Uses of Class
weka.core.matrix.Matrix

Packages that use Matrix

Package	Description
weka.classifiers.evaluation
weka.core
weka.core.matrix
weka.estimators

Uses of Matrix in weka.classifiers.evaluation

Subclasses of Matrix in weka.classifiers.evaluation

Modifier and Type	Class	Description
class	ConfusionMatrix	Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.

Uses of Matrix in weka.core

Methods in weka.core with parameters of type Matrix

Modifier and Type	Method	Description
static double[]	Optimization.solveTriangle(Matrix t, double[] b, boolean isLower, boolean[] isZero)	Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity

Uses of Matrix in weka.core.matrix

Methods in weka.core.matrix that return Matrix

Modifier and Type	Method	Description
Matrix	Matrix.arrayLeftDivide(Matrix B)	Element-by-element left division, C = A.\B
Matrix	Matrix.arrayLeftDivideEquals(Matrix B)	Element-by-element left division in place, A = A.\B
Matrix	Matrix.arrayRightDivide(Matrix B)	Element-by-element right division, C = A./B
Matrix	Matrix.arrayRightDivideEquals(Matrix B)	Element-by-element right division in place, A = A./B
Matrix	Matrix.arrayTimes(Matrix B)	Element-by-element multiplication, C = A.*B
Matrix	Matrix.arrayTimesEquals(Matrix B)	Element-by-element multiplication in place, A = A.*B
static Matrix	Matrix.constructWithCopy(double[][] A)	Construct a matrix from a copy of a 2-D array.
Matrix	Matrix.copy()	Make a deep copy of a matrix
Matrix	EigenvalueDecomposition.getD()	Return the block diagonal eigenvalue matrix
Matrix	QRDecomposition.getH()	Return the Householder vectors
Matrix	CholeskyDecomposition.getL()	Return triangular factor.
Matrix	LUDecomposition.getL()	Return lower triangular factor
Matrix	Matrix.getMatrix(int[] r, int[] c)	Get a submatrix.
Matrix	Matrix.getMatrix(int[] r, int j0, int j1)	Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int[] c)	Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int j0, int j1)	Get a submatrix.
Matrix	QRDecomposition.getQ()	Generate and return the (economy-sized) orthogonal factor
Matrix	QRDecomposition.getR()	Return the upper triangular factor
Matrix	SingularValueDecomposition.getS()	Return the diagonal matrix of singular values
Matrix	LUDecomposition.getU()	Return upper triangular factor
Matrix	SingularValueDecomposition.getU()	Return the left singular vectors
Matrix	EigenvalueDecomposition.getV()	Return the eigenvector matrix
Matrix	SingularValueDecomposition.getV()	Return the right singular vectors
static Matrix	Matrix.identity(int m, int n)	Generate identity matrix
Matrix	Matrix.inverse()	Matrix inverse or pseudoinverse
Matrix	Matrix.minus(Matrix B)	C = A - B
Matrix	Matrix.minusEquals(Matrix B)	A = A - B
static Matrix	Matrix.parseMatlab(String matlab)	creates a matrix from the given Matlab string.
Matrix	Matrix.plus(Matrix B)	C = A + B
Matrix	Matrix.plusEquals(Matrix B)	A = A + B
static Matrix	Matrix.random(int m, int n)	Generate matrix with random elements
static Matrix	Matrix.read(BufferedReader input)	Read a matrix from a stream.
Matrix	CholeskyDecomposition.solve(Matrix B)	Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B)	Solve A*X = B
Matrix	Matrix.solve(Matrix B)	Solve A*X = B
Matrix	QRDecomposition.solve(Matrix B)	Least squares solution of A*X = B
Matrix	Matrix.solveTranspose(Matrix B)	Solve X*A = B, which is also A'*X' = B'
Matrix	Matrix.sqrt()	returns the square root of the matrix, i.e., X from the equation X*X = A. Steps in the Calculation (see sqrtm in Matlab): perform eigenvalue decomposition [V,D]=eig(A) take the square root of all elements in D (only the ones with positive sign are considered for further computation) S=sqrt(D) calculate the root X=V*S/V, which can be also written as X=(V'\(V*S)')'
Matrix	Matrix.times(double s)	Multiply a matrix by a scalar, C = s*A
Matrix	Matrix.times(Matrix B)	Linear algebraic matrix multiplication, A * B
Matrix	Matrix.timesEquals(double s)	Multiply a matrix by a scalar in place, A = s*A
Matrix	Matrix.transpose()	Matrix transpose.
Matrix	Matrix.uminus()	Unary minus

Methods in weka.core.matrix with parameters of type Matrix

Modifier and Type	Method	Description
Matrix	Matrix.arrayLeftDivide(Matrix B)	Element-by-element left division, C = A.\B
Matrix	Matrix.arrayLeftDivideEquals(Matrix B)	Element-by-element left division in place, A = A.\B
Matrix	Matrix.arrayRightDivide(Matrix B)	Element-by-element right division, C = A./B
Matrix	Matrix.arrayRightDivideEquals(Matrix B)	Element-by-element right division in place, A = A./B
Matrix	Matrix.arrayTimes(Matrix B)	Element-by-element multiplication, C = A.*B
Matrix	Matrix.arrayTimesEquals(Matrix B)	Element-by-element multiplication in place, A = A.*B
Matrix	Matrix.minus(Matrix B)	C = A - B
Matrix	Matrix.minusEquals(Matrix B)	A = A - B
Matrix	Matrix.plus(Matrix B)	C = A + B
Matrix	Matrix.plusEquals(Matrix B)	A = A + B
LinearRegression	Matrix.regression(Matrix y, double ridge)	Performs a (ridged) linear regression.
LinearRegression	Matrix.regression(Matrix y, double[] w, double ridge)	Performs a weighted (ridged) linear regression.
void	Matrix.setMatrix(int[] r, int[] c, Matrix X)	Set a submatrix.
void	Matrix.setMatrix(int[] r, int j0, int j1, Matrix X)	Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int[] c, Matrix X)	Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int j0, int j1, Matrix X)	Set a submatrix.
Matrix	CholeskyDecomposition.solve(Matrix B)	Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B)	Solve A*X = B
Matrix	Matrix.solve(Matrix B)	Solve A*X = B
Matrix	QRDecomposition.solve(Matrix B)	Least squares solution of A*X = B
Matrix	Matrix.solveTranspose(Matrix B)	Solve X*A = B, which is also A'*X' = B'
Matrix	Matrix.times(Matrix B)	Linear algebraic matrix multiplication, A * B

Constructors in weka.core.matrix with parameters of type Matrix

Constructor	Description
CholeskyDecomposition(Matrix Arg)	Cholesky algorithm for symmetric and positive definite matrix.
EigenvalueDecomposition(Matrix Arg)	Check for symmetry, then construct the eigenvalue decomposition
LinearRegression(Matrix a, Matrix y, double ridge)	Performs a (ridged) linear regression.
LinearRegression(Matrix a, Matrix y, double[] w, double ridge)	Performs a weighted (ridged) linear regression.
LUDecomposition(Matrix A)	LU Decomposition
QRDecomposition(Matrix A)	QR Decomposition, computed by Householder reflections.
SingularValueDecomposition(Matrix Arg)	Construct the singular value decomposition

Uses of Matrix in weka.estimators

Constructors in weka.estimators with parameters of type Matrix

Constructor	Description
MahalanobisEstimator(Matrix covariance, double constDelta, double valueMean)	Constructor