NeuronalNetwork.cpp

#include "NeuronalNetwork.h"

NeuronalNetwork::NeuronalNetwork(int numInputs, int numOutputs, int numHiddenLayers, int numLayerNodes)
{

	_numInputs = numInputs;
	_numOutputs = numOutputs;
	_numHiddenLayers = numHiddenLayers;
	_numLayerNodes = numLayerNodes;

	// Init weights and biases
	_weights.reserve(numHiddenLayers + 1);
	_biases.reserve(numHiddenLayers + 1);
	
	float weightScale = 1.0;

	// Input Layer
	_weights.push_back(MatrixXf::Random(numLayerNodes, numInputs) * weightScale);
	_biases.push_back(MatrixXf::Random(numLayerNodes, 1) * weightScale);

	// Hidden Layers + Output Layer
	for (int i = 0; i < numHiddenLayers; i++) {
		int nextNodes = i == numHiddenLayers - 1 ? numOutputs : numLayerNodes;
		_weights.push_back(MatrixXf::Random(nextNodes, numLayerNodes) * weightScale);
		_biases.push_back(MatrixXf::Random(nextNodes, 1) * weightScale);
	}
}

MatrixXf NeuronalNetwork::simulate(MatrixXf &inputs) {
	MatrixXf currentActivatedValues = inputs;
	for (int i = 0; i < _weights.size(); i++) {
		MatrixXf summedValues = _weights[i] * currentActivatedValues + _biases[i];
		// Perform activation function with the summed values
		for (int k = 0; k < summedValues.size(); k++) {
			summedValues(k) = tanh( summedValues.coeff(k) );
		}
		currentActivatedValues = summedValues;
	}

	return currentActivatedValues;
	
}

vector<double> NeuronalNetwork::allCoeffsAsArray() {
	vector<double> result;
	
	// Add all weights
	for (int i = 0; i < _weights.size(); i++) {
		for (int k = 0; k < _weights[i].size(); k++) {
			result.push_back(_weights[i](k));
		}
	}
	
	// Add all biases
	for (int i = 0; i < _biases.size(); i++) {
		for (int k = 0; k < _biases[i].size(); k++) {
			result.push_back(_biases[i](k));
		}
	}

	return result;
}

double getRand(float factor) {
	// Returns a random number
	double range = 0.013;
	double exchange = (double)rand() / RAND_MAX;
	if (exchange > 1.0 - factor) {
		double f = (double)rand() / RAND_MAX;
		return -range + f * 2.0 * range;
	}
	return 0.0;
}

void NeuronalNetwork::setCoeffs(vector <double> coefficients, float randomizationFactor) {
	int currentIndex = 0;

	// Fetch all weights
	for (int i = 0; i < _weights.size(); i++) {
		for (int k = 0; k < _weights[i].size(); k++) {

			
			_weights[i](k) = coefficients[currentIndex] + getRand(randomizationFactor);
			currentIndex ++;
		}
	}

	// Fetch all biases
	for (int i = 0; i < _biases.size(); i++) {
		for (int k = 0; k < _biases[i].size(); k++) {
			_biases[i](k) = coefficients[currentIndex] + getRand(randomizationFactor);
			currentIndex ++;
		}
	}
}