public static void Mutate(MLP brain, float lo, float hi)
{
brain.inputWeights = Matrix.Add(brain.inputWeights, Matrix.UniformRandomMatrix(brain.inputs, brain.layerSize, 0.5f, -0.5f), 1);
for (int k = 0; k < brain.layers; k++)
{
brain.hiddenWeights[k] = Matrix.Add(brain.hiddenWeights[k], Matrix.UniformRandomMatrix(brain.layerSize, brain.layerSize, 0.5f, -0.5f), 2);
}
brain.outputWeights = Matrix.Add(brain.outputWeights, Matrix.UniformRandomMatrix(brain.layerSize, brain.outputs, 0.5f, -0.5f), 1);
return;
}
//Train function still in development----------------------------
public static void Train(MLP brain, float[][,] x, float[][,] y, int generations, float rate)
{
for (int i = 0; i < generations; i++)
{
float[][,] Y = MLP.Evaluate(brain, x);
for (int j = 0; j < generations; j++)
{
float step = -rate *Matrix.Error(Y[j], y[j], "dMeanSquare");
brain.outputWeights = Matrix.Increment(brain.outputWeights, Matrix.Map(Y[j], "dLogistic"), step);
}
}
return;
}
public static float[][,] Evaluate(MLP brain, float[][,] input)
{
int iter = brain.layers;
float[][,] output = new float[input.Length][, ];
for (int i = 0; i < input.Length; i++)
{
float[,] pre = new float[brain.layerSize, 1];
pre = Matrix.Map(Matrix.Inner(brain.inputWeights, input[i]), brain.activation);
float[,] hid = new float[brain.layerSize, 1];
for (int k = 0; k < brain.layers; k++)
{
hid = Matrix.Map(Matrix.Inner(brain.hiddenWeights[k], pre), brain.activation);
}
output[i] = new float[brain.outputs, 1];
output[i] = Matrix.Map(Matrix.Inner(brain.outputWeights, hid), brain.activation);
}
return(output);
}
//------------------------------------------------------------------
public static MLP Breed(MLP brain1, MLP brain2, string type)
{
MLP frenkenstein = new MLP(brain1.inputs, brain1.outputs, brain1.layers, brain1.layerSize, brain1.activation);
if (brain1.inputs != brain2.inputs || brain1.outputs != brain2.outputs || brain1.layerSize != brain2.layerSize || brain1.layers != brain2.layers || brain1.activation != brain2.activation)
{
Console.WriteLine("oops");
}
else
{
switch (type)
{
case "Swap":
frenkenstein.inputWeights = brain1.inputWeights;
for (int i = 0; i < frenkenstein.layers; i++)
{
if (i % 2 == 0)
{
frenkenstein.hiddenWeights[i] = brain2.hiddenWeights[i];
}
else
{
frenkenstein.hiddenWeights[i] = brain1.hiddenWeights[i];
}
}
frenkenstein.outputWeights = brain2.outputWeights;
break;
case "Average":
frenkenstein.inputWeights = Matrix.Add(brain1.inputWeights, brain2.inputWeights, 0.5f);
for (int i = 0; i < frenkenstein.layers; i++)
{
frenkenstein.hiddenWeights[i] = Matrix.Add(brain1.hiddenWeights[i], brain2.hiddenWeights[i], 0.5f);
}
frenkenstein.outputWeights = Matrix.Add(brain1.outputWeights, brain2.outputWeights, 0.5f);
break;
case "Conjoin":
int bI1 = (int)Decimal.Floor((brain1.inputs) / 2);
int bI2 = (int)Decimal.Floor((brain2.inputs) / 2);
int bH1 = (int)Decimal.Floor((brain1.layerSize) / 2);
int bH2 = (int)Decimal.Floor((brain2.layerSize) / 2);
frenkenstein.inputWeights = Matrix.Concatenate(Matrix.Slice(brain1.inputWeights, 0, bI1, 0, brain1.layerSize), Matrix.Slice(brain2.inputWeights, bI1, brain2.inputs, 0, brain1.layerSize), 0);
for (int i = 0; i < frenkenstein.layers; i++)
{
frenkenstein.hiddenWeights[i] = Matrix.Concatenate(Matrix.Slice(brain1.hiddenWeights[i], 0, bH1, 0, brain1.layerSize), Matrix.Slice(brain2.hiddenWeights[i], bH1, brain1.layerSize, 0, brain1.layerSize), 0);
}
frenkenstein.outputWeights = Matrix.Concatenate(Matrix.Slice(brain1.outputWeights, 0, bH1, 0, brain1.layerSize), Matrix.Slice(brain2.outputWeights, bH1, brain1.layerSize, 0, brain1.layerSize), 0);
break;
}
}
return(frenkenstein);
}
