| Package | Description |
|---|---|
| org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
| org.apache.commons.math3.filter |
Implementations of common discrete-time linear filters.
|
| org.apache.commons.math3.fitting.leastsquares |
This package provides algorithms that minimize the residuals
between observations and model values.
|
| org.apache.commons.math3.linear |
Linear algebra support.
|
| org.apache.commons.math3.ml.clustering |
Clustering algorithms.
|
| org.apache.commons.math3.optim.nonlinear.scalar |
Algorithms for optimizing a scalar function.
|
| org.apache.commons.math3.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
| org.apache.commons.math3.optim.nonlinear.vector |
Algorithms for optimizing a vector function.
|
| org.apache.commons.math3.optim.nonlinear.vector.jacobian |
This package provides optimization algorithms that require derivatives.
|
| org.apache.commons.math3.optimization |
All classes and sub-packages of this package are deprecated.
|
| org.apache.commons.math3.optimization.direct |
This package provides optimization algorithms that don't require derivatives.
|
| org.apache.commons.math3.optimization.general |
This package provides optimization algorithms that require derivatives.
|
| org.apache.commons.math3.random |
Random number and random data generators.
|
| org.apache.commons.math3.stat.correlation |
Correlations/Covariance computations.
|
| org.apache.commons.math3.stat.descriptive |
Generic univariate summary statistic objects.
|
| org.apache.commons.math3.stat.descriptive.moment |
Summary statistics based on moments.
|
| org.apache.commons.math3.stat.regression |
Statistical routines involving multivariate data.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
MultivariateNormalDistribution.getCovariances()
Gets the covariance matrix.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
ProcessModel.getControlMatrix()
Returns the control matrix.
|
RealMatrix |
DefaultProcessModel.getControlMatrix()
Returns the control matrix.
|
RealMatrix |
KalmanFilter.getErrorCovarianceMatrix()
Returns a copy of the current error covariance matrix.
|
RealMatrix |
ProcessModel.getInitialErrorCovariance()
Returns the initial error covariance matrix.
|
RealMatrix |
DefaultProcessModel.getInitialErrorCovariance()
Returns the initial error covariance matrix.
|
RealMatrix |
DefaultMeasurementModel.getMeasurementMatrix()
Returns the measurement matrix.
|
RealMatrix |
MeasurementModel.getMeasurementMatrix()
Returns the measurement matrix.
|
RealMatrix |
DefaultMeasurementModel.getMeasurementNoise()
Returns the measurement noise matrix.
|
RealMatrix |
MeasurementModel.getMeasurementNoise()
Returns the measurement noise matrix.
|
RealMatrix |
ProcessModel.getProcessNoise()
Returns the process noise matrix.
|
RealMatrix |
DefaultProcessModel.getProcessNoise()
Returns the process noise matrix.
|
RealMatrix |
ProcessModel.getStateTransitionMatrix()
Returns the state transition matrix.
|
RealMatrix |
DefaultProcessModel.getStateTransitionMatrix()
Returns the state transition matrix.
|
| Constructor and Description |
|---|
DefaultMeasurementModel(RealMatrix measMatrix,
RealMatrix measNoise)
Create a new
MeasurementModel, taking RealMatrix objects
as input parameters for the respective measurement matrix and noise. |
DefaultProcessModel(RealMatrix stateTransition,
RealMatrix control,
RealMatrix processNoise,
RealVector initialStateEstimate,
RealMatrix initialErrorCovariance)
Create a new
ProcessModel, taking double arrays as input parameters. |
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
ValueAndJacobianFunction.computeJacobian(double[] params)
Compute the Jacobian.
|
RealMatrix |
AbstractEvaluation.getCovariances(double threshold)
Get the covariance matrix of the optimized parameters.
|
RealMatrix |
LeastSquaresProblem.Evaluation.getCovariances(double threshold)
Get the covariance matrix of the optimized parameters.
|
RealMatrix |
LeastSquaresProblem.Evaluation.getJacobian()
Get the weighted Jacobian matrix.
|
| Modifier and Type | Method and Description |
|---|---|
Pair<RealVector,RealMatrix> |
MultivariateJacobianFunction.value(RealVector point)
Compute the function value and its Jacobian.
|
| Modifier and Type | Method and Description |
|---|---|
static LeastSquaresProblem |
LeastSquaresFactory.create(MultivariateJacobianFunction model,
RealVector observed,
RealVector start,
RealMatrix weight,
ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
int maxEvaluations,
int maxIterations)
Create a
LeastSquaresProblem
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 a
LeastSquaresProblem
from the given elements. |
static LeastSquaresProblem |
LeastSquaresFactory.create(MultivariateVectorFunction model,
MultivariateMatrixFunction jacobian,
double[] observed,
double[] start,
RealMatrix weight,
ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
int maxEvaluations,
int maxIterations)
Create a
LeastSquaresProblem
from the given elements. |
protected abstract RealVector |
GaussNewtonOptimizer.Decomposition.solve(RealMatrix jacobian,
RealVector residuals)
Solve the linear least squares problem Jx=r.
|
LeastSquaresBuilder |
LeastSquaresBuilder.weight(RealMatrix newWeight)
Configure the weight matrix.
|
static LeastSquaresProblem |
LeastSquaresFactory.weightMatrix(LeastSquaresProblem problem,
RealMatrix weights)
Apply a dense weight matrix to the
LeastSquaresProblem. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
SparseRealMatrix
Marker interface for
RealMatrix implementations that require sparse backing storage |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRealMatrix
Basic implementation of RealMatrix methods regardless of the underlying storage.
|
class |
Array2DRowRealMatrix
Implementation of
RealMatrix using a double[][] array to
store entries. |
class |
BlockRealMatrix
Cache-friendly implementation of RealMatrix using a flat arrays to store
square blocks of the matrix.
|
class |
DiagonalMatrix
Implementation of a diagonal matrix.
|
class |
OpenMapRealMatrix
Sparse matrix implementation based on an open addressed map.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
AbstractRealMatrix.add(RealMatrix m)
Returns the sum of
this and m. |
RealMatrix |
RealMatrix.add(RealMatrix m)
Returns the sum of
this and m. |
static RealMatrix |
MatrixUtils.blockInverse(RealMatrix m,
int splitIndex)
Computes the inverse of the given matrix by splitting it into
4 sub-matrices.
|
RealMatrix |
DiagonalMatrix.copy()
Returns a (deep) copy of this.
|
abstract RealMatrix |
AbstractRealMatrix.copy()
Returns a (deep) copy of this.
|
RealMatrix |
RealMatrix.copy()
Returns a (deep) copy of this.
|
RealMatrix |
Array2DRowRealMatrix.copy()
Returns a (deep) copy of this.
|
static RealMatrix |
MatrixUtils.createColumnRealMatrix(double[] columnData)
Creates a column
RealMatrix using the data from the input
array. |
RealMatrix |
DiagonalMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
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.
|
RealMatrix |
RealMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
RealMatrix |
Array2DRowRealMatrix.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)
Returns
dimension x dimension identity matrix. |
static RealMatrix |
MatrixUtils.createRealMatrix(double[][] data)
Returns a
RealMatrix whose entries are the the values in the
the input array. |
static RealMatrix |
MatrixUtils.createRealMatrix(int rows,
int columns)
Returns a
RealMatrix with specified dimensions. |
static RealMatrix |
MatrixUtils.createRowRealMatrix(double[] rowData)
Create a row
RealMatrix using the data from the input
array. |
RealMatrix |
AbstractRealMatrix.getColumnMatrix(int column)
Get the entries at the given column index as a column matrix.
|
RealMatrix |
RealMatrix.getColumnMatrix(int column)
Get the entries at the given column index as a column matrix.
|
RealMatrix |
SingularValueDecomposition.getCovariance(double minSingularValue)
Returns the n × n covariance matrix.
|
RealMatrix |
EigenDecomposition.getD()
Gets the block diagonal matrix D of the decomposition.
|
RealMatrix |
QRDecomposition.getH()
Returns the Householder reflector vectors.
|
RealMatrix |
DecompositionSolver.getInverse()
Get the pseudo-inverse
of the decomposed matrix.
|
RealMatrix |
LUDecomposition.getL()
Returns the matrix L of the decomposition.
|
RealMatrix |
CholeskyDecomposition.getL()
Returns the matrix L of the decomposition.
|
RealMatrix |
CholeskyDecomposition.getLT()
Returns the transpose of the matrix L of the decomposition.
|
RealMatrix |
LUDecomposition.getP()
Returns the P rows permutation matrix.
|
RealMatrix |
RRQRDecomposition.getP()
Returns the pivot matrix, P, used in the QR Decomposition of matrix A such that AP = QR.
|
RealMatrix |
QRDecomposition.getQ()
Returns the matrix Q of the decomposition.
|
RealMatrix |
QRDecomposition.getQT()
Returns the transpose of the matrix Q of the decomposition.
|
RealMatrix |
QRDecomposition.getR()
Returns the matrix R of the decomposition.
|
RealMatrix |
RectangularCholeskyDecomposition.getRootMatrix()
Get the root of the covariance matrix.
|
RealMatrix |
AbstractRealMatrix.getRowMatrix(int row)
Get the entries at the given row index as a row matrix.
|
RealMatrix |
RealMatrix.getRowMatrix(int row)
Get the entries at the given row index as a row matrix.
|
RealMatrix |
SingularValueDecomposition.getS()
Returns the diagonal matrix Σ of the decomposition.
|
RealMatrix |
EigenDecomposition.getSquareRoot()
Computes the square-root of the matrix.
|
RealMatrix |
AbstractRealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
RealMatrix |
RealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
RealMatrix |
AbstractRealMatrix.getSubMatrix(int startRow,
int endRow,
int startColumn,
int endColumn)
Gets a submatrix.
|
RealMatrix |
RealMatrix.getSubMatrix(int startRow,
int endRow,
int startColumn,
int endColumn)
Gets a submatrix.
|
RealMatrix |
LUDecomposition.getU()
Returns the matrix U of the decomposition.
|
RealMatrix |
SingularValueDecomposition.getU()
Returns the matrix U of the decomposition.
|
RealMatrix |
SingularValueDecomposition.getUT()
Returns the transpose of the matrix U of the decomposition.
|
RealMatrix |
EigenDecomposition.getV()
Gets the matrix V of the decomposition.
|
RealMatrix |
SingularValueDecomposition.getV()
Returns the matrix V of the decomposition.
|
RealMatrix |
EigenDecomposition.getVT()
Gets the transpose of the matrix V of the decomposition.
|
RealMatrix |
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.
|
RealMatrix |
DiagonalMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
AbstractRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
RealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
OpenMapRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
RealVector.outerProduct(RealVector v)
Compute the outer product.
|
RealMatrix |
ArrayRealVector.outerProduct(RealVector v)
Compute the outer product.
|
RealMatrix |
RealMatrixFormat.parse(String source)
Parse a string to produce a
RealMatrix object. |
RealMatrix |
RealMatrixFormat.parse(String source,
ParsePosition pos)
Parse a string to produce a
RealMatrix object. |
RealMatrix |
AbstractRealMatrix.power(int p)
Returns the result of multiplying
this with itself p
times. |
RealMatrix |
RealMatrix.power(int p)
Returns the result of multiplying
this with itself p
times. |
RealMatrix |
AbstractRealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m. |
RealMatrix |
RealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m. |
RealMatrix |
AbstractRealMatrix.scalarAdd(double d)
Returns the result of adding
d to each entry of this. |
RealMatrix |
RealMatrix.scalarAdd(double d)
Returns the result of adding
d to each entry of this. |
RealMatrix |
BlockRealMatrix.scalarMultiply(double d)
Returns the result of multiplying each entry of
this by
d. |
RealMatrix |
AbstractRealMatrix.scalarMultiply(double d)
Returns the result of multiplying each entry of
this by
d. |
RealMatrix |
RealMatrix.scalarMultiply(double d)
Returns the result of multiplying each entry of
this by
d. |
RealMatrix |
DecompositionSolver.solve(RealMatrix b)
Solve the linear equation A × X = B for matrices A.
|
RealMatrix |
AbstractRealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
RealMatrix |
RealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
RealMatrix |
AbstractRealMatrix.transpose()
Returns the transpose of this matrix.
|
RealMatrix |
RealMatrix.transpose()
Returns the transpose of this matrix.
|
| Modifier and Type | Method and Description |
|---|---|
BlockRealMatrix |
BlockRealMatrix.add(RealMatrix m)
Returns the sum of
this and m. |
RealMatrix |
AbstractRealMatrix.add(RealMatrix m)
Returns the sum of
this and m. |
RealMatrix |
RealMatrix.add(RealMatrix m)
Returns the sum of
this and m. |
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.
|
String |
RealMatrixFormat.format(RealMatrix m)
This method calls
RealMatrixFormat.format(RealMatrix,StringBuffer,FieldPosition). |
StringBuffer |
RealMatrixFormat.format(RealMatrix matrix,
StringBuffer toAppendTo,
FieldPosition pos)
Formats a
RealMatrix 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.
|
RealMatrix |
DiagonalMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
BlockRealMatrix |
BlockRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
AbstractRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
RealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
OpenMapRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m. |
RealMatrix |
AbstractRealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m. |
RealMatrix |
RealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m. |
static void |
MatrixUtils.serializeRealMatrix(RealMatrix matrix,
ObjectOutputStream oos)
Serialize a
RealMatrix. |
void |
BlockRealMatrix.setColumnMatrix(int column,
RealMatrix matrix)
Sets the specified
column of this matrix to the entries
of the specified column matrix. |
void |
AbstractRealMatrix.setColumnMatrix(int column,
RealMatrix matrix)
Sets the specified
column of this matrix to the entries
of the specified column matrix. |
void |
RealMatrix.setColumnMatrix(int column,
RealMatrix matrix)
Sets the specified
column of this matrix to the entries
of the specified column matrix. |
void |
BlockRealMatrix.setRowMatrix(int row,
RealMatrix matrix)
Sets the specified
row of this matrix to the entries of
the specified row matrix. |
void |
AbstractRealMatrix.setRowMatrix(int row,
RealMatrix matrix)
Sets the specified
row of this matrix to the entries of
the specified row matrix. |
void |
RealMatrix.setRowMatrix(int row,
RealMatrix matrix)
Sets the specified
row of this matrix to the entries of
the specified row matrix. |
RealMatrix |
DecompositionSolver.solve(RealMatrix b)
Solve the linear equation A × X = B for matrices A.
|
static void |
MatrixUtils.solveLowerTriangularSystem(RealMatrix rm,
RealVector b)
Solve a system of composed of a Lower Triangular Matrix
RealMatrix. |
static void |
MatrixUtils.solveUpperTriangularSystem(RealMatrix rm,
RealVector b)
Solver a system composed of an Upper Triangular Matrix
RealMatrix. |
BlockRealMatrix |
BlockRealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
RealMatrix |
AbstractRealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
RealMatrix |
RealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
OpenMapRealMatrix |
OpenMapRealMatrix.subtract(RealMatrix m)
Returns
this minus m. |
| Constructor and Description |
|---|
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
|
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.
|
RectangularCholeskyDecomposition(RealMatrix 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.
|
SingularValueDecomposition(RealMatrix matrix)
Calculates the compact Singular Value Decomposition of the given matrix.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
FuzzyKMeansClusterer.getMembershipMatrix()
Returns the
nxk membership matrix, where n is the number
of data points and k the number of clusters. |
| Constructor and Description |
|---|
LeastSquaresConverter(MultivariateVectorFunction function,
double[] observations,
RealMatrix scale)
Builds a simple converter for correlated residuals with the
specified weights.
|
| Modifier and Type | Method and Description |
|---|---|
List<RealMatrix> |
CMAESOptimizer.getStatisticsDHistory() |
List<RealMatrix> |
CMAESOptimizer.getStatisticsMeanHistory() |
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
Weight.getWeight()
Deprecated.
Gets the initial guess.
|
RealMatrix |
MultivariateVectorOptimizer.getWeight()
Deprecated.
Gets the weight matrix of the observations.
|
| Constructor and Description |
|---|
Weight(RealMatrix weight)
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
protected RealMatrix |
AbstractLeastSquaresOptimizer.computeWeightedJacobian(double[] params)
Deprecated.
Computes the weighted Jacobian matrix.
|
RealMatrix |
AbstractLeastSquaresOptimizer.getWeightSquareRoot()
Deprecated.
Gets the square-root of the weight matrix.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
Weight.getWeight()
Deprecated.
Gets the initial guess.
|
| Constructor and Description |
|---|
LeastSquaresConverter(MultivariateVectorFunction function,
double[] observations,
RealMatrix scale)
Deprecated.
Build a simple converter for correlated residuals with the specific weights.
|
Weight(RealMatrix weight)
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
BaseAbstractMultivariateVectorOptimizer.getWeight()
Deprecated.
Gets the weight matrix of the observations.
|
| Modifier and Type | Method and Description |
|---|---|
List<RealMatrix> |
CMAESOptimizer.getStatisticsDHistory()
Deprecated.
|
List<RealMatrix> |
CMAESOptimizer.getStatisticsMeanHistory()
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
protected RealMatrix |
AbstractLeastSquaresOptimizer.computeWeightedJacobian(double[] params)
Deprecated.
Computes the Jacobian matrix.
|
RealMatrix |
AbstractLeastSquaresOptimizer.getWeightSquareRoot()
Deprecated.
Gets the square-root of the weight matrix.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
CorrelatedRandomVectorGenerator.getRootMatrix()
Get the root of the covariance matrix.
|
| Constructor and Description |
|---|
CorrelatedRandomVectorGenerator(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.
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
PearsonsCorrelation.computeCorrelationMatrix(double[][] data)
Computes the correlation matrix for the columns of the
input rectangular array.
|
RealMatrix |
KendallsCorrelation.computeCorrelationMatrix(double[][] matrix)
Computes the Kendall's Tau rank correlation matrix for the columns of
the input rectangular array.
|
RealMatrix |
SpearmansCorrelation.computeCorrelationMatrix(double[][] matrix)
Computes the Spearman's rank correlation matrix for the columns of the
input rectangular array.
|
RealMatrix |
PearsonsCorrelation.computeCorrelationMatrix(RealMatrix matrix)
Computes the correlation matrix for the columns of the
input matrix, using
PearsonsCorrelation.correlation(double[], double[]). |
RealMatrix |
KendallsCorrelation.computeCorrelationMatrix(RealMatrix matrix)
Computes the Kendall's Tau rank correlation matrix for the columns of
the input matrix.
|
RealMatrix |
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.
|
RealMatrix |
PearsonsCorrelation.covarianceToCorrelation(RealMatrix covarianceMatrix)
Derives a correlation matrix from a covariance matrix.
|
RealMatrix |
PearsonsCorrelation.getCorrelationMatrix()
Returns the correlation matrix.
|
RealMatrix |
KendallsCorrelation.getCorrelationMatrix()
Returns the correlation matrix.
|
RealMatrix |
SpearmansCorrelation.getCorrelationMatrix()
Calculate the Spearman Rank Correlation Matrix.
|
RealMatrix |
PearsonsCorrelation.getCorrelationPValues()
Returns a matrix of p-values associated with the (two-sided) null
hypothesis that the corresponding correlation coefficient is zero.
|
RealMatrix |
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 with getCorrelationMatrix.getEntry(i,j) |
RealMatrix |
StorelessCovariance.getCovarianceMatrix()
Returns the covariance matrix
|
RealMatrix |
Covariance.getCovarianceMatrix()
Returns the covariance matrix
|
| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
PearsonsCorrelation.computeCorrelationMatrix(RealMatrix matrix)
Computes the correlation matrix for the columns of the
input matrix, using
PearsonsCorrelation.correlation(double[], double[]). |
RealMatrix |
KendallsCorrelation.computeCorrelationMatrix(RealMatrix matrix)
Computes the Kendall's Tau rank correlation matrix for the columns of
the input matrix.
|
RealMatrix |
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.
|
RealMatrix |
PearsonsCorrelation.covarianceToCorrelation(RealMatrix covarianceMatrix)
Derives a correlation matrix from a covariance matrix.
|
| Constructor and Description |
|---|
Covariance(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.
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PearsonsCorrelation(RealMatrix covarianceMatrix,
int numberOfObservations)
Create a PearsonsCorrelation from a covariance matrix.
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SpearmansCorrelation(RealMatrix dataMatrix)
Create a SpearmansCorrelation from the given data matrix.
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SpearmansCorrelation(RealMatrix dataMatrix,
RankingAlgorithm rankingAlgorithm)
Create a SpearmansCorrelation with the given input data matrix
and ranking algorithm.
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| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
MultivariateSummaryStatistics.getCovariance()
Returns the covariance matrix of the values that have been added.
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RealMatrix |
StatisticalMultivariateSummary.getCovariance()
Returns the covariance of the available values.
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RealMatrix |
SynchronizedMultivariateSummaryStatistics.getCovariance()
Returns the covariance matrix of the values that have been added.
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| Modifier and Type | Method and Description |
|---|---|
RealMatrix |
VectorialCovariance.getResult()
Get the covariance matrix.
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| Modifier and Type | Method and Description |
|---|---|
protected abstract RealMatrix |
AbstractMultipleLinearRegression.calculateBetaVariance()
Calculates the beta variance of multiple linear regression in matrix
notation.
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protected RealMatrix |
OLSMultipleLinearRegression.calculateBetaVariance()
Calculates the variance-covariance matrix of the regression parameters.
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protected RealMatrix |
GLSMultipleLinearRegression.calculateBetaVariance()
Calculates the variance on the beta.
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RealMatrix |
OLSMultipleLinearRegression.calculateHat()
Compute the "hat" matrix.
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protected RealMatrix |
GLSMultipleLinearRegression.getOmegaInverse()
Get the inverse of the covariance.
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protected RealMatrix |
AbstractMultipleLinearRegression.getX() |
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