org.astrogrid.matrix
Class Algorithms

java.lang.Object
  org.astrogrid.matrix.Algorithms

public class Algorithms
extends java.lang.Object

Some general matrix based algorithms.

Since:

VOTech Stage 8

Version:

$Name: $

Author:

Paul Harrison (paul.harrison@manchester.ac.uk) 8 Dec 2008

Field Summary

static double

eps

Constructor Summary

Algorithms()

Method Summary

static Matrix	centre_kmeans(Matrix data, int nclus, int ndim)
static Matrix	dist2(Matrix x, Matrix c) %DIST2 Calculates squared distance between two sets of points.
static Matrix	dist3_common(Matrix x, Matrix centres, no.uib.cipr.matrix.Vector covars)
static Matrix	dist3_diag(Matrix x, Matrix centres, Matrix covars)
static Matrix	dist3_free(Matrix x, Matrix centres, Matrix[] covars)
static double	gamma(double s)
static void	kmeans(Matrix centres, Matrix data, int niters, double absprec, double errprec, boolean fromData, boolean verbose) Comment for kmeans %KMEANS Trains a k means cluster model.
static no.uib.cipr.matrix.Vector	mean(Matrix m, int i)
static no.uib.cipr.matrix.Vector	multinorm(Matrix x, no.uib.cipr.matrix.Vector m, Matrix covar)
static int[]	randperm(int n) RANDPERM Random permutation.
static int	rem(int a, int b)
static no.uib.cipr.matrix.Vector	t_multinorm(Matrix x, no.uib.cipr.matrix.Vector m, Matrix covar, double v)

Methods inherited from class java.lang.Object

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

Field Detail

eps

public static final double eps

See Also:

Constant Field Values

Constructor Detail

Algorithms

public Algorithms()

Method Detail

kmeans

public static void kmeans(Matrix centres,
                          Matrix data,
                          int niters,
                          double absprec,
                          double errprec,
                          boolean fromData,
                          boolean verbose)

Comment for kmeans %KMEANS Trains a k means cluster model. Description CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES. The error value at that point is returned in OPTIONS(8). [CENTRES, OPTIONS, POST, ERRLOG] = KMEANS(CENTRES, DATA, OPTIONS) also returns the cluster number (in a one-of-N encoding) for each data point in POST and a log of the error values after each cycle in ERRLOG. The optional parameters have the following interpretations. OPTIONS(1) is set to 1 to display error values; also logs error values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then only warning messages are displayed. If OPTIONS(1) is -1, then nothing is displayed. OPTIONS(2) absprec is a measure of the absolute precision required for the value of CENTRES at the solution. If the absolute difference between the values of CENTRES between two successive steps is less than OPTIONS(2), then this condition is satisfied. OPTIONS(3) errprec is a measure of the precision required of the error function at the solution. If the absolute difference between the error functions between two successive steps is less than OPTIONS(3), then this condition is satisfied. Both this and the previous condition must be satisfied for termination. OPTIONS(14) is the maximum number of iterations; default 100.

centre_kmeans

public static Matrix centre_kmeans(Matrix data,
                                   int nclus,
                                   int ndim)

dist2

public static Matrix dist2(Matrix x,
                           Matrix c)

%DIST2 Calculates squared distance between two sets of points. Description D = DIST2(X, C) takes two matrices of vectors and calculates the squared Euclidean distance between them. Both matrices must be of the same column dimension. If X has M rows and N columns, and C has L rows and N columns, then the result has M rows and L columns. The I, Jth entry is the squared distance from the Ith row of X to the Jth row of C.

Parameters:

data -

centres -

Returns:

randperm

public static int[] randperm(int n)

RANDPERM Random permutation. RANDPERM(n) is a random permutation of the integers from 1 to n. For example, RANDPERM(6) might be [2 4 5 6 1 3].

Parameters:

n -

Returns:

dist3_common

public static Matrix dist3_common(Matrix x,
                                  Matrix centres,
                                  no.uib.cipr.matrix.Vector covars)

dist3_diag

public static Matrix dist3_diag(Matrix x,
                                Matrix centres,
                                Matrix covars)

dist3_free

public static Matrix dist3_free(Matrix x,
                                Matrix centres,
                                Matrix[] covars)

gamma

public static double gamma(double s)

mean

public static no.uib.cipr.matrix.Vector mean(Matrix m,
                                             int i)

rem

public static int rem(int a,
                      int b)

multinorm

public static no.uib.cipr.matrix.Vector multinorm(Matrix x,
                                                  no.uib.cipr.matrix.Vector m,
                                                  Matrix covar)

t_multinorm

public static no.uib.cipr.matrix.Vector t_multinorm(Matrix x,
                                                    no.uib.cipr.matrix.Vector m,
                                                    Matrix covar,
                                                    double v)