private void populateNeuralNet(ArrayList Structure, float [] Weights, float [] Biases)
{
//short[] neurons = structure;
net.initNeuralNet(Structure);
float[] w = Weights;
// create links
int totalLinks = 0;
// calculate total number of links
for (int i = 0; i < Structure.Count - 1; i++)
{
totalLinks += ((short[, ])(Structure[i]))[0, 0] * ((short[, ])(Structure[i + 1]))[0, 0];
}
// initialize WeightsCollection
WeightsMatrix weights = new WeightsMatrix(totalLinks);
for (int i = 0; i < totalLinks; i++)
{
weights.addWeight(w[i], i);
}
// initialize Links
int linkcount = 0;
for (short layerCount = 0; layerCount + 1 < Structure.Count; layerCount++)
{
// get adjacent layers
NeuralNet.Layer layerOne = net.getLayer(layerCount);
NeuralNet.Layer layerTwo = net.getLayer(layerCount + 1);
// traverse nerons of second layer
for (int toNeuronCount = 0; toNeuronCount < layerTwo.getNeuronCount(); toNeuronCount++)
{
// traverse all neurons of first layer
for (int fromNeuronCount = 0; fromNeuronCount < layerOne.getNeuronCount(); fromNeuronCount++)
{
// create a link
NeuralNet.Link link = new NeuralNet.Link(layerOne.getNeuron(fromNeuronCount), layerTwo.getNeuron(toNeuronCount));
link.setWeight(weights.getWeight(linkcount++));
}
}
}
// biases
float[] biases = Biases;
// initialize neurons with biases
int count = 0;
for (short layerCount = 1; layerCount < net.layers.Count; layerCount++)
{
for (int neuronCount = 0; neuronCount < net.getLayer(layerCount).getNeuronCount(); neuronCount++)
{
net.getLayer(layerCount).getNeuron(neuronCount).setBias(biases[count++]);
}
}
}
public void runNeuralNet()
{
lock (this)
{
// execute neurons
for (int inputCount = 0; inputCount < getInputDataLength(); inputCount++)
{
float[] inputRow = getInputRow(inputCount); //new float[]{-0.73626F};
// input data to input layer neurons
NeuralNet.Layer inputLayer = net.getLayer(0);
// for each input value
short neuronCount;
for (short inputValCount = 0; inputValCount < inputRow.Length; inputValCount++)
{
// for each input follow the following process
//set input value in everyToLink from this neuron
neuronCount = inputValCount;
object[] toLinks = (inputLayer.getNeuron(neuronCount).getToLinks()).ToArray();
for (int toLinkCount = 0; toLinkCount < toLinks.Length; toLinkCount++)
{
((NeuralNet.Link)(toLinks[toLinkCount])).setInputValue(inputRow[inputValCount]);
} // input layer neuron initialized with input
} // Inputs Dissimnated
// Execute next layer neurons now
for (int layerCount = 1; layerCount < net.layers.Count; layerCount++)
{
for (neuronCount = 0; neuronCount < net.getLayer(layerCount).getNeuronCount(); neuronCount++)
{
net.getLayer(layerCount).getNeuron(neuronCount).execute();
} // One Neuron Executed
} // One Layer Executed
} // inputs are finished
} // lock ends
}
/// <summary>
/// The main entry point for the application.
/// </summary>
public float[] getNNOutput(int denormType) // denormType 0 (NO Denorm) OR 1(0~1) OR 2 (-1~1)
{
int numOutputs = net.getLayer(net.layers.Count - 1).getNeuronCount();
float[] f = new float[numOutputs];
for (int count = 0; count < numOutputs; count++)
{
NeuralNet.Layer layer = net.getLayer(net.layers.Count - 1);
NeuralNet.OutputNeuron o = (NeuralNet.OutputNeuron)layer.getNeuron(count);
f[count] = (float)Convert.ToDecimal((o.getOutputValues())[0]);
}
if (denormType == 0)
{
return(f);
}
else if (denormType == 1)
{
return(Denormalize(f, true));
}
else
{
return(Denormalize(f, false));
}
}
