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 MLP mlp_create_model(int numLayers, int[] npl) { return(SourceMulti.mlp_create_model(numLayers, npl)); }