public string SetTrain()
{
string output = "";
_trainingData = _exemplarData.CopyData(0, 0, NumTrain);
output += "Trainig data:" + Environment.NewLine;
output += _trainingData.Head().ToString(5, 16, 300, true);
output += Environment.NewLine + Environment.NewLine;
return(output);
}
private MatrixData copyMatrix(string matrixRef, int rowStart, int colStart, int numRow, int numCols)
{
var md = Value(matrixRef);
if (md.GetType() == typeof(string))
{
return(new MatrixData(new string[] { "E", "r", "r", "o", "r" }));
}
MatrixData m = (MatrixData)md;
return(m.CopyData(rowStart, colStart, numRow, numCols));
}
public string RunIris()
{
string output = "";
_rawData = new MatrixData("iris", false, true, ' ');
_rawData.ChangeHeader(0, "Speal.Length");
_rawData.ChangeHeader(1, "Speal.Width");
_rawData.ChangeHeader(2, "Petal.Length");
_rawData.ChangeHeader(3, "Petal.Width");
_rawData.ChangeHeader(4, "Species");
output += "Raw data:" + Environment.NewLine;
output += _rawData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
output += "Normalizeing columns 1:4..." + Environment.NewLine;
_rawData.Normalize(0);
_rawData.Normalize(1);
_rawData.Normalize(2);
_rawData.Normalize(3);
output += "Normalised data:" + Environment.NewLine;
output += _rawData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
_exemplarData = _rawData.GetExemplar(4, 3, 1);
output += "Exemplar data:" + Environment.NewLine;
output += _exemplarData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
output += "Suffleing data..." + Environment.NewLine;
_exemplarData.Suffle();
output += Environment.NewLine;
output += "Setting trainig data as first 50 rows of suffled exemplar data..." + Environment.NewLine;
_trainingData = _exemplarData.CopyData(0, 0, 50);
output += "Trainig data:" + Environment.NewLine;
output += _trainingData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
output += "Setting test data as next 50 rows of suffled exemplar data..." + Environment.NewLine;
_testingData = _exemplarData.CopyData(50, 0, 50);
output += "Test data:" + Environment.NewLine;
output += _testingData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
output += "Setting validation data as next 50 rows of suffled exemplar data..." + Environment.NewLine;
_validationData = _exemplarData.CopyData(100, 0, 50);
output += "Validation data:" + Environment.NewLine;
output += _validationData.Head().ToString(5, 16);
output += Environment.NewLine + Environment.NewLine;
int num_inputs = 4; // from iris data set
int num_hidden = 1; // arbitary
int num_outputs = 3; // from iris data set
int epochs = 200; // For tute 3
double eta = 0.1; // learning_rate
output += "Initialising Neural Network with:" + Environment.NewLine;
output += num_inputs + " inputs, " + num_hidden + " hidden layers, " + num_outputs + " outputs, " + epochs + " epochs, " + eta + " learning eate" + Environment.NewLine;
Random rnd1 = new Random(102);
nn = new W4NeuralNetwork(num_inputs, num_hidden, num_outputs, rnd1);
nn.InitializeWeights(rnd1);
string dir = "Data\\";
nn.train(_trainingData.Data.ToJagged().ToDoubleArray(), _testingData.Data.ToJagged().ToDoubleArray(), epochs, eta, dir + "nnlog.txt");
_graphData = nn.GraphData;
double trainAcc = nn.Accuracy(_trainingData.Data.ToJagged().ToDoubleArray(), dir + "trainOut.txt");
string ConfusionTrain = nn.showConfusion(dir + "trainConfusion.txt");
_confusionMatrixTrain = nn.GetConfusionMatrix();
_outputMatrixTrain = new MatrixData(dir + "trainOut.txt", false, true, ' ');
double testAcc = nn.Accuracy(_testingData.Data.ToJagged().ToDoubleArray(), dir + "testOut.txt");
string ConfusionTest = nn.showConfusion(dir + "testConfusion.txt");
_confusionMatrixTest = nn.GetConfusionMatrix();
_outputMatrixTest = new MatrixData(dir + "testOut.txt", false, true, ' ');
double valAcc = nn.Accuracy(_validationData.Data.ToJagged().ToDoubleArray(), dir + "valOut.txt");
string ConfusionVal = nn.showConfusion(dir + "valConfusion.txt");
_confusionMatrixVal = nn.GetConfusionMatrix();
_outputMatrixVal = new MatrixData(dir + "valOut.txt", false, true, ' ');
trainAcc = trainAcc * 100;
testAcc = testAcc * 100;
valAcc = valAcc * 100;
output += Environment.NewLine;
output += "Train accuracy = " + trainAcc.ToString("F2") + Environment.NewLine;
output += "Test accuracy = " + testAcc.ToString("F2") + Environment.NewLine;
output += "Val accuracy = " + valAcc.ToString("F2") + Environment.NewLine;
output += Environment.NewLine;
output += "Train Confusion matrix \r\n" + ConfusionTrain + Environment.NewLine;
output += "Test Confusion matrix \r\n" + ConfusionTest + Environment.NewLine;
output += "Val Confusion matrix \r\n" + ConfusionVal + Environment.NewLine;
GenerateGraph();
return(output);
}
