static void Main(string[] args)
{
double[][] inputs = new double[4][];
double[] outputs = { 1, 1, -1, -1 };
inputs[0] = new double[] { -1, 1 };
inputs[1] = new double[] { 1, -1 };
inputs[2] = new double[] { 1, 1 };
inputs[3] = new double[] { -1, -1 };
MLP network = SourceMulti.mlp_create_model(3, new int[] { 2, 3, 1 });
//SourceMulti.mlp_fit_classification_backdrop(network, inputs, outputs, 1000, 0.1);
SourceMulti.mlp_fit_regression_backdrop(network, inputs, outputs, 1000, 0.1);
/*Console.WriteLine(SourceMulti.mlp_classify(network, new double[] { -1, 1 })[0]);
* Console.WriteLine(SourceMulti.mlp_classify(network, new double[] { 1, -1 })[0]);
* Console.WriteLine(SourceMulti.mlp_classify(network, new double[] { 1, 1 })[0]);
* Console.WriteLine(SourceMulti.mlp_classify(network, new double[] { -1, -1 })[0]);*/
Console.WriteLine(SourceMulti.mlp_predict(network, new double[] { -1, 1 })[0]);
Console.WriteLine(SourceMulti.mlp_predict(network, new double[] { 1, -1 })[0]);
Console.WriteLine(SourceMulti.mlp_predict(network, new double[] { 1, 1 })[0]);
Console.WriteLine(SourceMulti.mlp_predict(network, new double[] { -1, -1 })[0]);
Console.ReadLine();
}
public static double[] mlp_predict(object model, double[] input)
{
return(SourceMulti.mlp_predict((MLP)model, input));
}
public static double[] mlp_classify(object model, double[] input)
{
return(SourceMulti.mlp_classify((MLP)model, input));
}
public static int mlp_fit_classification_backdrop(object model, double[][] inputs, double[] outputs, int iterationNumber, double step)
{
return(SourceMulti.mlp_fit_classification_backdrop((MLP)model, inputs, outputs, iterationNumber, step));
}
public static MLP mlp_create_model(int numLayers, int[] npl)
{
return(SourceMulti.mlp_create_model(numLayers, npl));
}
