/// <summary>
///
/// </summary>
/// <param name="parent"></param>
public static NeuralNetwork NoMutations(NeuralNetwork parent)
{
NeuralNetwork ret = new NeuralNetwork();
ret.m_layers = parent.m_layers;
ret.m_inputNeurons = new InputNeuron[parent.m_inputNeurons.Length];
for (int i = 0; i < ret.m_inputNeurons.Length; i++)
{
ret.m_inputNeurons[i] = InputNeuron.NoMutations(parent.m_inputNeurons[i]);
}
ret.m_hiddenNeurons = new HiddenNeuron[parent.m_hiddenNeurons.Length][];
for (int i = 0; i < ret.m_hiddenNeurons.Length; i++)
{
ret.m_hiddenNeurons[i] = new HiddenNeuron[parent.m_hiddenNeurons[i].Length];
for (int j = 0; j < ret.m_hiddenNeurons[i].Length; j++)
{
ret.m_hiddenNeurons[i][j] = HiddenNeuron.NoMutations(parent.m_hiddenNeurons[i][j]);
}
}
ret.m_outputNeurons = new OutputNeuron[parent.m_outputNeurons.Length];
for (int i = 0; i < ret.m_outputNeurons.Length; i++)
{
ret.m_outputNeurons[i] = OutputNeuron.NoMutations(parent.m_outputNeurons[i]);
}
return(ret);
}
/// <summary>
/// Copy without mutations
/// </summary>
/// <param name="other"></param>
public static HiddenNeuron NoMutations(HiddenNeuron other)
{
HiddenNeuron ret = new HiddenNeuron(other.connections.Length, other.bias);
for (int i = 0; i < ret.connections.Length; i++)
{
ret.connections[i] = other.connections[i];
}
ret.bias = other.bias;
return(ret);
}
/// <summary>
/// Copy with mutations
/// </summary>
/// <param name="other"></param>
public static HiddenNeuron WithMutations(HiddenNeuron other)
{
HiddenNeuron ret = new HiddenNeuron(other.connections.Length, other.bias);
for (int i = 0; i < ret.connections.Length; i++)
{
ret.connections[i] = other.connections[i];
Mutate(ref ret.connections[i], 0.4f);
}
ret.bias = other.bias;
Mutate(ref ret.bias, 2);
return(ret);
}
/// <summary>
/// The constructor for initializing a neural network with pre-set values
/// </summary>
/// <param name="layers"></param>
/// <param name="connections"></param>
/// <param name="biases"></param>
public NeuralNetwork(int[] layers, float[] connections, float[] biases)
{
m_layers = layers;
m_inputNeurons = new InputNeuron[layers[0]];
m_hiddenNeurons = new HiddenNeuron[m_layers.Length - 2][];
m_outputNeurons = new OutputNeuron[m_layers[m_layers.Length - 1]];
int biasesIdx = 0;
int connectionsIdx = 0;
//Input neurons loop
for (int i = 0; i < m_layers[0]; i++)
{
int nextLayer = m_layers[1];
m_inputNeurons[i] = new InputNeuron(nextLayer);
for (int j = 0; j < nextLayer; j++)
{
m_inputNeurons[i].connections[j] = connections[connectionsIdx++];
}
}
//Hidden neurons loop
for (int i = 0; i < m_hiddenNeurons.Length; i++)
{
int layer = m_layers[i + 1];
m_hiddenNeurons[i] = new HiddenNeuron[layer];
for (int j = 0; j < layer; j++)
{
int nextLayer = m_layers[i + 2];
m_hiddenNeurons[i][j] = new HiddenNeuron(nextLayer, biases[biasesIdx++]);
for (int k = 0; k < nextLayer; k++)
{
m_hiddenNeurons[i][j].connections[k] = connections[connectionsIdx++];
}
}
}
//Output neurons loop
for (int i = 0; i < m_layers[m_layers.Length - 1]; i++)
{
m_outputNeurons[i] = new OutputNeuron(biases[biasesIdx++]);
}
}
/// <summary>
/// The constructor for initializing a neural network with random values
/// </summary>
/// <param name="layers"></param>
public NeuralNetwork(int[] layers)
{
m_layers = layers;
m_inputNeurons = new InputNeuron[layers[0]];
m_hiddenNeurons = new HiddenNeuron[m_layers.Length - 2][];
m_outputNeurons = new OutputNeuron[m_layers[m_layers.Length - 1]];
//Input neurons loop
for (int i = 0; i < m_layers[0]; i++)
{
int nextLayer = m_layers[1];
m_inputNeurons[i] = new InputNeuron(nextLayer);
for (int j = 0; j < nextLayer; j++)
{
m_inputNeurons[i].connections[j] = GetRandomNeuronValue(2);
}
}
//Hidden neurons loop
for (int i = 0; i < m_hiddenNeurons.Length; i++)
{
int layer = m_layers[i + 1];
m_hiddenNeurons[i] = new HiddenNeuron[layer];
for (int j = 0; j < layer; j++)
{
int nextLayer = m_layers[i + 2];
m_hiddenNeurons[i][j] = new HiddenNeuron(nextLayer, GetRandomNeuronValue(10));
for (int k = 0; k < nextLayer; k++)
{
m_hiddenNeurons[i][j].connections[k] = GetRandomNeuronValue(2);
}
}
}
//Output neurons loop
for (int i = 0; i < m_layers[m_layers.Length - 1]; i++)
{
m_outputNeurons[i] = new OutputNeuron(GetRandomNeuronValue(10));
}
}
/// <summary>
/// Takes another neural network and changes random values in it
/// </summary>
/// <param name="parent"></param>
public NeuralNetwork(NeuralNetwork parent)
{
m_layers = parent.m_layers;
m_inputNeurons = new InputNeuron[parent.m_inputNeurons.Length];
for (int i = 0; i < m_inputNeurons.Length; i++)
{
m_inputNeurons[i] = InputNeuron.WithMutations(parent.m_inputNeurons[i]);
}
m_hiddenNeurons = new HiddenNeuron[parent.m_hiddenNeurons.Length][];
for (int i = 0; i < m_hiddenNeurons.Length; i++)
{
m_hiddenNeurons[i] = new HiddenNeuron[parent.m_hiddenNeurons[i].Length];
for (int j = 0; j < m_hiddenNeurons[i].Length; j++)
{
m_hiddenNeurons[i][j] = HiddenNeuron.WithMutations(parent.m_hiddenNeurons[i][j]);
}
}
m_outputNeurons = new OutputNeuron[parent.m_outputNeurons.Length];
for (int i = 0; i < m_outputNeurons.Length; i++)
{
m_outputNeurons[i] = OutputNeuron.WithMutations(parent.m_outputNeurons[i]);
}
}
