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); }