de.lmu.ifi.dbs.elki.math.linearalgebra

Class CovarianceMatrix

java.lang.Object

de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix

public class CovarianceMatrix
extends java.lang.Object

Class for computing covariance matrixes using stable mean and variance computations. This class encapsulates the mathematical aspects of computing this matrix. See DatabaseUtil for easier to use APIs. For use in algorithms, it is more appropriate to use StandardCovarianceMatrixBuilder since this class can be overridden with a stabilized covariance matrix builder!

Since:

0.4.0

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
(package private) double[][]	elements The covariance matrix.
static java.lang.String	ERR_TOO_LITTLE_WEIGHT Error message reported when too little data (weight <= 1) in matrix.
(package private) double[]	mean The means.
(package private) double[]	nmea Temporary storage, to avoid reallocations.
protected double	wsum The current weight.

Constructor Summary

Constructors

Constructor and Description
CovarianceMatrix(int dim) Constructor.

Method Summary

Modifier and Type	Method and Description
double[][]	destroyToPopulationMatrix() Obtain the covariance matrix according to the population statistics: n degrees of freedom.
double[][]	destroyToSampleMatrix() Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom.
int	getDimensionality() Get the matrix dimensionality.
double[]	getMeanVector() Get the mean as vector.
<F extends NumberVector> F	getMeanVector(Relation<? extends F> relation) Get the mean as vector.
double	getWeight() Get the weight sum, to test whether the covariance matrix can be materialized.
static CovarianceMatrix	make(Relation<? extends NumberVector> relation) Static Constructor from a full relation.
static CovarianceMatrix	make(Relation<? extends NumberVector> relation, DBIDs ids) Static Constructor from a full relation.
double[][]	makePopulationMatrix() Obtain the covariance matrix according to the population statistics: n degrees of freedom.
double[][]	makeSampleMatrix() Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom.
void	put(double[] val) Add a single value with weight 1.0.
void	put(double[] val, double weight) Add data with a given weight.
void	put(NumberVector val) Add a single value with weight 1.0.
void	put(NumberVector val, double weight) Add data with a given weight.
void	reset() Reset the covariance matrix.

Methods inherited from class java.lang.Object

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

Field Detail

ERR_TOO_LITTLE_WEIGHT

public static final java.lang.String ERR_TOO_LITTLE_WEIGHT

Error message reported when too little data (weight <= 1) in matrix.

See Also:

Constant Field Values

mean

double[] mean

The means.

elements

double[][] elements

The covariance matrix.

nmea

double[] nmea

Temporary storage, to avoid reallocations.

wsum

protected double wsum

The current weight.

Constructor Detail

CovarianceMatrix

public CovarianceMatrix(int dim)

Constructor.

Parameters:

dim - Dimensionality

Method Detail

getDimensionality

public int getDimensionality()

Get the matrix dimensionality.

Returns:

Mean length.

put

public void put(double[] val)

Add a single value with weight 1.0.

Parameters:

val - Value

put

public void put(double[] val,
                double weight)

Add data with a given weight.

Parameters:

val - data

weight - weight

put

public void put(NumberVector val)

Add a single value with weight 1.0.

Parameters:

val - Value

put

public void put(NumberVector val,
                double weight)

Add data with a given weight.

Parameters:

val - data

weight - weight

getWeight

public double getWeight()

Get the weight sum, to test whether the covariance matrix can be materialized.

Returns:

Weight sum.

getMeanVector

public double[] getMeanVector()

Get the mean as vector.

Returns:

Mean vector

getMeanVector

public <F extends NumberVector> F getMeanVector(Relation<? extends F> relation)

Get the mean as vector.

Type Parameters:

F - vector type

Parameters:

relation - Data relation

Returns:

Mean vector

makeSampleMatrix

public double[][] makeSampleMatrix()

Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom. This method duplicates the matrix contents, so it does allow further updates. Use destroyToSampleMatrix() if you do not need further updates.

Returns:

New matrix

makePopulationMatrix

public double[][] makePopulationMatrix()

Obtain the covariance matrix according to the population statistics: n degrees of freedom. This method duplicates the matrix contents, so it does allow further updates. Use destroyToPopulationMatrix() if you do not need further updates.

Returns:

New matrix

destroyToSampleMatrix

public double[][] destroyToSampleMatrix()

Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom. This method doesn't require matrix duplication, but will not allow further updates, the object should be discarded. Use makeSampleMatrix() if you want to perform further updates.

Returns:

New matrix

destroyToPopulationMatrix

public double[][] destroyToPopulationMatrix()

Obtain the covariance matrix according to the population statistics: n degrees of freedom. This method doesn't require matrix duplication, but will not allow further updates, the object should be discarded. Use makePopulationMatrix() if you want to perform further updates.

Returns:

New matrix

reset

public void reset()

Reset the covariance matrix. This function may be called after a "destroy".

make

public static CovarianceMatrix make(Relation<? extends NumberVector> relation)

Static Constructor from a full relation.

Parameters:

relation - Relation to use.

Returns:

Covariance matrix

make

public static CovarianceMatrix make(Relation<? extends NumberVector> relation,
                                    DBIDs ids)

Static Constructor from a full relation.

Parameters:

relation - Relation to use.

ids - IDs to add

Returns:

Covariance matrix