private string trainNN(string nnRef, string matrixTrainRef, string matrixTestRef, int epochs, double eta, string outputFileName)
{
var nn = Value(nnRef);
if (nn.GetType() == typeof(string))
{
return(nn.ToString());
}
var matrixTrain = Value(matrixTrainRef);
if (matrixTrain.GetType() == typeof(string))
{
return(matrixTrain.ToString());
}
var matrixTest = Value(matrixTestRef);
if (matrixTest.GetType() == typeof(string))
{
return(matrixTest.ToString());
}
MatrixData train = (MatrixData)matrixTrain;
MatrixData test = (MatrixData)matrixTest;
W4NeuralNetwork m = (W4NeuralNetwork)nn;
m.train(train.Data.ToJagged().ToDoubleArray(), test.Data.ToJagged().ToDoubleArray(), epochs, eta, outputFileName + ".nnlog.txt");
return("Trained neural network \"" + nnRef + "\" with " + epochs + " epochs, " + eta.ToString() + " eta");
}
private string initializeNNWeights(string nnRef, Random r)
{
var nn = Value(nnRef);
if (nn.GetType() == typeof(string))
{
return(nn.ToString());
}
W4NeuralNetwork m = (W4NeuralNetwork)nn;
m.InitializeWeights(r);
return("Initialize weights of \"" + nnRef + "\"");
}
private MatrixData getGraphData(string nnRef)
{
var nn = Value(nnRef);
if (nn.GetType() == typeof(string))
{
return(new MatrixData(new string[] { "E", "r", "r", "o", "r" }));
}
W4NeuralNetwork m = (W4NeuralNetwork)nn;
return(m.GraphData);
}
private MatrixData getConfusionMatrix(string nnRef, string outputDir, string outputFileName)
{
var nn = Value(nnRef);
if (nn.GetType() == typeof(string))
{
return(new MatrixData(new string[] { "E", "r", "r", "o", "r" }));
}
W4NeuralNetwork m = (W4NeuralNetwork)nn;
m.showConfusion(outputDir + outputFileName + "confusion.txt");
return(new MatrixData(m.GetConfusionMatrix(), 0, 0, 0, 0));
}
private double getNNAccuracy(string nnRef, string matrixRef, string outputDir, string fileName)
{
var nn = Value(nnRef);
if (nn.GetType() == typeof(string))
{
return(0.0);
}
var matrixComp = Value(matrixRef);
if (matrixComp.GetType() == typeof(string))
{
return(0.0);
}
MatrixData comp = (MatrixData)matrixComp;
W4NeuralNetwork m = (W4NeuralNetwork)nn;
return(m.Accuracy(comp.Data.ToJagged().ToDoubleArray(), outputDir + fileName + ".txt"));
}
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);
}
public string RunNetwork()
{
string output = "";
int num_inputs = NumberInputsNodes; // from iris data set
int num_hidden = NumberHiddenNodes; // arbitary
int num_outputs = NumberOutputNodes; // from iris data set
int epochs = NumberOfEpochs; // For tute 3
double eta = LearningRate_eta; // 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\\";
if (!(_testingData == null))
{
nn.train(_trainingData.Data.ToJagged().ToDoubleArray(), _testingData.Data.ToJagged().ToDoubleArray(), epochs, eta, dir + "nnlog.txt");
}
else
{
nn.train(_trainingData.Data.ToJagged().ToDoubleArray(), _validationData.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 = 0;
string ConfusionTest = "";
bool testDataExists = false;
if (!(_testingData == null))
{
testAcc = nn.Accuracy(_testingData.Data.ToJagged().ToDoubleArray(), dir + "testOut.txt");
ConfusionTest = nn.showConfusion(dir + "testConfusion.txt");
_confusionMatrixTest = nn.GetConfusionMatrix();
_outputMatrixTest = new MatrixData(dir + "testOut.txt", false, true, ' ');
testDataExists = true;
}
else
{
output += "Testing data is not set, skipping step..." + Environment.NewLine;
}
double valAcc = 0;
string ConfusionVal = "";
bool valDataExists = false;
if (!(_validationData == null))
{
valAcc = nn.Accuracy(_validationData.Data.ToJagged().ToDoubleArray(), dir + "valOut.txt");
ConfusionVal = nn.showConfusion(dir + "valConfusion.txt");
_confusionMatrixVal = nn.GetConfusionMatrix();
_outputMatrixVal = new MatrixData(dir + "valOut.txt", false, true, ' ');
valDataExists = true;
}
else
{
output += "Validation data is not set, skipping step..." + Environment.NewLine;
}
trainAcc = trainAcc * 100;
testAcc = testAcc * 100;
valAcc = valAcc * 100;
output += Environment.NewLine;
output += "Train accuracy = " + trainAcc.ToString("F2") + Environment.NewLine;
output += (testDataExists)? "Test accuracy = " + testAcc.ToString("F2") + Environment.NewLine : "";
output += (valDataExists)? "Val accuracy = " + valAcc.ToString("F2") + Environment.NewLine : "";
output += Environment.NewLine;
output += "Train Confusion matrix \r\n" + ConfusionTrain + Environment.NewLine;
output += (testDataExists)? "Test Confusion matrix \r\n" + ConfusionTest + Environment.NewLine : "";
output += (valDataExists)? "Val Confusion matrix \r\n" + ConfusionVal + Environment.NewLine : "";
GenerateGraph();
return(output);
}
