weka.estimators

Class MultivariateGaussianEstimator

java.lang.Object

weka.estimators.MultivariateGaussianEstimator

All Implemented Interfaces:

java.io.Serializable, MultivariateEstimator

public class MultivariateGaussianEstimator
extends java.lang.Object
implements MultivariateEstimator, java.io.Serializable

Implementation of maximum likelihood Multivariate Distribution Estimation using Normal Distribution.

Version:

$Revision: 12904 $

Author:

Uday Kamath, PhD, George Mason University, Eibe Frank, University of Waikato

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	Log2PI Log of twice the number pi: log(2*pi).

Constructor Summary

Constructors

Constructor and Description
MultivariateGaussianEstimator()

Method Summary

Modifier and Type	Method and Description
void	estimate(double[][] observations, double[] weights) Generates the estimator based on the given observations and weight vector.
double[][]	estimatePooled(double[][][] observations, double[][] weights) Generates pooled estimator for linear discriminant analysis based on the given groups of observations and weight vectors.
double[]	getMean() Returns the mean vector.
double	getRidge() Get the value of Ridge.
double	logDensity(double[] valuePassed) Returns the log of the density value for the given vector.
static void	main(java.lang.String[] args) Main method for testing this class.
java.lang.String	ridgeTipText() Returns the tip text for this property
void	setRidge(double newRidge) Set the value of Ridge.
java.lang.String	toString() Returns string summarizing the estimator.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

Log2PI

public static final double Log2PI

Log of twice the number pi: log(2*pi).

Constructor Detail

MultivariateGaussianEstimator

public MultivariateGaussianEstimator()

Method Detail

toString

public java.lang.String toString()

Returns string summarizing the estimator.

Overrides:

toString in class java.lang.Object

getMean

public double[] getMean()

Returns the mean vector.

logDensity

public double logDensity(double[] valuePassed)

Returns the log of the density value for the given vector.

Specified by:

logDensity in interface MultivariateEstimator

Parameters:

valuePassed - input vector

Returns:

log density based on given distribution

estimate

public void estimate(double[][] observations,
                     double[] weights)

Generates the estimator based on the given observations and weight vector. Equal weights are assumed if the weight vector is null.

Specified by:

estimate in interface MultivariateEstimator

Parameters:

observations - the value to add

weights - the weight of the value

estimatePooled

public double[][] estimatePooled(double[][][] observations,
                                 double[][] weights)

Generates pooled estimator for linear discriminant analysis based on the given groups of observations and weight vectors. The pooled covariance matrix is the weighted mean of the per-group covariance matrices. The pooled mean vector is the mean vector for all observations.

Returns:

the per group mean vectors

ridgeTipText

public java.lang.String ridgeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getRidge

public double getRidge()

Get the value of Ridge.

Returns:

Value of Ridge.

setRidge

public void setRidge(double newRidge)

Set the value of Ridge.

Parameters:

newRidge - Value to assign to Ridge.

main

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

Main method for testing this class.

Parameters:

args - command-line parameters