public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.017238327841614508, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.13601421174394385, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.017238327841614508, actual, 0.0001);
}