Uses of Enum Class
com.imsl.datamining.PredictiveModel.VariableType

Packages that use PredictiveModel.VariableType

Package	Description
com.imsl.datamining	Data mining and machine learning.
com.imsl.datamining.decisionTree	Decision trees.
com.imsl.datamining.supportvectormachine	Support vector machines.

Uses of PredictiveModel.VariableType in com.imsl.datamining

Methods in com.imsl.datamining that return PredictiveModel.VariableType

Modifier and Type	Method	Description
PredictiveModel.VariableType[]	PredictiveModel.getPredictorTypes()	Returns an array of VariableType objects that correspond to the predictor data types in xy.
PredictiveModel.VariableType	LogisticRegressionModelObject.getResponseVariableType()	Returns the variable type of the response variable.
PredictiveModel.VariableType	PredictiveModel.getResponseVariableType()	Returns the variable type of the response variable.
PredictiveModel.VariableType[]	PredictiveModel.getVariableType()	Returns an array containing the variable types in xy.
static PredictiveModel.VariableType	PredictiveModel.VariableType.valueOf(String name)	Returns the enum constant of this class with the specified name.
static PredictiveModel.VariableType[]	PredictiveModel.VariableType.values()	Returns an array containing the constants of this enum class, in the order they are declared.

Methods in com.imsl.datamining with parameters of type PredictiveModel.VariableType

Modifier and Type	Method	Description
void	PredictiveModel.setPredictorTypes(PredictiveModel.VariableType[] predVarType)	Sets the VariableType objects that correspond to the predictor data types in xy.
void	GradientBoosting.setTrainingData(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Sets up the training data for the predictive model.
void	PredictiveModel.setTrainingData(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Sets up the training data for the predictive model.
void	PredictiveModel.setVariableType(PredictiveModel.VariableType[] varType)	Sets the variable types for the data.

Constructors in com.imsl.datamining with parameters of type PredictiveModel.VariableType

Modifier	Constructor	Description
	GradientBoosting(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a GradientBoosting object for a single response variable and multiple predictor variables.
	LogisticRegression(double[][] x, double[][] y, PredictiveModel.VariableType[] predictorVarType, PredictiveModel.VariableType responseVarType)	Constructs a logistic regression predictive model.
	LogisticRegression(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a logistic regression predictive model.
protected	PredictiveModel(double[][] x, double[][] y, PredictiveModel.VariableType[] predictorVarType, PredictiveModel.VariableType responseVarType)	Constructs a PredictiveModel object for a single response variable and multiple predictor variables.
protected	PredictiveModel(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a PredictiveModel object for a single response variable and multiple predictor variables.

Uses of PredictiveModel.VariableType in com.imsl.datamining.decisionTree

Methods in com.imsl.datamining.decisionTree that return PredictiveModel.VariableType

Modifier and Type	Method	Description
PredictiveModel.VariableType	Tree.getPredictorType(int i)	Returns the PredictiveModel.VariableType of a predictor variable.
PredictiveModel.VariableType	Tree.getResponseType()	Returns the PredictiveModel.VariableType of the response variable.

Constructors in com.imsl.datamining.decisionTree with parameters of type PredictiveModel.VariableType

Modifier	Constructor	Description
	ALACART(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs an ALACART decision tree for a single response variable and multiple predictor variables.
	C45(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a C45 object for a single response variable and multiple predictor variables.
	CHAID(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a CHAID object for a single response variable and multiple predictor variables.
	DecisionTree(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a DecisionTree object for a single response variable and multiple predictor variables.
	DecisionTreeInfoGain(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a DecisionTree object for a single response variable and multiple predictor variables.
	QUEST(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a QUEST object for a single response variable and multiple predictor variables.
	RandomTrees(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a RandomTrees random forest of ALACART decision trees.
	Tree(int nRows, int minObsAllowedInChild, int maxNumberOfCategories, int maxNumberOfNodes, int nClasses, int nPreds, PredictiveModel.VariableType varType, int[] predNValues, PredictiveModel.VariableType[] predType, int nyMissing)	Deprecated. Update to the other constructor that accepts the predictor indices in argument 10.
	Tree(int nRows, int minObsAllowedInChild, int maxNumberOfCategories, int maxNumberOfNodes, int nClasses, int nPreds, PredictiveModel.VariableType varType, int[] predNValues, PredictiveModel.VariableType[] predType, int[] predIdx, int nyMissing)	Creates the root node of a decision tree and contains information about the relationship of child nodes.

Uses of PredictiveModel.VariableType in com.imsl.datamining.supportvectormachine

Constructors in com.imsl.datamining.supportvectormachine with parameters of type PredictiveModel.VariableType

Modifier	Constructor	Description
	SupportVectorMachine(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a support vector machine for a single response variable and multiple predictor variables.
	SupportVectorMachine(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for a single response variable and multiple predictor variables.
	SVClassification(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a support vector machine for classification (SVC).
	SVClassification(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for classification (SVC).
	SVOneClass(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a one class support vector machine.
	SVOneClass(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a one class support vector machine.
	SVRegression(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)	Constructs a support vector machine for regression (SVR).
	SVRegression(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for regression (SVR).