Uses of Class
edu.cmu.tetrad.util.Matrix

Packages that use Matrix

Package	Description
edu.cmu.tetrad.cluster
edu.cmu.tetrad.data
edu.cmu.tetrad.regression
edu.cmu.tetrad.search
edu.cmu.tetrad.search.kernel
edu.cmu.tetrad.sem
edu.cmu.tetrad.util

Uses of Matrix in edu.cmu.tetrad.cluster

Methods in edu.cmu.tetrad.cluster with parameters of type Matrix

Modifier and Type	Method	Description
void	ClusteringAlgorithm.cluster(Matrix data)	Clusters the given data set.
void	KMeans.cluster(Matrix data)	Runs the batch K-means clustering algorithm on the data, returning a result.

Uses of Matrix in edu.cmu.tetrad.data

Methods in edu.cmu.tetrad.data that return Matrix

Modifier and Type	Method	Description
static Matrix	DataUtils.centerData(Matrix data)
static Matrix	DataUtils.concatenate(Matrix... dataSets)
static Matrix	DataUtils.cov(Matrix data)
static Matrix	DataUtils.getBootstrapSample(Matrix data, int sampleSize)
Matrix	BoxDataSet.getCorrelationMatrix()
Matrix	DataSet.getCorrelationMatrix()	If this is a continuous data set, returns the correlation matrix.
Matrix	NumberObjectDataSet.getCorrelationMatrix()
Matrix	BoxDataSet.getCovarianceMatrix()
Matrix	DataSet.getCovarianceMatrix()	If this is a continuous data set, returns the covariance matrix.
Matrix	NumberObjectDataSet.getCovarianceMatrix()
Matrix	TimeSeriesData.getData()
Matrix	BoxDataSet.getDoubleData()
Matrix	DataSet.getDoubleData()
Matrix	NumberObjectDataSet.getDoubleData()
final Matrix	CorrelationMatrixOnTheFly.getMatrix()
final Matrix	CovarianceMatrix.getMatrix()
final Matrix	CovarianceMatrixOnTheFly.getMatrix()
final Matrix	CovarianceMatrixOnTheFly.getMatrix(int[] rows)
Matrix	ICovarianceMatrix.getMatrix()
Matrix	CorrelationMatrix.getSelection(int[] rows, int[] cols)
Matrix	CorrelationMatrixOnTheFly.getSelection(int[] rows, int[] cols)
Matrix	CovarianceMatrix.getSelection(int[] rows, int[] cols)
Matrix	CovarianceMatrixOnTheFly.getSelection(int[] rows, int[] cols)
Matrix	CovarianceMatrixOnTheFly.getSelection(int[] rows, int[] cols, int[] dataRows)
Matrix	ICovarianceMatrix.getSelection(int[] rows, int[] cols)
static Matrix	DataUtils.standardizeData(Matrix data)
static Matrix	DataUtils.subMatrix(ICovarianceMatrix m, Node x, Node y, List<Node> z)
static Matrix	DataUtils.subMatrix(ICovarianceMatrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)
static Matrix	DataUtils.subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)
static Matrix	DataUtils.subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)

Methods in edu.cmu.tetrad.data with parameters of type Matrix

Modifier and Type	Method	Description
static Matrix	DataUtils.centerData(Matrix data)
static Matrix	DataUtils.concatenate(Matrix... dataSets)
static boolean	DataUtils.containsMissingValue(Matrix data)
static Matrix	DataUtils.cov(Matrix data)
static Matrix	DataUtils.getBootstrapSample(Matrix data, int sampleSize)
static Vector	DataUtils.mean(Matrix data)
static Vector	DataUtils.means(Matrix data)
void	CorrelationMatrix.setMatrix(Matrix matrix)
void	CorrelationMatrixOnTheFly.setMatrix(Matrix matrix)
void	CovarianceMatrix.setMatrix(Matrix matrix)
void	CovarianceMatrixOnTheFly.setMatrix(Matrix matrix)
void	ICovarianceMatrix.setMatrix(Matrix matrix)
static Matrix	DataUtils.standardizeData(Matrix data)
static Matrix	DataUtils.subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)
static Matrix	DataUtils.subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)

Constructors in edu.cmu.tetrad.data with parameters of type Matrix

Modifier	Constructor	Description
	CorrelationMatrix(List<Node> variables, Matrix matrix, int sampleSize)	Constructs a correlation matrix data set using the given information.
	CovarianceMatrix(List<Node> variables, Matrix matrix, int sampleSize)	Protected constructor to construct a new covariance matrix using the supplied continuous variables and the the given symmetric, positive definite matrix and sample size.
	TimeSeriesData(Matrix matrix, List<String> varNames)	Constructs a new time series data contains for the given row-major data array and the given list of variables.

Uses of Matrix in edu.cmu.tetrad.regression

Constructors in edu.cmu.tetrad.regression with parameters of type Matrix

Modifier	Constructor	Description
	RegressionDataset(Matrix data, List<Node> variables)

Uses of Matrix in edu.cmu.tetrad.search

Methods in edu.cmu.tetrad.search that return Matrix

Modifier and Type	Method	Description
static @NotNull Matrix	SemBicScore.bStar(Matrix b)
static @NotNull Matrix	ZhangShenBoundTest.bStar(Matrix b)
Matrix	FastIca.IcaResult.getK()
Matrix	PermutationMatrixPair.getMatrixW()
Matrix	FactorAnalysis.getResidual()
Matrix	FastIca.IcaResult.getS()
Matrix	FastIca.IcaResult.getW()
Matrix	FastIca.getWInit()	Initial un-mixing matrix of dimension (n.comp,n.comp).
Matrix	FastIca.IcaResult.getX()
Matrix	Ling.pruneEdgesByResampling(Matrix data)	This is the method used in Patrik's code.
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)
Matrix	FactorAnalysis.successiveResidual()	Successive method with residual matrix.

Methods in edu.cmu.tetrad.search that return types with arguments of type Matrix

Modifier and Type	Method	Description
List<Matrix>	IndTestScore.getCovMatrices()
List<Matrix>	Kci.getCovMatrices()

Methods in edu.cmu.tetrad.search with parameters of type Matrix

Modifier and Type	Method	Description
static boolean	TimeSeriesUtils.allEigenvaluesAreSmallerThanOneInModulus(Matrix mat)
static @NotNull Matrix	SemBicScore.bStar(Matrix b)
static @NotNull Matrix	ZhangShenBoundTest.bStar(Matrix b)
double	IndTestHsic.empiricalHSIC(Matrix Ky, Matrix Kx, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y
double	IndTestHsic.empiricalHSICincompleteCholesky(Matrix Gy, Matrix Gx, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y using incomplete Cholesky decomposition to approximate Gram matrices
double	IndTestHsic.empiricalHSICincompleteCholesky(Matrix Gy, Matrix Gx, Matrix Gz, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y given Z using incomplete Cholesky decomposition to approximate Gram matrices
static double	SemBicScore.getVarRy(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, boolean calculateRowSubsets)
static double	ZhangShenBoundTest.getVarRy(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, boolean calculateRowSubsets, boolean calculateSquareEuclideanNorms)
double	Ling.ngFullData(int rowIndex, Matrix data, Matrix W)
Matrix	Ling.pruneEdgesByResampling(Matrix data)	This is the method used in Patrik's code.
double	Lofs2.scoreRow(int rowIndex, Matrix data, List<List<Integer>> rows, List<List<Double>> parameters)
void	FastIca.setWInit(Matrix wInit)	Initial un-mixing matrix of dimension (n.comp,n.comp).
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)

Constructors in edu.cmu.tetrad.search with parameters of type Matrix

Modifier	Constructor	Description
	FastIca(Matrix X, int numComponents)	Constructs an instance of the Fast ICA algorithm, taking as arguments the two arguments that cannot be defaulted: the data matrix itself and the number of components to be extracted.
	IcaResult(Matrix X, Matrix K, Matrix W, Matrix S)
	IndTestFisherZ(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.
	IndTestFisherZRecursive(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.
	IndTestHsic(Matrix data, List<Node> variables, double alpha)
	PermutationMatrixPair(List<Integer> permutation, Matrix matrixW)
	RecursivePartialCorrelation(List<Node> nodes, Matrix cov, int sampleSize)

Uses of Matrix in edu.cmu.tetrad.search.kernel

Methods in edu.cmu.tetrad.search.kernel that return Matrix

Modifier and Type	Method	Description
static Matrix	KernelUtils.constructCentralizedGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs the centralized Gram matrix for a given vector valued sample.
static Matrix	KernelUtils.constructGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs Gram matrix for a given vector valued sample.
static Matrix	KernelUtils.constructH(int m)	Constructs the projection matrix on 1/m
static Matrix	KernelUtils.incompleteCholeskyGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes, double precision)	Approximates Gram matrix using incomplete Cholesky factorization

Uses of Matrix in edu.cmu.tetrad.sem

Methods in edu.cmu.tetrad.sem that return Matrix

Modifier and Type	Method	Description
Matrix	SemEstimatorGibbs.getDataSet()
Matrix	DagScorer.getEdgeCoef()
Matrix	Scorer.getEdgeCoef()
Matrix	SemIm.getEdgeCoef()
Matrix	SemIm.getErrCovar()
Matrix	DagScorer.getErrorCovar()
Matrix	Scorer.getErrorCovar()
Matrix	ISemIm.getImplCovar(boolean recalculate)
Matrix	SemIm.getImplCovar(boolean recalculate)
Matrix	SemIm.getImplCovar(List<Node> nodes)
Matrix	StandardizedSemIm.getImplCovar()
Matrix	ISemIm.getImplCovarMeas()
Matrix	SemIm.getImplCovarMeas()
Matrix	StandardizedSemIm.getImplCovarMeas()
Matrix	DagScorer.getSampleCovar()
Matrix	Scorer.getSampleCovar()
Matrix	SemIm.getSampleCovar()

Methods in edu.cmu.tetrad.sem with parameters of type Matrix

Modifier and Type	Method	Description
double	ISemIm.getStdDev(Node node, Matrix implCovar)
double	SemIm.getStdDev(Node node, Matrix implCovar)
double	ISemIm.getVariance(Node nodeA, Matrix implCovar)
double	SemIm.getVariance(Node node, Matrix implCovar)
SemIm	SemIm.updatedIm(Matrix covariances, Vector means)

Constructors in edu.cmu.tetrad.sem with parameters of type Matrix

Modifier	Constructor	Description
	Mapping(ISemIm semIm, Parameter parameter, Matrix matrix, int i, int j)	Constructs matrix new mapping using the given freeParameters.
	SemIm(SemPm semPm, List<Node> variableNodes, List<Node> measuredNodes, Matrix edgeCoef, double[] variableMeansStdDev)

Uses of Matrix in edu.cmu.tetrad.util

Methods in edu.cmu.tetrad.util that return Matrix

Modifier and Type	Method	Description
static Matrix	MatrixUtils.cholesky(Matrix covar)
static Matrix	MatrixUtils.convertCovToCorr(Matrix m)	Converts a covariance matrix to a correlation matrix in place; the same matrix is returned for convenience, but m is modified in the process.
Matrix	Matrix.copy()
Matrix	Vector.diag()
Matrix	Matrix.getPart(int i, int j, int k, int l)
Matrix	Matrix.getSelection(int[] rows, int[] cols)
Matrix	Matrix.ginverse()
static Matrix	Matrix.identity(int rows)
static Matrix	TetradAlgebra.identity(int rows)
static Matrix	MatrixUtils.impliedCovar(Matrix edgeCoef, Matrix errCovar)
static Matrix	MatrixUtils.impliedCovar2(Matrix edgeCoef, Matrix errCovar)
Matrix	Matrix.inverse()
Matrix	Matrix.like()
Matrix	Matrix.minus(Matrix mb)
static Matrix	TetradAlgebra.multOuter(Vector v1, Vector v2)
static Matrix	LingUtils.normalizeDiagonal(Matrix matrix)
Matrix	Matrix.plus(Matrix mb)
Matrix	Matrix.scalarMult(double scalar)
static Matrix	Matrix.serializableInstance()	Generates a simple exemplar of this class to test serialization.
static Matrix	TetradAlgebra.solve(Matrix a, Matrix b)
static Matrix	Matrix.sparseMatrix(int m, int n)
Matrix	Matrix.sqrt()
Matrix	Matrix.symmetricInverse()
Matrix	Matrix.times(Matrix m)
Matrix	Matrix.transpose()

Methods in edu.cmu.tetrad.util with parameters of type Matrix

Modifier and Type	Method	Description
void	Matrix.assign(Matrix matrix)
static Matrix	MatrixUtils.cholesky(Matrix covar)
static Matrix	MatrixUtils.convertCovToCorr(Matrix m)	Converts a covariance matrix to a correlation matrix in place; the same matrix is returned for convenience, but m is modified in the process.
boolean	Matrix.equals(Matrix m, double tolerance)
static Matrix	MatrixUtils.impliedCovar(Matrix edgeCoef, Matrix errCovar)
static Matrix	MatrixUtils.impliedCovar2(Matrix edgeCoef, Matrix errCovar)
static boolean	LingUtils.isPositiveDefinite(Matrix matrix)
static boolean	MatrixUtils.isPositiveDefinite(Matrix matrix)	Return true if the given matrix is symmetric positive definite--that is, if it would make a valid covariance matrix.
Matrix	Matrix.minus(Matrix mb)
static Matrix	LingUtils.normalizeDiagonal(Matrix matrix)
static double	StatUtils.partialCorrelation(Matrix submatrix)	Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial correlation one wants is correlation(X, Y | Z1,...,Zn).
static double	StatUtils.partialCorrelation(Matrix covariance, int x, int y, int... z)
static double	StatUtils.partialCorrelationPrecisionMatrix(Matrix submatrix)
static double	StatUtils.partialCovarianceWhittaker(Matrix submatrix)	Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial covariance one wants is covariance(X, Y | Z1,...,Zn).
static double	StatUtils.partialCovarianceWhittaker(Matrix covariance, int x, int y, int... z)
static double	StatUtils.partialStandardDeviation(Matrix covariance, int x, int... z)
static double	StatUtils.partialVariance(Matrix covariance, int x, int... z)
Matrix	Matrix.plus(Matrix mb)
static Vector	MatrixUtils.product(Vector ma, Matrix mb)
static Matrix	TetradAlgebra.solve(Matrix a, Matrix b)
Matrix	Matrix.times(Matrix m)

Constructors in edu.cmu.tetrad.util with parameters of type Matrix

Modifier	Constructor	Description
	Matrix(Matrix m)