public void TrainingTestSetSplit_Equals()
{
var trainingObservations1 = new F64Matrix(new double[] { 1, 2, 3, 4 }, 2, 2);
var trainingObservations2 = new F64Matrix(new double[] { 4, 3, 2, 1 }, 2, 2);
var trainingTargets1 = new double[] { 1, 2 };
var trainingTargets2 = new double[] { 2, 1 };
var testObservations1 = new F64Matrix(new double[] { 1, 2, 3, 4 }, 2, 2);
var testObservations2 = new F64Matrix(new double[] { 4, 3, 2, 1 }, 2, 2);
var testTargets1 = new double[] { 1, 2 };
var testTargets2 = new double[] { 2, 1 };
var sut = new TrainingTestSetSplit(trainingObservations1, trainingTargets1, testObservations1, testTargets1);
var equal = new TrainingTestSetSplit(trainingObservations1, trainingTargets1, testObservations1, testTargets1);
var notEqual1 = new TrainingTestSetSplit(trainingObservations2, trainingTargets1, testObservations1, testTargets1);
var notEqual2 = new TrainingTestSetSplit(trainingObservations1, trainingTargets2, testObservations1, testTargets1);
var notEqual3 = new TrainingTestSetSplit(trainingObservations1, trainingTargets1, testObservations2, testTargets1);
var notEqual4 = new TrainingTestSetSplit(trainingObservations1, trainingTargets1, testObservations1, testTargets2);
Assert.AreEqual(sut, equal);
Assert.AreNotEqual(sut, notEqual1);
Assert.AreNotEqual(sut, notEqual2);
Assert.AreNotEqual(sut, notEqual3);
Assert.AreNotEqual(sut, notEqual4);
}
public void TrainingTestIndexSplitterExtensions_SplitSet()
{
var observations = new F64Matrix(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, 10, 1);
var targets = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
var splitter = new NoShuffleTrainingTestIndexSplitter <double>(0.6);
var actual = splitter.SplitSet(observations, targets);
var trainingIndices = Enumerable.Range(0, 6).ToArray();
var testIndices = Enumerable.Range(6, 4).ToArray();
var expected = new TrainingTestSetSplit(
new ObservationTargetSet((F64Matrix)observations.Rows(trainingIndices),
targets.GetIndices(trainingIndices)),
new ObservationTargetSet((F64Matrix)observations.Rows(testIndices),
targets.GetIndices(testIndices)));
Assert.AreEqual(expected, actual);
}
public void RegressionGradientBoostLearner_LearnWithEarlyStopping_when_more_featuresPerSlit_than_featureCount_Throw()
{
var sut = new RegressionSquareLossGradientBoostLearner(500, 0.1, 10, 15, 0.01, 0.8,
featuresPrSplit: 4);
IRegressionMetric metric = new MeanSquaredErrorRegressionMetric();
var trainingRows = 5;
var testRows = 6;
var cols = 3;
var split = new TrainingTestSetSplit(
new F64Matrix(trainingRows, cols), new double[trainingRows],
new F64Matrix(testRows, cols), new double[testRows]);
var model = sut.LearnWithEarlyStopping(
split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets,
metric,
earlyStoppingRounds: 20);
}
