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	Contains classes for searching for (mostly structural) causal models given data.
edu.cmu.tetrad.search.score	Contains classes for various various sorts of scores for running score-based algorithms.
edu.cmu.tetrad.search.test	Contains classes for running conditional independence tests for various sorts of data.
edu.cmu.tetrad.search.utils	Contains some utility classes for search algorithms.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.
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	DataTransforms.centerData(Matrix data)
static Matrix	DataTransforms.concatenate(Matrix... dataSets)
static Matrix	DataUtils.cov(Matrix data)
static Matrix	DataTransforms.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	DataTransforms.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	DataTransforms.centerData(Matrix data)
static Matrix	DataTransforms.concatenate(Matrix... dataSets)
static boolean	DataUtils.containsMissingValue(Matrix data)
static Matrix	DataUtils.cov(Matrix data)
static Matrix	DataTransforms.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	DataTransforms.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 Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA)	Estimates the W matrix using FastICA.
Matrix	IcaLingam.fit(DataSet D)	Fits an ICA-LiNGAM model to the given dataset using a default method for estimating W.
Matrix	IcaLingam.fitW(Matrix W)	Searches given a W matrix is that is provided by the user (where WX = e).
Matrix	FastIca.IcaResult.getK()
Matrix	FactorAnalysis.getResidual()	Returns the matrix of residuals.
Matrix	FastIca.IcaResult.getS()
static Matrix	IcaLingD.getScaledBHat(PermutationMatrixPair pair, double bThreshold)	Returns the BHat matrix, permuted to the variable order of the original data and scaled so that the diagonal consists only of 1's.
Matrix	FastIca.IcaResult.getW()
Matrix	FastIca.IcaResult.getX()
static Matrix	IcaLingD.scale(Matrix M)	Scales the given matrix M by diving each entry (i, j) by M(j, j)
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)	Returns the matrix result for the varimax algorithm.
Matrix	FactorAnalysis.successiveResidual()	Successive method with residual matrix.
static Matrix	IcaLingD.threshold(Matrix M, double threshold)	Thresholds the given matrix, sending any small entries in absolute value to zero.

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

Modifier and Type	Method	Description
List<Matrix>	IcaLingD.fit(DataSet D)	Fits a LiNG-D model to the given dataset using a default method for estimating W.
List<Matrix>	IcaLingD.fitW(Matrix W)	Performs the LiNG-D algorithm given a W matrix, which needs to be discovered elsewhere.

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

Modifier and Type	Method	Description
Matrix	IcaLingam.fitW(Matrix W)	Searches given a W matrix is that is provided by the user (where WX = e).
List<Matrix>	IcaLingD.fitW(Matrix W)	Performs the LiNG-D algorithm given a W matrix, which needs to be discovered elsewhere.
static PermutationMatrixPair	IcaLingD.hungarianDiagonal(Matrix W)	Finds a column permutation of the W matrix that maximizes the sum of 1 / |Wii| for diagonal elements Wii in W.
static boolean	IcaLingD.isAcyclic(Matrix scaledBHat)	Determines whether a BHat matrix parses to an acyclic graph.
static boolean	IcaLingD.isStable(Matrix bHat)	Whether the BHat matrix represents a stable model.
static @NotNull Graph	IcaLingD.makeGraph(Matrix B, List<Node> variables)	Returns a graph given a coefficient matrix and a list of variables.
static Matrix	IcaLingD.scale(Matrix M)	Scales the given matrix M by diving each entry (i, j) by M(j, j)
double	Lofs.scoreRow(int rowIndex, Matrix data, List<List<Integer>> rows, List<List<Double>> parameters)
void	FastIca.setWInit(Matrix wInit)	Sets the initial un-mixing matrix of dimension (n.comp,n.comp).
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)	Returns the matrix result for the varimax algorithm.
static Matrix	IcaLingD.threshold(Matrix M, double threshold)	Thresholds the given matrix, sending any small entries in absolute value to zero.

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)

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

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

Modifier and Type	Method	Description
static @NotNull Matrix	SemBicScore.bStar(Matrix b)

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

Modifier and Type	Method	Description
static @NotNull Matrix	SemBicScore.bStar(Matrix b)
static double	SemBicScore.getVarRy(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, boolean calculateRowSubsets)

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

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

Modifier and Type	Method	Description
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

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

Modifier	Constructor	Description
	IndTestFisherZ(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)	Constructs a new HSIC Independence test.

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

Methods in edu.cmu.tetrad.search.utils 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
Matrix	PermutationMatrixPair.getPermutedMatrix()	Returns W, permuted rowwise by the permutation passed in through the constructor.
static Matrix	KernelUtils.incompleteCholeskyGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes, double precision)	Approximates Gram matrix using incomplete Cholesky factorization

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

Modifier and Type	Method	Description
static boolean	TsUtils.allEigenvaluesAreSmallerThanOneInModulus(Matrix mat)

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

Modifier	Constructor	Description
	PartialCorrelation(List<Node> nodes, Matrix cov, int sampleSize)	Constructor.
	PermutationMatrixPair(Matrix M, int[] rowPerm, int[] colPerm)	Constructs with a given matrix M and a row and column permutation (which may be null).

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

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

Modifier and Type	Method	Description
Matrix	Glasso.getSs()
Matrix	Glasso.Result.getWwi()

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

Modifier	Constructor	Description
	Glasso(Matrix cov)
	IndTestFisherZRecursive(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.
	Result(Matrix wwi)

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)
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	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 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)