Uses of Interface
org.apache.commons.math3.linear.RealMatrix
Packages that use RealMatrix
Package
Description
Implementations of common discrete and continuous distributions.
Implementations of common discrete-time linear filters.
This package provides algorithms that minimize the residuals
between observations and model values.
Linear algebra support.
Clustering algorithms.
This package provides classes to solve non-stiff Ordinary Differential Equations problems.
Algorithms for optimizing a scalar function.
This package provides optimization algorithms that do not require derivatives.
Algorithms for optimizing a vector function.
This package provides optimization algorithms that require derivatives.
All classes and sub-packages of this package are deprecated.
This package provides optimization algorithms that don't require derivatives.
This package provides optimization algorithms that require derivatives.
This package provides optimization algorithms for linear constrained problems.
Random number and random data generators.
Correlations/Covariance computations.
Generic univariate summary statistic objects.
Summary statistics based on moments.
Classes providing hypothesis testing.
Statistical routines involving multivariate data.
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Uses of RealMatrix in org.apache.commons.math3.distribution
Fields in org.apache.commons.math3.distribution declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
MultivariateNormalDistribution.covarianceMatrix
Covariance matrix.private final RealMatrix
MultivariateNormalDistribution.covarianceMatrixInverse
The matrix inverse of the covariance matrix.private final RealMatrix
MultivariateNormalDistribution.samplingMatrix
Matrix used in computation of samples.Methods in org.apache.commons.math3.distribution that return RealMatrixModifier and TypeMethodDescriptionMultivariateNormalDistribution.getCovariances()
Gets the covariance matrix. -
Uses of RealMatrix in org.apache.commons.math3.filter
Fields in org.apache.commons.math3.filter declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
DefaultProcessModel.controlMatrix
The control matrix, used to integrate a control input into the state estimation.private RealMatrix
KalmanFilter.controlMatrix
The control matrix, equivalent to B.private RealMatrix
KalmanFilter.errorCovariance
The error covariance matrix, equivalent to P.private RealMatrix
DefaultProcessModel.initialErrorCovMatrix
The initial error covariance matrix of the observed process.private RealMatrix
DefaultMeasurementModel.measurementMatrix
The measurement matrix, used to associate the measurement vector to the internal state estimation vector.private RealMatrix
KalmanFilter.measurementMatrix
The measurement matrix, equivalent to H.private RealMatrix
KalmanFilter.measurementMatrixT
The transposed measurement matrix.private RealMatrix
DefaultMeasurementModel.measurementNoise
The measurement noise covariance matrix.private RealMatrix
DefaultProcessModel.processNoiseCovMatrix
The process noise covariance matrix.private RealMatrix
DefaultProcessModel.stateTransitionMatrix
The state transition matrix, used to advance the internal state estimation each time-step.private RealMatrix
KalmanFilter.transitionMatrix
The transition matrix, equivalent to A.private RealMatrix
KalmanFilter.transitionMatrixT
The transposed transition matrix.Methods in org.apache.commons.math3.filter that return RealMatrixModifier and TypeMethodDescriptionDefaultProcessModel.getControlMatrix()
Returns the control matrix.ProcessModel.getControlMatrix()
Returns the control matrix.KalmanFilter.getErrorCovarianceMatrix()
Returns a copy of the current error covariance matrix.DefaultProcessModel.getInitialErrorCovariance()
Returns the initial error covariance matrix.ProcessModel.getInitialErrorCovariance()
Returns the initial error covariance matrix.DefaultMeasurementModel.getMeasurementMatrix()
Returns the measurement matrix.MeasurementModel.getMeasurementMatrix()
Returns the measurement matrix.DefaultMeasurementModel.getMeasurementNoise()
Returns the measurement noise matrix.MeasurementModel.getMeasurementNoise()
Returns the measurement noise matrix.DefaultProcessModel.getProcessNoise()
Returns the process noise matrix.ProcessModel.getProcessNoise()
Returns the process noise matrix.DefaultProcessModel.getStateTransitionMatrix()
Returns the state transition matrix.ProcessModel.getStateTransitionMatrix()
Returns the state transition matrix.Constructors in org.apache.commons.math3.filter with parameters of type RealMatrixModifierConstructorDescriptionDefaultMeasurementModel
(RealMatrix measMatrix, RealMatrix measNoise) Create a newMeasurementModel
, takingRealMatrix
objects as input parameters for the respective measurement matrix and noise.DefaultProcessModel
(RealMatrix stateTransition, RealMatrix control, RealMatrix processNoise, RealVector initialStateEstimate, RealMatrix initialErrorCovariance) Create a newProcessModel
, taking double arrays as input parameters. -
Uses of RealMatrix in org.apache.commons.math3.fitting.leastsquares
Fields in org.apache.commons.math3.fitting.leastsquares declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation.jacobian
Derivative at point.private RealMatrix
LeastSquaresBuilder.weight
weight matrixprivate final RealMatrix
DenseWeightedEvaluation.weightSqrt
reference to the weight square root matrixMethods in org.apache.commons.math3.fitting.leastsquares that return RealMatrixModifier and TypeMethodDescriptionLeastSquaresFactory.LocalValueAndJacobianFunction.computeJacobian
(double[] params) Compute the Jacobian.ValueAndJacobianFunction.computeJacobian
(double[] params) Compute the Jacobian.AbstractEvaluation.getCovariances
(double threshold) Get the covariance matrix of the optimized parameters.LeastSquaresProblem.Evaluation.getCovariances
(double threshold) Get the covariance matrix of the optimized parameters.OptimumImpl.getCovariances
(double threshold) Get the covariance matrix of the optimized parameters.DenseWeightedEvaluation.getJacobian()
Get the weighted Jacobian matrix.LeastSquaresFactory.LocalLeastSquaresProblem.LazyUnweightedEvaluation.getJacobian()
Get the weighted Jacobian matrix.LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation.getJacobian()
Get the weighted Jacobian matrix.LeastSquaresProblem.Evaluation.getJacobian()
Get the weighted Jacobian matrix.OptimumImpl.getJacobian()
Get the weighted Jacobian matrix.private static RealMatrix
LeastSquaresFactory.squareRoot
(RealMatrix m) Computes the square-root of the weight matrix.Methods in org.apache.commons.math3.fitting.leastsquares that return types with arguments of type RealMatrixModifier and TypeMethodDescriptionprivate static Pair
<RealMatrix, RealVector> GaussNewtonOptimizer.computeNormalMatrix
(RealMatrix jacobian, RealVector residuals) Compute the normal matrix, JTJ.LeastSquaresFactory.LocalValueAndJacobianFunction.value
(RealVector point) Compute the function value and its Jacobian.MultivariateJacobianFunction.value
(RealVector point) Compute the function value and its Jacobian.Methods in org.apache.commons.math3.fitting.leastsquares with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate static Pair
<RealMatrix, RealVector> GaussNewtonOptimizer.computeNormalMatrix
(RealMatrix jacobian, RealVector residuals) Compute the normal matrix, JTJ.static LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator) Create aLeastSquaresProblem
from the given elements.LevenbergMarquardtOptimizer.qrDecomposition
(RealMatrix jacobian, int solvedCols) Decompose a matrix A as A.P = Q.R using Householder transforms.protected abstract RealVector
GaussNewtonOptimizer.Decomposition.solve
(RealMatrix jacobian, RealVector residuals) Solve the linear least squares problem Jx=r.private static RealMatrix
LeastSquaresFactory.squareRoot
(RealMatrix m) Computes the square-root of the weight matrix.LeastSquaresBuilder.weight
(RealMatrix newWeight) Configure the weight matrix.static LeastSquaresProblem
LeastSquaresFactory.weightMatrix
(LeastSquaresProblem problem, RealMatrix weights) Apply a dense weight matrix to theLeastSquaresProblem
.Constructors in org.apache.commons.math3.fitting.leastsquares with parameters of type RealMatrixModifierConstructorDescription(package private)
DenseWeightedEvaluation
(LeastSquaresProblem.Evaluation unweighted, RealMatrix weightSqrt) Create a weighted evaluation from an unweighted one.private
UnweightedEvaluation
(RealVector values, RealMatrix jacobian, RealVector target, RealVector point) Create anLeastSquaresProblem.Evaluation
with no weights. -
Uses of RealMatrix in org.apache.commons.math3.linear
Subinterfaces of RealMatrix in org.apache.commons.math3.linearModifier and TypeInterfaceDescriptioninterface
Marker interface forRealMatrix
implementations that require sparse backing storageClasses in org.apache.commons.math3.linear that implement RealMatrixModifier and TypeClassDescriptionclass
Basic implementation of RealMatrix methods regardless of the underlying storage.class
Implementation ofRealMatrix
using adouble[][]
array to store entries.class
Cache-friendly implementation of RealMatrix using a flat arrays to store square blocks of the matrix.class
Implementation of a diagonal matrix.class
Sparse matrix implementation based on an open addressed map.Fields in org.apache.commons.math3.linear declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
BiDiagonalTransformer.cachedB
Cached value of B.private RealMatrix
EigenDecomposition.cachedD
Cached value of D.private RealMatrix
HessenbergTransformer.cachedH
Cached value of H.private RealMatrix
QRDecomposition.cachedH
Cached value of H.private RealMatrix
CholeskyDecomposition.cachedL
Cached value of L.private RealMatrix
LUDecomposition.cachedL
Cached value of L.private RealMatrix
CholeskyDecomposition.cachedLT
Cached value of LT.private RealMatrix
HessenbergTransformer.cachedP
Cached value of P.private RealMatrix
LUDecomposition.cachedP
Cached value of P.private RealMatrix
RRQRDecomposition.cachedP
Cached value of P.private RealMatrix
SchurTransformer.cachedP
Cached value of P.private RealMatrix
HessenbergTransformer.cachedPt
Cached value of Pt.private RealMatrix
SchurTransformer.cachedPt
Cached value of PT.private RealMatrix
QRDecomposition.cachedQ
Cached value of Q.private RealMatrix
TriDiagonalTransformer.cachedQ
Cached value of Q.private RealMatrix
TriDiagonalTransformer.cachedQt
Cached value of Qt.private RealMatrix
QRDecomposition.cachedQT
Cached value of QT.private RealMatrix
QRDecomposition.cachedR
Cached value of R.private RealMatrix
SingularValueDecomposition.cachedS
Cached value of S (diagonal) matrix.private RealMatrix
SchurTransformer.cachedT
Cached value of T.private RealMatrix
TriDiagonalTransformer.cachedT
Cached value of T.private RealMatrix
BiDiagonalTransformer.cachedU
Cached value of U.private RealMatrix
LUDecomposition.cachedU
Cached value of U.private final RealMatrix
SingularValueDecomposition.cachedU
Cached value of U matrix.private RealMatrix
SingularValueDecomposition.cachedUt
Cached value of transposed U matrix.private RealMatrix
BiDiagonalTransformer.cachedV
Cached value of V.private RealMatrix
EigenDecomposition.cachedV
Cached value of V.private final RealMatrix
SingularValueDecomposition.cachedV
Cached value of V matrix.private RealMatrix
EigenDecomposition.cachedVt
Cached value of Vt.private RealMatrix
SingularValueDecomposition.cachedVt
Cached value of transposed V matrix.private RealMatrix
RRQRDecomposition.Solver.p
A permutation matrix for the pivots used in the QR decompositionprivate final RealMatrix
SingularValueDecomposition.Solver.pseudoInverse
Pseudo-inverse of the initial matrix.private final RealMatrix
RectangularCholeskyDecomposition.root
Permutated Cholesky root of the symmetric positive semidefinite matrix.Methods in org.apache.commons.math3.linear that return RealMatrixModifier and TypeMethodDescriptionAbstractRealMatrix.add
(RealMatrix m) Returns the sum ofthis
andm
.RealMatrix.add
(RealMatrix m) Returns the sum ofthis
andm
.static RealMatrix
MatrixUtils.blockInverse
(RealMatrix m, int splitIndex) Computes the inverse of the given matrix by splitting it into 4 sub-matrices.abstract RealMatrix
AbstractRealMatrix.copy()
Returns a (deep) copy of this.Array2DRowRealMatrix.copy()
Returns a (deep) copy of this.DiagonalMatrix.copy()
Returns a (deep) copy of this.RealMatrix.copy()
Returns a (deep) copy of this.static RealMatrix
MatrixUtils.createColumnRealMatrix
(double[] columnData) Creates a columnRealMatrix
using the data from the input array.abstract RealMatrix
AbstractRealMatrix.createMatrix
(int rowDimension, int columnDimension) Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.Array2DRowRealMatrix.createMatrix
(int rowDimension, int columnDimension) Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.DiagonalMatrix.createMatrix
(int rowDimension, int columnDimension) Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.RealMatrix.createMatrix
(int rowDimension, int columnDimension) Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.static RealMatrix
MatrixUtils.createRealDiagonalMatrix
(double[] diagonal) Returns a diagonal matrix with specified elements.static RealMatrix
MatrixUtils.createRealIdentityMatrix
(int dimension) Returnsdimension x dimension
identity matrix.static RealMatrix
MatrixUtils.createRealMatrix
(double[][] data) Returns aRealMatrix
whose entries are the the values in the the input array.static RealMatrix
MatrixUtils.createRealMatrix
(int rows, int columns) Returns aRealMatrix
with specified dimensions.static RealMatrix
MatrixUtils.createRowRealMatrix
(double[] rowData) Create a rowRealMatrix
using the data from the input array.BiDiagonalTransformer.getB()
Returns the bi-diagonal matrix B of the transform.AbstractRealMatrix.getColumnMatrix
(int column) Get the entries at the given column index as a column matrix.RealMatrix.getColumnMatrix
(int column) Get the entries at the given column index as a column matrix.SingularValueDecomposition.getCovariance
(double minSingularValue) Returns the n × n covariance matrix.EigenDecomposition.getD()
Gets the block diagonal matrix D of the decomposition.HessenbergTransformer.getH()
Returns the Hessenberg matrix H of the transform.QRDecomposition.getH()
Returns the Householder reflector vectors.CholeskyDecomposition.Solver.getInverse()
Get the inverse of the decomposed matrix.DecompositionSolver.getInverse()
Get the pseudo-inverse of the decomposed matrix.EigenDecomposition.Solver.getInverse()
Get the inverse of the decomposed matrix.LUDecomposition.Solver.getInverse()
Get the inverse of the decomposed matrix.QRDecomposition.Solver.getInverse()
Get the pseudo-inverse of the decomposed matrix.RRQRDecomposition.Solver.getInverse()
Get the pseudo-inverse of the decomposed matrix.SingularValueDecomposition.Solver.getInverse()
Get the pseudo-inverse of the decomposed matrix.CholeskyDecomposition.getL()
Returns the matrix L of the decomposition.LUDecomposition.getL()
Returns the matrix L of the decomposition.CholeskyDecomposition.getLT()
Returns the transpose of the matrix L of the decomposition.HessenbergTransformer.getP()
Returns the matrix P of the transform.LUDecomposition.getP()
Returns the P rows permutation matrix.RRQRDecomposition.getP()
Returns the pivot matrix, P, used in the QR Decomposition of matrix A such that AP = QR.SchurTransformer.getP()
Returns the matrix P of the transform.HessenbergTransformer.getPT()
Returns the transpose of the matrix P of the transform.SchurTransformer.getPT()
Returns the transpose of the matrix P of the transform.QRDecomposition.getQ()
Returns the matrix Q of the decomposition.TriDiagonalTransformer.getQ()
Returns the matrix Q of the transform.QRDecomposition.getQT()
Returns the transpose of the matrix Q of the decomposition.TriDiagonalTransformer.getQT()
Returns the transpose of the matrix Q of the transform.QRDecomposition.getR()
Returns the matrix R of the decomposition.RectangularCholeskyDecomposition.getRootMatrix()
Get the root of the covariance matrix.AbstractRealMatrix.getRowMatrix
(int row) Get the entries at the given row index as a row matrix.RealMatrix.getRowMatrix
(int row) Get the entries at the given row index as a row matrix.SingularValueDecomposition.getS()
Returns the diagonal matrix Σ of the decomposition.EigenDecomposition.getSquareRoot()
Computes the square-root of the matrix.AbstractRealMatrix.getSubMatrix
(int[] selectedRows, int[] selectedColumns) Gets a submatrix.AbstractRealMatrix.getSubMatrix
(int startRow, int endRow, int startColumn, int endColumn) Gets a submatrix.RealMatrix.getSubMatrix
(int[] selectedRows, int[] selectedColumns) Gets a submatrix.RealMatrix.getSubMatrix
(int startRow, int endRow, int startColumn, int endColumn) Gets a submatrix.SchurTransformer.getT()
Returns the quasi-triangular Schur matrix T of the transform.TriDiagonalTransformer.getT()
Returns the tridiagonal matrix T of the transform.BiDiagonalTransformer.getU()
Returns the matrix U of the transform.LUDecomposition.getU()
Returns the matrix U of the decomposition.SingularValueDecomposition.getU()
Returns the matrix U of the decomposition.SingularValueDecomposition.getUT()
Returns the transpose of the matrix U of the decomposition.BiDiagonalTransformer.getV()
Returns the matrix V of the transform.EigenDecomposition.getV()
Gets the matrix V of the decomposition.SingularValueDecomposition.getV()
Returns the matrix V of the decomposition.EigenDecomposition.getVT()
Gets the transpose of the matrix V of the decomposition.SingularValueDecomposition.getVT()
Returns the transpose of the matrix V of the decomposition.static RealMatrix
MatrixUtils.inverse
(RealMatrix matrix) Computes the inverse of the given matrix.static RealMatrix
MatrixUtils.inverse
(RealMatrix matrix, double threshold) Computes the inverse of the given matrix.AbstractRealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.DiagonalMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.OpenMapRealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.RealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.ArrayRealVector.outerProduct
(RealVector v) Compute the outer product.RealVector.outerProduct
(RealVector v) Compute the outer product.Parse a string to produce aRealMatrix
object.RealMatrixFormat.parse
(String source, ParsePosition pos) Parse a string to produce aRealMatrix
object.AbstractRealMatrix.power
(int p) Returns the result of multiplyingthis
with itselfp
times.RealMatrix.power
(int p) Returns the result of multiplyingthis
with itselfp
times.AbstractRealMatrix.preMultiply
(RealMatrix m) Returns the result of premultiplyingthis
bym
.RealMatrix.preMultiply
(RealMatrix m) Returns the result of premultiplyingthis
bym
.AbstractRealMatrix.scalarAdd
(double d) Returns the result of addingd
to each entry ofthis
.RealMatrix.scalarAdd
(double d) Returns the result of addingd
to each entry ofthis
.AbstractRealMatrix.scalarMultiply
(double d) Returns the result of multiplying each entry ofthis
byd
.BlockRealMatrix.scalarMultiply
(double d) Returns the result of multiplying each entry ofthis
byd
.RealMatrix.scalarMultiply
(double d) Returns the result of multiplying each entry ofthis
byd
.CholeskyDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.DecompositionSolver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.EigenDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.LUDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.QRDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.RRQRDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.SingularValueDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B in least square sense.AbstractRealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.RealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.AbstractRealMatrix.transpose()
Returns the transpose of this matrix.RealMatrix.transpose()
Returns the transpose of this matrix.Methods in org.apache.commons.math3.linear with parameters of type RealMatrixModifier and TypeMethodDescriptionAbstractRealMatrix.add
(RealMatrix m) Returns the sum ofthis
andm
.BlockRealMatrix.add
(RealMatrix m) Returns the sum ofthis
andm
.RealMatrix.add
(RealMatrix m) Returns the sum ofthis
andm
.static RealMatrix
MatrixUtils.blockInverse
(RealMatrix m, int splitIndex) Computes the inverse of the given matrix by splitting it into 4 sub-matrices.static void
MatrixUtils.checkSymmetric
(RealMatrix matrix, double eps) Checks whether a matrix is symmetric.RealMatrixFormat.format
(RealMatrix m) This method callsRealMatrixFormat.format(RealMatrix,StringBuffer,FieldPosition)
.RealMatrixFormat.format
(RealMatrix matrix, StringBuffer toAppendTo, FieldPosition pos) Formats aRealMatrix
object to produce a string.static RealMatrix
MatrixUtils.inverse
(RealMatrix matrix) Computes the inverse of the given matrix.static RealMatrix
MatrixUtils.inverse
(RealMatrix matrix, double threshold) Computes the inverse of the given matrix.static boolean
MatrixUtils.isSymmetric
(RealMatrix matrix, double eps) Checks whether a matrix is symmetric.private static boolean
MatrixUtils.isSymmetricInternal
(RealMatrix matrix, double relativeTolerance, boolean raiseException) Checks whether a matrix is symmetric, within a given relative tolerance.AbstractRealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.BlockRealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.DiagonalMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.OpenMapRealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.RealMatrix.multiply
(RealMatrix m) Returns the result of postmultiplyingthis
bym
.AbstractRealMatrix.preMultiply
(RealMatrix m) Returns the result of premultiplyingthis
bym
.RealMatrix.preMultiply
(RealMatrix m) Returns the result of premultiplyingthis
bym
.static void
MatrixUtils.serializeRealMatrix
(RealMatrix matrix, ObjectOutputStream oos) Serialize aRealMatrix
.void
AbstractRealMatrix.setColumnMatrix
(int column, RealMatrix matrix) Sets the specifiedcolumn
ofthis
matrix to the entries of the specified columnmatrix
.void
BlockRealMatrix.setColumnMatrix
(int column, RealMatrix matrix) Sets the specifiedcolumn
ofthis
matrix to the entries of the specified columnmatrix
.void
RealMatrix.setColumnMatrix
(int column, RealMatrix matrix) Sets the specifiedcolumn
ofthis
matrix to the entries of the specified columnmatrix
.void
AbstractRealMatrix.setRowMatrix
(int row, RealMatrix matrix) Sets the specifiedrow
ofthis
matrix to the entries of the specified rowmatrix
.void
BlockRealMatrix.setRowMatrix
(int row, RealMatrix matrix) Sets the specifiedrow
ofthis
matrix to the entries of the specified rowmatrix
.void
RealMatrix.setRowMatrix
(int row, RealMatrix matrix) Sets the specifiedrow
ofthis
matrix to the entries of the specified rowmatrix
.CholeskyDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.DecompositionSolver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.EigenDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.LUDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.QRDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.RRQRDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B for matrices A.SingularValueDecomposition.Solver.solve
(RealMatrix b) Solve the linear equation A × X = B in least square sense.static void
MatrixUtils.solveLowerTriangularSystem
(RealMatrix rm, RealVector b) Solve a system of composed of a Lower Triangular MatrixRealMatrix
.static void
MatrixUtils.solveUpperTriangularSystem
(RealMatrix rm, RealVector b) Solver a system composed of an Upper Triangular MatrixRealMatrix
.AbstractRealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.BlockRealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.OpenMapRealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.RealMatrix.subtract
(RealMatrix m) Returnsthis
minusm
.private SchurTransformer
EigenDecomposition.transformToSchur
(RealMatrix matrix) Transforms the matrix to Schur form and calculates the eigenvalues.private void
EigenDecomposition.transformToTridiagonal
(RealMatrix matrix) Transforms the matrix to tridiagonal form.Constructors in org.apache.commons.math3.linear with parameters of type RealMatrixModifierConstructorDescription(package private)
BiDiagonalTransformer
(RealMatrix matrix) Build the transformation to bi-diagonal shape of a matrix.CholeskyDecomposition
(RealMatrix matrix) Calculates the Cholesky decomposition of the given matrix.CholeskyDecomposition
(RealMatrix matrix, double relativeSymmetryThreshold, double absolutePositivityThreshold) Calculates the Cholesky decomposition of the given matrix.EigenDecomposition
(RealMatrix matrix) Calculates the eigen decomposition of the given real matrix.EigenDecomposition
(RealMatrix matrix, double splitTolerance) Deprecated.in 3.1 (to be removed in 4.0) due to unused parameter(package private)
HessenbergTransformer
(RealMatrix matrix) Build the transformation to Hessenberg form of a general matrix.LUDecomposition
(RealMatrix matrix) Calculates the LU-decomposition of the given matrix.LUDecomposition
(RealMatrix matrix, double singularityThreshold) Calculates the LU-decomposition of the given matrix.QRDecomposition
(RealMatrix matrix) Calculates the QR-decomposition of the given matrix.QRDecomposition
(RealMatrix matrix, double threshold) Calculates the QR-decomposition of the given matrix.Decompose a symmetric positive semidefinite matrix.RectangularCholeskyDecomposition
(RealMatrix matrix, double small) Decompose a symmetric positive semidefinite matrix.RRQRDecomposition
(RealMatrix matrix) Calculates the QR-decomposition of the given matrix.RRQRDecomposition
(RealMatrix matrix, double threshold) Calculates the QR-decomposition of the given matrix.(package private)
SchurTransformer
(RealMatrix matrix) Build the transformation to Schur form of a general real matrix.SingularValueDecomposition
(RealMatrix matrix) Calculates the compact Singular Value Decomposition of the given matrix.private
Solver
(DecompositionSolver upper, RealMatrix p) Build a solver from decomposed matrix.private
Solver
(double[] singularValues, RealMatrix uT, RealMatrix v, boolean nonSingular, double tol) Build a solver from decomposed matrix.(package private)
TriDiagonalTransformer
(RealMatrix matrix) Build the transformation to tridiagonal shape of a symmetrical matrix. -
Uses of RealMatrix in org.apache.commons.math3.ml.clustering
Methods in org.apache.commons.math3.ml.clustering that return RealMatrixModifier and TypeMethodDescriptionFuzzyKMeansClusterer.getMembershipMatrix()
Returns thenxk
membership matrix, wheren
is the number of data points andk
the number of clusters. -
Uses of RealMatrix in org.apache.commons.math3.ode.nonstiff
Methods in org.apache.commons.math3.ode.nonstiff with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate double
AdamsBashforthIntegrator.errorEstimation
(double[] previousState, double[] predictedState, double[] predictedScaled, RealMatrix predictedNordsieck) Estimate error. -
Uses of RealMatrix in org.apache.commons.math3.optim.nonlinear.scalar
Fields in org.apache.commons.math3.optim.nonlinear.scalar declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
LeastSquaresConverter.scale
Optional scaling matrix (weight and correlations) for the residuals.Constructors in org.apache.commons.math3.optim.nonlinear.scalar with parameters of type RealMatrixModifierConstructorDescriptionLeastSquaresConverter
(MultivariateVectorFunction function, double[] observations, RealMatrix scale) Builds a simple converter for correlated residuals with the specified weights. -
Uses of RealMatrix in org.apache.commons.math3.optim.nonlinear.scalar.noderiv
Fields in org.apache.commons.math3.optim.nonlinear.scalar.noderiv declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
CMAESOptimizer.B
Coordinate system.private RealMatrix
CMAESOptimizer.BD
B*D, stored for efficiency.private RealMatrix
CMAESOptimizer.C
Covariance matrix.private RealMatrix
CMAESOptimizer.D
Scaling.private RealMatrix
CMAESOptimizer.diagC
Diagonal of C, used for diagonalOnly.private RealMatrix
CMAESOptimizer.diagD
Diagonal of sqrt(D), stored for efficiency.private RealMatrix
CMAESOptimizer.pc
Evolution path.private RealMatrix
CMAESOptimizer.ps
Evolution path for sigma.private RealMatrix
CMAESOptimizer.weights
Array for weighted recombination.private RealMatrix
CMAESOptimizer.xmean
Objective variables.Fields in org.apache.commons.math3.optim.nonlinear.scalar.noderiv with type parameters of type RealMatrixModifier and TypeFieldDescriptionprivate final List
<RealMatrix> CMAESOptimizer.statisticsDHistory
History of D matrix.private final List
<RealMatrix> CMAESOptimizer.statisticsMeanHistory
History of mean matrix.Methods in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that return RealMatrixModifier and TypeMethodDescriptionprivate static RealMatrix
CMAESOptimizer.diag
(RealMatrix m) private static RealMatrix
CMAESOptimizer.divide
(RealMatrix m, RealMatrix n) private static RealMatrix
CMAESOptimizer.eye
(int n, int m) private static RealMatrix
CMAESOptimizer.log
(RealMatrix m) private static RealMatrix
CMAESOptimizer.ones
(int n, int m) private RealMatrix
CMAESOptimizer.randn1
(int size, int popSize) private static RealMatrix
CMAESOptimizer.repmat
(RealMatrix mat, int n, int m) private static RealMatrix
CMAESOptimizer.selectColumns
(RealMatrix m, int[] cols) private static RealMatrix
CMAESOptimizer.sequence
(double start, double end, double step) private static RealMatrix
CMAESOptimizer.sqrt
(RealMatrix m) private static RealMatrix
CMAESOptimizer.square
(RealMatrix m) private static RealMatrix
CMAESOptimizer.sumRows
(RealMatrix m) private static RealMatrix
CMAESOptimizer.times
(RealMatrix m, RealMatrix n) private static RealMatrix
CMAESOptimizer.triu
(RealMatrix m, int k) private static RealMatrix
CMAESOptimizer.zeros
(int n, int m) Methods in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that return types with arguments of type RealMatrixModifier and TypeMethodDescriptionCMAESOptimizer.getStatisticsDHistory()
CMAESOptimizer.getStatisticsMeanHistory()
Methods in org.apache.commons.math3.optim.nonlinear.scalar.noderiv with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate static void
CMAESOptimizer.copyColumn
(RealMatrix m1, int col1, RealMatrix m2, int col2) Copies a column from m1 to m2.private static RealMatrix
CMAESOptimizer.diag
(RealMatrix m) private static RealMatrix
CMAESOptimizer.divide
(RealMatrix m, RealMatrix n) private static RealMatrix
CMAESOptimizer.log
(RealMatrix m) private static double
CMAESOptimizer.max
(RealMatrix m) private static double
CMAESOptimizer.min
(RealMatrix m) private static RealMatrix
CMAESOptimizer.repmat
(RealMatrix mat, int n, int m) private static RealMatrix
CMAESOptimizer.selectColumns
(RealMatrix m, int[] cols) private static RealMatrix
CMAESOptimizer.sqrt
(RealMatrix m) private static RealMatrix
CMAESOptimizer.square
(RealMatrix m) private static RealMatrix
CMAESOptimizer.sumRows
(RealMatrix m) private static RealMatrix
CMAESOptimizer.times
(RealMatrix m, RealMatrix n) private static RealMatrix
CMAESOptimizer.triu
(RealMatrix m, int k) private void
CMAESOptimizer.updateCovariance
(boolean hsig, RealMatrix bestArx, RealMatrix arz, int[] arindex, RealMatrix xold) Update of the covariance matrix C.private void
CMAESOptimizer.updateCovarianceDiagonalOnly
(boolean hsig, RealMatrix bestArz) Update of the covariance matrix C for diagonalOnly > 0private boolean
CMAESOptimizer.updateEvolutionPaths
(RealMatrix zmean, RealMatrix xold) Update of the evolution paths ps and pc. -
Uses of RealMatrix in org.apache.commons.math3.optim.nonlinear.vector
Fields in org.apache.commons.math3.optim.nonlinear.vector declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
MultivariateVectorOptimizer.weightMatrix
Deprecated.Weight matrix.private final RealMatrix
Weight.weightMatrix
Deprecated.Weight matrix.Methods in org.apache.commons.math3.optim.nonlinear.vector that return RealMatrixModifier and TypeMethodDescriptionMultivariateVectorOptimizer.getWeight()
Deprecated.Gets the weight matrix of the observations.Weight.getWeight()
Deprecated.Gets the initial guess.Constructors in org.apache.commons.math3.optim.nonlinear.vector with parameters of type RealMatrix -
Uses of RealMatrix in org.apache.commons.math3.optim.nonlinear.vector.jacobian
Fields in org.apache.commons.math3.optim.nonlinear.vector.jacobian declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
AbstractLeastSquaresOptimizer.weightMatrixSqrt
Deprecated.Square-root of the weight matrix.Methods in org.apache.commons.math3.optim.nonlinear.vector.jacobian that return RealMatrixModifier and TypeMethodDescriptionprotected RealMatrix
AbstractLeastSquaresOptimizer.computeWeightedJacobian
(double[] params) Deprecated.Computes the weighted Jacobian matrix.AbstractLeastSquaresOptimizer.getWeightSquareRoot()
Deprecated.Gets the square-root of the weight matrix.private RealMatrix
AbstractLeastSquaresOptimizer.squareRoot
(RealMatrix m) Deprecated.Computes the square-root of the weight matrix.Methods in org.apache.commons.math3.optim.nonlinear.vector.jacobian with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate void
LevenbergMarquardtOptimizer.qrDecomposition
(RealMatrix jacobian) Deprecated.Decompose a matrix A as A.P = Q.R using Householder transforms.private RealMatrix
AbstractLeastSquaresOptimizer.squareRoot
(RealMatrix m) Deprecated.Computes the square-root of the weight matrix. -
Uses of RealMatrix in org.apache.commons.math3.optimization
Fields in org.apache.commons.math3.optimization declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
LeastSquaresConverter.scale
Deprecated.Optional scaling matrix (weight and correlations) for the residuals.private final RealMatrix
Weight.weightMatrix
Deprecated.Weight matrix.Methods in org.apache.commons.math3.optimization that return RealMatrixConstructors in org.apache.commons.math3.optimization with parameters of type RealMatrixModifierConstructorDescriptionLeastSquaresConverter
(MultivariateVectorFunction function, double[] observations, RealMatrix scale) Deprecated.Build a simple converter for correlated residuals with the specific weights.Weight
(RealMatrix weight) Deprecated. -
Uses of RealMatrix in org.apache.commons.math3.optimization.direct
Fields in org.apache.commons.math3.optimization.direct declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
CMAESOptimizer.B
Deprecated.Coordinate system.private RealMatrix
CMAESOptimizer.BD
Deprecated.B*D, stored for efficiency.private RealMatrix
CMAESOptimizer.C
Deprecated.Covariance matrix.private RealMatrix
CMAESOptimizer.D
Deprecated.Scaling.private RealMatrix
CMAESOptimizer.diagC
Deprecated.Diagonal of C, used for diagonalOnly.private RealMatrix
CMAESOptimizer.diagD
Deprecated.Diagonal of sqrt(D), stored for efficiency.private RealMatrix
CMAESOptimizer.pc
Deprecated.Evolution path.private RealMatrix
CMAESOptimizer.ps
Deprecated.Evolution path for sigma.private RealMatrix
BaseAbstractMultivariateVectorOptimizer.weightMatrix
Deprecated.Weight matrix.private RealMatrix
CMAESOptimizer.weights
Deprecated.Array for weighted recombination.private RealMatrix
CMAESOptimizer.xmean
Deprecated.Objective variables.Fields in org.apache.commons.math3.optimization.direct with type parameters of type RealMatrixModifier and TypeFieldDescriptionprivate List
<RealMatrix> CMAESOptimizer.statisticsDHistory
Deprecated.History of D matrix.private List
<RealMatrix> CMAESOptimizer.statisticsMeanHistory
Deprecated.History of mean matrix.Methods in org.apache.commons.math3.optimization.direct that return RealMatrixModifier and TypeMethodDescriptionprivate static RealMatrix
CMAESOptimizer.diag
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.divide
(RealMatrix m, RealMatrix n) Deprecated.private static RealMatrix
CMAESOptimizer.eye
(int n, int m) Deprecated.BaseAbstractMultivariateVectorOptimizer.getWeight()
Deprecated.Gets the weight matrix of the observations.private static RealMatrix
CMAESOptimizer.log
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.ones
(int n, int m) Deprecated.private RealMatrix
CMAESOptimizer.randn1
(int size, int popSize) Deprecated.private static RealMatrix
CMAESOptimizer.repmat
(RealMatrix mat, int n, int m) Deprecated.private static RealMatrix
CMAESOptimizer.selectColumns
(RealMatrix m, int[] cols) Deprecated.private static RealMatrix
CMAESOptimizer.sequence
(double start, double end, double step) Deprecated.private static RealMatrix
CMAESOptimizer.sqrt
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.square
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.sumRows
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.times
(RealMatrix m, RealMatrix n) Deprecated.private static RealMatrix
CMAESOptimizer.triu
(RealMatrix m, int k) Deprecated.private static RealMatrix
CMAESOptimizer.zeros
(int n, int m) Deprecated.Methods in org.apache.commons.math3.optimization.direct that return types with arguments of type RealMatrixModifier and TypeMethodDescriptionCMAESOptimizer.getStatisticsDHistory()
Deprecated.CMAESOptimizer.getStatisticsMeanHistory()
Deprecated.Methods in org.apache.commons.math3.optimization.direct with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate static void
CMAESOptimizer.copyColumn
(RealMatrix m1, int col1, RealMatrix m2, int col2) Deprecated.Copies a column from m1 to m2.private static RealMatrix
CMAESOptimizer.diag
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.divide
(RealMatrix m, RealMatrix n) Deprecated.private static RealMatrix
CMAESOptimizer.log
(RealMatrix m) Deprecated.private static double
CMAESOptimizer.max
(RealMatrix m) Deprecated.private static double
CMAESOptimizer.min
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.repmat
(RealMatrix mat, int n, int m) Deprecated.private static RealMatrix
CMAESOptimizer.selectColumns
(RealMatrix m, int[] cols) Deprecated.private static RealMatrix
CMAESOptimizer.sqrt
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.square
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.sumRows
(RealMatrix m) Deprecated.private static RealMatrix
CMAESOptimizer.times
(RealMatrix m, RealMatrix n) Deprecated.private static RealMatrix
CMAESOptimizer.triu
(RealMatrix m, int k) Deprecated.private void
CMAESOptimizer.updateCovariance
(boolean hsig, RealMatrix bestArx, RealMatrix arz, int[] arindex, RealMatrix xold) Deprecated.Update of the covariance matrix C.private void
CMAESOptimizer.updateCovarianceDiagonalOnly
(boolean hsig, RealMatrix bestArz) Deprecated.Update of the covariance matrix C for diagonalOnly > 0private boolean
CMAESOptimizer.updateEvolutionPaths
(RealMatrix zmean, RealMatrix xold) Deprecated.Update of the evolution paths ps and pc. -
Uses of RealMatrix in org.apache.commons.math3.optimization.general
Fields in org.apache.commons.math3.optimization.general declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
AbstractLeastSquaresOptimizer.weightMatrixSqrt
Deprecated.Square-root of the weight matrix.Methods in org.apache.commons.math3.optimization.general that return RealMatrixModifier and TypeMethodDescriptionprotected RealMatrix
AbstractLeastSquaresOptimizer.computeWeightedJacobian
(double[] params) Deprecated.Computes the Jacobian matrix.AbstractLeastSquaresOptimizer.getWeightSquareRoot()
Deprecated.Gets the square-root of the weight matrix.private RealMatrix
AbstractLeastSquaresOptimizer.squareRoot
(RealMatrix m) Deprecated.Computes the square-root of the weight matrix.Methods in org.apache.commons.math3.optimization.general with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate void
LevenbergMarquardtOptimizer.qrDecomposition
(RealMatrix jacobian) Deprecated.Decompose a matrix A as A.P = Q.R using Householder transforms.private RealMatrix
AbstractLeastSquaresOptimizer.squareRoot
(RealMatrix m) Deprecated.Computes the square-root of the weight matrix. -
Uses of RealMatrix in org.apache.commons.math3.optimization.linear
Fields in org.apache.commons.math3.optimization.linear declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
SimplexTableau.tableau
Deprecated.Simple tableau.Methods in org.apache.commons.math3.optimization.linear that return RealMatrixModifier and TypeMethodDescriptionprotected RealMatrix
SimplexTableau.createTableau
(boolean maximize) Deprecated.Create the tableau by itself. -
Uses of RealMatrix in org.apache.commons.math3.random
Fields in org.apache.commons.math3.random declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
CorrelatedRandomVectorGenerator.root
Root of the covariance matrix.Methods in org.apache.commons.math3.random that return RealMatrixModifier and TypeMethodDescriptionCorrelatedRandomVectorGenerator.getRootMatrix()
Get the root of the covariance matrix.Constructors in org.apache.commons.math3.random with parameters of type RealMatrixModifierConstructorDescriptionCorrelatedRandomVectorGenerator
(double[] mean, RealMatrix covariance, double small, NormalizedRandomGenerator generator) Builds a correlated random vector generator from its mean vector and covariance matrix.CorrelatedRandomVectorGenerator
(RealMatrix covariance, double small, NormalizedRandomGenerator generator) Builds a null mean random correlated vector generator from its covariance matrix. -
Uses of RealMatrix in org.apache.commons.math3.stat.correlation
Fields in org.apache.commons.math3.stat.correlation declared as RealMatrixModifier and TypeFieldDescriptionprivate final RealMatrix
KendallsCorrelation.correlationMatrix
correlation matrixprivate final RealMatrix
PearsonsCorrelation.correlationMatrix
correlation matrixprivate final RealMatrix
Covariance.covarianceMatrix
covariance matrixprivate final RealMatrix
SpearmansCorrelation.data
Input dataMethods in org.apache.commons.math3.stat.correlation that return RealMatrixModifier and TypeMethodDescriptionKendallsCorrelation.computeCorrelationMatrix
(double[][] matrix) Computes the Kendall's Tau rank correlation matrix for the columns of the input rectangular array.KendallsCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the Kendall's Tau rank correlation matrix for the columns of the input matrix.PearsonsCorrelation.computeCorrelationMatrix
(double[][] data) Computes the correlation matrix for the columns of the input rectangular array.PearsonsCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the correlation matrix for the columns of the input matrix, usingPearsonsCorrelation.correlation(double[], double[])
.SpearmansCorrelation.computeCorrelationMatrix
(double[][] matrix) Computes the Spearman's rank correlation matrix for the columns of the input rectangular array.SpearmansCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the Spearman's rank correlation matrix for the columns of the input matrix.protected RealMatrix
Covariance.computeCovarianceMatrix
(double[][] data) Create a covariance matrix from a rectangular array whose columns represent covariates.protected RealMatrix
Covariance.computeCovarianceMatrix
(double[][] data, boolean biasCorrected) Compute a covariance matrix from a rectangular array whose columns represent covariates.protected RealMatrix
Covariance.computeCovarianceMatrix
(RealMatrix matrix) Create a covariance matrix from a matrix whose columns represent covariates.protected RealMatrix
Covariance.computeCovarianceMatrix
(RealMatrix matrix, boolean biasCorrected) Compute a covariance matrix from a matrix whose columns represent covariates.PearsonsCorrelation.covarianceToCorrelation
(RealMatrix covarianceMatrix) Derives a correlation matrix from a covariance matrix.KendallsCorrelation.getCorrelationMatrix()
Returns the correlation matrix.PearsonsCorrelation.getCorrelationMatrix()
Returns the correlation matrix.SpearmansCorrelation.getCorrelationMatrix()
Calculate the Spearman Rank Correlation Matrix.PearsonsCorrelation.getCorrelationPValues()
Returns a matrix of p-values associated with the (two-sided) null hypothesis that the corresponding correlation coefficient is zero.PearsonsCorrelation.getCorrelationStandardErrors()
Returns a matrix of standard errors associated with the estimates in the correlation matrix.
getCorrelationStandardErrors().getEntry(i,j)
is the standard error associated withgetCorrelationMatrix.getEntry(i,j)
Covariance.getCovarianceMatrix()
Returns the covariance matrixStorelessCovariance.getCovarianceMatrix()
Returns the covariance matrixprivate RealMatrix
SpearmansCorrelation.rankTransform
(RealMatrix matrix) Applies rank transform to each of the columns ofmatrix
using the currentrankingAlgorithm
.Methods in org.apache.commons.math3.stat.correlation with parameters of type RealMatrixModifier and TypeMethodDescriptionprivate void
Covariance.checkSufficientData
(RealMatrix matrix) Throws MathIllegalArgumentException if the matrix does not have at least one column and two rows.private void
PearsonsCorrelation.checkSufficientData
(RealMatrix matrix) Throws MathIllegalArgumentException if the matrix does not have at least two columns and two rows.KendallsCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the Kendall's Tau rank correlation matrix for the columns of the input matrix.PearsonsCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the correlation matrix for the columns of the input matrix, usingPearsonsCorrelation.correlation(double[], double[])
.SpearmansCorrelation.computeCorrelationMatrix
(RealMatrix matrix) Computes the Spearman's rank correlation matrix for the columns of the input matrix.protected RealMatrix
Covariance.computeCovarianceMatrix
(RealMatrix matrix) Create a covariance matrix from a matrix whose columns represent covariates.protected RealMatrix
Covariance.computeCovarianceMatrix
(RealMatrix matrix, boolean biasCorrected) Compute a covariance matrix from a matrix whose columns represent covariates.PearsonsCorrelation.covarianceToCorrelation
(RealMatrix covarianceMatrix) Derives a correlation matrix from a covariance matrix.private RealMatrix
SpearmansCorrelation.rankTransform
(RealMatrix matrix) Applies rank transform to each of the columns ofmatrix
using the currentrankingAlgorithm
.Constructors in org.apache.commons.math3.stat.correlation with parameters of type RealMatrixModifierConstructorDescriptionCovariance
(RealMatrix matrix) Create a covariance matrix from a matrix whose columns represent covariates.Covariance
(RealMatrix matrix, boolean biasCorrected) Create a covariance matrix from a matrix whose columns represent covariates.KendallsCorrelation
(RealMatrix matrix) Create a KendallsCorrelation from a RealMatrix whose columns represent variables to be correlated.PearsonsCorrelation
(RealMatrix matrix) Create a PearsonsCorrelation from a RealMatrix whose columns represent variables to be correlated.PearsonsCorrelation
(RealMatrix covarianceMatrix, int numberOfObservations) Create a PearsonsCorrelation from a covariance matrix.SpearmansCorrelation
(RealMatrix dataMatrix) Create a SpearmansCorrelation from the given data matrix.SpearmansCorrelation
(RealMatrix dataMatrix, RankingAlgorithm rankingAlgorithm) Create a SpearmansCorrelation with the given input data matrix and ranking algorithm. -
Uses of RealMatrix in org.apache.commons.math3.stat.descriptive
Methods in org.apache.commons.math3.stat.descriptive that return RealMatrixModifier and TypeMethodDescriptionMultivariateSummaryStatistics.getCovariance()
Returns the covariance matrix of the values that have been added.StatisticalMultivariateSummary.getCovariance()
Returns the covariance of the available values.SynchronizedMultivariateSummaryStatistics.getCovariance()
Returns the covariance matrix of the values that have been added. -
Uses of RealMatrix in org.apache.commons.math3.stat.descriptive.moment
Methods in org.apache.commons.math3.stat.descriptive.moment that return RealMatrix -
Uses of RealMatrix in org.apache.commons.math3.stat.inference
Methods in org.apache.commons.math3.stat.inference that return RealMatrixModifier and TypeMethodDescriptionprivate RealMatrix
KolmogorovSmirnovTest.createRoundedH
(double d, int n) CreatesH
of sizem x m
as described in [1] (see above) using double-precision. -
Uses of RealMatrix in org.apache.commons.math3.stat.regression
Fields in org.apache.commons.math3.stat.regression declared as RealMatrixModifier and TypeFieldDescriptionprivate RealMatrix
GLSMultipleLinearRegression.Omega
Covariance matrix.private RealMatrix
GLSMultipleLinearRegression.OmegaInverse
Inverse of covariance matrix.private RealMatrix
AbstractMultipleLinearRegression.xMatrix
X sample data.Methods in org.apache.commons.math3.stat.regression that return RealMatrixModifier and TypeMethodDescriptionprotected abstract RealMatrix
AbstractMultipleLinearRegression.calculateBetaVariance()
Calculates the beta variance of multiple linear regression in matrix notation.protected RealMatrix
GLSMultipleLinearRegression.calculateBetaVariance()
Calculates the variance on the beta.protected RealMatrix
OLSMultipleLinearRegression.calculateBetaVariance()
Calculates the variance-covariance matrix of the regression parameters.OLSMultipleLinearRegression.calculateHat()
Compute the "hat" matrix.protected RealMatrix
GLSMultipleLinearRegression.getOmegaInverse()
Get the inverse of the covariance.protected RealMatrix
AbstractMultipleLinearRegression.getX()