@@ -22,11 +28,16 @@ void Neuron::feedForward(double inputValue)
 	outputValue = Neuron::transferFunction(inputValue);
 }
 
-double Neuron::getWeightedOutputValue(int outputNeuron) const
+double Neuron::getWeightedOutputValue(unsigned int outputNeuron) const
+{
+	if (outputNeuron < outputWeights.size())
 	{
 		return outputValue * outputWeights[outputNeuron];
 	}
 
+	return 0.0;
+}
+
 void Neuron::createOutputWeights(unsigned int number)
 {
 	outputWeights.clear();

Neuron.h

@@ -9,11 +9,13 @@ private:
 	std::vector<double> outputWeights;
 
 public:
+	Neuron(double value = 1.0);
+
 	void setOutputValue(double value);
 	static double transferFunction(double inputValue);
 	static double transferFunctionDerivative(double inputValue);
 	void feedForward(double inputValue);
-	double getWeightedOutputValue(int outputNeuron) const;
+	double getWeightedOutputValue(unsigned int outputNeuron) const;
 	void createOutputWeights(unsigned int number);
 	double getOutputValue() const;
 };