static void Main(string[] args)
{
Console.WriteLine("\nBegin Build 2013 neural network demo");
Console.WriteLine("\nData is the famous Iris flower set.");
Console.WriteLine("Data is sepal length, sepal width, petal length, petal width -> iris species");
Console.WriteLine("Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0 ");
Console.WriteLine("The goal is to predict species from sepal length, width, petal length, width\n");
Console.WriteLine("Raw data resembles:\n");
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");
double[][] allData = new double[150][];
allData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 }; // sepal length, width, petal length, width
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 };
Console.WriteLine("\nFirst 6 rows of entire 150-item 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, out trainData, out testData);
Console.WriteLine("\nFirst 5 rows of training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of test data:");
ShowMatrix(testData, 3, 1, true);
Normalize(trainData, new int[] { 0, 1, 2, 3 });
Normalize(testData, new int[] { 0, 1, 2, 3 });
Console.WriteLine("\nFirst 5 rows of normalized training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of normalized test data:");
ShowMatrix(testData, 3, 1, true);
Console.WriteLine("\nCreating a 4-input, 7-hidden, 3-output neural network");
Console.Write("Hard-coded tanh function for input-to-hidden and softmax for ");
Console.WriteLine("hidden-to-output activations");
const int numInput = 4;
const int numHidden = 7;
const int numOutput = 3;
NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nInitializing weights and bias to small random values");
nn.InitializeWeights();
int maxEpochs = 2000;
double learnRate = 0.05;
double momentum = 0.01;
double weightDecay = 0.0001;
Console.WriteLine("Setting maxEpochs = 2000, learnRate = 0.05, momentum = 0.01, weightDecay = 0.0001");
Console.WriteLine("Training has hard-coded mean squared error < 0.020 stopping condition");
Console.WriteLine("\nBeginning training using incremental back-propagation\n");
nn.Train(trainData, maxEpochs, learnRate, momentum, weightDecay);
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 Build 2013 neural network demo\n");
Console.ReadLine();
} // Main
static void Main(string[] args)
{
Console.WriteLine("\nBegin Build 2013 neural network demo");
Console.WriteLine("\nData is the famous Iris flower set.");
Console.WriteLine("Data is sepal length, sepal width, petal length, petal width -> iris species");
Console.WriteLine("Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0 ");
Console.WriteLine("The goal is to predict species from sepal length, width, petal length, width\n");
Console.WriteLine("Raw data resembles:\n");
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");
double[][] allData = new double[150][];
allData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 }; // sepal length, width, petal length, width
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 };
Console.WriteLine("\nFirst 6 rows of entire 150-item 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, out trainData, out testData);
Console.WriteLine("\nFirst 5 rows of training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of test data:");
ShowMatrix(testData, 3, 1, true);
Normalize(trainData, new int[] { 0, 1, 2, 3 });
Normalize(testData, new int[] { 0, 1, 2, 3 });
Console.WriteLine("\nFirst 5 rows of normalized training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of normalized test data:");
ShowMatrix(testData, 3, 1, true);
Console.WriteLine("\nCreating a 4-input, 7-hidden, 3-output neural network");
Console.Write("Hard-coded tanh function for input-to-hidden and softmax for ");
Console.WriteLine("hidden-to-output activations");
const int numInput = 4;
const int numHidden = 7;
const int numOutput = 3;
NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nInitializing weights and bias to small random values");
nn.InitializeWeights();
int maxEpochs = 2000;
double learnRate = 0.05;
double momentum = 0.01;
double weightDecay = 0.0001;
Console.WriteLine("Setting maxEpochs = 2000, learnRate = 0.05, momentum = 0.01, weightDecay = 0.0001");
Console.WriteLine("Training has hard-coded mean squared error < 0.020 stopping condition");
Console.WriteLine("\nBeginning training using incremental back-propagation\n");
nn.Train(trainData, maxEpochs, learnRate, momentum, weightDecay);
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 Build 2013 neural network demo\n");
Console.ReadLine();
}
static void Main(string[] args)
{
Console.WriteLine("\nBegin Build 2017 neural network demo");
Console.WriteLine("\nData is cubic function set with three variables.");
Console.WriteLine("Data is x, y, z -> result");
Console.WriteLine("The goal is to predict result from x, y, z\n");
Console.WriteLine("Raw data resembles:\n");
Console.WriteLine(" 0, 0, 0, 16");
Console.WriteLine(" 0, 0, 1, 15");
Console.WriteLine(" 0, 1, 0, 16");
Console.WriteLine(" ......\n");
double[][] allData = FunzioneCubica.SetInputOutput().Select(r => new double [4] {r.argx, r.argy, r.argz, r.risultato}).ToArray();
Console.WriteLine("\nFirst 6 rows of entire 150-item 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, out trainData, out testData);
Console.WriteLine("\nFirst 5 rows of training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of test data:");
ShowMatrix(testData, 3, 1, true);
Normalize(trainData, new int[] { 0, 1, 2, 3 });
Normalize(testData, new int[] { 0, 1, 2, 3 });
Console.WriteLine("\nFirst 5 rows of normalized training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of normalized test data:");
ShowMatrix(testData, 3, 1, true);
Console.WriteLine("\nCreating a 4-input, 7-hidden, 3-output neural network");
Console.Write("Hard-coded tanh function for input-to-hidden and softmax for ");
Console.WriteLine("hidden-to-output activations");
const int numInput = 3;
const int numHidden = 7;
const int numOutput = 1;
NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nInitializing weights and bias to small random values");
nn.InitializeWeights();
int maxEpochs = 2000;
double learnRate = 0.05;
double momentum = 0.01;
double weightDecay = 0.0001;
Console.WriteLine("Setting maxEpochs = 2000, learnRate = 0.05, momentum = 0.01, weightDecay = 0.0001");
Console.WriteLine("Training has hard-coded mean squared error < 0.020 stopping condition");
Console.WriteLine("\nBeginning training using incremental back-propagation\n");
nn.Train(trainData, maxEpochs, learnRate, momentum, weightDecay);
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 Build 2013 neural network demo\n");
Console.ReadLine();
} // Main
// Save Player XML fil
// Get the data from the XML-File
/*
* public DataType GetDataType()
* {
* System.Xml.Serialization.XmlSerializer reader = new System.Xml.Serialization.XmlSerializer(typeof(DataType));
*
* var path = "../../save.xml";
*
* System.IO.StreamReader file = new System.IO.StreamReader(path);
*
* DataType data = (DataType)reader.Deserialize(file);
*
* file.Close();
*
* return data;
* }
*
*/
/*
* 1. Age of patient at time of operation (numerical)
* 2. Patient's year of operation (year - 1900, numerical)
* 3. Number of positive axillary nodes detected (numerical)
* 4. Survival status (class attribute)
* -- 1 = the patient survived 5 years or longer
* -- 2 = the patient died within 5 year
*/
// 1 0 = alive
// 0 1 = død
//36,69,0,1
static void Main(string[] args)
{
double[][] allData = new double[306][];
string line;
string[] numbers;
int counter = 0;
// Read the file and display it line by line.
System.IO.StreamReader file = new System.IO.StreamReader("../../TestData.txt");
while ((line = file.ReadLine()) != null)
{
numbers = line.Split(',');
double epsilon = 0.001;
allData[counter] = new double[5];
allData[counter][0] = epsilon + double.Parse(numbers[0]);
allData[counter][0] /= 10;
allData[counter][1] = epsilon + double.Parse(numbers[1]);
allData[counter][1] /= 10;
allData[counter][2] = epsilon + double.Parse(numbers[2]);
if (double.Parse(numbers[3]) == 1)
{
allData[counter][3] = 1;
allData[counter][4] = 0;
}
else
{
allData[counter][3] = 0;
allData[counter][4] = 1;
}
counter++;
line = "";
}
file.Close();
// Suspend the screen.
Console.ReadLine();
// mock dataexample
// allData[0] = new double[] {43, 43, 7, 0, 1};
/*
* Console.WriteLine("\nBegin Build 2013 neural network demo");
* Console.WriteLine("\nData is the famous Iris flower set.");
* Console.WriteLine("Data is sepal length, sepal width, petal length, petal width -> iris species");
* Console.WriteLine("Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0 ");
* Console.WriteLine("The goal is to predict species from sepal length, width, petal length, width\n");
*
* Console.WriteLine("Raw data resembles:\n");
* 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");
*/
/*
*
* double[][] allData = new double[150][];
* allData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 }; // sepal length, width, petal length, width
* 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 };
* 1. Age of patient at time of operation (numerical)
* 2. Patient's year of operation (year - 1900, numerical)
* 3. Number of positive axillary nodes detected (numerical)
* 4. Survival status (class attribute
*
*/
//
Console.WriteLine("\nFirst 6 rows of entire 150-item 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, out trainData, out testData);
Console.WriteLine("\nFirst 5 rows of training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of test data:");
ShowMatrix(testData, 3, 1, true);
Normalize(trainData, new int[] { 0, 1, 2, 3 });
Normalize(testData, new int[] { 0, 1, 2, 3 });
Console.WriteLine("\nFirst 5 rows of normalized training data:");
ShowMatrix(trainData, 5, 1, true);
Console.WriteLine("First 3 rows of normalized test data:");
ShowMatrix(testData, 3, 1, true);
Console.WriteLine("\nCreating a 4-input, 7-hidden, 3-output neural network");
Console.Write("Hard-coded tanh function for input-to-hidden and softmax for ");
Console.WriteLine("hidden-to-output activations");
const int numInput = 3;
const int numHidden = 3;
const int numOutput = 2;
NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nInitializing weights and bias to small random values");
nn.InitializeWeights();
int maxEpochs = 2000;
double learnRate = 0.05;
double momentum = 0.01;
double weightDecay = 0.0001;
Console.WriteLine("Setting maxEpochs = 2000, learnRate = 0.05, momentum = 0.01, weightDecay = 0.0001");
Console.WriteLine("Training has hard-coded mean squared error < 0.020 stopping condition");
Console.WriteLine("\nBeginning training using incremental back-propagation\n");
nn.Train(trainData, maxEpochs, learnRate, momentum, weightDecay);
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 Build 2013 neural network demo\n");
Console.ReadLine();
/*
*
* string[] lines = { "First line", "Second line", "Third line" };
* System.IO.File.WriteAllLines(@"C:\Users\Public\TestFolder\WriteLines.txt", lines);
*
*
* string text = "A class is the most powerful data type in C#. Like a structure, " +
* "a class defines the data and behavior of the data type. ";
* System.IO.File.WriteAllText(@"C:\Users\Public\TestFolder\WriteText.txt", text);
*
* using (System.IO.StreamWriter file = new System.IO.StreamWriter(@"C:\Users\Public\TestFolder\WriteLines2.txt"))
* {
* foreach (string line in lines)
* {
* if (!line.Contains("Second"))
* {
* file.WriteLine(line);
* }
* }
* }
*
* using (System.IO.StreamWriter file = new System.IO.StreamWriter(@"C:\Users\Public\TestFolder\WriteLines2.txt", true))
* {
* file.WriteLine("Fourth line");
* }
*
*
*/
} // Main
