public void RandomRegressionEnsembleSelection_Constructor_Number_Of_Availible_Models_Lower_Than_Number_Of_Models_To_Select()
{
var sut = new RandomRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 5, 1, true);
var observations = new F64Matrix(10, 3);
var targets = new double[10];
sut.Select(observations, targets);
}
public void RandomRegressionEnsembleSelection_Select_No_Replacements()
{
var sut = new RandomRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 3, 10, false);
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[3] {
3, 8, 2
};
CollectionAssert.AreEqual(expected, actual);
}
public void RandomRegressionEnsembleSelection_Constructor_EnsembleStratey_Null()
{
var sut = new RandomRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(), null, 5, 1, true);
}
public void RandomRegressionEnsembleSelection_Constructor_Number_Of_Models_From_Start_Too_Low()
{
var sut = new RandomRegressionEnsembleSelection(new MeanSquaredErrorRegressionMetric(),
new MeanRegressionEnsembleStrategy(), 5, 0, true);
}
public void RandomRegressionEnsembleSelection_Constructor_Metric_Null()
{
var sut = new RandomRegressionEnsembleSelection(null, new MeanRegressionEnsembleStrategy(), 5, 1, true);
}
