Uses of Interface
edu.ucla.sspace.matrix.Matrix

Packages that use Matrix

Package	Description
edu.ucla.sspace.clustering
edu.ucla.sspace.clustering.criterion
edu.ucla.sspace.clustering.seeding
edu.ucla.sspace.common
edu.ucla.sspace.matrix
edu.ucla.sspace.matrix.factorization
edu.ucla.sspace.util

Uses of Matrix in edu.ucla.sspace.clustering

Fields in edu.ucla.sspace.clustering declared as Matrix

Modifier and Type	Field and Description
protected Matrix	BaseSpectralCut.dataMatrix The Matrix containing the data points.
protected Matrix	BaseSpectralCut.leftSplit The data points in the left region.
protected Matrix	BaseSpectralCut.rightSplit The data points in the right region.

Methods in edu.ucla.sspace.clustering with type parameters of type Matrix

Modifier and Type	Method and Description
protected static <T extends Matrix> DoubleVector	BaseSpectralCut.computeMatrixRowSum(T matrix) Compute the row sums of the values in matrix and returns the values in a vector of length matrix.columns().

Methods in edu.ucla.sspace.clustering that return Matrix

Modifier and Type	Method and Description
Matrix	EigenCut.getLeftCut() Returns the data set in the first (left) region.
Matrix	BaseSpectralCut.getLeftCut() Returns the data set in the first (left) region.
Matrix	EigenCut.getRightCut() Returns the data set in the second (right) region.
Matrix	BaseSpectralCut.getRightCut() Returns the data set in the second (right) region.

Methods in edu.ucla.sspace.clustering with parameters of type Matrix

Modifier and Type	Method and Description
List<Merge>	HierarchicalAgglomerativeClustering.buildDendogram(Matrix m, HierarchicalAgglomerativeClustering.ClusterLinkage linkage, Similarity.SimType similarityFunction) Builds a dendrogram of the rows of similarity matrix by iteratelyve linking each row according to the linkage policy in a bottom up manner.
List<Merge>	HierarchicalAgglomerativeClustering.buildDendrogram(Matrix similarityMatrix, HierarchicalAgglomerativeClustering.ClusterLinkage linkage) Builds a dendrogram of the rows of similarity matrix by iteratively linking each row according to the linkage policy in a bottom up manner.
Assignments	SpectralClustering.cluster(Matrix matrix) Returns the Cluster Assignments of each data point in matrix.
Assignments	SpectralClustering.cluster(Matrix matrix, int maxClusters, boolean useKMeans) Returns the Cluster for each data point in matrix.
Assignments	ClutoClustering.cluster(Matrix matrix, int numClusters, ClutoClustering.Method clusterMethod, ClutoClustering.Criterion criterionMethod) Clusters the set of rows in the given Matrix into a specified number of clusters using the specified CLUTO clustering method.
Assignments	Streemer.cluster(Matrix matrix, int numClusters, double backgroundClusterPerc, double similarityThreshold, int minClusterSize, SimilarityFunction simFunc)
static Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, int numRepetitions) Clusters matrix using the RandomSeed seeding algorithm and the default kmeans CriterionFunction.
static Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, int numRepetitions, CriterionFunction criterion) Clusters matrix using the RandomSeed seeding algorithm and the specified CriterionFunction.
Assignments	FastStreamingKMeans.cluster(Matrix matrix, int numClusters, int kappa, double beta, SimilarityFunction simFunc) Clusters the rows of the provided matrix into the specified number of clusters in a single pass using the parameters to guide how clusters are formed.
static Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, int numRepetitions, KMeansSeed seedType, CriterionFunction criterion) Clusters matrix using the specified SeedAlgorithm and the specified CriterionFunction.
Assignments	AutomaticStopClustering.cluster(Matrix m, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	FastStreamingKMeans.cluster(Matrix matrix, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters and using the default values for beta, kappa, and the SimilarityFunction, unless otherwise specified in the properties.
Assignments	Streemer.cluster(Matrix matrix, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	BisectingKMeans.cluster(Matrix dataPoints, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	HierarchicalAgglomerativeClustering.cluster(Matrix m, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	CKVWSpectralClustering03.cluster(Matrix matrix, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	ClutoClustering.cluster(Matrix matrix, int numClusters, Properties properties) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, Properties properties) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	LinkClustering.cluster(Matrix matrix, int numClusters, Properties props) Ignores the specified number of clusters and returns the clustering solution according to the partition density.
Assignments	CKVWSpectralClustering06.cluster(Matrix matrix, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	ClusteringByCommittee.cluster(Matrix m, int numClusters, Properties props) Ignores the provided number of clusters and clusters the rows of the provided matrix using the CBC algorithm.
Assignments	NeighborChainAgglomerativeClustering.cluster(Matrix m, int numClusters, Properties props) Returns the agglomerative clustering result using NeighborChainAgglomerativeClustering.ClusterLink and SimilarityFunction specified from the constructor.
Assignments	Clustering.cluster(Matrix matrix, int numClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	DataMatrixLinkClustering.cluster(Matrix matrix, int numClusters, Properties props)
Assignments	GapStatistic.cluster(Matrix m, int maxClusters, Properties props) Clusters the set of rows in the given Matrix into the specified number of clusters.
Assignments	AutomaticStopClustering.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	FastStreamingKMeans.cluster(Matrix matrix, Properties props) Throws an UnsupportedOperationException if called.
Assignments	Streemer.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters
Assignments	BisectingKMeans.cluster(Matrix dataPoints, Properties props) Not implemented.
Assignments	HierarchicalAgglomerativeClustering.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	CKVWSpectralClustering03.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	ClutoClustering.cluster(Matrix matrix, Properties properties) Throws an UnsupportedOperationException if called, as CLUTO requires the number of clusters to be specified.
Assignments	DirectClustering.cluster(Matrix matrix, Properties properties) Throws UnsupportedOperationException.
Assignments	LinkClustering.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	CKVWSpectralClustering06.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	ClusteringByCommittee.cluster(Matrix m, Properties props) Clusters the rows of m according to the CBC algorithm, using props to specify the configurable parameters of the algorithm.
Assignments	NeighborChainAgglomerativeClustering.cluster(Matrix m, Properties props) Unsupported
Assignments	Clustering.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
Assignments	DataMatrixLinkClustering.cluster(Matrix matrix, Properties props)
Assignments	GapStatistic.cluster(Matrix matrix, Properties props) Clusters the set of rows in the given Matrix without a specified number of clusters (optional operation).
static Assignments	NeighborChainAgglomerativeClustering.clusterAdjacencyMatrix(Matrix adj, NeighborChainAgglomerativeClustering.ClusterLink method, int numClusters) Clusters the points represented as an adjacency matrix in adj using the supplied ClusterLink method into numCluster.
static int[]	HierarchicalAgglomerativeClustering.clusterRows(Matrix m, double clusterSimilarityThreshold, HierarchicalAgglomerativeClustering.ClusterLinkage linkage, Similarity.SimType similarityFunction) Clusters all rows in the matrix using the specified cluster similarity measure for comparison and threshold for when to stop clustering.
void	EigenCut.computeCut(Matrix matrix) Compute the cut with the lowest conductance for the data set.
void	BaseSpectralCut.computeCut(Matrix matrix) Compute the cut with the lowest conductance for the data set.
protected static int	BaseSpectralCut.computeCut(Matrix matrix, DoubleVector rho, double rhoSum, DoubleVector matrixRowSums) Returns the index at which matrix should be cut such that the conductance between the two partitions is minimized.
protected static void	BaseSpectralCut.computeMatrixDotV(Matrix matrix, DoubleVector newV, DoubleVector v) Computes the dot product between a given matrix and a given vector newV.
protected static DoubleVector	BaseSpectralCut.computeMatrixTransposeV(Matrix matrix, DoubleVector v) Returns the dot product between the transpose of a given matrix and a given vector.
DoubleVector	EigenCut.computeRhoSum(Matrix matrix) Computes the similarity between each data point and centroid of the data set.
DoubleVector	BaseSpectralCut.computeRhoSum(Matrix matrix) Computes the similarity between each data point and centroid of the data set.
protected abstract DoubleVector	BaseSpectralCut.computeSecondEigenVector(Matrix matrix, int vectorLength) Returns a DoubleVector representing the secord largest eigen vector for the data set.
protected DoubleVector	CKVWSpectralClustering03.SpectralCut.computeSecondEigenVector(Matrix matrix, int vectorLength) Returns a DoubleVector representing the secord largest eigen vector for the data set.
protected DoubleVector	CKVWSpectralClustering06.SuperSpectralCut.computeSecondEigenVector(Matrix matrix, int vectorLength) Returns a DoubleVector representing the secord largest eigen vector for the data set.
static double	BaseSpectralCut.kMeansObjective(int numClusters, int[] assignments, Matrix data) Returns the K-Means objective over an arbitrary clustering assignment for the data set.
static int[]	HierarchicalAgglomerativeClustering.partitionRows(Matrix m, int numClusters, HierarchicalAgglomerativeClustering.ClusterLinkage linkage, Similarity.SimType similarityFunction) Clusters all rows in the matrix using the specified cluster similarity measure for comparison and stopping when the number of clusters is equal to the specified number.

Constructors in edu.ucla.sspace.clustering with parameters of type Matrix

Constructor and Description
Assignments(int numClusters, Assignment[] initialAssignments, Matrix matrix) Creates a new Assignments instance that takes ownership of the initialAssignments array.
Assignments(int numClusters, int numAssignments, Matrix matrix) Creates a new Assignments instance that can hold up to numAssignments Assignments.

Uses of Matrix in edu.ucla.sspace.clustering.criterion

Methods in edu.ucla.sspace.clustering.criterion with parameters of type Matrix

Modifier and Type	Method and Description
void	CriterionFunction.setup(Matrix m, int[] initialAssignments, int numClusters) Creates the cluster centroids and any other meta data needed by this CriterionFunction.
void	BaseFunction.setup(Matrix m, int[] initialAssignments, int numClusters) Creates the cluster centroids and any other meta data needed by this CriterionFunction.
void	HybridBaseFunction.setup(Matrix m, int[] initialAssignments, int numClusters) Creates the cluster centroids and any other meta data needed by this CriterionFunction.
protected void	BaseFunction.subSetup(Matrix m) Setup any extra information needed before computing the cost values for each cluster.
protected void	E1Function.subSetup(Matrix m) Setup any extra information needed before computing the cost values for each cluster.
protected void	G1Function.subSetup(Matrix m) Setup any extra information needed before computing the cost values for each cluster.

Uses of Matrix in edu.ucla.sspace.clustering.seeding

Methods in edu.ucla.sspace.clustering.seeding with parameters of type Matrix

Modifier and Type	Method and Description
DoubleVector[]	OrssSeed.chooseSeeds(int numCentroids, Matrix dataPoints) Returns an array of length numCentroids that contains centroids composed of either vectors from dataPoints or a linear combination of vectors from dataPoints.
DoubleVector[]	KMeansPlusPlusSeed.chooseSeeds(int numCentroids, Matrix dataPoints) Selects the best scattered numCentroids data points from dataPoints.
DoubleVector[]	GeneralizedOrssSeed.chooseSeeds(int k, Matrix dataPoints) Selects k rows of dataPoints to be seeds of a k-means instance.
DoubleVector[]	RandomSeed.chooseSeeds(int numCentroids, Matrix dataPoints) Returns an array of length numCentroids that contains centroids composed of either vectors from dataPoints or a linear combination of vectors from dataPoints.
DoubleVector[]	KMeansSeed.chooseSeeds(int numCentroids, Matrix dataPoints) Returns an array of length numCentroids that contains centroids composed of either vectors from dataPoints or a linear combination of vectors from dataPoints.
DoubleVector[]	GeneralizedOrssSeed.chooseSeeds(Matrix dataPoints, int k, int[] weights) Selects k rows of dataPoints, weighted by the specified amount, to be seeds of a k-means instance.

Uses of Matrix in edu.ucla.sspace.common

Fields in edu.ucla.sspace.common declared as Matrix

Modifier and Type	Field and Description
protected Matrix	GenericTermDocumentVectorSpace.wordSpace The word space of the term document based word space model.

Uses of Matrix in edu.ucla.sspace.matrix

Subinterfaces of Matrix in edu.ucla.sspace.matrix

Modifier and Type	Interface and Description
interface	AtomicMatrix An interface for any Matrix which wants to support atomic behavior.
interface	SparseMatrix An interface for sparse Matrix implementations whose backing data storage permits accessing rows and columns with SparseVector objects.

Classes in edu.ucla.sspace.matrix that implement Matrix

Modifier and Type	Class and Description
class	AbstractMatrix An abstract Matrix class that provides common implementations for generic matrix operations.
class	ArrayMatrix A Matrix backed by an array.
class	AtomicGrowingMatrix A concurrent, thread-safe, growable Matrix class.
class	AtomicGrowingSparseHashMatrix A concurrent, thread-safe, growable SparseMatrix class that is optimized for operations only access one value of the matrix at a time.
class	AtomicGrowingSparseMatrix A concurrent, thread-safe, growable SparseMatrix class.
class	CellMaskedMatrix This Matrix decorator allows every row and column index to be remapped to new indices.
class	CellMaskedSparseMatrix This SparseMatrix decorator allows every row and column index to be remapped to new indices.
class	DiagonalMatrix A special-case Matrix implementation for diagonal matrices.
class	GrowingSparseMatrix A growing sparse Matrix based on the Yale Sparse Matrix Format.
class	OnDiskMatrix A Matrix implementation that uses a binary file to read and write Returns a copy of the specified rowvalues of the matrix.
class	RowMaskedMatrix A tiled view of a Matrix instance where selected rows of the instance a represented as a single, contiguous matrix.
class	RowScaledMatrix A decorator over Matrixs.
class	RowScaledSparseMatrix A decorator over SparseMatrixs.
class	SparseHashMatrix A SparseMatrix backed by vectors that provide amortized O(1) access to their elements.
class	SparseOnDiskMatrix A SparseMatrix implementation that uses a binary file to read and write.
class	SparseRowMaskedMatrix A tiled view of a SparseMatrix instance where selected rows of the instance a represented as a single, contiguous matrix.
class	SparseSymmetricMatrix A decorator around a SparseMatrix that keeps only the upper triangular values while providing a symmetric view of the data.
class	SymmetricMatrix A symmetric dense matrix that only stores the values of the lower triangular.
class	SynchronizedMatrix A Matrix decorator class that provides thread safe access to a backing Matrix instance.
class	SynchronizedSparseMatrix A SparseMatrix decorator class that provides thread safe access to a backing SparseMatrix instance.
class	TransposedMatrix A Matrix decorator class that tranposes the data in the backing matrix.
class	YaleSparseMatrix A sparse Matrix based on the Yale Sparse Matrix Format, as implemented in CompactSparseVector.

Methods in edu.ucla.sspace.matrix that return Matrix

Modifier and Type	Method and Description
static <T extends DoubleVector> Matrix	Matrices.asMatrix(List<T> vectors) Returns a Matrix from a list of DoubleVectors.
static Matrix	Statistics.average(Matrix m, Statistics.Dimension dim) Return a matrix containing the averages for the dimension specificed.
static Matrix	Statistics.average(Matrix m, Statistics.Dimension dim, int errorCode) Return a matrix containing the averages for the dimension specificed.
Matrix	RowMaskedMatrix.backingMatrix() Returns the backingMatrix that is being masked.
Matrix	MatrixFactorization.classFeatures() Returns the latent class by feature Matrix.
static Matrix	Matrices.copy(Matrix matrix) Returns a copied version of a given matrix.
static Matrix	Matrices.copyTo(Matrix matrix, Matrix output) Copies values from matrix to output and returns output.
static Matrix	Matrices.create(int rows, int cols, boolean isDense) Creates a matrix of the given dimensions and selects the matrix implementation by considering the size and density of the new matrix with respect to the available memory for the JVM.
static Matrix	Matrices.create(int rows, int cols, Matrix.Type matrixType) Creates a new Matrix based on the provided type, with the provided dimensions
Matrix	MatrixFactorization.dataClasses() Returns the data point by latent class Matrix.
Matrix	MatrixFile.load() Loads the matrix from disk and returns a copy of its data.
static Matrix	Matrices.multiply(Matrix m1, Matrix m2)
static Matrix	LocalityPreservingProjection.project(Matrix m, MatrixFile affinityMatrix, int dimensions) Projects the rows of the input matrix into a lower dimensional subspace using the Locality Preserving Projection (LPP) algorithm and the affinity matrix as a guide to locality.
static Matrix	LocalityPreservingProjection.project(Matrix m, Matrix affinityMatrix, int dimensions) Projects the rows of the input matrix into a lower dimensional subspace using the Locality Preserving Projection (LPP) algorithm and the affinity matrix as a guide to locality.
static Matrix	MatrixIO.readMatrix(File matrix, MatrixIO.Format format) Converts the contents of a matrix file as a Matrix object, using the provided format as a hint for what kind to create.
static Matrix	MatrixIO.readMatrix(File matrix, MatrixIO.Format format, Matrix.Type matrixType) Converts the contents of a matrix file as a Matrix object, using the provided type description as a hint for what kind to create.
static Matrix	MatrixIO.readMatrix(File matrix, MatrixIO.Format format, Matrix.Type matrixType, boolean transposeOnRead) Converts the contents of a matrix file as a Matrix object, using the provided type description as a hint for what kind to create.
static Matrix	Matrices.resize(Matrix matrix, int rows, int columns) Returns a new Matrix that has been resized from the original, truncating values if smaller, or adding zero elements if larger.
static Matrix	Statistics.std(Matrix m, Matrix average, Statistics.Dimension dim) Return a matrix containing the standard deviation for the dimension specificed.
static Matrix	Statistics.std(Matrix m, Matrix average, Statistics.Dimension dim, int errorCode) Return a matrix containing the standard deviation for the dimension specificed.
static Matrix[]	SVD.svd(File matrix, int dimensions) Deprecated.
static Matrix[]	SVD.svd(File matrix, MatrixIO.Format format, int dimensions) Deprecated.
static Matrix[]	SvdlibjDriver.svd(File matrix, MatrixIO.Format format, int dimensions) Computes the SVD of the matrix in the provided file in the specified format.
static Matrix[]	SVD.svd(File matrix, SVD.Algorithm alg, int dimensions) Deprecated.
static Matrix[]	SVD.svd(File matrix, SVD.Algorithm alg, MatrixIO.Format format, int dimensions) Deprecated.
static Matrix[]	SVD.svd(Matrix m, int dimensions) Deprecated.
static Matrix[]	SvdlibjDriver.svd(Matrix m, int dimensions) Computes the SVD of the matrix.
static Matrix[]	SVD.svd(Matrix m, SVD.Algorithm algorithm, int dimensions) Deprecated.
Matrix	BaseTransform.transform(Matrix matrix) Returns a transformed matrix based on the given matrix.
Matrix	Transform.transform(Matrix input) Returns a transformed matrix based on the given matrix.
Matrix	NoTransform.transform(Matrix input) Returns a transformed matrix based on the given matrix.
Matrix	BaseTransform.transform(Matrix matrix, Matrix transformed) Returns a transformed matrix based on the given matrix.
Matrix	Transform.transform(Matrix input, Matrix output) Returns a transformed matrix based on the given matrix.
Matrix	NoTransform.transform(Matrix input, Matrix output) Returns a transformed matrix based on the given matrix.
static Matrix	Matrices.transpose(Matrix matrix) Returns the transpose of the input matrix, i.e.

Methods in edu.ucla.sspace.matrix with parameters of type Matrix

Modifier and Type	Method and Description
static Matrix	Statistics.average(Matrix m, Statistics.Dimension dim) Return a matrix containing the averages for the dimension specificed.
static Matrix	Statistics.average(Matrix m, Statistics.Dimension dim, int errorCode) Return a matrix containing the averages for the dimension specificed.
static void	Normalize.byColumn(Matrix m) Normalize the values of each column in the Matrix to be normalized by the length of each column.
static void	Normalize.byCorrelation(Matrix m, boolean saveNegatives) Normalize the values of the Matrix by using the Pearson correlation.
static void	Normalize.byLength(Matrix m) Normalize the values of each row in the Matrix to be normalized by the l2 norm of each row.
static void	Normalize.byMagnitude(Matrix m) Normalize the all the values values in the Matrix to be normalized by the l2 norm of the entire Matrix.
static void	Normalize.byRow(Matrix m) Normalize the values of each row in Matrix to be normalized by the length of each row.
MatrixFile	MinSimilarityAffinityMatrixCreator.calculate(Matrix input) Computes the affinity matrix for the input matrix according to the specified similarity metrics, returning the result as a file on disk.
MatrixFile	AffinityMatrixCreator.calculate(Matrix input) Computes the affinity matrix for the input matrix according to the specified similarity metrics, returning the result as a file on disk.
MatrixFile	NearestNeighborAffinityMatrixCreator.calculate(Matrix input) Computes the affinity matrix for the input matrix according to the specified similarity metrics, returning the result as a file on disk.
static Matrix	Matrices.copy(Matrix matrix) Returns a copied version of a given matrix.
static Matrix	Matrices.copyTo(Matrix matrix, Matrix output) Copies values from matrix to output and returns output.
static TransformStatistics.MatrixStatistics	TransformStatistics.extractStatistics(Matrix matrix) Extracts the full row, column, and matrix summations based on entries in the given Matrix.
static TransformStatistics.MatrixStatistics	TransformStatistics.extractStatistics(Matrix matrix, boolean countRowOccurrances, boolean countColumnOccurrances) Extracts the row, column, and matrix summations based on entries in the given Matrix.
SortedMultiMap<Double,Integer>	RowComparator.getMostSimilar(Matrix m, int row, int kNearestRows, Similarity.SimType similarityType) Compares the specified row to all other rows, returning the k-nearest rows according to the similarity metric.
SortedMultiMap<Double,Integer>	RowComparator.getMostSimilar(Matrix m, int row, int kNearestRows, SimilarityFunction simFunction) Compares the specified row to all other rows, returning the k-nearest rows according to the similarity metric.
protected GlobalTransform	CorrelationTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	LogLikelihoodTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	LogEntropyTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	TfIdfTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	PointWiseMutualInformationTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected abstract GlobalTransform	BaseTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	TfIdfDocStripedTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
protected GlobalTransform	RowMagnitudeTransform.getTransform(Matrix matrix) Returns a GlobalTransform for a Matrix.
static Matrix	Matrices.multiply(Matrix m1, Matrix m2)
static Matrix	LocalityPreservingProjection.project(Matrix m, MatrixFile affinityMatrix, int dimensions) Projects the rows of the input matrix into a lower dimensional subspace using the Locality Preserving Projection (LPP) algorithm and the affinity matrix as a guide to locality.
static Matrix	LocalityPreservingProjection.project(Matrix m, Matrix affinityMatrix, int dimensions) Projects the rows of the input matrix into a lower dimensional subspace using the Locality Preserving Projection (LPP) algorithm and the affinity matrix as a guide to locality.
static Matrix	Matrices.resize(Matrix matrix, int rows, int columns) Returns a new Matrix that has been resized from the original, truncating values if smaller, or adding zero elements if larger.
static Matrix	Statistics.std(Matrix m, Matrix average, Statistics.Dimension dim) Return a matrix containing the standard deviation for the dimension specificed.
static Matrix	Statistics.std(Matrix m, Matrix average, Statistics.Dimension dim, int errorCode) Return a matrix containing the standard deviation for the dimension specificed.
static Matrix[]	SVD.svd(Matrix m, int dimensions) Deprecated.
static Matrix[]	SvdlibjDriver.svd(Matrix m, int dimensions) Computes the SVD of the matrix.
static Matrix[]	SVD.svd(Matrix m, SVD.Algorithm algorithm, int dimensions) Deprecated.
static AtomicMatrix	Matrices.synchronizedMatrix(Matrix m) Returns a synchronized (thread-safe) matrix backed by the provided Matrix.
Matrix	BaseTransform.transform(Matrix matrix) Returns a transformed matrix based on the given matrix.
Matrix	Transform.transform(Matrix input) Returns a transformed matrix based on the given matrix.
Matrix	NoTransform.transform(Matrix input) Returns a transformed matrix based on the given matrix.
Matrix	BaseTransform.transform(Matrix matrix, Matrix transformed) Returns a transformed matrix based on the given matrix.
Matrix	Transform.transform(Matrix input, Matrix output) Returns a transformed matrix based on the given matrix.
Matrix	NoTransform.transform(Matrix input, Matrix output) Returns a transformed matrix based on the given matrix.
static Matrix	Matrices.transpose(Matrix matrix) Returns the transpose of the input matrix, i.e.
static void	MatrixIO.writeMatrix(Matrix matrix, File output, MatrixIO.Format format) Writes the matrix to the specified output file in the provided format

Constructors in edu.ucla.sspace.matrix with parameters of type Matrix

Constructor and Description
CellMaskedMatrix(Matrix matrix, int[] rowMaskMap, int[] colMaskMap) Creates a new CellMaskedMatrix from a given Matrix and maps, one for the row indices and one for the column indices.
CorrelationTransform.CorrelationGlobalTransform(Matrix matrix) Creates an instance of CorrelationTransform from a Matrix.
LogEntropyTransform.LogEntropyGlobalTransform(Matrix matrix) Creates an instance of LogEntropyGlobalTransform from a Matrix.
LogLikelihoodTransform.LogLikelihoodGlobalTransform(Matrix matrix) Creates an instance of LogLikelihoodTransform from a given Matrix.
PointWiseMutualInformationTransform.PointWiseMutualInformationGlobalTransform(Matrix matrix) Creates an instance of PointWiseMutualInformationTransform from a given Matrix.
RowMagnitudeTransform.RowMagnitudeGlobalTransform(Matrix matrix) Creates an instance of RowMagnitudeGlobalTransform from a Matrix.
RowMaskedMatrix(Matrix matrix, BitSet included) Creates a partial view of the provided matrix using the bits set to true as the rows that should be included
RowMaskedMatrix(Matrix matrix, int[] reordering) Creates a partial view of the provided matrix using the integers in the array of indices.
RowMaskedMatrix(Matrix matrix, LinkedHashSet<Integer> included) Creates a partial view of the provided matrix using the integers in the ordered set.
RowMaskedMatrix(Matrix matrix, Set<Integer> included) Creates a partial view of the provided matrix using the integers in the set to specify which rows should be included in the matrix.
RowScaledMatrix(Matrix matrix, DoubleVector v) Creates a RowScaledMatrix that provides scaled read only access to the provided Matrix instance.
SynchronizedMatrix(Matrix matrix) Creates a SynchronizedMatrix that provides thread-safe access to the provided Matrix instance.
TfIdfDocStripedTransform.TfIdfGlobalTransform(Matrix matrix) Creates an instance of TfIdfGlobalTransform from a Matrix.
TfIdfTransform.TfIdfGlobalTransform(Matrix matrix) Creates an instance of TfIdfGlobalTransform from a Matrix.
TransposedMatrix(Matrix matrix) Creates a Matrix that provides a transposed view of the original matrix.

Uses of Matrix in edu.ucla.sspace.matrix.factorization

Fields in edu.ucla.sspace.matrix.factorization declared as Matrix

Modifier and Type	Field and Description
protected Matrix	AbstractSvd.classFeatures The class by feature type matrix.
protected Matrix	AbstractSvd.dataClasses The data point by class matrix.

Methods in edu.ucla.sspace.matrix.factorization that return Matrix

Modifier and Type	Method and Description
Matrix	NonNegativeMatrixFactorizationOPL.classFeatures() Returns the latent class by feature Matrix.
Matrix	AbstractSvd.classFeatures() Returns the latent class by feature Matrix.
Matrix	NonNegativeMatrixFactorizationMultiplicative.classFeatures() Returns the latent class by feature Matrix.
Matrix	SingularValueDecompositionLibC.classFeatures() Returns the latent class by feature Matrix.
Matrix	NonNegativeMatrixFactorizationOPL.dataClasses() Returns the data point by latent class Matrix.
Matrix	AbstractSvd.dataClasses() Returns the data point by latent class Matrix.
Matrix	NonNegativeMatrixFactorizationMultiplicative.dataClasses() Returns the data point by latent class Matrix.
Matrix	SingularValueDecompositionLibC.dataClasses() Returns the data point by latent class Matrix.

Methods in edu.ucla.sspace.matrix.factorization with parameters of type Matrix

Modifier and Type	Method and Description
static void	NonNegativeMatrixFactorizationOPL.initialize(Matrix m) Initializes every value in m to be a random value between 0 and 1, inclusive.
static void	NonNegativeMatrixFactorizationMultiplicative.initialize(Matrix m) Initializes every value in m to be a random value between 0 and 1, inclusive.
static void	NonNegativeMatrixFactorizationOPL.makeNonZero(Matrix m) Sets any negative values of m to zero.

Uses of Matrix in edu.ucla.sspace.util

Classes in edu.ucla.sspace.util that implement Matrix

Modifier and Type	Class and Description
class	SemanticSpaceMatrix A Matrix implementation whose data is backed by a SemanticSpace.

Methods in edu.ucla.sspace.util with parameters of type Matrix

Modifier and Type	Method and Description
double	KrippendorffsAlpha.compute(Matrix ratings, KrippendorffsAlpha.DifferenceFunction diffFunc)
double[]	KrippendorffsAlpha.computeConfidenceIntervals(Matrix ratings, KrippendorffsAlpha.DifferenceFunction diffFunc, int numResamples, double[] confidenceIntervals)