public void MultivariateLinearRegression()
{
int outputIndex = 2;
double[] c = new double[] { -1.0, 2.0, -3.0, 4.0 };
Random rng = new Random(1001110000);
UniformDistribution pointDistribution = new UniformDistribution(Interval.FromEndpoints(-4.0, 4.0));
MultivariateSample sample = new MultivariateSample(c.Length);
for (int k = 0; k < 1000; k++) {
double[] row = new double[sample.Dimension];
double z = 0.0;
for (int i = 0; i < row.Length; i++) {
if (i == outputIndex) {
z += c[i];
} else {
row[i] = pointDistribution.GetRandomValue(rng);
z += row[i] * c[i];
}
}
double ez = Math.Exp(z);
double p = ez / (1.0 + ez);
row[outputIndex] = (rng.NextDouble() < p) ? 1.0 : 0.0;
sample.Add(row);
}
FitResult result = sample.LogisticLinearRegression(outputIndex);
for (int i = 0; i < result.Dimension; i++) {
Console.WriteLine(result.Parameter(i));
Assert.IsTrue(result.Parameter(i).ConfidenceInterval(0.99).ClosedContains(c[i]));
}
}
public void BivariateLogisticRegression()
{
double[] c = new double[] { -0.1, 1.0 };
Random rng = new Random(1);
UniformDistribution pointDistribution = new UniformDistribution(Interval.FromEndpoints(-4.0, 4.0));
BivariateSample sample1 = new BivariateSample();
MultivariateSample sample2 = new MultivariateSample(2);
for (int k = 0; k < 1000; k++) {
double x = pointDistribution.GetRandomValue(rng);
double z = c[0] * x + c[1];
double ez = Math.Exp(z);
double p = ez / (1.0 + ez);
double y = (rng.NextDouble() < p) ? 1.0 : 0.0;
sample1.Add(x, y);
sample2.Add(x, y);
}
Console.WriteLine(sample1.Covariance / sample1.X.Variance / sample1.Y.Mean / (1.0 - sample1.Y.Mean));
Console.WriteLine(sample1.Covariance / sample1.X.Variance / sample1.Y.Variance);
FitResult result1 = sample1.LinearLogisticRegression();
FitResult result2 = sample2.TwoColumns(0, 1).LinearLogisticRegression();
FitResult result3 = sample2.LogisticLinearRegression(1);
for (int i = 0; i < result1.Dimension; i++) {
Console.WriteLine("{0} {1} {2}", i, result1.Parameter(i), result3.Parameter(i) );
}
}