public MultiValueDecisionTreeModelBuilderTests()
{
this.categoricalSubject = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator <string>(this.shannonEntropy, this.shannonEntropy as ICategoricalImpurityMeasure <string>),
new MultiValueSplitSelectorForCategoricalOutcome(
new MultiValueDiscreteDataSplitter(),
new BinaryNumericDataSplitter(),
new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
}
public MultiValueDecisionTreeModelBuilderTests()
{
this.categoricalSubject = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator<string>(this.shannonEntropy, this.shannonEntropy as ICategoricalImpurityMeasure<string>),
new MultiValueSplitSelectorForCategoricalOutcome(
new MultiValueDiscreteDataSplitter(),
new BinaryNumericDataSplitter(),
new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
}
public RandomForestModelBuilder(
IDecisionTreeModelBuilder decisionTreeModelBuilder,
IPredictor <TPredictionVal> treePredictor,
IDataQualityMeasure <TPredictionVal> dataQualityMeasurer,
Func <int, int> featuresToUseCountSelector,
Func <IDecisionTreeModelBuilderParams> decisionTreePramsFactory)
{
this.decisionTreeModelBuilder = decisionTreeModelBuilder;
decisionTreePredictor = treePredictor;
dataQualityMeasure = dataQualityMeasurer;
featuresToUseCountCalculator = featuresToUseCountSelector;
decisionTreeModelBuilderParamsFactory = decisionTreePramsFactory;
}
public DecisionTreePredictorTests()
{
binaryTreeBuilder = new BinaryDecisionTreeModelBuilder(
new InformationGainRatioCalculator<string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure<string>),
new BinarySplitSelectorForCategoricalOutcome(new BinaryDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
multiValueTreeBuilder = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator<string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure<string>),
new MultiValueSplitSelectorForCategoricalOutcome(new MultiValueDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
multiValueTreeBuilderWithBetterNumercValsHandler = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator<string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure<string>),
new MultiValueSplitSelectorForCategoricalOutcome(new MultiValueDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new DynamicProgrammingNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
}
public RandomForestsTests()
{
binaryTreeBuilder = new BinaryDecisionTreeModelBuilder(
new InformationGainRatioCalculator <string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure <string>),
new BinarySplitSelectorForCategoricalOutcome(new BinaryDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
multiValueTreeBuilder = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator <string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure <string>),
new MultiValueSplitSelectorForCategoricalOutcome(new MultiValueDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new ClassBreakpointsNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
multiValueTreeBuilderWithBetterNumercValsHandler = new MultiSplitDecisionTreeModelBuilder(
new InformationGainRatioCalculator <string>(shannonEntropy, shannonEntropy as ICategoricalImpurityMeasure <string>),
new MultiValueSplitSelectorForCategoricalOutcome(new MultiValueDiscreteDataSplitter(), new BinaryNumericDataSplitter(), new DynamicProgrammingNumericSplitFinder()),
new CategoricalDecisionTreeLeafBuilder());
}
