static void Main(string[] args)
{
Console.WriteLine("\nBegin neural network training demo");
Console.WriteLine("\nData is the famous Iris flower set.");
Console.WriteLine("Predict species from sepal length, width, petal length, width");
Console.WriteLine("Iris setosa = 0 0 1, versicolor = 0 1 0, virginica = 1 0 0 \n");
Console.WriteLine("Rad data resembles:");
Console.WriteLine(" 5.1, 3.5, 1.4, 0.2, Iris setosa");
Console.WriteLine(" 7.0, 3.2, 4.7, 1.4, Iris versicolor");
Console.WriteLine(" 6.3, 3.3, 6.0, 2.5, Iris virginica");
Console.WriteLine(" ......\n");
#region Data for Input
double[][] allData = new double[150][];
allData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 };
allData[1] = new double[] { 4.9, 3.0, 1.4, 0.2, 0, 0, 1 }; // Iris setosa = 0 0 1
allData[2] = new double[] { 4.7, 3.2, 1.3, 0.2, 0, 0, 1 }; // Iris versicolor = 0 1 0
allData[3] = new double[] { 4.6, 3.1, 1.5, 0.2, 0, 0, 1 }; // Iris virginica = 1 0 0
allData[4] = new double[] { 5.0, 3.6, 1.4, 0.2, 0, 0, 1 };
allData[5] = new double[] { 5.4, 3.9, 1.7, 0.4, 0, 0, 1 };
allData[6] = new double[] { 4.6, 3.4, 1.4, 0.3, 0, 0, 1 };
allData[7] = new double[] { 5.0, 3.4, 1.5, 0.2, 0, 0, 1 };
allData[8] = new double[] { 4.4, 2.9, 1.4, 0.2, 0, 0, 1 };
allData[9] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
allData[10] = new double[] { 5.4, 3.7, 1.5, 0.2, 0, 0, 1 };
allData[11] = new double[] { 4.8, 3.4, 1.6, 0.2, 0, 0, 1 };
allData[12] = new double[] { 4.8, 3.0, 1.4, 0.1, 0, 0, 1 };
allData[13] = new double[] { 4.3, 3.0, 1.1, 0.1, 0, 0, 1 };
allData[14] = new double[] { 5.8, 4.0, 1.2, 0.2, 0, 0, 1 };
allData[15] = new double[] { 5.7, 4.4, 1.5, 0.4, 0, 0, 1 };
allData[16] = new double[] { 5.4, 3.9, 1.3, 0.4, 0, 0, 1 };
allData[17] = new double[] { 5.1, 3.5, 1.4, 0.3, 0, 0, 1 };
allData[18] = new double[] { 5.7, 3.8, 1.7, 0.3, 0, 0, 1 };
allData[19] = new double[] { 5.1, 3.8, 1.5, 0.3, 0, 0, 1 };
allData[20] = new double[] { 5.4, 3.4, 1.7, 0.2, 0, 0, 1 };
allData[21] = new double[] { 5.1, 3.7, 1.5, 0.4, 0, 0, 1 };
allData[22] = new double[] { 4.6, 3.6, 1.0, 0.2, 0, 0, 1 };
allData[23] = new double[] { 5.1, 3.3, 1.7, 0.5, 0, 0, 1 };
allData[24] = new double[] { 4.8, 3.4, 1.9, 0.2, 0, 0, 1 };
allData[25] = new double[] { 5.0, 3.0, 1.6, 0.2, 0, 0, 1 };
allData[26] = new double[] { 5.0, 3.4, 1.6, 0.4, 0, 0, 1 };
allData[27] = new double[] { 5.2, 3.5, 1.5, 0.2, 0, 0, 1 };
allData[28] = new double[] { 5.2, 3.4, 1.4, 0.2, 0, 0, 1 };
allData[29] = new double[] { 4.7, 3.2, 1.6, 0.2, 0, 0, 1 };
allData[30] = new double[] { 4.8, 3.1, 1.6, 0.2, 0, 0, 1 };
allData[31] = new double[] { 5.4, 3.4, 1.5, 0.4, 0, 0, 1 };
allData[32] = new double[] { 5.2, 4.1, 1.5, 0.1, 0, 0, 1 };
allData[33] = new double[] { 5.5, 4.2, 1.4, 0.2, 0, 0, 1 };
allData[34] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
allData[35] = new double[] { 5.0, 3.2, 1.2, 0.2, 0, 0, 1 };
allData[36] = new double[] { 5.5, 3.5, 1.3, 0.2, 0, 0, 1 };
allData[37] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
allData[38] = new double[] { 4.4, 3.0, 1.3, 0.2, 0, 0, 1 };
allData[39] = new double[] { 5.1, 3.4, 1.5, 0.2, 0, 0, 1 };
allData[40] = new double[] { 5.0, 3.5, 1.3, 0.3, 0, 0, 1 };
allData[41] = new double[] { 4.5, 2.3, 1.3, 0.3, 0, 0, 1 };
allData[42] = new double[] { 4.4, 3.2, 1.3, 0.2, 0, 0, 1 };
allData[43] = new double[] { 5.0, 3.5, 1.6, 0.6, 0, 0, 1 };
allData[44] = new double[] { 5.1, 3.8, 1.9, 0.4, 0, 0, 1 };
allData[45] = new double[] { 4.8, 3.0, 1.4, 0.3, 0, 0, 1 };
allData[46] = new double[] { 5.1, 3.8, 1.6, 0.2, 0, 0, 1 };
allData[47] = new double[] { 4.6, 3.2, 1.4, 0.2, 0, 0, 1 };
allData[48] = new double[] { 5.3, 3.7, 1.5, 0.2, 0, 0, 1 };
allData[49] = new double[] { 5.0, 3.3, 1.4, 0.2, 0, 0, 1 };
allData[50] = new double[] { 7.0, 3.2, 4.7, 1.4, 0, 1, 0 };
allData[51] = new double[] { 6.4, 3.2, 4.5, 1.5, 0, 1, 0 };
allData[52] = new double[] { 6.9, 3.1, 4.9, 1.5, 0, 1, 0 };
allData[53] = new double[] { 5.5, 2.3, 4.0, 1.3, 0, 1, 0 };
allData[54] = new double[] { 6.5, 2.8, 4.6, 1.5, 0, 1, 0 };
allData[55] = new double[] { 5.7, 2.8, 4.5, 1.3, 0, 1, 0 };
allData[56] = new double[] { 6.3, 3.3, 4.7, 1.6, 0, 1, 0 };
allData[57] = new double[] { 4.9, 2.4, 3.3, 1.0, 0, 1, 0 };
allData[58] = new double[] { 6.6, 2.9, 4.6, 1.3, 0, 1, 0 };
allData[59] = new double[] { 5.2, 2.7, 3.9, 1.4, 0, 1, 0 };
allData[60] = new double[] { 5.0, 2.0, 3.5, 1.0, 0, 1, 0 };
allData[61] = new double[] { 5.9, 3.0, 4.2, 1.5, 0, 1, 0 };
allData[62] = new double[] { 6.0, 2.2, 4.0, 1.0, 0, 1, 0 };
allData[63] = new double[] { 6.1, 2.9, 4.7, 1.4, 0, 1, 0 };
allData[64] = new double[] { 5.6, 2.9, 3.6, 1.3, 0, 1, 0 };
allData[65] = new double[] { 6.7, 3.1, 4.4, 1.4, 0, 1, 0 };
allData[66] = new double[] { 5.6, 3.0, 4.5, 1.5, 0, 1, 0 };
allData[67] = new double[] { 5.8, 2.7, 4.1, 1.0, 0, 1, 0 };
allData[68] = new double[] { 6.2, 2.2, 4.5, 1.5, 0, 1, 0 };
allData[69] = new double[] { 5.6, 2.5, 3.9, 1.1, 0, 1, 0 };
allData[70] = new double[] { 5.9, 3.2, 4.8, 1.8, 0, 1, 0 };
allData[71] = new double[] { 6.1, 2.8, 4.0, 1.3, 0, 1, 0 };
allData[72] = new double[] { 6.3, 2.5, 4.9, 1.5, 0, 1, 0 };
allData[73] = new double[] { 6.1, 2.8, 4.7, 1.2, 0, 1, 0 };
allData[74] = new double[] { 6.4, 2.9, 4.3, 1.3, 0, 1, 0 };
allData[75] = new double[] { 6.6, 3.0, 4.4, 1.4, 0, 1, 0 };
allData[76] = new double[] { 6.8, 2.8, 4.8, 1.4, 0, 1, 0 };
allData[77] = new double[] { 6.7, 3.0, 5.0, 1.7, 0, 1, 0 };
allData[78] = new double[] { 6.0, 2.9, 4.5, 1.5, 0, 1, 0 };
allData[79] = new double[] { 5.7, 2.6, 3.5, 1.0, 0, 1, 0 };
allData[80] = new double[] { 5.5, 2.4, 3.8, 1.1, 0, 1, 0 };
allData[81] = new double[] { 5.5, 2.4, 3.7, 1.0, 0, 1, 0 };
allData[82] = new double[] { 5.8, 2.7, 3.9, 1.2, 0, 1, 0 };
allData[83] = new double[] { 6.0, 2.7, 5.1, 1.6, 0, 1, 0 };
allData[84] = new double[] { 5.4, 3.0, 4.5, 1.5, 0, 1, 0 };
allData[85] = new double[] { 6.0, 3.4, 4.5, 1.6, 0, 1, 0 };
allData[86] = new double[] { 6.7, 3.1, 4.7, 1.5, 0, 1, 0 };
allData[87] = new double[] { 6.3, 2.3, 4.4, 1.3, 0, 1, 0 };
allData[88] = new double[] { 5.6, 3.0, 4.1, 1.3, 0, 1, 0 };
allData[89] = new double[] { 5.5, 2.5, 4.0, 1.3, 0, 1, 0 };
allData[90] = new double[] { 5.5, 2.6, 4.4, 1.2, 0, 1, 0 };
allData[91] = new double[] { 6.1, 3.0, 4.6, 1.4, 0, 1, 0 };
allData[92] = new double[] { 5.8, 2.6, 4.0, 1.2, 0, 1, 0 };
allData[93] = new double[] { 5.0, 2.3, 3.3, 1.0, 0, 1, 0 };
allData[94] = new double[] { 5.6, 2.7, 4.2, 1.3, 0, 1, 0 };
allData[95] = new double[] { 5.7, 3.0, 4.2, 1.2, 0, 1, 0 };
allData[96] = new double[] { 5.7, 2.9, 4.2, 1.3, 0, 1, 0 };
allData[97] = new double[] { 6.2, 2.9, 4.3, 1.3, 0, 1, 0 };
allData[98] = new double[] { 5.1, 2.5, 3.0, 1.1, 0, 1, 0 };
allData[99] = new double[] { 5.7, 2.8, 4.1, 1.3, 0, 1, 0 };
allData[100] = new double[] { 6.3, 3.3, 6.0, 2.5, 1, 0, 0 };
allData[101] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
allData[102] = new double[] { 7.1, 3.0, 5.9, 2.1, 1, 0, 0 };
allData[103] = new double[] { 6.3, 2.9, 5.6, 1.8, 1, 0, 0 };
allData[104] = new double[] { 6.5, 3.0, 5.8, 2.2, 1, 0, 0 };
allData[105] = new double[] { 7.6, 3.0, 6.6, 2.1, 1, 0, 0 };
allData[106] = new double[] { 4.9, 2.5, 4.5, 1.7, 1, 0, 0 };
allData[107] = new double[] { 7.3, 2.9, 6.3, 1.8, 1, 0, 0 };
allData[108] = new double[] { 6.7, 2.5, 5.8, 1.8, 1, 0, 0 };
allData[109] = new double[] { 7.2, 3.6, 6.1, 2.5, 1, 0, 0 };
allData[110] = new double[] { 6.5, 3.2, 5.1, 2.0, 1, 0, 0 };
allData[111] = new double[] { 6.4, 2.7, 5.3, 1.9, 1, 0, 0 };
allData[112] = new double[] { 6.8, 3.0, 5.5, 2.1, 1, 0, 0 };
allData[113] = new double[] { 5.7, 2.5, 5.0, 2.0, 1, 0, 0 };
allData[114] = new double[] { 5.8, 2.8, 5.1, 2.4, 1, 0, 0 };
allData[115] = new double[] { 6.4, 3.2, 5.3, 2.3, 1, 0, 0 };
allData[116] = new double[] { 6.5, 3.0, 5.5, 1.8, 1, 0, 0 };
allData[117] = new double[] { 7.7, 3.8, 6.7, 2.2, 1, 0, 0 };
allData[118] = new double[] { 7.7, 2.6, 6.9, 2.3, 1, 0, 0 };
allData[119] = new double[] { 6.0, 2.2, 5.0, 1.5, 1, 0, 0 };
allData[120] = new double[] { 6.9, 3.2, 5.7, 2.3, 1, 0, 0 };
allData[121] = new double[] { 5.6, 2.8, 4.9, 2.0, 1, 0, 0 };
allData[122] = new double[] { 7.7, 2.8, 6.7, 2.0, 1, 0, 0 };
allData[123] = new double[] { 6.3, 2.7, 4.9, 1.8, 1, 0, 0 };
allData[124] = new double[] { 6.7, 3.3, 5.7, 2.1, 1, 0, 0 };
allData[125] = new double[] { 7.2, 3.2, 6.0, 1.8, 1, 0, 0 };
allData[126] = new double[] { 6.2, 2.8, 4.8, 1.8, 1, 0, 0 };
allData[127] = new double[] { 6.1, 3.0, 4.9, 1.8, 1, 0, 0 };
allData[128] = new double[] { 6.4, 2.8, 5.6, 2.1, 1, 0, 0 };
allData[129] = new double[] { 7.2, 3.0, 5.8, 1.6, 1, 0, 0 };
allData[130] = new double[] { 7.4, 2.8, 6.1, 1.9, 1, 0, 0 };
allData[131] = new double[] { 7.9, 3.8, 6.4, 2.0, 1, 0, 0 };
allData[132] = new double[] { 6.4, 2.8, 5.6, 2.2, 1, 0, 0 };
allData[133] = new double[] { 6.3, 2.8, 5.1, 1.5, 1, 0, 0 };
allData[134] = new double[] { 6.1, 2.6, 5.6, 1.4, 1, 0, 0 };
allData[135] = new double[] { 7.7, 3.0, 6.1, 2.3, 1, 0, 0 };
allData[136] = new double[] { 6.3, 3.4, 5.6, 2.4, 1, 0, 0 };
allData[137] = new double[] { 6.4, 3.1, 5.5, 1.8, 1, 0, 0 };
allData[138] = new double[] { 6.0, 3.0, 4.8, 1.8, 1, 0, 0 };
allData[139] = new double[] { 6.9, 3.1, 5.4, 2.1, 1, 0, 0 };
allData[140] = new double[] { 6.7, 3.1, 5.6, 2.4, 1, 0, 0 };
allData[141] = new double[] { 6.9, 3.1, 5.1, 2.3, 1, 0, 0 };
allData[142] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
allData[143] = new double[] { 6.8, 3.2, 5.9, 2.3, 1, 0, 0 };
allData[144] = new double[] { 6.7, 3.3, 5.7, 2.5, 1, 0, 0 };
allData[145] = new double[] { 6.7, 3.0, 5.2, 2.3, 1, 0, 0 };
allData[146] = new double[] { 6.3, 2.5, 5.0, 1.9, 1, 0, 0 };
allData[147] = new double[] { 6.5, 3.0, 5.2, 2.0, 1, 0, 0 };
allData[148] = new double[] { 6.2, 3.4, 5.4, 2.3, 1, 0, 0 };
allData[149] = new double[] { 5.9, 3.0, 5.1, 1.8, 1, 0, 0 };
#endregion
//Define remaining data here
//Create train and test data
// string datafile = "..\\irisdata.txt
// allData = LoadData(dataFile, 150, 7);
Console.WriteLine("\nFirst 6 rows of the 150-imte data set:");
ShowMatrix(allData, 6, 1, true);
Console.WriteLine("Creating 80% training and 20% test data matrices");
double[][] trainData = null;
double[][] testData = null;
MakeTrainTest(allData, 72, out trainData, out testData); // seed = 72 gives a prety demo
Console.WriteLine("\nFirst 3 rows of training data:");
ShowMatrix(trainData, 3, 1, true);
Console.WriteLine("First 3 rows of test dat:");
ShowMatrix(testData, 3, 1, true);
Console.WriteLine("\nCreating a 4-input, 7-hidden, 3-output neural network");
Console.WriteLine("Hard-coded tanh function for input-to-hidden and softmax for ");
Console.WriteLine("hidden-to-output activations");
int numInput = 4;
int numHidden = 7;
int numOutput = 3;
NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);
int maxEpochs = 1000;
double learningRate = 0.05;
double momentum = 0.01;
Console.WriteLine("Setting maxEpochs = " + maxEpochs + ", learnRate = " + learningRate + " , momentum" +
momentum);
Console.WriteLine("Training has hard-coded mean squared error < 0.040 < stopping condition");
Console.WriteLine("\nBeginning training using incremental back-propagation\n");
nn.Train(trainData, maxEpochs, learningRate, momentum);
Console.WriteLine("Training complete");
double[] weights = nn.GetWeights();
Console.WriteLine("Final neural network weights and bias values:");
ShowVector(weights, 10, 3, true);
double trainAcc = nn.Accuracy(trainData);
Console.WriteLine("\nAccuracy on training data = " + trainAcc.ToString("F4"));
double testAcc = nn.Accuracy(testData);
Console.WriteLine("\nAccuracy on test data = " + testAcc.ToString("F4"));
Console.WriteLine("\nEnd neural network training demo\n");
Console.ReadLine();
}
