public void BackwardEliminationRegressionEnsembleSelection_Constructor_Number_Of_Availible_Models_Lower_Than_Number_Of_Models_To_Select()
{
var sut = new BackwardEliminationRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 5);
var observations = new F64Matrix(10, 3);
var targets = new double[10];
sut.Select(observations, targets);
}
public void BackwardEliminationRegressionEnsembleSelection_Select()
{
var sut = new BackwardEliminationRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 3);
var random = new Random(42);
var observations = new F64Matrix(10, 10);
observations.Map(() => random.Next());
var targets = Enumerable.Range(0, 10).Select(v => random.NextDouble()).ToArray();
var actual = sut.Select(observations, targets);
var expected = new int[] { 2 };
CollectionAssert.AreEqual(expected, actual);
}
public void BackwardEliminationRegressionEnsembleSelection_Constructor_Number_Of_Models_Too_Low()
{
var sut = new BackwardEliminationRegressionEnsembleSelection(
new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 0);
}
public void BackwardEliminationRegressionEnsembleSelection_Constructor_EnsembleStratey_Null()
{
var sut = new BackwardEliminationRegressionEnsembleSelection(
new MeanSquaredErrorRegressionMetric(), null, 5);
}
public void BackwardEliminationRegressionEnsembleSelection_Constructor_Metric_Null()
{
var sut = new BackwardEliminationRegressionEnsembleSelection(null,
new MeanRegressionEnsembleStrategy(), 5);
}
