weka.estimators
Class MahalanobisEstimator

java.lang.Object
  weka.estimators.MahalanobisEstimator

All Implemented Interfaces:

Estimator, java.io.Serializable

public class MahalanobisEstimator

extends java.lang.Object

implements Estimator

Simple probability estimator that places a single normal distribution over the observed values.

Version:

$Revision: 1.4 $

Author:

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

See Also:

Serialized Form

Field Summary

private double	m_ConstDelta The difference between the conditioning value and the conditioning mean
private Matrix	m_CovarianceInverse The inverse of the covariance matrix
private double	m_Determinant The determinant of the covariance matrix
private double	m_ValueMean The mean of the values
private static double	TWO_PI 2 * PI

Constructor Summary

MahalanobisEstimator(Matrix covariance, double constDelta, double valueMean)
Constructor

Method Summary

void	addValue(double data, double weight) Add a new data value to the current estimator.
double	getProbability(double data) 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) 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_CovarianceInverse

private Matrix m_CovarianceInverse

The inverse of the covariance matrix

m_Determinant

private double m_Determinant

The determinant of the covariance matrix

m_ConstDelta

private double m_ConstDelta

The difference between the conditioning value and the conditioning mean

m_ValueMean

private double m_ValueMean

The mean of the values

TWO_PI

private static double TWO_PI

2 * PI

Constructor Detail

MahalanobisEstimator

public MahalanobisEstimator(Matrix covariance,
                            double constDelta,
                            double valueMean)

Constructor

Method Detail

normalKernel

private double normalKernel(double x)

Returns value for normal kernel

Parameters:

x - the argument to the kernel function

Returns:

the value for a normal kernel

addValue

public void addValue(double data,
                     double weight)

Add a new data value to the current estimator. Does nothing because the data is provided in the constructor.

Specified by:

addValue in interface Estimator

Parameters:

data - the new data value

weight - the weight assigned to the data value

getProbability

public double getProbability(double data)

Get a probability estimate for a value

Specified by:

getProbability in interface Estimator

Parameters:

data - the value to estimate the probability of

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