public void TrainOne(Gidran.Matrix Data)
{
this._gradients = new double[3, 3];
this.sse = 0;
for (int i = 0; i < Data.RowCount; i++)
{
double[] values = Data[i];
double nu1 = 1 * this._weights[0, 0] + values[0] * this._weights[0, 1] + values[1] * this._weights[0, 2];
double h1 = this._act.Evaluate(nu1);
double nu2 = 1 * this._weights[1, 0] + values[0] * this._weights[1, 1] + values[1] * this._weights[1, 2];
double h2 = this._act.Evaluate(nu2);
double nuf = 1 * this._weights[2, 0] + h1 * this._weights[2, 1] + h2 * this._weights[2, 2];
double y = this._act.Evaluate(nuf);
Console.WriteLine("H1 : {0}", h1);
Console.WriteLine("H2 : {0}", h2);
Console.WriteLine("Y : {0}", y);
double dx = y * (1 - y) * (values[2] - y);
this.sse += (y - values[2]) * (y - values[2]);
//Console.WriteLine(y);
this._gradients[2, 0] += 1D * dx;
this._gradients[2, 1] += h1 * dx;
this._gradients[2, 2] += h2 * dx;
this._gradients[0, 0] += h1 * (1 - h1) * dx * this._weights[2, 1] * 1;
this._gradients[0, 1] += h1 * (1 - h1) * dx * this._weights[2, 1] * values[0];
this._gradients[0, 2] += h1 * (1 - h1) * dx * this._weights[2, 1] * values[1];
this._gradients[1, 0] += h2 * (1 - h2) * dx * this._weights[2, 2] * 1;
this._gradients[1, 1] += h2 * (1 - h2) * dx * this._weights[2, 2] * values[0];
this._gradients[1, 2] += h2 * (1 - h2) * dx * this._weights[2, 2] * values[1];
}
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
this._weights[i, j] += this._gradients[i, j] * 0.3;
}
}
}
public void TrainN(Gidran.Matrix Data, int N)
{
for (int i = 0; i < N; i++)
{
TrainOne(Data);
if (i % 100 == 0)
{
Console.WriteLine("{0} : {1}", i, sse);
}
}
}
