public void NoShuffleLearningCurvesCalculator_Calculate()
{
var sut = new NoShuffleLearningCurvesCalculator <double>(new MeanSquaredErrorRegressionMetric(),
new double[] { 0.2, 0.8 }, 0.8);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var actual = sut.Calculate(new RegressionDecisionTreeLearner(),
observations, targets);
var expected = new List <LearningCurvePoint>()
{
new LearningCurvePoint(32, 0, 0.12874833873980004),
new LearningCurvePoint(128, 0.0, 0.067720786718774989)
};
CollectionAssert.AreEqual(expected, actual);
}
public void NoShuffleLearningCurvesCalculator_Calculate()
{
var sut = new NoShuffleLearningCurvesCalculator <double>(new MeanSquaredErrorRegressionMetric(),
new double[] { 0.2, 0.8 }, 0.8);
var targetName = "T";
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var actual = sut.Calculate(new RegressionDecisionTreeLearner(),
observations, targets);
var expected = new List <LearningCurvePoint>()
{
new LearningCurvePoint(32, 0, 0.12874833873980004),
new LearningCurvePoint(128, 0.0, 0.067720786718774989)
};
CollectionAssert.AreEqual(expected, actual);
}
