Uses of Class
weka.classifiers.functions.pace.PaceMatrix

Packages that use PaceMatrix

weka.classifiers.functions.pace

Uses of PaceMatrix in weka.classifiers.functions.pace

Methods in weka.classifiers.functions.pace that return PaceMatrix

PaceMatrix	NormalMixture.fittingIntervals(DoubleVector data) Contructs the set of fitting intervals for mixture estimation.
PaceMatrix	NormalMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
PaceMatrix	PaceMatrix.rbind(PaceMatrix b) Returns a new matrix which binds two matrices together with rows.
PaceMatrix	PaceMatrix.cbind(PaceMatrix b) Returns a new matrix which binds two matrices with columns.
PaceMatrix	ChisqMixture.fittingIntervals(DoubleVector data) Contructs the set of fitting intervals for mixture estimation.
PaceMatrix	ChisqMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
abstract PaceMatrix	MixtureDistribution.fittingIntervals(DoubleVector data) Contructs the set of fitting intervals for mixture estimation.
abstract PaceMatrix	MixtureDistribution.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
PaceMatrix	MixtureDistribution.empiricalProbability(DoubleVector data, PaceMatrix intervals) Computes the empirical probabilities of the data over a set of intervals.

Methods in weka.classifiers.functions.pace with parameters of type PaceMatrix

PaceMatrix	NormalMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
double	PaceMatrix.times(int i, int j0, int j1, PaceMatrix B, int l) Multiplication between a row (or part of a row) of the first matrix and a column (or part or a column) of the second matrix.
void	PaceMatrix.h2(int j, int k, double q, PaceMatrix b, int l) Performs single Householder transformation on one column of a matrix
void	PaceMatrix.forward(PaceMatrix b, IntVector pvt, int k0) Forward ordering of columns in terms of response explanation.
int	PaceMatrix.mostExplainingColumn(PaceMatrix b, IntVector pvt, int ks) Returns the index of the column that has the largest (squared) response, when each of columns pvt[ks:] is moved to become the ks-th column.
void	PaceMatrix.backward(PaceMatrix b, IntVector pvt, int ks, int k0) Backward ordering of columns in terms of response explanation.
int	PaceMatrix.leastExplainingColumn(PaceMatrix b, IntVector pvt, int ks, int k0) Returns the index of the column that has the smallest (squared) response, when the column is moved to become the (ks-1)-th column.
double	PaceMatrix.columnResponseExplanation(PaceMatrix b, IntVector pvt, int j, int ks) Returns the squared ks-th response value if the j-th column becomes the ks-th after orthogonal transformation.
void	PaceMatrix.lsqr(PaceMatrix b, IntVector pvt, int k0) QR transformation for a least squares problem A x = b
void	PaceMatrix.lsqrSelection(PaceMatrix b, IntVector pvt, int k0) QR transformation for a least squares problem A x = b
void	PaceMatrix.positiveDiagonal(PaceMatrix Y, IntVector pvt) Sets all diagonal elements to be positive (or nonnegative) without changing the least squares solution
void	PaceMatrix.steplsqr(PaceMatrix b, IntVector pvt, int ks, int j, boolean adjoin) Stepwise least squares QR-decomposition of the problem A x = b
void	PaceMatrix.rsolve(PaceMatrix b, IntVector pvt, int kp) Solves upper-triangular equation R x = b
PaceMatrix	PaceMatrix.rbind(PaceMatrix b) Returns a new matrix which binds two matrices together with rows.
PaceMatrix	PaceMatrix.cbind(PaceMatrix b) Returns a new matrix which binds two matrices with columns.
DoubleVector	PaceMatrix.nnls(PaceMatrix b, IntVector pvt) Solves the nonnegative linear squares problem.
DoubleVector	PaceMatrix.nnlse(PaceMatrix b, PaceMatrix c, PaceMatrix d, IntVector pvt) Solves the nonnegative least squares problem with equality constraint.
DoubleVector	PaceMatrix.nnlse1(PaceMatrix b, IntVector pvt) Solves the nonnegative least squares problem with equality constraint.
PaceMatrix	ChisqMixture.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
abstract PaceMatrix	MixtureDistribution.probabilityMatrix(DoubleVector s, PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
PaceMatrix	MixtureDistribution.empiricalProbability(DoubleVector data, PaceMatrix intervals) Computes the empirical probabilities of the data over a set of intervals.