Index (Regression-Tree 4.2.2 API)

A B C D F G I J M N O P R S T U V W
All Classes and Interfaces|All Packages|Serialized Form

A

add(int) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Adds an index where the feature value occurs.

B

buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).

C

CART_INDEPENDENT - Enum constant in enum org.tribuo.regression.rtree.TrainTest.TreeType: Creates a CARTRegressionTrainer which treats each regression dimension independently.
CART_JOINT - Enum constant in enum org.tribuo.regression.rtree.TrainTest.TreeType: Creates a CARTJointRegressionTrainer which jointly minimises the impurity across all output dimensions.
CARTJointRegressionTrainer - Class in org.tribuo.regression.rtree: A Trainer that uses an approximation of the CART algorithm to build a decision tree.
CARTJointRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer: Creates a CART Trainer.
CARTJointRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer: Creates a CART Trainer.
CARTJointRegressionTrainer(int, boolean) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer: Creates a CART Trainer.
CARTJointRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer: Creates a CART Trainer.
CARTJointRegressionTrainer(int, float, float, float, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer: Creates a CART Trainer.
CARTRegressionTrainer - Class in org.tribuo.regression.rtree: A Trainer that uses an approximation of the CART algorithm to build a decision tree.
CARTRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer: Creates a CART trainer.
CARTRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer: Creates a CART trainer.
CARTRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer: Creates a CART Trainer.
CARTRegressionTrainer(int, float, float, float, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer: Creates a CART Trainer.
compareTo(InvertedFeature) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
convertTree() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
convertTree() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

D

deepCopy() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Copies this inverted feature.
deepCopy() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Returns a deep copy of this tree feature.
depth - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Maximum depth in the decision tree.

F

fixSize() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Fixes the size of the backing array.
fixSize() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Fixes the size of each InvertedFeature's inner arrays.
fraction - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Fraction of features in split.

G

general - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: The data loading options.
getDepth() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel: Probes the trees to find the depth.
getExcuse(Example<Regressor>) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getFeature() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Gets the inverted feature values for this feature.
getFeatures() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getImpurity() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
getImpurity() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode
getOptionsDescription() - Method in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
getProvenance() - Method in class org.tribuo.regression.rtree.CARTJointRegressionTrainer
getProvenance() - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
getProvenance() - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
getProvenance() - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
getRoot() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel: Returns null, as this model contains multiple roots, one per regression output dimension.
getRoots() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel: Returns an unmodifiable view on the root node collection.
getTopFeatures(int) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getWeightSum() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
getWeightSum() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode

I

impurity - Variable in class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple: The impurity value.
impurity(float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurity(float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurity(float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the impurity based on the supplied weights and targets.
impurity(int[], float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurity(int[], int, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurity(List<int[]>, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in all the indices arrays.
impurity(IntArrayContainer, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices container.
impurityTuple(int[], int, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurityTuple(int[], int, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurityTuple(int[], int, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurityTuple(List<int[]>, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurityTuple(List<int[]>, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurityTuple(List<int[]>, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in all the indices arrays.
ImpurityTuple(float, float) - Constructor for class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple: Construct an impurity tuple.
impurityType - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Impurity measure to use.
IndependentRegressionTreeModel - Class in org.tribuo.regression.rtree: A Model wrapped around a list of decision tree root Nodes used to generate independent predictions for each dimension in a regression.
indices() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Gets the indices where this feature value occurs.
invertData(Dataset<Regressor>) - Static method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Inverts a training dataset from row major to column major.
InvertedFeature - Class in org.tribuo.regression.rtree.impl: Internal datastructure for implementing a decision tree.
InvertedFeature(double, int) - Constructor for class org.tribuo.regression.rtree.impl.InvertedFeature: Constructs an inverted feature for the specifed value which occurs at a single index.
InvertedFeature(double, int[]) - Constructor for class org.tribuo.regression.rtree.impl.InvertedFeature: Constructs an inverted feature for the specified value which occurs at the specified indices.
iterator() - Method in class org.tribuo.regression.rtree.impl.TreeFeature

J

JointRegressorTrainingNode - Class in org.tribuo.regression.rtree.impl: A decision tree node used at training time.
JointRegressorTrainingNode(RegressorImpurity, Dataset<Regressor>, boolean, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Constructor which creates the inverted file.

M

MAE - Enum constant in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Use MeanAbsoluteError.
main(String[]) - Static method in class org.tribuo.regression.rtree.TrainTest: Runs a TrainTest CLI.
MeanAbsoluteError - Class in org.tribuo.regression.rtree.impurity: Measures the mean absolute error over a set of inputs.
MeanAbsoluteError() - Constructor for class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
MeanSquaredError - Class in org.tribuo.regression.rtree.impurity: Measures the mean squared error over a set of inputs.
MeanSquaredError() - Constructor for class org.tribuo.regression.rtree.impurity.MeanSquaredError
minChildWeight - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Minimum child weight.
minImpurityDecrease - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Minimumum decrease in impurity required in order for the node to be split.
MSE - Enum constant in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Use MeanSquaredError.

N

normalize - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Normalize the leaf outputs so each leaf sums to 1.0.

O

observeValue(double, int) - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Observes a value for this feature.
org.tribuo.regression.rtree - package org.tribuo.regression.rtree: Provides an implementation of decision trees for regression problems.
org.tribuo.regression.rtree.impl - package org.tribuo.regression.rtree.impl: Provides internal implementation classes for the regression trees.
org.tribuo.regression.rtree.impurity - package org.tribuo.regression.rtree.impurity: Provides implementations of regression tree impurity metrics.

P

predict(Example<Regressor>) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
printTree - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Prints the decision tree.

R

RegressionTreeOptions() - Constructor for class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
RegressorImpurity - Interface in org.tribuo.regression.rtree.impurity: Calculates a tree impurity score based on the regression targets.
RegressorImpurity.ImpurityTuple - Class in org.tribuo.regression.rtree.impurity: Tuple class for the impurity and summed weight.
RegressorTrainingNode - Class in org.tribuo.regression.rtree.impl: A decision tree node used at training time.
RegressorTrainingNode(RegressorImpurity, RegressorTrainingNode.InvertedData, int, String, int, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Constructs a tree training node for regression problems.
RegressorTrainingNode.InvertedData - Class in org.tribuo.regression.rtree.impl: Tuple containing an inverted dataset (i.e., feature-wise not exmaple-wise).

S

sort() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Sort the list using InvertedFeature's natural ordering.
split(int[], int[], IntArrayContainer, IntArrayContainer) - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Splits this tree feature into two.
split(IntArrayContainer, IntArrayContainer) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Relies upon allLeftIndices being sorted in ascending order.
splitChar - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Character to split the CSV response on to generate multiple regression dimensions.

T

toString() - Method in class org.tribuo.regression.rtree.CARTJointRegressionTrainer
toString() - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
toString() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
toString() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
toString() - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
toString() - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
toString() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
train(Dataset<Regressor>, Map<String, Provenance>, int) - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
TrainTest - Class in org.tribuo.regression.rtree: Build and run a regression tree for a standard dataset.
TrainTest() - Constructor for class org.tribuo.regression.rtree.TrainTest
TrainTest.ImpurityType - Enum in org.tribuo.regression.rtree: Impurity function.
TrainTest.RegressionTreeOptions - Class in org.tribuo.regression.rtree: Command line options.
TrainTest.TreeType - Enum in org.tribuo.regression.rtree: Type of tree trainer.
TreeFeature - Class in org.tribuo.regression.rtree.impl: An inverted feature, which stores a reference to all the values of this feature.
TreeFeature(int) - Constructor for class org.tribuo.regression.rtree.impl.TreeFeature: Constructs an inverted feature with the specified feature id.
treeType - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Tree type.

U

useRandomSplitPoints - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions: Whether to choose split points for features at random.

V

value - Variable in class org.tribuo.regression.rtree.impl.InvertedFeature: The feature value of this object.
valueOf(String) - Static method in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.rtree.TrainTest.TreeType: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.rtree.TrainTest.TreeType: Returns an array containing the constants of this enum type, in the order they are declared.

W

weight - Variable in class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple: The sum of the weights.

A B C D F G I J M N O P R S T U V W
All Classes and Interfaces|All Packages|Serialized Form