static void Main(string[] args)
{
Console.WriteLine("\nbegin ANN.\n");
int numInput2 = 4; // number features
int numHidden3 = 5;
int numHidden4 = 5;
int numOutput2 = 3; // number of classes for Y
int numRows2 = 1000;
int seed2 = 1; // gives nice demo
Console.WriteLine("\nGenerating " + numRows2 +
" artificial data items with " + numInput2 + " features");
double[][] allData = MakeAllData(numInput2, numHidden3, numHidden4, numOutput2,
numRows2, seed2);
Console.WriteLine("Done");
//ShowMatrix(allData, allData.Length, 2, true);
Console.WriteLine("\nCreating train (80%) and test (20%) matrices");
double[][] trainData2;
double[][] testData2;
SplitTrainTest(allData, 0.80, seed2, out trainData2, out testData2);
Console.WriteLine("Done\n");
Console.WriteLine("Training data:");
ShowMatrix(trainData2, 4, 2, true);
Console.WriteLine("Test data:");
ShowMatrix(testData2, 4, 2, true);
Console.WriteLine("Creating a " + numInput2 + "-" + numHidden3 +
"-" + numHidden4 + "-" + numOutput2 + " neural network");
NeuralNetwork1 nn = new NeuralNetwork1(numInput2, numHidden3, numHidden4, numOutput2, numRows2, seed2);
int maxEpochs = 1000;
double learnRate = 0.05;
double momentum = 0.01;
Console.WriteLine("\nSetting maxEpochs = " + maxEpochs);
Console.WriteLine("Setting learnRate = " + learnRate.ToString("F2"));
Console.WriteLine("Setting momentum = " + momentum.ToString("F2"));
Console.WriteLine("\nStarting training");
double[] weights = nn.Train(trainData2, maxEpochs, learnRate, momentum);
Console.WriteLine("Done");
Console.WriteLine("\nFinal neural network model weights and biases:\n");
// ShowVector(weights, 2, 10, true);
//double[] y = nn.ComputeOutputs(new double[] { 1.0, 2.0, 3.0, 4.0 });
//ShowVector(y, 3, 3, true);
double trainAcc = nn.Accuracy(trainData2);
Console.WriteLine("\nFinal accuracy on training data = " +
trainAcc.ToString("F4"));
double testAcc = nn.Accuracy(testData2);
Console.WriteLine("Final accuracy on test data = " +
testAcc.ToString("F4"));
Console.WriteLine("\nBegin van Vector.\n");
double[][] trainData = new double[4][];
trainData[0] = new double[] { 2.0, 2.0 }; // data cannot be perfectly classified
trainData[1] = new double[] { 3.5, -4.0 };
trainData[2] = new double[] { 4.0, -5.5 };
trainData[3] = new double[] { 4.5, -2.0 };
int[] Y = new int[4] {
0, 1, 1, 1
};
Console.WriteLine("\nTraining data: \n");
ShowTrainData(trainData, Y);
double[] weights2 = null;
double bias = 0.0;
double alpha = 0.001;
int maxEpochs2 = 500;
Console.Write("\nSetting learning rate to " + alpha.ToString("F3"));
Console.WriteLine(" and maxEpochs to " + maxEpochs2);
Console.WriteLine("\nBeginning training the perceptron");
Train(trainData, alpha, maxEpochs2, Y, out weights2, out bias);
Console.WriteLine("Training complete");
Console.WriteLine("\nBest percetron weights found: ");
ShowVector(weights2, 4);
Console.WriteLine("\nBest perceptron bias found = " + bias.ToString("F4"));
double acc = Accuracy(trainData, weights2, bias, Y);
Console.Write("\nAccuracy of the perceptron on the training data = ");
Console.WriteLine(acc.ToString("F2"));
Console.WriteLine("\nDone\n");
Console.ReadLine();
} //Main
