public PreceptorLayer(int numOfPreceptors, int prevLayerNumOfPreceptors, Neuron.NormalizedMethod method)
{
this.numOfPreceptors = numOfPreceptors;
preceptors = new Neuron[numOfPreceptors];
for (int i = 0; i < numOfPreceptors; i++)
{
preceptors[i] = new Neuron(prevLayerNumOfPreceptors, method);
}
}
public NeuralNetwork(int numOfInputs, int numOfOutputs,
int[] numOfHiddens) : base()
{
this.numOfHiddens = numOfHiddens;
this.numOfInputs = numOfInputs;
this.numOfOutputs = numOfOutputs;
numOfLayers = this.numOfHiddens.Length + 1;
layers = new PreceptorLayer[numOfLayers];
int prevNumOfPreceptors = this.numOfInputs;
for (int i = 0; i < numOfLayers; i++)
{
int numOfPreceptors = i < (numOfLayers - 1) ? this.numOfHiddens[i] : this.numOfOutputs;
Neuron.NormalizedMethod method = i == (numOfLayers - 1) ? Neuron.NormalizedMethod.SigmoidZeroTo1 : Neuron.NormalizedMethod.HyperTan;
layers[i] = new PreceptorLayer(numOfPreceptors, prevNumOfPreceptors, method);
numOfFeatures += numOfPreceptors;
numOfConnections += prevNumOfPreceptors * numOfPreceptors;
prevNumOfPreceptors = numOfPreceptors;
}
int idx = 0;
featureIndex = new FeatureIndex[numOfFeatures];
for (int i = 0; i < numOfLayers; i++)
{
for (int j = 0; j < layers[i].numOfPreceptors; j++)
{
featureIndex[idx] = new FeatureIndex(i, j);
idx += 1;
}
}
idx = 0;
connectionIndex = new ConnectionIndex[numOfConnections];
for (int i = 0; i < numOfLayers; i++)
{
for (int j = 0; j < layers[i].numOfPreceptors; j++)
{
for (int w = 0; w < layers[i].preceptors[j].GetNumWeights(); w++)
{
connectionIndex[idx] = new ConnectionIndex(i, j, w);
idx += 1;
}
}
}
}
