public override void train(int trainCount)
{
int epochs = 0;
bool moreError = true;
double minError = 1E-2;
double[][] error = new double[this.classMask.Length][];
double[] mse = new double[MAX_EPOCHS]; //Mean square error.
bool weightChanged = true;
int classIndex;
double[] lineData;
/*REAL WORK*/
while (weightChanged && epochs < NeuralNetwork.MAX_EPOCHS && moreError)
{
weightChanged = false;
for (int i = 0; i < this.classMask.Length; i++) //Class index.
{
error[i] = new double[trainCount];
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);
net = getvalue(net);
if (net != this.target[i])
{
weightChanged = true;
lineData = VectorTools.trim(this.data[classIndex][j], this.featureMask);
lineData = VectorTools.prepend(lineData, this.bias);
error[i][j] = this.target[i] - net;
double[] mulOut = VectorTools.multiply(lineData, error[i][j] * this.eta);
error[i][j] = 0.5 * error[i][j] * error[i][j];
this.weight = VectorTools.sum(this.weight, mulOut);
}
}
} //End of inner for.
double[] temp = VectorTools.get1D(error);
mse[epochs] = VectorTools.mean(temp);
if (mse[epochs] < minError)
{
moreError = false;
}
epochs++;
} //End of outer while.
}
/// <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.
}
