/// <summary>Classify one sample in the machine using the perceptron algorithm (used after training).</summary>
/// <param name="input">The sample to classify</param>
public override double classify(double[] input)
{
if (input.Length > this.featureMask.Length)
{
throw new ArgumentOutOfRangeException("Input features is bigger than features in feature mask");
}
input = VectorTools.prepend(input, this.bias); //Add the bias.
double net = VectorTools.multiply(this.weight, input);
int sgnOut = ActivationFunctions.signum(net);
return(sgnOut);
}
/// <summary>Train the machine using the perceptron algorithm.</summary>
/// <param name="trainCount">Number of data set smaples to use in training.</param>
public override void train(int trainCount)
{
int epochs = 0;
bool weightChanged = true;
int classIndex;
double[] lineData;
/*REAL WORK*/
while (weightChanged && epochs < NeuralNetwork.MAX_EPOCHS)
{
weightChanged = false;
for (int i = 0; i < this.classMask.Length; i++) //Class index.
{
classIndex = (int)classMask[i] - 1;
for (int j = 0; j < trainCount; j++) //Data index.
{
lineData = VectorTools.trim(this.data[classIndex][j], this.featureMask);
lineData = VectorTools.prepend(lineData, this.bias); //Prepend the bias.
double net = VectorTools.multiply(this.weight, lineData);
int sgnOut = ActivationFunctions.signum(net);
if (sgnOut != this.target[i])
{
weightChanged = true;
lineData = VectorTools.trim(this.data[classIndex][j], this.featureMask);
lineData = VectorTools.prepend(lineData, this.bias);
double error = this.target[i] - sgnOut;
double[] mulOut = VectorTools.multiply(lineData, error * this.eta);
this.weight = VectorTools.sum(this.weight, mulOut);
}
}
} //End of inner for.
epochs++;
} //End of outer while.
}
public Layer(List <Neuron> neurons, ActivationFunctions activationFunction)
{
this.Neurons = neurons;
this.activationFunction = activationFunction;
}
public Layer(int numNeurons, ActivationFunctions activationFunction)
{
this.activationFunction = activationFunction;
Neurons = new List <Neuron>(numNeurons);
}
