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 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);
}
