org.tribuo.regression.rtree.impl

Class JointRegressorTrainingNode

java.lang.Object

org.tribuo.common.tree.AbstractTrainingNode<Regressor>

org.tribuo.regression.rtree.impl.JointRegressorTrainingNode

All Implemented Interfaces:

Serializable, Node<Regressor>

public class JointRegressorTrainingNode
extends AbstractTrainingNode<Regressor>

A decision tree node used at training time. Contains a list of the example indices currently found in this node, the current impurity and a bunch of other statistics.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class org.tribuo.common.tree.AbstractTrainingNode

AbstractTrainingNode.LeafDeterminer

Field Summary

Fields inherited from class org.tribuo.common.tree.AbstractTrainingNode

DEFAULT_SIZE, depth, greaterThan, impurityScore, leafDeterminer, lessThanOrEqual, numExamples, split, splitID, splitValue

Constructor Summary

Constructors

Constructor and Description
JointRegressorTrainingNode(RegressorImpurity impurity, Dataset<Regressor> examples, boolean normalize, AbstractTrainingNode.LeafDeterminer leafDeterminer) Constructor which creates the inverted file.

Method Summary

Modifier and Type	Method and Description
List<AbstractTrainingNode<Regressor>>	buildTree(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
Node<Regressor>	convertTree() Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
double	getImpurity() The impurity score of this node.
float	getWeightSum() The sum of the weights associated with this node's examples.

Methods inherited from class org.tribuo.common.tree.AbstractTrainingNode

copy, createSplitNode, getDepth, getNextNode, getNumExamples, isLeaf, shouldMakeLeaf

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

JointRegressorTrainingNode

public JointRegressorTrainingNode(RegressorImpurity impurity,
                                  Dataset<Regressor> examples,
                                  boolean normalize,
                                  AbstractTrainingNode.LeafDeterminer leafDeterminer)

Constructor which creates the inverted file.

Parameters:

impurity - The impurity function to use.

examples - The training data.

normalize - Normalizes the leaves so each leaf has a distribution which sums to 1.0.

leafDeterminer - Contains parameters needed to determine whether a node is a leaf.

Method Detail

getImpurity

public double getImpurity()

Description copied from interface: Node

The impurity score of this node.

Returns:

The node impurity.

getWeightSum

public float getWeightSum()

Description copied from class: AbstractTrainingNode

The sum of the weights associated with this node's examples.

Specified by:

getWeightSum in class AbstractTrainingNode<Regressor>

Returns:

the sum of the weights associated with this node's examples.

buildTree

public List<AbstractTrainingNode<Regressor>> buildTree(int[] featureIDs,
                                                       SplittableRandom rng,
                                                       boolean useRandomSplitPoints)

Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).

Specified by:

buildTree in class AbstractTrainingNode<Regressor>

Parameters:

featureIDs - Indices of the features available in this split.

rng - Splittable random number generator.

useRandomSplitPoints - Whether to choose split points for features at random.

Returns:

A possibly empty list of TrainingNodes.

convertTree

public Node<Regressor> convertTree()

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

Specified by:

convertTree in class AbstractTrainingNode<Regressor>

Returns:

A subtree using the SplitNode and LeafNode classes.