public Generate ( Matrix X, |
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X | Matrix | The Matrix to process. |
y | The Vector to process. | |
Résultat | IModel |
public void ArbitraryPrediction_Test_With_Named_Iterator() { var data = ArbitraryPrediction.GetDataUsingNamedIterator(); var description = Descriptor.Create<ArbitraryPrediction>(); var generator = new DecisionTreeGenerator(50); var model = generator.Generate(description, data); ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction { FirstTestFeature = 1.0m, SecondTestFeature = 10.0m, ThirdTestFeature = 1.2m }; ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction { FirstTestFeature = 1.0m, SecondTestFeature = 57.0m, ThirdTestFeature = 1.2m }; var expectedMinimum = model.Predict<ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel; var expectedMaximum = model.Predict<ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel; Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum); Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum); }
public void Save_And_Load_Iris_DT() { var data = Iris.Load(); var description = Descriptor.Create<Iris>(); var generator = new DecisionTreeGenerator(50); var model = generator.Generate(description, data) as DecisionTreeModel; Serialize(model); var lmodel = Deserialize<DecisionTreeModel>(); Assert.AreEqual(model.Hint, lmodel.Hint); AreEqual(model.Tree, lmodel.Tree, false); }
public void Save_And_Load_HouseDT() { var data = House.GetData(); var description = Descriptor.Create<House>(); var generator = new DecisionTreeGenerator { Depth = 50 }; var model = generator.Generate(description, data) as DecisionTreeModel; Serialize(model); var lmodel = Deserialize<DecisionTreeModel>(); Assert.AreEqual(model.Hint, lmodel.Hint); AreEqual(model.Tree, lmodel.Tree, false); }