public double getPredictionErrorFromData(double[][] input, double[][] output)
{
double[] buyOutput = new double[output.Length];
double[] sellOutput = new double[output.Length];
for (int i = 0; i < output.Length; i++)
{
buyOutput[i] = output[i][0];
sellOutput[i] = output[i][1];
}
return((teacherBuy.ComputeError(input, buyOutput) + teacherSell.ComputeError(input, sellOutput)) / 2);
}
public void RegularizationTest2()
{
CsvReader reader = CsvReader.FromText(Properties.Resources.regression, true);
double[][] data = reader.ToTable().ToArray(System.Globalization.CultureInfo.InvariantCulture);
double[][] inputs = data.GetColumns(new[] { 0, 1 });
double[] output = data.GetColumn(2);
var regression = new LogisticRegression(2);
var irls = new IterativeReweightedLeastSquares(regression);
double error = irls.Run(inputs, output);
double newError = 0;
for (int i = 0; i < 50; i++)
{
newError = irls.Run(inputs, output);
}
double actual = irls.ComputeError(inputs, output);
Assert.AreEqual(30.507262964894068, actual, 1e-8);
Assert.AreEqual(3, regression.Coefficients.Length);
Assert.AreEqual(-0.38409721299838279, regression.Coefficients[0], 1e-7);
Assert.AreEqual(0.1065137931017601, regression.Coefficients[1], 1e-7);
Assert.AreEqual(22.010378526331344, regression.Coefficients[2], 1e-7);
for (int i = 0; i < 50; i++)
{
newError = irls.Run(inputs, output);
}
Assert.AreEqual(-0.38409721299838279, regression.Coefficients[0], 1e-7);
Assert.AreEqual(0.1065137931017601, regression.Coefficients[1], 1e-8);
Assert.AreEqual(22.010378588337979, regression.Coefficients[2], 1e-8);
}