gov.sandia.cognition.learning.algorithm.tree
Class RegressionTreeNode<InputType,InteriorType>

java.lang.Object
  gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode<InputType,Double,InteriorType>
      gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode<InputType,InteriorType>

Type Parameters:

InputType - The input type for the tree.

InteriorType - The type that is the output of the decision made at this tree node.

All Implemented Interfaces:

DecisionTreeNode<InputType,Double>, CloneableSerializable, Serializable, Cloneable

public class RegressionTreeNode<InputType,InteriorType>
extends AbstractDecisionTreeNode<InputType,Double,InteriorType>

The RegressionTreeNode implements a DecisionTreeNode for a tree that does regression.

Since:

2.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

static double	DEFAULT_VALUE The default value for the node is 0.0.
protected Evaluator<? super InputType,Double>	scalarFunction The function to apply for leaf nodes.
protected double	value The value stored at the tree node.

Fields inherited from class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode

childMap, decider, incomingValue, parent

Constructor Summary

RegressionTreeNode()
Creates a new instance of RegressionTreeNode.

RegressionTreeNode(DecisionTreeNode<InputType,Double> parent, Categorizer<? super InputType,? extends InteriorType> decider, double value)
Creates a new instance of RegressionTreeNode.

RegressionTreeNode(DecisionTreeNode<InputType,Double> parent, Categorizer<? super InputType,? extends InteriorType> decider, Evaluator<? super InputType,Double> scalarFunction, double value, Object incomingValue)
Creates a new instance of RegressionTreeNode.

RegressionTreeNode(DecisionTreeNode<InputType,Double> parent, double value)
Creates a new instance of RegressionTreeNode.

RegressionTreeNode(DecisionTreeNode<InputType,Double> parent, Evaluator<? super InputType,Double> scalarFunction, double value)
Creates a new instance of RegressionTreeNode.

RegressionTreeNode(DecisionTreeNode<InputType,Double> parent, Evaluator<? super InputType,Double> scalarFunction, double value, Object incomingValue)
Creates a new instance of RegressionTreeNode.

Method Summary

RegressionTreeNode<InputType,InteriorType>	clone() Creates a new clone (shallow copy) of this object.
Double	getOutput(InputType input) Gets the local output of this node for the given input.
Evaluator<? super InputType,Double>	getScalarFunction() Gets the scalar function applied to the input when the node is a leaf node.
double	getValue() Gets the value stored at the node, which is usually the mean value.
void	setScalarFunction(Evaluator<? super InputType,Double> scalarFunction) Sets the scalar function applied to the input when the node is a leaf node.
void	setValue(double value) Sets the value stored at the node, which is usually the mean value.

Methods inherited from class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode

addChild, chooseChild, getChildMap, getChildren, getDecider, getDepth, getIncomingValue, getParent, getTreeSize, isLeaf, setChildMap, setDecider, setIncomingValue, setParent

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_VALUE

public static final double DEFAULT_VALUE

The default value for the node is 0.0.

See Also:

Constant Field Values

scalarFunction

protected Evaluator<? super InputType,Double> scalarFunction

The function to apply for leaf nodes.

value

protected double value

The value stored at the tree node. It is used as a backup value if no scalar function exists for the node but the output is requested.

Constructor Detail

RegressionTreeNode

public RegressionTreeNode()

Creates a new instance of RegressionTreeNode.

RegressionTreeNode

public RegressionTreeNode(DecisionTreeNode<InputType,Double> parent,
                          double value)

Creates a new instance of RegressionTreeNode.

Parameters:

parent - The parent node of this node. Null if this is a root.

value - The value stored at the node.

RegressionTreeNode

public RegressionTreeNode(DecisionTreeNode<InputType,Double> parent,
                          Categorizer<? super InputType,? extends InteriorType> decider,
                          double value)

Creates a new instance of RegressionTreeNode.

Parameters:

parent - The parent node of this node. Null if this is a root.

decider - The decision function for interior nodes.

value - The value stored at the node.

RegressionTreeNode

public RegressionTreeNode(DecisionTreeNode<InputType,Double> parent,
                          Evaluator<? super InputType,Double> scalarFunction,
                          double value)

Creates a new instance of RegressionTreeNode.

Parameters:

parent - The parent node of this node. Null if this is a root.

scalarFunction - The scalar function to apply at leaf nodes.

value - The value stored at the node.

RegressionTreeNode

public RegressionTreeNode(DecisionTreeNode<InputType,Double> parent,
                          Evaluator<? super InputType,Double> scalarFunction,
                          double value,
                          Object incomingValue)

Creates a new instance of RegressionTreeNode.

Parameters:

parent - The parent node of this node. Null if this is a root.

scalarFunction - The scalar function to apply at leaf nodes.

value - The value stored at the node.

incomingValue - The incoming value.

RegressionTreeNode

public RegressionTreeNode(DecisionTreeNode<InputType,Double> parent,
                          Categorizer<? super InputType,? extends InteriorType> decider,
                          Evaluator<? super InputType,Double> scalarFunction,
                          double value,
                          Object incomingValue)

Creates a new instance of RegressionTreeNode.

Parameters:

parent - The parent node of this node. Null if this is a root.

decider - The decision function for interior nodes.

scalarFunction - The scalar function to apply at leaf nodes.

value - The value stored at the node.

incomingValue - The incoming value.

Method Detail

clone

public RegressionTreeNode<InputType,InteriorType> clone()

Description copied from interface: CloneableSerializable

Creates a new clone (shallow copy) of this object.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractDecisionTreeNode<InputType,Double,InteriorType>

Returns:

A new clone (shallow copy) of this object.

getOutput

public Double getOutput(InputType input)

Description copied from interface: DecisionTreeNode

Gets the local output of this node for the given input. This is done to determine the output value for a leaf node or the output value in the case that there is no corresponding child node for an input.

Parameters:

input - The input.

Returns:

The local output value for the given input.

getScalarFunction

public Evaluator<? super InputType,Double> getScalarFunction()

Gets the scalar function applied to the input when the node is a leaf node.

Returns:

The scalar function applied to the input for leaves.

setScalarFunction

public void setScalarFunction(Evaluator<? super InputType,Double> scalarFunction)

Sets the scalar function applied to the input when the node is a leaf node.

Parameters:

scalarFunction - The scalar function applied to the input for leaves.

getValue

public double getValue()

Gets the value stored at the node, which is usually the mean value.

Returns:

The value stored at the node.

setValue

public void setValue(double value)

Sets the value stored at the node, which is usually the mean value.

Parameters:

value - The value stored at the node.