public void StratifiedLearningCurvesCalculator_Calculate()
{
var sut = new StratifiedLearningCurvesCalculator <double>(new TotalErrorClassificationMetric <double>(),
new double[] { 0.2, 0.8 }, 0.8, 5, 42);
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var actual = sut.Calculate(new ClassificationDecisionTreeLearner(),
observations, targets);
var expected = new List <LearningCurvePoint>()
{
new LearningCurvePoint(4, 0, 0.39999999999999997),
new LearningCurvePoint(16, 0.0625, 0.33333333333333331)
};
CollectionAssert.AreEqual(expected, actual);
}
public void StratifiedLearningCurvesCalculator_Calculate()
{
var sut = new StratifiedLearningCurvesCalculator <double>(new TotalErrorClassificationMetric <double>(),
new double[] { 0.2, 0.8 }, 0.8, 5, 42);
var targetName = "Pass";
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var actual = sut.Calculate(new ClassificationDecisionTreeLearner(),
observations, targets);
var expected = new List <LearningCurvePoint>()
{
new LearningCurvePoint(4, 0, 0.39999999999999997),
new LearningCurvePoint(16, 0.0625, 0.33333333333333331)
};
CollectionAssert.AreEqual(expected, actual);
}