CovarianceMatrix (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix

```
public class CovarianceMatrix
extends 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!

Author:

Erich Schubert

Field Summary

Fields
Modifier and Type	Field and Description
`(package private) double[][]`	`elements` The covariance matrix.
`static 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.

Constructors
Constructor and Description
`CovarianceMatrix(int dim)` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`Matrix`	`destroyToNaiveMatrix()` Obtain the covariance matrix according to the population statistics: n degrees of freedom.
`Matrix`	`destroyToSampleMatrix()` Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom.
`int`	`getDimensionality()` Get the matrix dimensionality.
`Vector`	`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(Matrix mat)` Static Constructor.
`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.
`Matrix`	`makeNaiveMatrix()` Obtain the covariance matrix according to the population statistics: n degrees of freedom.
`Matrix`	`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`	`put(Vector val)` Add a single value with weight 1.0.
`void`	`put(Vector 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 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 final void put(Vector val)
```
    Add a single value with weight 1.0.
    
    Parameters:
    val - Value
  - put
```
public final void put(Vector 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 Vector 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 Matrix 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
  - makeNaiveMatrix
```
public Matrix makeNaiveMatrix()
```
    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 destroyToNaiveMatrix() if you do not need further updates.
    
    Returns:
    New matrix
  - destroyToSampleMatrix
```
public Matrix 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
  - destroyToNaiveMatrix
```
public Matrix destroyToNaiveMatrix()
```
    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 makeNaiveMatrix() 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(Matrix mat)
```
    Static Constructor.
    
    Parameters:
    mat - Matrix to use the columns of
    
    Returns:
    Covariance matrix
  - 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

Class CovarianceMatrix

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

ERR_TOO_LITTLE_WEIGHT

mean

elements

nmea

wsum

Constructor Detail

CovarianceMatrix

Method Detail

getDimensionality

put

put

put

put

put

put

getWeight

getMeanVector

getMeanVector

makeSampleMatrix

makeNaiveMatrix

destroyToSampleMatrix

destroyToNaiveMatrix

reset

make

make

make