Uses of Interface
gov.sandia.cognition.math.matrix.Matrix

Packages that use Matrix

Package	Description
gov.sandia.cognition.framework.learning.converter	Provides implementations of CogxelConverters.
gov.sandia.cognition.framework.lite	Provides a lightweight implementation of the Cognitive Framework.
gov.sandia.cognition.learning.algorithm.annealing	Provides the Simulated Annealing algorithm.
gov.sandia.cognition.learning.algorithm.factor.machine	Provides factorization machine algorithms.
gov.sandia.cognition.learning.algorithm.gradient	Provides gradient based learning algorithms.
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.algorithm.minimization	Provides minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.matrix	Provides matrix solving algorithms.
gov.sandia.cognition.learning.algorithm.pca	Provides implementations of Principle Components Analysis (PCA).
gov.sandia.cognition.learning.data	Provides data set utilities for learning.
gov.sandia.cognition.learning.data.feature	Provides data feature extractors.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.learning.function.cost	Provides cost functions.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.math	Provides classes for mathematical computation.
gov.sandia.cognition.math.matrix	Provides interfaces and classes for linear algebra.
gov.sandia.cognition.math.matrix.custom	Provides a custom linear algebra package implementation for both dense and sparse classes.
gov.sandia.cognition.math.matrix.decomposition	Provides matrix decompositions.
gov.sandia.cognition.math.matrix.mtj	Provides a linear algebra package implementation wrapper using the Matrix Toolkits for Java (MTJ) library.
gov.sandia.cognition.math.matrix.mtj.decomposition	Provides matrix decomposition implementations using the Matrix Toolkits for Java (MTJ) library.
gov.sandia.cognition.math.signals	Provides mathematical signal processing methods.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.bayesian.conjugate	Provides Bayesian estimation routines based on conjugate prior distribution of parameters of specific conditional distributions.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.method	Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
gov.sandia.cognition.text.term.relation	Provides relationships between terms.
gov.sandia.cognition.text.topic	Provides topic modeling algorithms.

Uses of Matrix in gov.sandia.cognition.framework.learning.converter

Methods in gov.sandia.cognition.framework.learning.converter that return Matrix

Modifier and Type	Method and Description
Matrix	CogxelMatrixConverter.fromCogxels(CogxelState cogxels) Converts from a CogxelState object to an object of type DataType.

Methods in gov.sandia.cognition.framework.learning.converter with parameters of type Matrix

Modifier and Type	Method and Description
void	CogxelMatrixConverter.toCogxels(Matrix data, CogxelState cogxels) Converts from an object of type DataType to an updated CogxelState.

Uses of Matrix in gov.sandia.cognition.framework.lite

Methods in gov.sandia.cognition.framework.lite with parameters of type Matrix

Modifier and Type	Method and Description
protected void	SimplePatternRecognizer.setMatrix(Matrix matrix) Sets the underlying matrix.

Uses of Matrix in gov.sandia.cognition.learning.algorithm.annealing

Methods in gov.sandia.cognition.learning.algorithm.annealing that return Matrix

Modifier and Type	Method and Description
Matrix	VectorizablePerturber.getCovarianceSqrt() Gets the covariance square root matrix.

Methods in gov.sandia.cognition.learning.algorithm.annealing with parameters of type Matrix

Modifier and Type	Method and Description
void	VectorizablePerturber.setCovariance(Matrix covariance) Sets the covariance of the perturber.
protected void	VectorizablePerturber.setCovarianceSqrt(Matrix covarianceSqrt) Sets the covariance square root matrix.

Constructors in gov.sandia.cognition.learning.algorithm.annealing with parameters of type Matrix

Constructor and Description
VectorizablePerturber(java.util.Random random, Matrix covariance) Creates a new instance of VectorizablePerturber.

Uses of Matrix in gov.sandia.cognition.learning.algorithm.factor.machine

Fields in gov.sandia.cognition.learning.algorithm.factor.machine declared as Matrix

Modifier and Type	Field and Description
protected Matrix	FactorizationMachine.factors The k x d factor matrix (v) with k factors for each dimension.

Methods in gov.sandia.cognition.learning.algorithm.factor.machine that return Matrix

Modifier and Type	Method and Description
Matrix	FactorizationMachine.getFactors() Gets the matrix of factors.

Methods in gov.sandia.cognition.learning.algorithm.factor.machine with parameters of type Matrix

Modifier and Type	Method and Description
void	FactorizationMachine.setFactors(Matrix factors) Sets the matrix of factors.

Constructors in gov.sandia.cognition.learning.algorithm.factor.machine with parameters of type Matrix

Constructor and Description
FactorizationMachine(double bias, Vector weights, Matrix factors) Creates a new FactorizationMachine with the given parameters.

Uses of Matrix in gov.sandia.cognition.learning.algorithm.gradient

Methods in gov.sandia.cognition.learning.algorithm.gradient that return Matrix

Modifier and Type	Method and Description
Matrix	GradientDescendable.computeParameterGradient(Vector input) Computes the derivative of the function about the input with respect to the parameters of the function.
Matrix	GradientDescendableApproximator.computeParameterGradient(Vector input) Computes a forward-differences approximation to the parameter Jacobian
static Matrix	GradientDescendableApproximator.computeParameterGradient(VectorizableVectorFunction function, Vector input, double deltaSize) Computes a forward-differences approximation to the parameter Jacobian

Uses of Matrix in gov.sandia.cognition.learning.algorithm.hmm

Fields in gov.sandia.cognition.learning.algorithm.hmm declared as Matrix

Modifier and Type	Field and Description
protected Matrix	MarkovChain.transitionProbability Transition probability matrix.

Methods in gov.sandia.cognition.learning.algorithm.hmm that return Matrix

Modifier and Type	Method and Description
Matrix	ParallelHiddenMarkovModel.ComputeTransitionsTask.call()
protected Matrix	HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b) Computes the stochastic transition-probability matrix from the given probabilities.
protected Matrix	ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b)
protected static Matrix	HiddenMarkovModel.computeTransitions(Vector alphan, Vector betanp1, Vector bnp1) Computes the stochastic transition-probability matrix from the given probabilities.
protected static Matrix	MarkovChain.createUniformTransitionProbability(int numStates) Creates a uniform transition-probability Matrix
Matrix	MarkovChain.getTransitionProbability() Getter for transitionProbability.
protected Matrix	BaumWelchAlgorithm.updateTransitionMatrix(java.util.ArrayList<Matrix> sequenceTransitionMatrices) Computes an updated transition matrix from the scaled estimates

Methods in gov.sandia.cognition.learning.algorithm.hmm that return types with arguments of type Matrix

Modifier and Type	Method and Description
protected Pair<java.util.ArrayList<java.util.ArrayList<Vector>>,java.util.ArrayList<Matrix>>	BaumWelchAlgorithm.computeSequenceParameters() Computes the gammas and A matrices for each sequence.

Methods in gov.sandia.cognition.learning.algorithm.hmm with parameters of type Matrix

Modifier and Type	Method and Description
protected void	MarkovChain.normalizeTransitionMatrix(Matrix A) Normalizes the transition-probability matrix
protected void	ParallelHiddenMarkovModel.normalizeTransitionMatrix(Matrix A)
protected static void	MarkovChain.normalizeTransitionMatrix(Matrix A, int j) Normalizes a column of the transition-probability matrix
void	MarkovChain.setTransitionProbability(Matrix transitionProbability) Setter for transitionProbability.

Method parameters in gov.sandia.cognition.learning.algorithm.hmm with type arguments of type Matrix

Modifier and Type	Method and Description
protected Matrix	BaumWelchAlgorithm.updateTransitionMatrix(java.util.ArrayList<Matrix> sequenceTransitionMatrices) Computes an updated transition matrix from the scaled estimates

Constructors in gov.sandia.cognition.learning.algorithm.hmm with parameters of type Matrix

Constructor and Description
HiddenMarkovModel(Vector initialProbability, Matrix transitionProbability, java.util.Collection<? extends ComputableDistribution<ObservationType>> emissionFunctions) Creates a new instance of ContinuousDensityHiddenMarkovModel
MarkovChain(Vector initialProbability, Matrix transitionProbability) Creates a new instance of ContinuousDensityHiddenMarkovModel
ParallelHiddenMarkovModel(Vector initialProbability, Matrix transitionProbability, java.util.Collection<? extends ComputableDistribution<ObservationType>> emissionFunctions) Creates a new instance of ParallelHiddenMarkovModel

Uses of Matrix in gov.sandia.cognition.learning.algorithm.minimization

Methods in gov.sandia.cognition.learning.algorithm.minimization with parameters of type Matrix

Modifier and Type	Method and Description
static boolean	FunctionMinimizerBFGS.BFGSupdateRule(Matrix hessianInverse, Vector delta, Vector gamma, double tolerance) BFGS Quasi-Newton update rule
boolean	FunctionMinimizerBFGS.updateHessianInverse(Matrix hessianInverse, Vector delta, Vector gamma)
protected boolean	FunctionMinimizerDFP.updateHessianInverse(Matrix hessianInverse, Vector delta, Vector gamma)
protected abstract boolean	FunctionMinimizerQuasiNewton.updateHessianInverse(Matrix hessianInverse, Vector delta, Vector gamma) The step that makes BFGS/DFP/SR1 different from each other.

Uses of Matrix in gov.sandia.cognition.learning.algorithm.minimization.matrix

Fields in gov.sandia.cognition.learning.algorithm.minimization.matrix declared as Matrix

Modifier and Type	Field and Description
protected Matrix	MatrixVectorMultiplier.m The matrix to multiply with.

Constructors in gov.sandia.cognition.learning.algorithm.minimization.matrix with parameters of type Matrix

Constructor and Description
MatrixVectorMultiplier(Matrix m) Clones the input matrix to prevent any later edits to the input from changing the results of iterative multiplications.
MatrixVectorMultiplierDiagonalPreconditioner(Matrix m) Creates a new MatrixVectorMultiplierDiagonalPreconditioner for the given matrix.

Uses of Matrix in gov.sandia.cognition.learning.algorithm.pca

Fields in gov.sandia.cognition.learning.algorithm.pca declared as Matrix

Modifier and Type	Field and Description
protected Matrix	KernelPrincipalComponentsAnalysis.Function.components The matrix of components for the function.
protected Matrix	KernelPrincipalComponentsAnalysis.Function.kernelMatrix The kernel matrix for all the data the KPCA was done over.

Methods in gov.sandia.cognition.learning.algorithm.pca that return Matrix

Modifier and Type	Method and Description
Matrix	KernelPrincipalComponentsAnalysis.Function.getComponents() Gets the matrix of components from the analysis.
Matrix	KernelPrincipalComponentsAnalysis.Function.getKernelMatrix() Gets the kernel matrix for the data that the analysis was done over.

Methods in gov.sandia.cognition.learning.algorithm.pca with parameters of type Matrix

Modifier and Type	Method and Description
void	KernelPrincipalComponentsAnalysis.Function.setComponents(Matrix components) Sets the matrix of components from the analysis.
void	KernelPrincipalComponentsAnalysis.Function.setKernelMatrix(Matrix kernelMatrix) Sets the kernel matrix for the data that the analysis was done over.

Constructors in gov.sandia.cognition.learning.algorithm.pca with parameters of type Matrix

Constructor and Description
Function(Kernel<? super DataType> kernel, java.util.List<? extends DataType> data, Matrix components, boolean centerData, Matrix kernelMatrix) Creates a new Kernel Principal Components Analysis function.

Uses of Matrix in gov.sandia.cognition.learning.data

Methods in gov.sandia.cognition.learning.data that return Matrix

Modifier and Type	Method and Description
static Matrix	DatasetUtil.computeOuterProductDataMatrix(java.util.ArrayList<? extends Vector> data) Computes the outer-product Matrix of the given set of data: XXt = [ x1 x2 ...

Uses of Matrix in gov.sandia.cognition.learning.data.feature

Methods in gov.sandia.cognition.learning.data.feature that return Matrix

Modifier and Type	Method and Description
Matrix	MultivariateDecorrelator.getCovariance() Gets the covariance.
Matrix	MultivariateDecorrelator.getCovarianceInverseSquareRoot() Gets the square root of the inverse of the covariance matrix.

Constructors in gov.sandia.cognition.learning.data.feature with parameters of type Matrix

Constructor and Description
MultivariateDecorrelator(Vector mean, Matrix covariance) Creates a new instance of MultivariateDecorrelator with the given mean and variance.

Uses of Matrix in gov.sandia.cognition.learning.function.categorization

Fields in gov.sandia.cognition.learning.function.categorization declared as Matrix

Modifier and Type	Field and Description
protected Matrix	DefaultConfidenceWeightedBinaryCategorizer.covariance The covariance matrix.

Methods in gov.sandia.cognition.learning.function.categorization that return Matrix

Modifier and Type	Method and Description
Matrix	ConfidenceWeightedBinaryCategorizer.getCovariance() Gets the covariance matrix of the categorizer.
Matrix	DefaultConfidenceWeightedBinaryCategorizer.getCovariance()

Methods in gov.sandia.cognition.learning.function.categorization with parameters of type Matrix

Modifier and Type	Method and Description
void	DefaultConfidenceWeightedBinaryCategorizer.setCovariance(Matrix covariance) Sets the covariance matrix.

Constructors in gov.sandia.cognition.learning.function.categorization with parameters of type Matrix

Constructor and Description
DefaultConfidenceWeightedBinaryCategorizer(Vector mean, Matrix covariance) Creates a new DefaultConfidenceWeightedBinaryCategorizer with the given mean and covariance.

Uses of Matrix in gov.sandia.cognition.learning.function.cost

Fields in gov.sandia.cognition.learning.function.cost declared as Matrix

Modifier and Type	Field and Description
Matrix	SumSquaredErrorCostFunction.Cache.J Jacobian
Matrix	SumSquaredErrorCostFunction.Cache.JtJ Inner-product of the Jacobian matrix: J.transpose().times( J )

Constructors in gov.sandia.cognition.learning.function.cost with parameters of type Matrix

Constructor and Description
Cache(Matrix J, Matrix JtJ, Vector Jte, double parameterCost) Creates a new instance of Cache

Uses of Matrix in gov.sandia.cognition.learning.function.vector

Fields in gov.sandia.cognition.learning.function.vector declared as Matrix

Modifier and Type	Field and Description
protected Matrix	ThreeLayerFeedforwardNeuralNetwork.hiddenToOutputWeights Matrix of weights to pre-multiply the hidden-unit activations by.
protected Matrix	ThreeLayerFeedforwardNeuralNetwork.inputToHiddenWeights Matrix of weights to pre-multiply the inputs by.

Methods in gov.sandia.cognition.learning.function.vector that return Matrix

Modifier and Type	Method and Description
static Matrix	MultivariateDiscriminant.computeParameterGradient(Matrix matrix, Vector input) Computes the parameter gradient of the given matrix post-multiplied by the input Vector
Matrix	DifferentiableFeedforwardNeuralNetwork.computeParameterGradient(Vector input)
Matrix	DifferentiableGeneralizedLinearModel.computeParameterGradient(Vector input)
Matrix	MultivariateDiscriminant.computeParameterGradient(Vector input)
Matrix	MultivariateDiscriminantWithBias.computeParameterGradient(Vector input)
Matrix	ThreeLayerFeedforwardNeuralNetwork.computeParameterGradient(Vector input)
Matrix	DifferentiableGeneralizedLinearModel.differentiate(Vector input)
Matrix	ElementWiseDifferentiableVectorFunction.differentiate(Vector input)
Matrix	LinearVectorFunction.differentiate(Vector input)
Matrix	MultivariateDiscriminant.differentiate(Vector input)
Matrix	MultivariateDiscriminant.getDiscriminant() Getter for discriminant.

Methods in gov.sandia.cognition.learning.function.vector with parameters of type Matrix

Modifier and Type	Method and Description
static Matrix	MultivariateDiscriminant.computeParameterGradient(Matrix matrix, Vector input) Computes the parameter gradient of the given matrix post-multiplied by the input Vector
protected void	MultivariateDiscriminant.setDiscriminant(Matrix discriminant) Setter for discriminant

Constructors in gov.sandia.cognition.learning.function.vector with parameters of type Matrix

Constructor and Description
MultivariateDiscriminant(Matrix discriminant) Creates a new instance of MatrixVectorMultiplyFunction.
MultivariateDiscriminantWithBias(Matrix discriminant) Creates a new instance of MultivariateDiscriminantWithBias.
MultivariateDiscriminantWithBias(Matrix discriminant, Vector bias) Creates a new instance of MultivariateDiscriminantWithBias.

Uses of Matrix in gov.sandia.cognition.math

Methods in gov.sandia.cognition.math that return Matrix

Modifier and Type	Method and Description
static Matrix	MultivariateStatisticsUtil.computeVariance(java.util.Collection<? extends Vector> data) Computes the variance (second central moment, squared standard deviation) of a dataset.
static Matrix	MultivariateStatisticsUtil.computeVariance(java.util.Collection<? extends Vector> data, Vector mean) Computes the variance (second central moment, squared standard deviation) of a dataset

Methods in gov.sandia.cognition.math that return types with arguments of type Matrix

Modifier and Type	Method and Description
static Pair<Vector,Matrix>	MultivariateStatisticsUtil.computeMeanAndCovariance(java.lang.Iterable<? extends Vectorizable> data) Computes the mean and unbiased covariance Matrix of a multivariate data set.
static Pair<Vector,Matrix>	MultivariateStatisticsUtil.computeWeightedMeanAndCovariance(java.lang.Iterable<? extends WeightedValue<? extends Vectorizable>> data) Computes the mean and biased covariance Matrix of a multivariate weighted data set.

Uses of Matrix in gov.sandia.cognition.math.matrix

Classes in gov.sandia.cognition.math.matrix with type parameters of type Matrix

Modifier and Type	Class and Description
class	MatrixFactory<MatrixType extends Matrix> Abstract factory for creating Matrix objects.

Subinterfaces of Matrix in gov.sandia.cognition.math.matrix

Modifier and Type	Interface and Description
interface	DiagonalMatrix Interface describing a diagonal matrix.

Classes in gov.sandia.cognition.math.matrix that implement Matrix

Modifier and Type	Class and Description
class	AbstractMatrix Abstract implementation of some low-hanging functions in the Matrix interface.

Fields in gov.sandia.cognition.math.matrix with type parameters of type Matrix

Modifier and Type	Field and Description
protected static MatrixFactory<? extends Matrix>	MatrixFactory.DEFAULT_DENSE_INSTANCE The default dense implementation of a MatrixFactory.
protected static MatrixFactory<? extends Matrix>	MatrixFactory.DEFAULT_SPARSE_INSTANCE The default sparse implementation of a MatrixFactory.

Methods in gov.sandia.cognition.math.matrix that return Matrix

Modifier and Type	Method and Description
Matrix	Matrix.clone()
Matrix	Quaternion.convertToRotationMatrix() Converts the quaternion to a 3-by-3 rotation matrix.
Matrix	DifferentiableVectorFunction.differentiate(Vector input) Differentiate the VectorFunction at input and return the Jacobian
Matrix	NumericalDifferentiator.MatrixJacobian.differentiate(Vector input)
static Matrix	NumericalDifferentiator.MatrixJacobian.differentiate(Vector input, Evaluator<? super Vector,Vector> f) Static access to the numerical differentiation procedure.
static Matrix	NumericalDifferentiator.MatrixJacobian.differentiate(Vectorizable input, Evaluator<? super Vector,Vector> f, double h) Static access to the numerical differentiation procedure.
Matrix	AbstractMatrix.dotDivide(Matrix other)
Matrix	Matrix.dotDivide(Matrix other) Element-wise division of this by other.
Matrix	Matrix.getSubMatrix(int minRow, int maxRow, int minColumn, int maxColumn) Gets the embedded submatrix inside of the Matrix, specified by the inclusive, zero-based indices such that the result matrix will have size (maxRow-minRow+1) x (maxColum-minCcolumn+1)
Matrix	Matrix.inverse() Computes the full-blown inverse of this, which must be a square matrix
Matrix	AbstractVector.outerProduct(Vector other)
Matrix	Vector.outerProduct(Vector other) Computes the outer matrix product between the two vectors
Matrix	AbstractMatrix.pseudoInverse()
Matrix	Matrix.pseudoInverse() Computes the effective pseudo-inverse of this, using a rather expensive procedure (SVD)
Matrix	Matrix.pseudoInverse(double effectiveZero) Computes the effective pseudo-inverse of this, using a rather expensive procedure (SVD)
Matrix	MatrixReader.read() Reads the next Matrix found in the specified Reader
Matrix	Matrix.solve(Matrix B) Solves for "X" in the equation: this*X = B
Matrix	AbstractMatrix.times(Matrix other)
Matrix	Matrix.times(Matrix matrix) Matrix multiplication of this and matrix, operates like the "*" operator in Matlab
Matrix	Matrix.transpose() Returns the transpose of this

Methods in gov.sandia.cognition.math.matrix that return types with arguments of type Matrix

Modifier and Type	Method and Description
static MatrixFactory<? extends Matrix>	MatrixFactory.getDefault() Gets the default implementation of MatrixFactory.
static MatrixFactory<? extends Matrix>	MatrixFactory.getDenseDefault() Gets the default implementation of MatrixFactory for dense matrices.
MatrixFactory<? extends Matrix>	MatrixFactoryContainer.getMatrixFactory() Gets the matrix factory the object to use to create new matrices.
static MatrixFactory<? extends Matrix>	MatrixFactory.getSparseDefault() Gets the default implementation of MatrixFactory for sparse matrices.

Methods in gov.sandia.cognition.math.matrix with parameters of type Matrix

Modifier and Type	Method and Description
void	AbstractMatrix.assertMultiplicationDimensions(Matrix postMultiplicationMatrix)
void	Matrix.assertMultiplicationDimensions(Matrix postMultiplicationMatrix) Checks to see if the dimensions are appropriate for: this.times(postMultiplicationMatrix).
void	AbstractMatrix.assertSameDimensions(Matrix other)
void	Matrix.assertSameDimensions(Matrix otherMatrix) Throws a DimensionalityMismatchException if dimensions between this and otherMatrix aren't the same
boolean	AbstractMatrix.checkMultiplicationDimensions(Matrix postMultiplicationMatrix)
boolean	Matrix.checkMultiplicationDimensions(Matrix postMultiplicationMatrix) Checks to see if the dimensions are appropriate for: this.times( postMultiplicationMatrix )
boolean	AbstractMatrix.checkSameDimensions(Matrix otherMatrix)
boolean	Matrix.checkSameDimensions(Matrix otherMatrix) Checks to see if the dimensions are the same between this and otherMatrix
void	Quaternion.convertFromRotationMatrix(Matrix rotationMatrix) Sets the quaternion to be equivalent to the given a 3-by-3 rotation matrix.
abstract MatrixType	MatrixFactory.copyMatrix(Matrix m) Creates a deep copy new Matrix given another, argument is unchanged
Matrix	AbstractMatrix.dotDivide(Matrix other)
Matrix	Matrix.dotDivide(Matrix other) Element-wise division of this by other.
void	AbstractMatrix.dotDivideEquals(Matrix other)
void	Matrix.dotDivideEquals(Matrix other) Inline element-wise division of this by other.
DiagonalMatrix	DiagonalMatrix.dotTimes(Matrix matrix)
void	AbstractMatrix.dotTimesEquals(Matrix other)
boolean	AbstractMatrix.equals(Matrix other, double effectiveZero)
void	AbstractMatrix.minusEquals(Matrix other)
void	AbstractMatrix.plusEquals(Matrix other)
void	AbstractMatrix.scaledPlusEquals(double scaleFactor, Matrix other)
void	AbstractMatrix.setSubMatrix(int minRow, int minColumn, Matrix submatrix)
void	Matrix.setSubMatrix(int minRow, int minColumn, Matrix submatrix) Sets the submatrix inside of the Matrix, specified by the zero-based indices.
Matrix	Matrix.solve(Matrix B) Solves for "X" in the equation: this*X = B
Matrix	AbstractMatrix.times(Matrix other)
Vector	AbstractVector.times(Matrix matrix)
Matrix	Matrix.times(Matrix matrix) Matrix multiplication of this and matrix, operates like the "*" operator in Matlab
Vector	Vector.times(Matrix matrix) Premultiplies the matrix by the vector "this"
void	MatrixWriter.write(Matrix matrix) Writes the given matrix to the Java-based writer

Uses of Matrix in gov.sandia.cognition.math.matrix.custom

Classes in gov.sandia.cognition.math.matrix.custom that implement Matrix

Modifier and Type	Class and Description
class	DenseMatrix A dense matrix implementation.
class	DiagonalMatrix Diagonal matrices are a special case, but a rather common one with very quick and simple solutions to multiplications, inverses, etc.
class	ParallelSparseMatrix A sparse matrix implementation.
class	SparseMatrix A sparse matrix implementation.

Fields in gov.sandia.cognition.math.matrix.custom declared as Matrix

Modifier and Type	Field and Description
Matrix	DenseMatrix.LU.L The lower triangular matrix resulting from the factorization.
Matrix	DenseMatrix.QR.Q The orthonormal basis matrix
Matrix	DenseMatrix.QR.R The upper-triangular matrix
Matrix	DenseMatrix.SVD.Sigma * The singular value diagonal matrix.
Matrix	DenseMatrix.SVD.U The left basis matrix.
Matrix	DenseMatrix.LU.U The upper triangular matrix resulting from the factorization.
Matrix	DenseMatrix.SVD.V The right basis matrix.

Methods in gov.sandia.cognition.math.matrix.custom that return Matrix

Modifier and Type	Method and Description
Matrix	DenseMatrix.clone() Returns a deep copy of this.
Matrix	DiagonalMatrix.clone()
Matrix	ParallelSparseMatrix.clone() This makes public the clone method on the Object class and removes the exception that it throws.
Matrix	SparseMatrix.clone() This makes public the clone method on the Object class and removes the exception that it throws.
Matrix	DenseMatrix.LU.getPivotMatrix() Helper method converts P into a pivot matrix that can pre-multiply L*U.
Matrix	DenseMatrix.getSubMatrix(int minRow, int maxRow, int minColumn, int maxColumn) Gets the embedded submatrix inside of the Matrix, specified by the inclusive, zero-based indices such that the result matrix will have size (maxRow-minRow+1) x (maxColum-minCcolumn+1)
Matrix	DiagonalMatrix.getSubMatrix(int minRow, int maxRow, int minColumn, int maxColumn)
Matrix	SparseMatrix.getSubMatrix(int minRow, int maxRow, int minColumn, int maxColumn) Gets the embedded submatrix inside of the Matrix, specified by the inclusive, zero-based indices such that the result matrix will have size (maxRow-minRow+1) x (maxColum-minCcolumn+1)
Matrix	DenseMatrix.inverse()
Matrix	DiagonalMatrix.inverse() Computes the full-blown inverse of this, which must be a square matrix
Matrix	SparseMatrix.inverse() Computes the full-blown inverse of this, which must be a square matrix
Matrix	DenseVector.outerProduct(DenseVector other)
Matrix	SparseVector.outerProduct(DenseVector other)
Matrix	DenseVector.outerProduct(SparseVector other)
Matrix	SparseVector.outerProduct(SparseVector other)
Matrix	SparseMatrix.preTimes(DiagonalMatrix other) Package-private helper for the diagonal matrix class.
Matrix	DenseMatrix.pseudoInverse(double effectiveZero)
Matrix	DiagonalMatrix.pseudoInverse(double effectiveZero)
Matrix	SparseMatrix.pseudoInverse(double effectiveZero) Computes the effective pseudo-inverse of this, using a rather expensive procedure (SVD)
Matrix	DenseMatrix.solve(Matrix B)
Matrix	DiagonalMatrix.solve(Matrix B)
Matrix	SparseMatrix.solve(Matrix B) Solves for "X" in the equation: this*X = B
Matrix	DenseMatrix.times(DenseMatrix other)
Matrix	DiagonalMatrix.times(DenseMatrix other)
Matrix	SparseMatrix.times(DenseMatrix other) Type-specific version of times for combining whatever type this is with the input dense matrix.
Matrix	DenseMatrix.times(DiagonalMatrix other)
Matrix	DiagonalMatrix.times(DiagonalMatrix other)
Matrix	SparseMatrix.times(DiagonalMatrix other) Type-specific version of times for combining whatever type this is with the input diagonal matrix.
Matrix	DenseMatrix.times(SparseMatrix other)
Matrix	DiagonalMatrix.times(SparseMatrix other)
Matrix	SparseMatrix.times(SparseMatrix other) Type-specific version of times for combining whatever type this is with the input sparse matrix.
Matrix	DenseMatrix.transpose()
Matrix	DiagonalMatrix.transpose()
Matrix	SparseMatrix.transpose() Returns the transpose of this

Methods in gov.sandia.cognition.math.matrix.custom with parameters of type Matrix

Modifier and Type	Method and Description
DenseMatrix	CustomDenseMatrixFactory.copyMatrix(Matrix m) Creates a deep copy of m into a DenseMatrix and returns it.
DiagonalMatrix	CustomDiagonalMatrixFactory.copyMatrix(Matrix m) Creates a deep copy new Matrix given another, argument is unchanged
SparseMatrix	CustomSparseMatrixFactory.copyMatrix(Matrix m) Creates a deep copy new Matrix given another, argument is unchanged
Matrix	DenseMatrix.solve(Matrix B)
Matrix	DiagonalMatrix.solve(Matrix B)
Matrix	SparseMatrix.solve(Matrix B) Solves for "X" in the equation: this*X = B

Constructors in gov.sandia.cognition.math.matrix.custom with parameters of type Matrix

Constructor and Description
DenseMatrix(Matrix m) Copy constructor that copies any input Matrix -- creating a deep copy of the matrix.
DiagonalMatrix(Matrix m) Creates a diagonal matrix as a copy of the given matrix.

Uses of Matrix in gov.sandia.cognition.math.matrix.decomposition

Methods in gov.sandia.cognition.math.matrix.decomposition that return Matrix

Modifier and Type	Method and Description
Matrix	AbstractEigenDecomposition.getEigenVectorsImaginaryPart() gets the imaginary part of the eienvector, where the ith eigenvector is the ith column
Matrix	EigenDecomposition.getEigenVectorsImaginaryPart() Returns a matrix with the imaginary parts of the right eigenvalues of the underlying matrix
Matrix	AbstractEigenDecomposition.getEigenVectorsRealPart() getter for eigenvectorsrealPart
Matrix	EigenDecomposition.getEigenVectorsRealPart() Returns a matrix with the real parts of the right eigenvalues of the underlying matrix
Matrix	AbstractSingularValueDecomposition.getS()
Matrix	SingularValueDecomposition.getS() Gets the PSD diagonal matrix containing the sorted singular values
Matrix	AbstractSingularValueDecomposition.getU()
Matrix	SingularValueDecomposition.getU() Gets the orthonormal matrix containing the left singular vectors of the underlying matrix
Matrix	AbstractSingularValueDecomposition.getVtranspose()
Matrix	SingularValueDecomposition.getVtranspose() Gets the transpose of the orthonormal matrix containing the right singular vectors of the underlying matrix
Matrix	AbstractSingularValueDecomposition.pseudoInverse()
Matrix	SingularValueDecomposition.pseudoInverse() Computes the Least Squares pseudoinverse of the underlying matrix
Matrix	AbstractSingularValueDecomposition.pseudoInverse(double effectiveZero)
Matrix	SingularValueDecomposition.pseudoInverse(double effectiveZero) Computes the Least Squares pseudoinverse of the underlying matrix, while clipping the singular values at effectiveZero

Methods in gov.sandia.cognition.math.matrix.decomposition with parameters of type Matrix

Modifier and Type	Method and Description
static double	EigenvectorPowerIteration.estimateEigenvalue(Matrix A, Vector v) Finds the eigenvalue associated with the given Matrix and eigenvector.
static Vector	EigenvectorPowerIteration.estimateEigenvector(Vector initial, Matrix A, double stoppingThreshold, int maxIterations) Estimates the eigenvector corresponding to the largest magnitude eigenvalue.
static java.util.ArrayList<Vector>	EigenvectorPowerIteration.estimateEigenvectors(Matrix A, int numEigenvectors) Estimates the top eigenvectors of the given matrix using the power iteration algorithm.
static java.util.ArrayList<Vector>	EigenvectorPowerIteration.estimateEigenvectors(Matrix A, int numEigenvectors, double stoppingThreshold, int maxIterations) Estimates the top eigenvectors of the given matrix using the power iteration algorithm.
protected void	AbstractEigenDecomposition.setEigenDecomposition(ComplexNumber[] eigenValues, Matrix eigenVectorsRealPart, Matrix eigenVectorsImaginaryPart, boolean sort) Sets the eigen decomposition for this
void	AbstractEigenDecomposition.setEigenVectorsImaginaryPart(Matrix eigenVectorsImaginaryPart) setter for the imaginary part of the eienvector, where the ith eigenvector is the ith column
protected void	AbstractEigenDecomposition.setEigenVectorsRealPart(Matrix eigenVectorsRealPart) setter for eigenVectorsRealPart, where the ith eigenvector is the ith column
protected void	AbstractSingularValueDecomposition.setS(Matrix S) setter for the singular values matrix
protected void	AbstractSingularValueDecomposition.setU(Matrix U) setter for left singular vectors
protected void	AbstractEigenDecomposition.setUnsortedEigenDecomposition(ComplexNumber[] eigenValues, Matrix eigenVectorsRealPart, Matrix eigenVectorsImaginaryPart) Creates a new eigendecomposition using the given eigenvalues and eigenvectors...
protected void	AbstractSingularValueDecomposition.setVtranspose(Matrix Vtranspose) sets the transpose of the right singular vectors matrix
protected void	AbstractEigenDecomposition.sortAndSetEigenDecomposition(ComplexNumber[] eigenValues, Matrix eigenVectorsRealPart, Matrix eigenVectorsImaginaryPart) Sorts the eigendecomposition in descending order of the value of the magnitudes of the eigenvalues

Constructors in gov.sandia.cognition.math.matrix.decomposition with parameters of type Matrix

Constructor and Description
AbstractEigenDecomposition(ComplexNumber[] eigenValues, Matrix eigenVectorsRealPart, Matrix eigenVectorsImaginaryPart) Stores the given eigenvalues and eigenvectors internally, the eigenvalues and eigenvectors will not be sorted.
AbstractEigenDecomposition(ComplexNumber[] eigenValues, Matrix eigenVectorsRealPart, Matrix eigenVectorsImaginaryPart, boolean sort) Creates a new eigendecomposition using the given eigenvalues and eigenvectors...
AbstractSingularValueDecomposition(Matrix U, Matrix S, Matrix Vtranspose) Creates a new instance of AbstractSingularValueDecomposition where U*S*Vtranspose = original_matrix

Uses of Matrix in gov.sandia.cognition.math.matrix.mtj

Classes in gov.sandia.cognition.math.matrix.mtj that implement Matrix

Modifier and Type	Class and Description
class	AbstractMTJMatrix Relies on internal MTJ matrix to do some of the heavy lifting, without assuming that the underlying matrix is Dense or Sparse
class	AbstractSparseMatrix Implements some generic operations that any sparse-matrix representation must do.
class	DiagonalMatrixMTJ A diagonal matrix that wraps MTJ's BandMatrix class.
class	SparseColumnMatrix A sparse matrix, represented as a collection of sparse column vectors.
class	SparseRowMatrix A sparse matrix, represented as a collection of sparse row vectors.

Methods in gov.sandia.cognition.math.matrix.mtj that return Matrix

Modifier and Type	Method and Description
Matrix	AbstractMTJMatrix.inverse()
Matrix	AbstractMTJVector.outerProduct(Vector other)
Matrix	AbstractMTJMatrix.solve(AbstractMTJMatrix B) Solves for "X" in the equation this * X = B, where X is a DenseMatrix, "this" and "B" will be converted to a DenseMatrix (if not already)
Matrix	AbstractMTJMatrix.solve(Matrix B)
Matrix	DiagonalMatrixMTJ.solve(Matrix B)
Matrix	AbstractMTJMatrix.times(Matrix matrix)

Methods in gov.sandia.cognition.math.matrix.mtj with parameters of type Matrix

Modifier and Type	Method and Description
DenseMatrix	DenseMatrixFactoryMTJ.copyMatrix(Matrix m)
DiagonalMatrixMTJ	DiagonalMatrixFactoryMTJ.copyMatrix(Matrix m)
SparseMatrix	SparseMatrixFactoryMTJ.copyMatrix(Matrix m)
DiagonalMatrixMTJ	DiagonalMatrixMTJ.dotTimes(Matrix matrix)
void	AbstractMTJMatrix.dotTimesEquals(Matrix other)
void	AbstractMTJMatrix.minusEquals(Matrix other)
void	AbstractMTJMatrix.plusEquals(Matrix other)
void	AbstractMTJMatrix.scaledPlusEquals(double scaleFactor, Matrix other)
Matrix	AbstractMTJMatrix.solve(Matrix B)
Matrix	DiagonalMatrixMTJ.solve(Matrix B)
Matrix	AbstractMTJMatrix.times(Matrix matrix)
Vector	AbstractMTJVector.times(Matrix matrix)

Constructors in gov.sandia.cognition.math.matrix.mtj with parameters of type Matrix

Constructor and Description
DenseMatrix(Matrix matrix) Creates a new instance of DenseMatrix
SparseColumnMatrix(Matrix matrix) Copy constructor for general matrices, copies over nonzero values.
SparseMatrix(Matrix matrix) Converts the given matrix to a SparseMatrix.
SparseRowMatrix(Matrix matrix) Copy constructor for general matrices, copies over nonzero values.

Uses of Matrix in gov.sandia.cognition.math.matrix.mtj.decomposition

Methods in gov.sandia.cognition.math.matrix.mtj.decomposition with parameters of type Matrix

Modifier and Type	Method and Description
static SingularValueDecompositionMTJ	SingularValueDecompositionMTJ.create(Matrix matrix) Creates a new instance of SingularValueDecompositionMTJ

Uses of Matrix in gov.sandia.cognition.math.signals

Methods in gov.sandia.cognition.math.signals that return Matrix

Modifier and Type	Method and Description
Matrix	LinearDynamicalSystem.getA() Getter for A.
Matrix	LinearDynamicalSystem.getB() Getter for B.
Matrix	LinearDynamicalSystem.getC() Getter for C.

Methods in gov.sandia.cognition.math.signals with parameters of type Matrix

Modifier and Type	Method and Description
void	LinearDynamicalSystem.setA(Matrix A) Setter for A.
void	LinearDynamicalSystem.setB(Matrix B) Setter for B.
void	LinearDynamicalSystem.setC(Matrix C) Setter for C.

Constructors in gov.sandia.cognition.math.signals with parameters of type Matrix

Constructor and Description
LinearDynamicalSystem(Matrix A, Matrix B) Creates a new instance of LinearDynamicalSystem
LinearDynamicalSystem(Matrix A, Matrix B, Matrix C) Creates a new instance of LinearDynamicalSystem

Uses of Matrix in gov.sandia.cognition.statistics.bayesian

Fields in gov.sandia.cognition.statistics.bayesian declared as Matrix

Modifier and Type	Field and Description
protected Matrix	AbstractKalmanFilter.measurementCovariance Covariance associated with the measurements.
protected Matrix	AbstractKalmanFilter.modelCovariance Covariance associated with the system's model.

Methods in gov.sandia.cognition.statistics.bayesian that return Matrix

Modifier and Type	Method and Description
Matrix	AbstractKalmanFilter.computePredictionCovariance(Matrix A, Matrix beliefCovariance) Computes the prediction covariance from the Jacobian and believe covariance
Matrix	BayesianLinearRegression.IncrementalEstimator.SufficientStatistic.getCovarianceInverse() Getter for covarianceInverse
Matrix	BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic.getCovarianceInverse() Getter for covarianceInverse
Matrix	AbstractKalmanFilter.getMeasurementCovariance() Getter for measurementCovariance
Matrix	AbstractKalmanFilter.getModelCovariance() Getter for modelCovariance

Methods in gov.sandia.cognition.statistics.bayesian with parameters of type Matrix

Modifier and Type	Method and Description
void	AbstractKalmanFilter.computeMeasurementBelief(MultivariateGaussian belief, Vector innovation, Matrix C) Updates the measurement belief by computing the Kalman gain and incorporating the innovation into the estimate
Matrix	AbstractKalmanFilter.computePredictionCovariance(Matrix A, Matrix beliefCovariance) Computes the prediction covariance from the Jacobian and believe covariance
void	AbstractKalmanFilter.setMeasurementCovariance(Matrix measurementCovariance) Setter for measurementCovariance
void	AbstractKalmanFilter.setModelCovariance(Matrix modelCovariance) Setter for modelCovariance

Constructors in gov.sandia.cognition.statistics.bayesian with parameters of type Matrix

Constructor and Description
AbstractKalmanFilter(Vector currentInput, Matrix modelCovariance, Matrix measurementCovariance) Creates a new instance of AbstractKalmanFilter
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel, Evaluator<Vector,Vector> observationModel, Vector currentInput, Matrix modelCovariance, Matrix measurementCovariance) Creates a new instance of ExtendedKalmanFilter
KalmanFilter(LinearDynamicalSystem model, Matrix modelCovariance, Matrix measurementCovariance) Creates a new instance of LinearUpdater

Uses of Matrix in gov.sandia.cognition.statistics.bayesian.conjugate

Methods in gov.sandia.cognition.statistics.bayesian.conjugate that return Matrix

Modifier and Type	Method and Description
Matrix	MultivariateGaussianMeanBayesianEstimator.getKnownCovarianceInverse() Getter for knownCovarianceInverse.
Matrix	MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter.getValue()

Methods in gov.sandia.cognition.statistics.bayesian.conjugate with parameters of type Matrix

Modifier and Type	Method and Description
void	MultivariateGaussianMeanBayesianEstimator.setKnownCovarianceInverse(Matrix knownCovarianceInverse) Setter for knownCovarianceInverse.
void	MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter.setValue(Matrix value)

Constructors in gov.sandia.cognition.statistics.bayesian.conjugate with parameters of type Matrix

Constructor and Description
MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse) Creates a new instance of MultivariateGaussianMeanBayesianEstimator
MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse, MultivariateGaussian belief) Creates a new instance of MultivariateGaussianMeanBayesianEstimator

Constructor parameters in gov.sandia.cognition.statistics.bayesian.conjugate with type arguments of type Matrix

Constructor and Description
MultivariateGaussianMeanCovarianceBayesianEstimator(BayesianParameter<Matrix,MultivariateGaussian,NormalInverseWishartDistribution> parameter) Creates a new instance

Uses of Matrix in gov.sandia.cognition.statistics.distribution

Fields in gov.sandia.cognition.statistics.distribution declared as Matrix

Modifier and Type	Field and Description
protected Matrix	InverseWishartDistribution.inverseScale Inverse scale matrix, must be symmetric and positive definite.

Methods in gov.sandia.cognition.statistics.distribution that return Matrix

Modifier and Type	Method and Description
Matrix	MultivariateGaussian.getCovariance() Gets the covariance matrix of the Gaussian.
Matrix	MultivariateGaussian.SufficientStatistic.getCovariance() Gets the covariance of the Gaussian.
Matrix	MultivariateStudentTDistribution.getCovariance() Computes the covariance of the distribution, which involves inverting the precision matrix.
Matrix	MultivariateGaussian.getCovarianceInverse() Gets the inverse of the covariance matrix.
Matrix	MultivariateGaussian.SufficientStatisticCovarianceInverse.getCovarianceInverse() Gets the covariance Inverse of the Gaussian.
Matrix	InverseWishartDistribution.getInverseScale() Getter for inverseScale
Matrix	InverseWishartDistribution.getMean()
Matrix	NormalInverseWishartDistribution.getMean()
Matrix	MultivariateStudentTDistribution.getPrecision() Getter for precision
Matrix	InverseWishartDistribution.getScaleSqrt() Getter for scaleSqrt
Matrix	MultivariateGaussian.SufficientStatistic.getSumSquaredDifferences() Getter for sumSquaredDifferences
Matrix	MultivariateGaussian.SufficientStatisticCovarianceInverse.getSumSquaredDifferencesInverse() Getter for sumSquaredDifferences
static Matrix	InverseWishartDistribution.sample(java.util.Random random, Vector mean, Matrix covarianceSqrt, int degreesOfFreedom) Creates a single sample covariance matrix inverse from the given parameters.

Methods in gov.sandia.cognition.statistics.distribution with parameters of type Matrix

Modifier and Type	Method and Description
java.lang.Double	InverseWishartDistribution.PDF.evaluate(Matrix input)
java.lang.Double	NormalInverseWishartDistribution.PDF.evaluate(Matrix input)
double	InverseWishartDistribution.PDF.logEvaluate(Matrix input)
double	NormalInverseWishartDistribution.PDF.logEvaluate(Matrix input)
static Matrix	InverseWishartDistribution.sample(java.util.Random random, Vector mean, Matrix covarianceSqrt, int degreesOfFreedom) Creates a single sample covariance matrix inverse from the given parameters.
static Vector	MultivariateGaussian.sample(Vector mean, Matrix covarianceSquareRoot, java.util.Random random) Returns a single draw from the Gaussian with the given mean and covariance.
static java.util.ArrayList<Vector>	MultivariateGaussian.sample(Vector mean, Matrix covarianceSquareRoot, java.util.Random random, int sampleCount) Returns a collection of draws this Gaussian with the given mean and covariance.
static void	MultivariateGaussian.sampleInto(Vector mean, Matrix covarianceSquareRoot, java.util.Random random, int sampleCount, java.util.Collection<? super Vector> output) Performs a collection of draws this Gaussian with the given mean and covariance.
MultivariateGaussian	MultivariateGaussian.scale(Matrix premultiplyMatrix) Scales the MultivariateGaussian by premultiplying by the given Matrix
void	MultivariateGaussian.setCovariance(Matrix covariance) Sets the covariance matrix.
void	MultivariateGaussian.setCovariance(Matrix covariance, double symmetryTolerance) Sets the covariance matrix.
void	MultivariateGaussian.setCovarianceInverse(Matrix covarianceInverse) Sets the covariance inverse
void	MultivariateGaussian.setCovarianceInverse(Matrix covarianceInverse, double symmetryTolerance) Sets the covariance inverse
void	InverseWishartDistribution.setInverseScale(Matrix inverseScale) Setter for inverseScale
void	MultivariateStudentTDistribution.setPrecision(Matrix precision) Setter for precision
void	MultivariateStudentTDistribution.PDF.setPrecision(Matrix precision)

Method parameters in gov.sandia.cognition.statistics.distribution with type arguments of type Matrix

Modifier and Type	Method and Description
void	InverseWishartDistribution.sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super Matrix> output)
void	NormalInverseWishartDistribution.sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super Matrix> output)

Constructors in gov.sandia.cognition.statistics.distribution with parameters of type Matrix

Constructor and Description
InverseWishartDistribution(Matrix inverseScale, int degreesOfFreedom) Creates a new instance of InverseWishartDistribution
MultivariateGaussian(Vector mean, Matrix covariance) Creates a new instance of MultivariateGaussian.
MultivariateStudentTDistribution(double degreesOfFreedom, Vector mean, Matrix precision) Creates a distribution with the given dimensionality.
PDF(double degreesOfFreedom, Vector mean, Matrix precision) Creates a distribution with the given dimensionality.
PDF(Matrix inverseScale, int degreesOfFreedom) Creates a new instance of InverseWishartDistribution
PDF(Vector mean, Matrix covariance) Creates a new instance of MultivariateGaussian.

Uses of Matrix in gov.sandia.cognition.statistics.method

Fields in gov.sandia.cognition.statistics.method declared as Matrix

Modifier and Type	Field and Description
protected Matrix	HolmCorrection.Statistic.adjustedAlphas Matrix of adjusted alphas (p-value thresholds) for each comparison
protected Matrix	ShafferStaticCorrection.Statistic.adjustedAlphas Matrix of adjusted alphas (p-value thresholds) for each comparison
protected Matrix	AbstractMultipleHypothesisComparison.Statistic.nullHypothesisProbabilities Null Hypothesis probability associated with the (i,j) treatment comparison
protected Matrix	TukeyKramerConfidence.Statistic.standardErrors Gets the standard errors in the experiment
protected Matrix	AbstractMultipleHypothesisComparison.Statistic.testStatistics Test statistic associated with the (i,j) treatment comparison

Methods in gov.sandia.cognition.statistics.method that return Matrix

Modifier and Type	Method and Description
protected Matrix	TukeyKramerConfidence.Statistic.computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts, Matrix Z) Computes null-hypothesis probability for the (i,j) treatment comparison
protected Matrix	NemenyiConfidence.Statistic.computeNullHypothesisProbabilities(int subjectCount, Matrix Z) Computes null-hypothesis probability for the (i,j) treatment comparison
Matrix	TukeyKramerConfidence.Statistic.computeTestStatistics(java.util.ArrayList<java.lang.Integer> subjectCounts, java.util.ArrayList<java.lang.Double> treatmentMeans, double meanSquaredResiduals) Computes the test statistic for all treatments
Matrix	NemenyiConfidence.Statistic.computeTestStatistics(int subjectCount, java.util.ArrayList<java.lang.Double> treatmentRankMeans, double standardError) Computes the test statistic for all treatments
Matrix	TukeyKramerConfidence.Statistic.getStandardErrors() Getter for standardErrors

Methods in gov.sandia.cognition.statistics.method with parameters of type Matrix

Modifier and Type	Method and Description
protected Matrix	TukeyKramerConfidence.Statistic.computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts, Matrix Z) Computes null-hypothesis probability for the (i,j) treatment comparison
protected Matrix	NemenyiConfidence.Statistic.computeNullHypothesisProbabilities(int subjectCount, Matrix Z) Computes null-hypothesis probability for the (i,j) treatment comparison

Uses of Matrix in gov.sandia.cognition.text.term.relation

Fields in gov.sandia.cognition.text.term.relation declared as Matrix

Modifier and Type	Field and Description
protected Matrix	MatrixBasedTermSimilarityNetwork.similarities The similarities between terms.

Fields in gov.sandia.cognition.text.term.relation with type parameters of type Matrix

Modifier and Type	Field and Description
protected MatrixFactory<? extends Matrix>	TermVectorSimilarityNetworkCreator.matrixFactory The matrix factory to create the matrix that backs the similarity network.

Methods in gov.sandia.cognition.text.term.relation that return Matrix

Modifier and Type	Method and Description
Matrix	MatrixBasedTermSimilarityNetwork.getSimilarities() Gets the similarities between terms.

Methods in gov.sandia.cognition.text.term.relation that return types with arguments of type Matrix

Modifier and Type	Method and Description
MatrixFactory<? extends Matrix>	TermVectorSimilarityNetworkCreator.getMatrixFactory() Gets the matrix factory to create the matrix that backs the similarity network.

Methods in gov.sandia.cognition.text.term.relation with parameters of type Matrix

Modifier and Type	Method and Description
protected void	MatrixBasedTermSimilarityNetwork.setSimilarities(Matrix similarities) Gets the similarities between terms.

Method parameters in gov.sandia.cognition.text.term.relation with type arguments of type Matrix

Modifier and Type	Method and Description
void	TermVectorSimilarityNetworkCreator.setMatrixFactory(MatrixFactory<? extends Matrix> matrixFactory) Sets the matrix factory to create the matrix that backs the similarity network.

Constructors in gov.sandia.cognition.text.term.relation with parameters of type Matrix

Constructor and Description
MatrixBasedTermSimilarityNetwork(TermIndex termIndex, Matrix similarities) Creates a new MatrixBasedTermSimilarityNetwork.

Constructor parameters in gov.sandia.cognition.text.term.relation with type arguments of type Matrix

Constructor and Description
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction, double effectiveZero, MatrixFactory<? extends Matrix> matrixFactory) Creates a new TermVectorSimilarityNetworkCreator.

Uses of Matrix in gov.sandia.cognition.text.topic

Fields in gov.sandia.cognition.text.topic declared as Matrix

Modifier and Type	Field and Description
protected Matrix	ProbabilisticLatentSemanticAnalysis.documentsByTerms The document-by-term matrix.
protected Matrix	LatentSemanticAnalysis.Transform.singularValues The diagonal matrix of singular values.
protected Matrix	LatentSemanticAnalysis.Transform.termBasis The matrix of orthogonal term column vectors.
protected Matrix	LatentSemanticAnalysis.Transform.transform The cached transform matrix.

Fields in gov.sandia.cognition.text.topic with type parameters of type Matrix

Modifier and Type	Field and Description
protected MatrixFactory<? extends Matrix>	ProbabilisticLatentSemanticAnalysis.matrixFactory The matrix factory.

Methods in gov.sandia.cognition.text.topic that return Matrix

Modifier and Type	Method and Description
Matrix	LatentSemanticAnalysis.Transform.getSingularValues() Gets the diagonal matrix of singular values.
Matrix	LatentSemanticAnalysis.Transform.getTermBasis() Gets the matrix of orthogonal term column vectors.
Matrix	LatentSemanticAnalysis.Transform.getTransform() Gets the cached transform matrix.

Methods in gov.sandia.cognition.text.topic that return types with arguments of type Matrix

Modifier and Type	Method and Description
MatrixFactory<? extends Matrix>	ProbabilisticLatentSemanticAnalysis.getMatrixFactory() Gets the matrix factory to use.

Methods in gov.sandia.cognition.text.topic with parameters of type Matrix

Modifier and Type	Method and Description
protected void	LatentSemanticAnalysis.Transform.setSingularValues(Matrix singularValues) Sets the diagonal matrix of singular values.
protected void	LatentSemanticAnalysis.Transform.setTermBasis(Matrix termBasis) Sets the matrix of orthogonal term column vectors.
protected void	LatentSemanticAnalysis.Transform.setTransform(Matrix transform) Gets the cached transform matrix.
protected double	ProbabilisticLatentSemanticAnalysis.Result.step(Vector query, Matrix pLatentGivenQueryTerm, Vector pQueryGivenLatent) Take a step of the expectation-maximization algorithm for computing the probability of the query given each latent variable.

Method parameters in gov.sandia.cognition.text.topic with type arguments of type Matrix

Modifier and Type	Method and Description
void	ProbabilisticLatentSemanticAnalysis.setMatrixFactory(MatrixFactory<? extends Matrix> matrixFactory) Sets the matrix factory to use.

Constructors in gov.sandia.cognition.text.topic with parameters of type Matrix

Constructor and Description
Transform(Matrix termBasis, Matrix singularValues) Create a new Transform