gov.sandia.cognition.math

Class MultivariateStatisticsUtil

java.lang.Object

gov.sandia.cognition.math.MultivariateStatisticsUtil

public class MultivariateStatisticsUtil
extends java.lang.Object

Some static methods for computing generally useful multivariate statistics.

Since:

2.0

Author:

Kevin Dixon

Constructor Summary

Constructors

Constructor and Description
MultivariateStatisticsUtil()

Method Summary

Modifier and Type	Method and Description
static <RingType extends Ring<RingType>> RingType	computeMean(java.lang.Iterable<? extends RingType> data) Computes the arithmetic mean (average, expectation, first central moment) of a dataset
static Pair<Vector,Matrix>	computeMeanAndCovariance(java.lang.Iterable<? extends Vectorizable> data) Computes the mean and unbiased covariance Matrix of a multivariate data set.
static <RingType extends Ring<RingType>> RingType	computeSum(java.lang.Iterable<? extends RingType> data) Computes the arithmetic sum of the dataset
static Matrix	computeVariance(java.util.Collection<? extends Vector> data) Computes the variance (second central moment, squared standard deviation) of a dataset.
static Matrix	computeVariance(java.util.Collection<? extends Vector> data, Vector mean) Computes the variance (second central moment, squared standard deviation) of a dataset
static Pair<Vector,Matrix>	computeWeightedMeanAndCovariance(java.lang.Iterable<? extends WeightedValue<? extends Vectorizable>> data) Computes the mean and biased covariance Matrix of a multivariate weighted data set.

Methods inherited from class java.lang.Object

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

Constructor Detail

MultivariateStatisticsUtil

public MultivariateStatisticsUtil()

Method Detail

computeSum

public static <RingType extends Ring<RingType>> RingType computeSum(java.lang.Iterable<? extends RingType> data)

Computes the arithmetic sum of the dataset

Type Parameters:

RingType - The type of data to compute the sum over, which must implement the Ring interface.

Parameters:

data - Dataset to consider

Returns:

Arithmetic sum of the given dataset

computeMean

public static <RingType extends Ring<RingType>> RingType computeMean(java.lang.Iterable<? extends RingType> data)

Computes the arithmetic mean (average, expectation, first central moment) of a dataset

Type Parameters:

RingType - The type of data to compute the sum over, which must implement the Ring interface.

Parameters:

data - Collection of Vectors to consider

Returns:

Arithmetic mean of the given dataset

computeVariance

public static Matrix computeVariance(java.util.Collection<? extends Vector> data)

Computes the variance (second central moment, squared standard deviation) of a dataset. Computes the mean first, then computes the variance. If you already have the mean, then use the two-argument computeVariance(data,mean) method to save duplication of effort

Parameters:

data - Collection of Vector to consider

Returns:

Variance of the given dataset

computeVariance

public static Matrix computeVariance(java.util.Collection<? extends Vector> data,
                                     Vector mean)

Computes the variance (second central moment, squared standard deviation) of a dataset

Parameters:

data - Collection of Doubles to consider

mean - Pre-computed mean (or central value) of the dataset

Returns:

Full covariance matrix of the given dataset

computeMeanAndCovariance

public static Pair<Vector,Matrix> computeMeanAndCovariance(java.lang.Iterable<? extends Vectorizable> data)

Computes the mean and unbiased covariance Matrix of a multivariate data set.

Parameters:

data - Data set to consider

Returns:

Mean and unbiased Covariance

computeWeightedMeanAndCovariance

public static Pair<Vector,Matrix> computeWeightedMeanAndCovariance(java.lang.Iterable<? extends WeightedValue<? extends Vectorizable>> data)

Computes the mean and biased covariance Matrix of a multivariate weighted data set.

Parameters:

data - Data set to consider

Returns:

Mean and biased Covariance