weka.estimators
Class NNConditionalEstimator

java.lang.Object
  weka.estimators.NNConditionalEstimator

All Implemented Interfaces:

ConditionalEstimator

public class NNConditionalEstimator

extends java.lang.Object

implements ConditionalEstimator

Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).

Version:

$Revision: 1.4 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

Field Summary

private boolean	m_AllWeightsOne Whether we can optimise the kernel summation
private double	m_CondMean Current Conditional mean
private java.util.Vector	m_CondValues Vector containing all of the conditioning values seen
private Matrix	m_Covariance Current covariance matrix
private double	m_SumOfWeights The sum of the weights so far
private double	m_ValueMean Current Values mean
private java.util.Vector	m_Values Vector containing all of the values seen
private java.util.Vector	m_Weights Vector containing the associated weights
private static double	TWO_PI 2 * PI

Constructor Summary

NNConditionalEstimator()

Method Summary

void	addValue(double data, double given, double weight) Add a new data value to the current estimator.
private void	calculateCovariance() Calculate covariance and value means
private int	findNearestPair(double key, double secondaryKey) Execute a binary search to locate the nearest data value
Estimator	getEstimator(double given) Get a probability estimator for a value
double	getProbability(double data, double given) Get a probability estimate for a value
static void	main(java.lang.String[] argv) Main method for testing this class.
private double	normalKernel(double x, double variance) Returns value for normal kernel
java.lang.String	toString() Display a representation of this estimator

Methods inherited from class java.lang.Object

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

Field Detail

m_Values

private java.util.Vector m_Values

Vector containing all of the values seen

m_CondValues

private java.util.Vector m_CondValues

Vector containing all of the conditioning values seen

m_Weights

private java.util.Vector m_Weights

Vector containing the associated weights

m_SumOfWeights

private double m_SumOfWeights

The sum of the weights so far

m_CondMean

private double m_CondMean

Current Conditional mean

m_ValueMean

private double m_ValueMean

Current Values mean

m_Covariance

private Matrix m_Covariance

Current covariance matrix

m_AllWeightsOne

private boolean m_AllWeightsOne

Whether we can optimise the kernel summation

TWO_PI

private static double TWO_PI

2 * PI

Constructor Detail

NNConditionalEstimator

public NNConditionalEstimator()

Method Detail

findNearestPair

private int findNearestPair(double key,
                            double secondaryKey)

Execute a binary search to locate the nearest data value

Parameters:

key - the data value to locate

secondaryKey - the data value to locate

Returns:

the index of the nearest data value

calculateCovariance

private void calculateCovariance()

Calculate covariance and value means

normalKernel

private double normalKernel(double x,
                            double variance)

Returns value for normal kernel

Parameters:

x - the argument to the kernel function

variance - the variance

Returns:

the value for a normal kernel

addValue

public void addValue(double data,
                     double given,
                     double weight)

Add a new data value to the current estimator.

Specified by:

addValue in interface ConditionalEstimator

Parameters:

data - the new data value

given - the new value that data is conditional upon

weight - the weight assigned to the data value

getEstimator

public Estimator getEstimator(double given)

Get a probability estimator for a value

Specified by:

getEstimator in interface ConditionalEstimator

Parameters:

given - the new value that data is conditional upon

Returns:

the estimator for the supplied value given the condition

getProbability

public double getProbability(double data,
                             double given)

Get a probability estimate for a value

Specified by:

getProbability in interface ConditionalEstimator

Parameters:

data - the value to estimate the probability of

given - the new value that data is conditional upon

Returns:

the estimated probability of the supplied value

toString

public java.lang.String toString()

Display a representation of this estimator

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain a sequence of numeric values