public void OneVariableLogisticRegressionRegularizedTest(double lambda)
{
var revenuePerPopulationFilePath = FileManagerTest.GetTestDataByName("ResultExamsAdmission.txt");
var reader = new CsvReader <ResultExamsAdmission>();
var revenuePerPopulationRows = reader.GetData(revenuePerPopulationFilePath, ",");
var dataMatrix = new Matrix(revenuePerPopulationRows.Select(x => new ResultExamsAdmissionRow(x)));
var dataY = dataMatrix.Columns[0].ToMatrix();
var dataX = dataMatrix.ToMatrix(new int[2] {
1, 2
});
var logisticRegression = new LogisticRegression();
var logisticRegressionParameters = new LogisticRegressionParameters
{
IterationCount = 100,
ThetaInit = new Matrix(new double[3, 1] {
{ 0 }, { 0 }, { 0 }
}),
X = dataX,
Y = dataY,
Lambda = lambda
};
var output = logisticRegression.Compute(logisticRegressionParameters);
var expectedOutput = new Matrix(new double[3, 1] {
{ -25.052148050018360 }, { 0.205354461994741 }, { 0.200583555605940 }
});
AssertMatrixAreEqual(expectedOutput, output.Theta);
}
public void SimpleNeuralNetworkTest()
{
var takeN = 100;
var xPath = FileManagerTest.GetTestDataByName("HandwrittenDigit", "dataX.csv");
var doubleReader = new CsvDoubleReader();
var xArray = doubleReader.GetData(xPath, ",");
var x = new Matrix(xArray.Take(takeN));
var yPath = FileManagerTest.GetTestDataByName("HandwrittenDigit", "datay.csv");
var intReader = new CsvIntReader();
var yArray = intReader.GetData(yPath, ",").Take(takeN).ToArray();
var y = MatrixUtils.VectorToBinaryMatrix(yArray.Select(z => z[0]).ToArray(), 10);
var neuralNetworkParameters = new SimpleNeuralNetworkParameters()
{
Alpha = 1,
HiddenLayerSize = 25,
InputLayerSize = 400,
IterationCount = 10,
LabelCount = 10,
Lambda = 1,
X = x,
Y = y,
};
var neuralNetwork = new SimpleNeuralNetwork();
var neuralNetworkResults = neuralNetwork.Compute(neuralNetworkParameters);
var prediction = neuralNetwork.Predict(neuralNetworkResults.Theta1, neuralNetworkResults.Theta2, x, x.RowCount);
}
public void OneVariableLogisticRegressionTest()
{
var revenuePerPopulationFilePath = FileManagerTest.GetTestDataByName("ResultExamsAdmission.txt");
var reader = new CsvReader <ResultExamsAdmission>();
var revenuePerPopulationRows = reader.GetData(revenuePerPopulationFilePath, ",");
var dataMatrix = new Matrix(revenuePerPopulationRows.Select(x => new ResultExamsAdmissionRow(x)));
var dataY = dataMatrix.Columns[0].ToMatrix();
var dataX = dataMatrix.ToMatrix(new int[2] {
1, 2
});
var logisticRegression = new LogisticRegression();
var logisticRegressionParameters = new LogisticRegressionParameters
{
IterationCount = 100,
ThetaInit = new Matrix(new double[3, 1] {
{ 0 }, { 0 }, { 0 }
}),
X = dataX,
Y = dataY
};
var output = logisticRegression.Compute(logisticRegressionParameters);
var expectedOutput = new Matrix(new double[3, 1] {
{ -25.161333566639530 }, { 0.206231713293983 }, { 0.201471600441963 }
});
AssertMatrixAreEqual(expectedOutput, output.Theta);
}