## Uses of Interface
org.apache.mahout.math.Matrix

Packages that use Matrix
org.apache.mahout.math: Core base classes; Operations on primitive arrays such as sorting, partitioning and permuting.
org.apache.mahout.math.decomposer.hebbian
org.apache.mahout.math.decomposer.lanczos
org.apache.mahout.math.random
org.apache.mahout.math.solver
org.apache.mahout.math.ssvd

Uses of Matrix in org.apache.mahout.math

Classes in org.apache.mahout.math that implement Matrix
`class` `AbstractMatrix` A few universal implementations of convenience functions
`class` `DenseMatrix` Matrix of doubles implemented using a 2-d array
`class` `DenseSymmetricMatrix` Economy packaging for a dense symmetric in-core matrix.
`class` `DiagonalMatrix`
`class` `FileBasedMatrix` Provides a way to get data from a file and treat it as if it were a matrix, but avoids putting all that data onto the Java heap.
`class` `FileBasedSparseBinaryMatrix` Provides a way to get data from a file and treat it as if it were a matrix, but avoids putting all that data onto the Java heap.
`class` `MatrixView` Implements subset view of a Matrix
`class` `PivotedMatrix` Matrix that allows transparent row and column permutation.
`class` `RandomTrinaryMatrix` Random matrix.
`class` `SparseColumnMatrix` sparse matrix with general element values whose columns are accessible quickly.
`class` `SparseMatrix` Doubly sparse matrix.
`class` `SparseRowMatrix` sparse matrix with general element values whose rows are accessible quickly.
`class` `UpperTriangular` Quick and dirty implementation of some `Matrix` methods over packed upper triangular matrix.

Methods in org.apache.mahout.math that return Matrix
`Matrix` `DenseMatrix.assign(DenseMatrix matrix)`
`Matrix` `Matrix.assign(double value)` Assign the value to all elements of the receiver
`Matrix` `DenseMatrix.assign(double value)`
`Matrix` `AbstractMatrix.assign(double value)`
`Matrix` `Matrix.assign(double[][] values)` Assign the values to the receiver
`Matrix` `AbstractMatrix.assign(double[][] values)`
`Matrix` `Matrix.assign(DoubleFunction function)` Apply the function to each element of the receiver
`Matrix` `AbstractMatrix.assign(DoubleFunction function)`
`Matrix` `Matrix.assign(Matrix other)` Assign the other vector values to the receiver
`Matrix` `AbstractMatrix.assign(Matrix other)`
`Matrix` `Matrix.assign(Matrix other, DoubleDoubleFunction function)` Apply the function to each element of the receiver and the corresponding element of the other argument
`Matrix` `AbstractMatrix.assign(Matrix other, DoubleDoubleFunction function)`
`Matrix` `UpperTriangular.assignColumn(int column, Vector other)`
`Matrix` `SparseRowMatrix.assignColumn(int column, Vector other)`
`Matrix` `SparseMatrix.assignColumn(int column, Vector other)`
`Matrix` `SparseColumnMatrix.assignColumn(int column, Vector other)`
`Matrix` `RandomTrinaryMatrix.assignColumn(int column, Vector other)`
`Matrix` `PivotedMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `MatrixView.assignColumn(int column, Vector other)`
`Matrix` `Matrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `FileBasedSparseBinaryMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `FileBasedMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `DiagonalMatrix.assignColumn(int column, Vector other)`
`Matrix` `DenseMatrix.assignColumn(int column, Vector other)`
`Matrix` `UpperTriangular.assignNonZeroElementsInRow(int row, double[] other)`
`Matrix` `UpperTriangular.assignRow(int row, Vector other)`
`Matrix` `SparseRowMatrix.assignRow(int row, Vector other)`
`Matrix` `SparseMatrix.assignRow(int row, Vector other)`
`Matrix` `SparseColumnMatrix.assignRow(int row, Vector other)`
`Matrix` `RandomTrinaryMatrix.assignRow(int row, Vector other)`
`Matrix` `PivotedMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `MatrixView.assignRow(int row, Vector other)`
`Matrix` `Matrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `FileBasedSparseBinaryMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `FileBasedMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `DiagonalMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `DenseMatrix.assignRow(int row, Vector other)`
`Matrix` `SparseRowMatrix.clone()`
`Matrix` `SparseMatrix.clone()`
`Matrix` `SparseColumnMatrix.clone()`
`Matrix` `PivotedMatrix.clone()`
`Matrix` `MatrixView.clone()`
`Matrix` `Matrix.clone()` Return a copy of the recipient
`Matrix` `DenseMatrix.clone()`
`Matrix` `AbstractMatrix.clone()`
`Matrix` `Vector.cross(Vector other)` Return the cross product of the receiver and the other vector
`Matrix` `NamedVector.cross(Vector other)`
`Matrix` `DelegatingVector.cross(Vector other)`
`Matrix` `AbstractVector.cross(Vector other)`
`Matrix` `Matrix.divide(double x)` Return a new matrix containing the values of the recipient divided by the argument
`Matrix` `AbstractMatrix.divide(double x)`
`static Matrix` `Matrices.functionalMatrixView(int rows, int columns, IntIntFunction gf)` Shorter form of `Matrices.functionalMatrixView(int, int, org.apache.mahout.math.function.IntIntFunction, boolean)`.
`static Matrix` `Matrices.functionalMatrixView(int rows, int columns, IntIntFunction gf, boolean denseLike)` Create a matrix view based on a function generator.
`static Matrix` `Matrices.gaussianView(int rows, int columns, long seed)` Random Gaussian matrix view.
`Matrix` `PivotedMatrix.getBase()`
`Matrix` `CholeskyDecomposition.getL()`
`Matrix` `QRDecomposition.getQ()` Generates and returns the (economy-sized) orthogonal factor `Q`.
`Matrix` `QR.getQ()`
`Matrix` `OldQRDecomposition.getQ()` Generates and returns the (economy-sized) orthogonal factor `Q`.
`Matrix` `QRDecomposition.getR()` Returns the upper triangular factor, `R`.
`Matrix` `QR.getR()`
`Matrix` `OldQRDecomposition.getR()` Returns the upper triangular factor, `R`.
`Matrix` `SingularValueDecomposition.getS()`
`Matrix` `SingularValueDecomposition.getU()` Returns the left singular vectors `U`.
`Matrix` `SingularValueDecomposition.getV()` Returns the right singular vectors `V`.
`Matrix` `UpperTriangular.like()`
`Matrix` `SparseRowMatrix.like()`
`Matrix` `SparseMatrix.like()`
`Matrix` `SparseColumnMatrix.like()`
`Matrix` `RandomTrinaryMatrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `PivotedMatrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `MatrixView.like()`
`Matrix` `Matrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `FileBasedSparseBinaryMatrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `FileBasedMatrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `DiagonalMatrix.like()` Return an empty matrix of the same underlying class as the receiver
`Matrix` `DenseMatrix.like()`
`Matrix` `UpperTriangular.like(int rows, int columns)`
`Matrix` `SparseRowMatrix.like(int rows, int columns)`
`Matrix` `SparseMatrix.like(int rows, int columns)`
`Matrix` `SparseColumnMatrix.like(int rows, int columns)`
`Matrix` `RandomTrinaryMatrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `PivotedMatrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `MatrixView.like(int rows, int columns)`
`Matrix` `Matrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `FileBasedSparseBinaryMatrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `FileBasedMatrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `DiagonalMatrix.like(int rows, int columns)` Returns an empty matrix of the same underlying class as the receiver and of the specified size.
`Matrix` `DenseMatrix.like(int rows, int columns)`
`protected Matrix` `VectorView.matrixLike(int rows, int columns)`
`protected Matrix` `SequentialAccessSparseVector.matrixLike(int rows, int columns)`
`protected Matrix` `RandomAccessSparseVector.matrixLike(int rows, int columns)`
`protected Matrix` `PermutedVectorView.matrixLike(int rows, int columns)` Subclasses must override to return an appropriately sparse or dense result
`protected Matrix` `MatrixVectorView.matrixLike(int rows, int columns)`
`protected Matrix` `DenseVector.matrixLike(int rows, int columns)`
`protected Matrix` `ConstantVector.matrixLike(int rows, int columns)` Subclasses must override to return an appropriately sparse or dense result
`protected abstract Matrix` `AbstractVector.matrixLike(int rows, int columns)` Subclasses must override to return an appropriately sparse or dense result
`protected Matrix` `AbstractMatrix.TransposeViewVector.matrixLike(int rows, int columns)`
`Matrix` `Matrix.minus(Matrix x)` Return a new matrix containing the element by element difference of the recipient and the argument
`Matrix` `AbstractMatrix.minus(Matrix other)`
`Matrix` `Matrix.plus(double x)` Return a new matrix containing the sum of each value of the recipient and the argument
`Matrix` `AbstractMatrix.plus(double x)`
`Matrix` `Matrix.plus(Matrix x)` Return a new matrix containing the element by element sum of the recipient and the argument
`Matrix` `AbstractMatrix.plus(Matrix other)`
`Matrix` `QRDecomposition.solve(Matrix B)` Least squares solution of `A*X = B`; `returns X`.
`Matrix` `QR.solve(Matrix B)`
`Matrix` `OldQRDecomposition.solve(Matrix B)` Least squares solution of `A*X = B`; `returns X`.
`Matrix` `CholeskyDecomposition.solveLeft(Matrix z)` Compute inv(L) * z efficiently.
`Matrix` `CholeskyDecomposition.solveRight(Matrix z)` Compute z * inv(L') efficiently
`static Matrix` `Matrices.symmetricUniformView(int rows, int columns, int seed)` Matrix view based on uniform [-1,1) distribution.
`Matrix` `Matrix.times(double x)` Return a new matrix containing the product of each value of the recipient and the argument
`Matrix` `AbstractMatrix.times(double x)`
`Matrix` `Matrix.times(Matrix x)` Return a new matrix containing the product of the recipient and the argument
`Matrix` `DiagonalMatrix.times(Matrix other)`
`Matrix` `AbstractMatrix.times(Matrix other)`
`Matrix` `MatrixTimesOps.timesLeft(Matrix that)` Computes matrix product of (that * this)
`Matrix` `DiagonalMatrix.timesLeft(Matrix that)`
`Matrix` `MatrixTimesOps.timesRight(Matrix that)` computes matrix product of (this * that)
`Matrix` `DiagonalMatrix.timesRight(Matrix that)`
`Matrix` `SparseRowMatrix.transpose()`
`Matrix` `SparseColumnMatrix.transpose()`
`Matrix` `Matrix.transpose()` Return a new matrix that is the transpose of the receiver
`Matrix` `AbstractMatrix.transpose()`
`static Matrix` `Matrices.transposedView(Matrix m)` A read-only transposed view of a matrix argument.
`static Matrix` `Matrices.uniformView(int rows, int columns, int seed)` Matrix view based on uniform [0,1) distribution.
`Matrix` `UpperTriangular.viewPart(int[] offset, int[] size)`
`Matrix` `SparseRowMatrix.viewPart(int[] offset, int[] size)`
`Matrix` `SparseMatrix.viewPart(int[] offset, int[] size)`
`Matrix` `SparseColumnMatrix.viewPart(int[] offset, int[] size)`
`Matrix` `RandomTrinaryMatrix.viewPart(int[] offset, int[] size)` Return a new matrix containing the subset of the recipient
`Matrix` `PivotedMatrix.viewPart(int[] offset, int[] size)` Return a new matrix containing the subset of the recipient
`Matrix` `MatrixView.viewPart(int[] offset, int[] size)`
`Matrix` `Matrix.viewPart(int[] offset, int[] size)` Return a view into part of a matrix.
`Matrix` `FileBasedSparseBinaryMatrix.viewPart(int[] offset, int[] size)` Return a view into part of a matrix.
`Matrix` `FileBasedMatrix.viewPart(int[] offset, int[] size)` Return a view into part of a matrix.
`Matrix` `DiagonalMatrix.viewPart(int[] offset, int[] size)` Return a new matrix containing the subset of the recipient
`Matrix` `DenseMatrix.viewPart(int[] offset, int[] size)`
`Matrix` `AbstractMatrix.viewPart(int[] offset, int[] size)`
`Matrix` `Matrix.viewPart(int rowOffset, int rowsRequested, int columnOffset, int columnsRequested)` Return a view into part of a matrix.
`Matrix` `DenseMatrix.viewPart(int rowOffset, int rowsRequested, int columnOffset, int columnsRequested)`
`Matrix` `AbstractMatrix.viewPart(int rowOffset, int rowsRequested, int columnOffset, int columnsRequested)`

Methods in org.apache.mahout.math with parameters of type Matrix
`Matrix` `Matrix.assign(Matrix other)` Assign the other vector values to the receiver
`Matrix` `AbstractMatrix.assign(Matrix other)`
`Matrix` `Matrix.assign(Matrix other, DoubleDoubleFunction function)` Apply the function to each element of the receiver and the corresponding element of the other argument
`Matrix` `AbstractMatrix.assign(Matrix other, DoubleDoubleFunction function)`
`static double` `Algebra.getNorm(Matrix m)` Compute Maximum Absolute Row Sum Norm of input Matrix m http://mathworld.wolfram.com/MaximumAbsoluteRowSumNorm.html
`Matrix` `Matrix.minus(Matrix x)` Return a new matrix containing the element by element difference of the recipient and the argument
`Matrix` `AbstractMatrix.minus(Matrix other)`
`static Vector` `Algebra.mult(Matrix m, Vector v)`
`Matrix` `Matrix.plus(Matrix x)` Return a new matrix containing the element by element sum of the recipient and the argument
`Matrix` `AbstractMatrix.plus(Matrix other)`
`Matrix` `QRDecomposition.solve(Matrix B)` Least squares solution of `A*X = B`; `returns X`.
`Matrix` `QR.solve(Matrix B)`
`Matrix` `OldQRDecomposition.solve(Matrix B)` Least squares solution of `A*X = B`; `returns X`.
`Matrix` `CholeskyDecomposition.solveLeft(Matrix z)` Compute inv(L) * z efficiently.
`Matrix` `CholeskyDecomposition.solveRight(Matrix z)` Compute z * inv(L') efficiently
`Matrix` `Matrix.times(Matrix x)` Return a new matrix containing the product of the recipient and the argument
`Matrix` `DiagonalMatrix.times(Matrix other)`
`Matrix` `AbstractMatrix.times(Matrix other)`
`Matrix` `MatrixTimesOps.timesLeft(Matrix that)` Computes matrix product of (that * this)
`Matrix` `DiagonalMatrix.timesLeft(Matrix that)`
`Matrix` `MatrixTimesOps.timesRight(Matrix that)` computes matrix product of (this * that)
`Matrix` `DiagonalMatrix.timesRight(Matrix that)`
`static Matrix` `Matrices.transposedView(Matrix m)` A read-only transposed view of a matrix argument.
`static void` `FileBasedSparseBinaryMatrix.writeMatrix(File f, Matrix m)`
`static void` `FileBasedMatrix.writeMatrix(File f, Matrix m)`

Constructors in org.apache.mahout.math with parameters of type Matrix
`AbstractMatrix.TransposeViewVector(Matrix m, int offset)`
`AbstractMatrix.TransposeViewVector(Matrix m, int offset, boolean rowToColumn)`
`CholeskyDecomposition(Matrix a)`
`CholeskyDecomposition(Matrix a, boolean pivot)`
`DiagonalMatrix(Matrix values)`
`MatrixVectorView(Matrix matrix, int row, int column, int rowStride, int columnStride)`
`MatrixVectorView(Matrix matrix, int row, int column, int rowStride, int columnStride, boolean isDense)`
`MatrixView(Matrix matrix, int[] offset, int[] size)` Construct a view of the matrix with given offset and cardinality
`OldQRDecomposition(Matrix a)` Constructs and returns a new QR decomposition object; computed by Householder reflections; The decomposed matrices can be retrieved via instance methods of the returned decomposition object.
`PivotedMatrix(Matrix base)`
`PivotedMatrix(Matrix base, int[] pivot)`
`PivotedMatrix(Matrix base, int[] rowPivot, int[] columnPivot)`
`QRDecomposition(Matrix a)` Constructs and returns a new QR decomposition object; computed by Householder reflections; The decomposed matrices can be retrieved via instance methods of the returned decomposition object.
`SingularValueDecomposition(Matrix arg)` Constructs and returns a new singular value decomposition object; The decomposed matrices can be retrieved via instance methods of the returned decomposition object.

Uses of Matrix in org.apache.mahout.math.decomposer.hebbian

Methods in org.apache.mahout.math.decomposer.hebbian that return Matrix
`Matrix` `TrainingState.getCurrentEigens()`
`Matrix` `TrainingState.getTrainingProjections()`

Methods in org.apache.mahout.math.decomposer.hebbian with parameters of type Matrix
`protected boolean` `HebbianSolver.hasNotConverged(Vector currentPseudoEigen, Matrix corpus, TrainingState state)` Uses the `SingularVectorVerifier` to check for convergence
`void` `TrainingState.setCurrentEigens(Matrix currentEigens)`
`void` `TrainingState.setTrainingProjections(Matrix trainingProjections)`
`TrainingState` `HebbianSolver.solve(Matrix corpus, int desiredRank)` Primary singular vector solving method.
`protected EigenStatus` `HebbianSolver.verify(Matrix corpus, Vector currentPseudoEigen)`

Uses of Matrix in org.apache.mahout.math.decomposer.lanczos

Fields in org.apache.mahout.math.decomposer.lanczos declared as Matrix
`protected Matrix` `LanczosState.diagonalMatrix`

Methods in org.apache.mahout.math.decomposer.lanczos that return Matrix
`Matrix` `LanczosState.getDiagonalMatrix()`

Uses of Matrix in org.apache.mahout.math.random

Constructors in org.apache.mahout.math.random with parameters of type Matrix
`MultiNormal(Matrix a, Vector mean)` Constructs a sampler with non-trivial scale matrix and mean.

Uses of Matrix in org.apache.mahout.math.solver

Methods in org.apache.mahout.math.solver that return Matrix
`Matrix` `EigenDecomposition.getD()` Return the block diagonal eigenvalue matrix
`Matrix` `EigenDecomposition.getV()` Return the eigenvector matrix

Methods in org.apache.mahout.math.solver with parameters of type Matrix
`Vector` `LSMR.solve(Matrix A, Vector b)`

Constructors in org.apache.mahout.math.solver with parameters of type Matrix
`EigenDecomposition(Matrix x)`
`EigenDecomposition(Matrix x, boolean isSymmetric)`
`JacobiConditioner(Matrix a)`

Uses of Matrix in org.apache.mahout.math.ssvd

Methods in org.apache.mahout.math.ssvd that return Matrix
`Matrix` `SequentialBigSvd.getU()`
`Matrix` `SequentialBigSvd.getV()`

Constructors in org.apache.mahout.math.ssvd with parameters of type Matrix
`SequentialBigSvd(Matrix A, int p)`

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