weka.estimators

Class MultivariateGaussianEstimator

java.lang.Object

weka.estimators.MultivariateGaussianEstimator

All Implemented Interfaces:

Cloneable, MultivariateEstimator

public class MultivariateGaussianEstimator
extends Object
implements MultivariateEstimator, Cloneable

Implementation of Multivariate Distribution Estimation using Normal Distribution. *

Version:

$Revision: 10459 $

Author:

Uday Kamath, PhD, George Mason University

Field Summary

Fields

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

Constructor Summary

Constructors

Constructor and Description
MultivariateGaussianEstimator()
MultivariateGaussianEstimator(double[] means, double[][] covariance)

Method Summary

Modifier and Type	Method and Description
MultivariateGaussianEstimator	clone()
static double[][]	covariance(double[][] matrix, double[] means) Calculates the covariance matrix of a sample matrix.
void	estimate(double[][] observations, double[] weights) Fits the value to the density estimator.
double[][]	getCovariance()
double[]	getMean()
double	getProbability(double[] value) Returns the probability density estimate at the given point.
double	logDensity(double[] valuePassed) Returns the log likelihood of density value for the Multivariate distribution
static void	main(String[] args)
static double[][]	scatter(double[][] matrix, double[] means, double divisor, int dimension) Calculates the scatter matrix of a sample matrix.

Methods inherited from class java.lang.Object

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

Field Detail

Log2PI

public static final double Log2PI

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

See Also:

Constant Field Values

Constructor Detail

MultivariateGaussianEstimator

public MultivariateGaussianEstimator()

MultivariateGaussianEstimator

public MultivariateGaussianEstimator(double[] means,
                                     double[][] covariance)

Method Detail

clone

public MultivariateGaussianEstimator clone()

Overrides:

clone in class Object

getProbability

public double getProbability(double[] value)

Returns the probability density estimate at the given point.

Specified by:

getProbability in interface MultivariateEstimator

Parameters:

value - the value at which to evaluate

Returns:

the the density estimate at the given value

logDensity

public double logDensity(double[] valuePassed)

Returns the log likelihood of density value for the Multivariate distribution

Specified by:

logDensity in interface MultivariateEstimator

Parameters:

input - vector

Returns:

log density based on given distribution

estimate

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

Description copied from interface: MultivariateEstimator

Fits the value to the density estimator.

Specified by:

estimate in interface MultivariateEstimator

Parameters:

observations - the value to add

weights - the weight of the value

See Also:

MultivariateEstimator.estimate(double[][], double[])

getMean

public double[] getMean()

getCovariance

public double[][] getCovariance()

covariance

public static double[][] covariance(double[][] matrix,
                                    double[] means)

Calculates the covariance matrix of a sample matrix. In statistics and probability theory, the covariance matrix is a matrix of covariances between elements of a vector. It is the natural generalization to higher dimensions of the concept of the variance of a scalar-valued random variable.

Parameters:

matrix - A number multi-dimensional array containing the matrix values.

means - The values' mean vector, if already known.

Returns:

The covariance matrix.

scatter

public static double[][] scatter(double[][] matrix,
                                 double[] means,
                                 double divisor,
                                 int dimension)

Calculates the scatter matrix of a sample matrix. By dividing the Scatter matrix by the sample size, we get the population Covariance matrix. By dividing by the sample size minus one, we get the sample Covariance matrix.

Parameters:

matrix - A number multi-dimensional array containing the matrix values.

means - The values' mean vector, if already known.

divisor - A real number to divide each member of the matrix.

dimension - Pass 0 to if mean vector is a row vector, 1 otherwise. Default value is 0.

Returns:

The covariance matrix.

main

public static void main(String[] args)