public void Train(double[,] training_inputs, double[] training_outputs, int training_iterations)
{
for (int i = 0; i < training_iterations; i++)
{
double[] outputs = new double[training_inputs.Length / synaptic_weights.Length];
double[] error = new double[training_inputs.Length / synaptic_weights.Length];
double[] adjustments = new double[training_inputs.Length / synaptic_weights.Length];
outputs = Think(training_inputs);
error = NNMath.createError(training_outputs, outputs);
adjustments = NNMath.createAdjustments(error, training_inputs, outputs, synaptic_weights.Length);
synaptic_weights = NNMath.AdjustWeights(synaptic_weights, adjustments);
}
}
public static double[] createAdjustments(double[] error, double[,] inputs, double[] outputs, int numofweights)
{
double[] adjustments = new double[numofweights];
for (int i = 0; i < numofweights; i++)
{
double adjust = 0;
for (int y = 0; y < error.Length; y++)
{
adjust += error[y] * inputs[y, i] * NNMath.Sigmoid_Derivative(outputs[y]);
}
adjustments[i] = adjust;
}
return(adjustments);
}
public double[] Think(double[,] inputs)
{
double[] outputs = new double[inputs.Length / synaptic_weights.Length];
for (int i = 0; i < outputs.Length; i++)
{
double output = 0;
for (int j = 0; j < 3; j++)
{
output += inputs[i, j] * synaptic_weights[j];
}
outputs[i] = NNMath.Sigmoid(output);
}
return(outputs);
}