/// <summary>
/// This method creates a 1D array containing random weight and bias values.
/// A 1D Array makes storing the weights easier
/// The SetWeights method will then take the created array and initialize all arrays with the values
/// Order: ihweights - hhWeights[] - hoWeights - hBiases[] - oBiases
/// </summary>
public void InitializeWeights()
{
// make wts
double lo = -0.75f;
double hi = +0.75f;
int numWts = DeepNeuralNetwork.NumWeights(this.nInput, this.nHidden, this.nOutput);
double[] wts = new double[numWts];
for (int i = 0; i < numWts; ++i)
{
wts[i] = (hi - lo) * rnd.NextDouble() + lo;
}
this.SetWeights(wts);
}
} // SetWeights
public void updateWeights(double[] wts, double chance, double mutationRate)
{
// scale the weights with the factor
double[] weights = this.GetWeights();
double[] newWeights = new double[DeepNeuralNetwork.NumWeights(this.nInput, this.nHidden, this.nOutput)];
for (int i = 0; i < wts.Length; i++)
{
if (rnd.Next(1, 101) <= mutationRate)
{
int x = rnd.Next(1, 101);
// 20% chance for one of these cases to occure
if (x <= 20)
{
newWeights[i] = (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
else if (x <= 40)
{
newWeights[i] += (0.375f - -0.375f) * rnd.NextDouble() + -0.375f;
}
else if (x <= 60)
{
newWeights[i] -= (0.375f - -0.375f) * rnd.NextDouble() + -0.375f;
}
else if (x <= 80)
{
newWeights[i] += (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
else
{
newWeights[i] -= (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
}
else
{
if (rnd.Next(1, 101) <= chance)
{
newWeights[i] = wts[i];
}
else
{
newWeights[i] = weights[i];
}
}
}
// order: ihweights - hhWeights[] - hoWeights - hBiases[] - oBiases
int nw = NumWeights(this.nInput, this.nHidden, this.nOutput); // total num wts + biases
if (wts.Length != nw)
{
return;
}
int ptr = 0; // pointer into wts[]
for (int i = 0; i < nInput; ++i) // input node
{
for (int j = 0; j < hNodes[0].Length; ++j) // 1st hidden layer nodes
{
ihWeights[i][j] = newWeights[ptr++];
}
}
for (int h = 0; h < nLayers - 1; ++h) // not last h layer
{
for (int j = 0; j < nHidden[h]; ++j) // from node
{
for (int jj = 0; jj < nHidden[h + 1]; ++jj) // to node
{
hhWeights[h][j][jj] = newWeights[ptr++];
}
}
}
int hi = this.nLayers - 1; // if 3 hidden layers (0,1,2) last is 3-1 = [2]
for (int j = 0; j < this.nHidden[hi]; ++j)
{
for (int k = 0; k < this.nOutput; ++k)
{
hoWeights[j][k] = newWeights[ptr++];
}
}
for (int h = 0; h < nLayers; ++h) // hidden node biases
{
for (int j = 0; j < this.nHidden[h]; ++j)
{
hBiases[h][j] = newWeights[ptr++];
}
}
for (int k = 0; k < nOutput; ++k)
{
oBiases[k] = newWeights[ptr++];
}
} // updateWeights