org.openimaj.math.matrix

Class MatlibMatrixUtils

java.lang.Object

org.openimaj.math.matrix.MatlibMatrixUtils

public class MatlibMatrixUtils
extends Object

Some helpful operations on Matrix instances from Adrian Kuhn's library.

Author:

Sina Samangooei (ss@ecs.soton.ac.uk)

Constructor Summary

Constructors

Constructor and Description
MatlibMatrixUtils()

Method Summary

Modifier and Type	Method and Description
static boolean	any(Vector v)
static <T extends Matrix> T	appendColumn(T m, Vector col)
static <T extends Matrix> T	appendRow(T m, Vector row)
static com.jmatio.types.MLDouble	asMatlab(Matrix m)
static <T extends Matrix> T	copy(T sparseMatrix) Copy a matrix
static Matrix	dotProduct(Matrix X, Matrix W) Compute the dot product X.W
static Matrix	dotProductTranspose(Matrix A, Matrix B) Compute Y = A .
static <T extends Matrix> T	dotProductTranspose(Matrix A, Matrix B, T Y) Y = A .
static Matrix	dotProductTransposeTranspose(Matrix A, Matrix B) Perform: A.T.dot(B.T) without performing the transpose.
static Matrix	fromCF(gov.sandia.cognition.math.matrix.Matrix init) Create a Matrix from the Cognitive Foundry equivalent
static Matrix	fromJama(Jama.Matrix sol)
static Matrix	fromMatlab(com.jmatio.types.MLArray mlArray) Create a Matrix from a matlab MLArray
static Vector	lessThan(Vector v, double d)
static double	max(Matrix mat) uses the second value returned by minmaxmean(Matrix)
static SparseMatrix	maxInplace(SparseMatrix A, SparseMatrix B)
static double	mean(Matrix mat) uses the third value returned by minmaxmean(Matrix)
static double	min(Matrix mat) uses the first value returned by minmaxmean(Matrix)
static SparseMatrix	minInplace(SparseMatrix A, SparseMatrix B)
static double[]	minmaxmean(Matrix mat) Calculate all 3, used by min(Matrix), max(Matrix) and mean(Matrix)
static <T extends Matrix> T	minus(double v, T l)
static Matrix	minus(Matrix A, Matrix B) Subtract matrices A-B
static <T extends Matrix> T	minus(T l, double v)
static Vector	minus(Vector l, Vector v)
static <T extends Matrix> T	minusInplace(DiagonalMatrix D, T A) Subtract two matrices, storing the result in the second: A = D - A
static Matrix	minusInplace(Matrix A, Matrix B) Subtract two matrices, storing the result in the first: A = A - B
static <T extends Vector> T	minusInplace(T A, Vector D) Subtract a vector from another vector A = A - D
static double	norm2(Vector row) Compute the 2-norm (Euclidean norm) of the vector
static double	normF(Matrix A) Compute the Frobenius norm
static <T extends Matrix> T	plusInplace(DiagonalMatrix D, T A) Add two matrices, storing the results in the second: A = D + A
static SparseMatrix	plusInplace(SparseMatrix A, Matrix B) Add two matrices, storing the results in the first: A = A + B
static <T extends Matrix> T	plusInplace(T A, double d) Add a constant inplace A = A + d
static <T extends Matrix> T	plusInplace(T A, Matrix B) Add two matrices, storing the results in the first: A = A + B
static <T extends Vector> T	plusInplace(T A, Vector D) Subtract a vector from another vector A = A + D
static <T extends Matrix> T	powInplace(T matrix, double d) Raise each element to the power d, operating on the matrix itself
static <T extends Matrix> T	scaleInplace(T A, double s) A = A .
static void	setSubMatrix(Matrix to, int row, int col, Matrix from) Set a submatrix of a larger matrix
static void	setSubMatrixCol(Matrix to, int row, int col, Vector v) Starting from a given row of a column, set the values of a matrix to the values of v
static void	setSubMatrixRow(Matrix to, int row, int col, Vector v) Starting from a given column of a row, set the values of a matrix to the values of v
static void	setSubVector(Vector to, int startindex, Vector from)
static SparseDoubleArray	sparseVectorToSparseArray(SparseVector row) Convert a SparseVector to a SparseDoubleArray.
static double	sparsity(Matrix mat) Compute the matrix sparsity (i.e.
static <T extends Matrix> T	subMatrix(T mat, int rowstart, int rowend, int colstart, int colend) Extract the submatrix of the same type of mat
static <T extends Matrix> T	subMatrix(T mat, int rowstart, int rowend, gnu.trove.list.array.TIntArrayList cols)
static <T extends Matrix> T	subMatrix(T mat, gnu.trove.list.array.TIntArrayList rows, int colstart, int colend)
static <T extends Matrix> T	subMatrix(T mat, gnu.trove.TIntCollection rows, gnu.trove.TIntCollection cols) Extract a submatrix from the given rows and cols
static double	sum(DiagonalMatrix d) Sum the diagonal of the given matrix
static SparseMatrix	threshold(SparseMatrix data, double thresh) Set values below the given threshold to zero in the output matrix.
static SparseMatrix	times(DiagonalMatrix D, SparseMatrix A) Left multiply two matrices: R = D .
static SparseMatrix	times(SparseMatrix A, DiagonalMatrix D) Right multiply two matrices: R = A .
static Matrix	timesInplace(Matrix A, Matrix B)
static SparseMatrix	timesInplace(SparseMatrix A, SparseMatrix B)
static Jama.Matrix	toColJama(Vector vector)
static Jama.Matrix	toJama(Matrix laplacian)
static no.uib.cipr.matrix.Matrix	toMTJ(Matrix sol)
static Jama.Matrix	toRowJama(Vector vector)
static <T extends Matrix> T	transpose(T mat) Transpose a matrix, returning a new matrix.
static Matrix	transposeDotProduct(Matrix A, Matrix B) Compute A^T .
static Matrix	vstack(Matrix... matricies) Stack matrices vertically

Methods inherited from class java.lang.Object

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

Constructor Detail

MatlibMatrixUtils

public MatlibMatrixUtils()

Method Detail

sparsity

public static double sparsity(Matrix mat)

Compute the matrix sparsity (i.e. proportion of zero elements)

Parameters:

mat - the matrix

Returns:

the sparsity

See Also:

Matrix.density()

powInplace

public static <T extends Matrix> T powInplace(T matrix,
                                              double d)

Raise each element to the power d, operating on the matrix itself

Parameters:

matrix - the matrix

d - the power

Returns:

the input matrix, with elements raised to the given power

times

public static SparseMatrix times(DiagonalMatrix D,
                                 SparseMatrix A)

Left multiply two matrices: R = D . A

Parameters:

D - first matrix

A - second matrix

Returns:

result of multiplication

times

public static SparseMatrix times(SparseMatrix A,
                                 DiagonalMatrix D)

Right multiply two matrices: R = A . D

Parameters:

D - first matrix

A - second matrix

Returns:

result of multiplication

plusInplace

public static SparseMatrix plusInplace(SparseMatrix A,
                                       Matrix B)

Add two matrices, storing the results in the first: A = A + B

Parameters:

A - first matrix

B - matrix to add

Returns:

A first matrix

plusInplace

public static <T extends Matrix> T plusInplace(T A,
                                               Matrix B)

Add two matrices, storing the results in the first: A = A + B

Parameters:

A - first matrix

B - matrix to add

Returns:

A first matrix

plusInplace

public static <T extends Matrix> T plusInplace(T A,
                                               double d)

Add a constant inplace A = A + d

Parameters:

A - first matrix

d - the constant to add

Returns:

A first matrix

minusInplace

public static <T extends Matrix> T minusInplace(DiagonalMatrix D,
                                                T A)

Subtract two matrices, storing the result in the second: A = D - A

Parameters:

D - first matrix

A - second matrix

Returns:

second matrix

minusInplace

public static Matrix minusInplace(Matrix A,
                                  Matrix B)

Subtract two matrices, storing the result in the first: A = A - B

Parameters:

A - first matrix

B - second matrix

Returns:

first matrix

minusInplace

public static <T extends Vector> T minusInplace(T A,
                                                Vector D)

Subtract a vector from another vector A = A - D

Parameters:

A - first matrix

D - second matrix

Returns:

second matrix

plusInplace

public static <T extends Vector> T plusInplace(T A,
                                               Vector D)

Subtract a vector from another vector A = A + D

Parameters:

A - first matrix

D - second matrix

Returns:

second matrix

plusInplace

public static <T extends Matrix> T plusInplace(DiagonalMatrix D,
                                               T A)

Add two matrices, storing the results in the second: A = D + A

Parameters:

D - first matrix

A - second matrix

Returns:

second matrix

transposeDotProduct

public static Matrix transposeDotProduct(Matrix A,
                                         Matrix B)

Compute A^T . B

Parameters:

A -

B -

Returns:

A^T . B

dotProductTranspose

public static Matrix dotProductTranspose(Matrix A,
                                         Matrix B)

Compute Y = A . B^T

Parameters:

A -

B -

Returns:

Y

dotProductTransposeTranspose

public static Matrix dotProductTransposeTranspose(Matrix A,
                                                  Matrix B)

Perform: A.T.dot(B.T) without performing the transpose. This is fine for dense matricies but is very inefficient for sparse matrices, consider performing the transpose manually.

Parameters:

A -

B -

Returns:

A.T.dot(B.T)

dotProductTranspose

public static <T extends Matrix> T dotProductTranspose(Matrix A,
                                                       Matrix B,
                                                       T Y)

Y = A . Bt

Parameters:

A -

B -

Y -

Returns:

Y

scaleInplace

public static <T extends Matrix> T scaleInplace(T A,
                                                double s)

A = A . s

Parameters:

A -

s -

Returns:

A

toJama

public static Jama.Matrix toJama(Matrix laplacian)

Parameters:

laplacian -

Returns:

returns a dense jama matrix

toColJama

public static Jama.Matrix toColJama(Vector vector)

Parameters:

vector -

Returns:

the vector as a column in a matrix

toRowJama

public static Jama.Matrix toRowJama(Vector vector)

Parameters:

vector -

Returns:

the vector as a row in a matrix

fromJama

public static Matrix fromJama(Jama.Matrix sol)

Parameters:

sol -

Returns:

Dense matrix from a Matrix

toMTJ

public static no.uib.cipr.matrix.Matrix toMTJ(Matrix sol)

Parameters:

sol -

Returns:

Dense matrix from a Matrix

subMatrix

public static <T extends Matrix> T subMatrix(T mat,
                                             int rowstart,
                                             int rowend,
                                             int colstart,
                                             int colend)

Extract the submatrix of the same type of mat

Parameters:

mat -

rowstart -

rowend -

colstart -

colend -

Returns:

new instance

subMatrix

public static <T extends Matrix> T subMatrix(T mat,
                                             gnu.trove.TIntCollection rows,
                                             gnu.trove.TIntCollection cols)

Extract a submatrix from the given rows and cols

Parameters:

mat - the matrix to extract from

rows - the rows to extract

cols - the columns to extract

Returns:

the extracted matrix

subMatrix

public static <T extends Matrix> T subMatrix(T mat,
                                             gnu.trove.list.array.TIntArrayList rows,
                                             int colstart,
                                             int colend)

Parameters:

mat -

rows -

colstart -

colend -

Returns:

the submatrix

subMatrix

public static <T extends Matrix> T subMatrix(T mat,
                                             int rowstart,
                                             int rowend,
                                             gnu.trove.list.array.TIntArrayList cols)

Parameters:

mat -

rowstart -

rowend -

cols -

Returns:

the submatrix

asMatlab

public static com.jmatio.types.MLDouble asMatlab(Matrix m)

Parameters:

m -

Returns:

a MLDouble for matlab

minmaxmean

public static double[] minmaxmean(Matrix mat)

Calculate all 3, used by min(Matrix), max(Matrix) and mean(Matrix)

Parameters:

mat -

Returns:

the min, max and mean of the provided matrix

min

public static double min(Matrix mat)

uses the first value returned by minmaxmean(Matrix)

Parameters:

mat -

Returns:

the min

max

public static double max(Matrix mat)

uses the second value returned by minmaxmean(Matrix)

Parameters:

mat -

Returns:

the min

mean

public static double mean(Matrix mat)

uses the third value returned by minmaxmean(Matrix)

Parameters:

mat -

Returns:

the min

minus

public static <T extends Matrix> T minus(T l,
                                         double v)

Parameters:

l -

v -

Returns:

performs l - v returning a matrix of type T

minus

public static Vector minus(Vector l,
                           Vector v)

Parameters:

l -

v -

Returns:

performs l - v returning a matrix of type T

minus

public static <T extends Matrix> T minus(double v,
                                         T l)

Parameters:

v -

l -

Returns:

performs v - l returning a matrix of type T

fromMatlab

public static Matrix fromMatlab(com.jmatio.types.MLArray mlArray)

Create a Matrix from a matlab MLArray

Parameters:

mlArray - the matlab array

Returns:

the matrix

sum

public static double sum(DiagonalMatrix d)

Sum the diagonal of the given matrix

Parameters:

d - the matrix

Returns:

the sum along the diagonal

setSubMatrix

public static void setSubMatrix(Matrix to,
                                int row,
                                int col,
                                Matrix from)

Set a submatrix of a larger matrix

Parameters:

to - the matrix to write into

row - the row to start inserting from

col - the column to start inserting from

from - the matrix to insert

transpose

public static <T extends Matrix> T transpose(T mat)

Transpose a matrix, returning a new matrix.

Parameters:

mat - the matrix to transpose

Returns:

the transposed matrix

maxInplace

public static SparseMatrix maxInplace(SparseMatrix A,
                                      SparseMatrix B)

Parameters:

A -

B -

Returns:

A = MAX(A,B)

minInplace

public static SparseMatrix minInplace(SparseMatrix A,
                                      SparseMatrix B)

Parameters:

A -

B -

Returns:

A = MIN(A,B)

timesInplace

public static SparseMatrix timesInplace(SparseMatrix A,
                                        SparseMatrix B)

Parameters:

A -

B -

Returns:

A = A.*B

timesInplace

public static Matrix timesInplace(Matrix A,
                                  Matrix B)

Parameters:

A -

B -

Returns:

A = A.*B

copy

public static <T extends Matrix> T copy(T sparseMatrix)

Copy a matrix

Parameters:

sparseMatrix - the matrix to copy

Returns:

the copy

threshold

public static SparseMatrix threshold(SparseMatrix data,
                                     double thresh)

Set values below the given threshold to zero in the output matrix.

Parameters:

data - the input matrix

thresh - the threshold

Returns:

a new matrix with values in the input matrix set to zero.

sparseVectorToSparseArray

public static SparseDoubleArray sparseVectorToSparseArray(SparseVector row)

Convert a SparseVector to a SparseDoubleArray.

Parameters:

row - the vector to convert

Returns:

the converted vector

setSubVector

public static void setSubVector(Vector to,
                                int startindex,
                                Vector from)

Parameters:

to - add items to this

startindex - starting index in to

from - add items from this

setSubMatrixRow

public static void setSubMatrixRow(Matrix to,
                                   int row,
                                   int col,
                                   Vector v)

Starting from a given column of a row, set the values of a matrix to the values of v

Parameters:

to -

row -

col -

v -

setSubMatrixCol

public static void setSubMatrixCol(Matrix to,
                                   int row,
                                   int col,
                                   Vector v)

Starting from a given row of a column, set the values of a matrix to the values of v

Parameters:

to -

row -

col -

v -

lessThan

public static Vector lessThan(Vector v,
                              double d)

Parameters:

v - the value vector

d - the check value

Returns:

for each item in the vector, returns 1 if the value is less than the check value

any

public static boolean any(Vector v)

Parameters:

v -

Returns:

any values of the vector are not-zero

appendColumn

public static <T extends Matrix> T appendColumn(T m,
                                                Vector col)

Parameters:

m -

col -

Returns:

m with the added column

appendRow

public static <T extends Matrix> T appendRow(T m,
                                             Vector row)

Parameters:

m -

row -

Returns:

m with the added column

fromCF

public static Matrix fromCF(gov.sandia.cognition.math.matrix.Matrix init)

Create a Matrix from the Cognitive Foundry equivalent

Parameters:

init - the matrix

Returns:

the converted matrix

dotProduct

public static Matrix dotProduct(Matrix X,
                                Matrix W)

Compute the dot product X.W

Parameters:

X -

W -

Returns:

dot product

norm2

public static double norm2(Vector row)

Compute the 2-norm (Euclidean norm) of the vector

Parameters:

row - the vector

Returns:

the Euclidean norm

minus

public static Matrix minus(Matrix A,
                           Matrix B)

Subtract matrices A-B

Parameters:

A -

B -

Returns:

A-B

normF

public static double normF(Matrix A)

Compute the Frobenius norm

Parameters:

A - the matrix

Returns:

the F norm

vstack

public static Matrix vstack(Matrix... matricies)

Stack matrices vertically

Parameters:

matricies - matrices to stack

Returns:

matrix created from the stacking