public PerceptronNetwork(double[] input, int neuronsAmount)
{
Input = input;
Neurons = new Neuron[neuronsAmount];
for (int i = 0; i < neuronsAmount; i++)
Neurons[i] = new Neuron(Input, 0.1);
}
public MultiLayerPerceptronNetwork(double[] input, int hiddenLayerAmount, int outLayerAmount, double learningRate)
{
Input = input;
LearningRate = learningRate;
// Allocating Hidden Layer
HiddenLayer = new Neuron[ hiddenLayerAmount ];
// Instantiating Neurons from Hidden Layer
for (int i = 0; i < HiddenLayer.Length; i++)
HiddenLayer[i] = new Neuron(LearningRate);
//
// Allocating Output Layer
OutLayer = new Neuron[ outLayerAmount ];
// Instantiating Neurons from Output Layer
for (int i = 0; i < OutLayer.Length; i++)
OutLayer[i] = new Neuron(LearningRate);
//
// Setting Weights manually
SetManuallyWeights();
// Set input on the hidden layer
SetInputOnHiddenLayer();
}
static void BackwardNeuron()
{
Neuron neuron = new Neuron(0.1);
neuron.Weights = new double[] { -1.0, 1.0, 0.5 };
for (int i = 1; i <= 10; i++)
{
neuron.Backward(new double[] { -1.0, -1.0 }, -1.0);
Console.WriteLine("{0}: -1.0, -1.0 = {1:f6}", i, neuron.Output);
neuron.Backward(new double[] { 1.0, -1.0 }, -1.0);
Console.WriteLine("{0}: 1.0, -1.0 = {1:f6}", i, neuron.Output);
neuron.Backward(new double[] { -1.0, 1.0 }, -1.0);
Console.WriteLine("{0}: -1.0, 1.0 = {1:f6}", i, neuron.Output);
neuron.Backward(new double[] { 1.0, 1.0 }, 1.0);
Console.WriteLine("{0}: 1.0, 1.0 = {1:f6}", i, neuron.Output);
Console.WriteLine();
}
}
static void ForwardNeuron()
{
double[] inputs = { -1.0, -1.0 };
// { bias, entradas... }
double[] weights = { -0.1, 0.4, 0.1 };
Neuron neuron = new Neuron(inputs, 0.1);
neuron.Weights = weights;
neuron.Forward();
Console.WriteLine("bias: {0}", Neuron.Bias);
Console.Write("Inputs: ");
foreach (double value in neuron.Input)
Console.Write("{0:f4} ", value);
Console.WriteLine();
Console.Write("Weights:");
foreach (double value in neuron.Weights)
Console.Write("{0:f4} ", value);
Console.WriteLine();
Console.Write("Output: {0:f4}", neuron.Output);
}
