create NEAT app repo

NeuralNetwork.cpp

@@ -0,0 +1,159 @@
+#include "NeuralNetwork.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+
+#include "IteratorHelper.h"
+
+NeuralNetwork::Neuron::Neuron(int id, float x, float y, bool fixed, float activation):
+	ID(id),
+	X(x),
+	Y(y),
+	Fixed(fixed),
+	Activation(activation)
+{
+	// empty
+}
+
+bool NeuralNetwork::Neuron::operator< (const NeuralNetwork::Neuron& rhs) const
+{
+	return
+		(Y < rhs.Y) ||
+		((Y == rhs.Y) && (X < rhs.X));
+}
+
+NeuralNetwork::NeuralNetwork(const Genotype& genotype)
+{
+	// reset out input/output count
+	NumInputs = NumOutputs = 0;
+
+	// copy the neurons
+	for ( std::vector<NeuronGene>::const_iterator it = genotype.Neurons.begin(); it != genotype.Neurons.end(); ++it )
+	{
+		switch ( (*it).GetType() )
+		{
+			case NeuronGene::BIAS:
+				Neurons.push_back(Neuron((*it).GetInnovation(),(*it).GetX(),(*it).GetY(),true,1.0));
+				break;
+
+			case NeuronGene::INPUT:
+				++NumInputs;
+				Neurons.push_back(Neuron((*it).GetInnovation(),(*it).GetX(),(*it).GetY(),true,0.0));
+				break;
+
+			case NeuronGene::OUTPUT:
+				++NumOutputs;
+				// fall through
+
+			case NeuronGene::HIDDEN:
+				Neurons.push_back(Neuron((*it).GetInnovation(),(*it).GetX(),(*it).GetY()));
+				break;
+		}
+	}
+
+	// sort neurons by position
+	std::sort(Neurons.begin(), Neurons.end());
+
+	// copy the connections
+	for ( std::vector<ConnectionGene>::const_iterator it = genotype.Connections.begin(); it != genotype.Connections.end(); ++it )
+	{
+		if ( (*it).IsEnabled() )
+		{
+			// figure out which two neurons this thing connects
+			int index_a = GetNeuronIndexFromID((*it).GetInputNeuron());
+			int index_b = GetNeuronIndexFromID((*it).GetOutputNeuron());
+			assert(index_a < (int)Neurons.size() && index_b < (int)Neurons.size());
+
+			// now add to the neuron
+			Neurons[index_b].Inputs.push_back( Connection(index_a,(*it).GetWeight()) );
+		}
+	}
+
+	// print out this network
+	#if 0
+	int i = 0;
+	iterate_each ( std::vector<Neuron>::iterator, it_neuron, Neurons )
+	{
+		printf("Neuron %2d (%2d) %0.2f %0.2f\n",i,(*it_neuron).ID,(*it_neuron).X,(*it_neuron).Y);
+		iterate_each ( std::vector<Connection>::iterator, it_connection, (*it_neuron).Inputs )
+		{
+			printf("       %d %0.5f\n",(*it_connection).id,(*it_connection).weight);
+		}
+		++i;
+	}
+	#endif
+}
+
+
+NeuralNetwork::~NeuralNetwork()
+{
+}
+
+void NeuralNetwork::Reset()
+{
+	iterate_each ( std::vector<Neuron>::iterator, it_neuron, Neurons )
+	{
+		if ( !(*it_neuron).Fixed )
+		{
+			(*it_neuron).Activation = 0.0;
+		}
+	}
+}
+
+void NeuralNetwork::Update(const std::vector<float>& inputs, std::vector<float>& outputs)  // TODO make input STL-like, begin(), end() & array compatible
+{
+	// assign inputs
+	for ( int i=0;i<NumInputs;++i )
+	{
+		// TODO range check this!!!
+		Neurons[i+1].Activation = inputs[i];  // +1 is because Bias is #0 - this is messy TODO: clean up, make safer
+		// TODO make sure this is really an input Neuron!!!  and its ID matches up, etc.
+	}
+
+	// update each neuron
+	iterate_each ( std::vector<Neuron>::iterator, it_neuron, Neurons )
+	{
+		// if this is a fixed neuron, skip
+		if ( (*it_neuron).Fixed )
+		{
+			continue;
+		}
+
+		// sum the inputs into this neuron
+		float sum = 0.0f;
+		iterate_each ( std::vector<Connection>::iterator, it_connection, (*it_neuron).Inputs )
+		{
+			sum += Neurons[(*it_connection).id].Activation * (*it_connection).weight;
+		}
+
+		// activate
+		(*it_neuron).Activation = 1.0f / (1.0f + (expf(-4.9f * sum)));
+	}
+
+	// clear outputs
+	outputs.clear();
+
+	// copy outputs
+	for ( int i=0;i<NumOutputs;++i )
+	{
+		size_t index = Neurons.size() - NumOutputs + i;
+		outputs.push_back( Neurons[index].Activation );
+	}
+}
+
+
+int NeuralNetwork::GetNeuronIndexFromID(int id)
+{
+	for ( int i=0;i<(int)Neurons.size();++i )
+	{
+		if ( Neurons[i].ID == id )
+		{
+			return i;
+		}
+	}
+	assert(0);
+	return 0;
+}
+
+