public void GBMBinomialLoss_UpdateResiduals()
{
var targets = new double[] { 1, 1, 1, 1, 0, 0, 0, 0, 0 };
var predictions = new double[] { 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 };
var actual = new double[targets.Length];
var sut = new GradientBoostBinomialLoss();
sut.UpdateResiduals(targets, predictions, actual, targets.Select(t => true).ToArray());
var expected = new double[] { 0.268941421369995, 0.268941421369995, 0.5, 0.268941421369995, -0.731058578630005, -0.731058578630005, -0.5, -0.5, -0.5 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.001);
}
}
public void GBMBinomialLoss_UpdateResiduals_Indexed()
{
var targets = new double[] { 1, 1, 1, 1, 0, 0, 0, 0, 0 };
var predictions = new double[] { 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 };
var actual = new double[targets.Length];
var inSample = new bool[] { true, false, true, false, true, false, true, false, true };
var sut = new GradientBoostBinomialLoss();
sut.UpdateResiduals(targets, predictions, actual, inSample);
var expected = new double[] { 0.268941421369995, 0.0, 0.5, 0.0, -0.731058578630005, 0.0, -0.5, 0.0, -0.5 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.001);
}
}
