# CorrelationMatrix¶

class CorrelationMatrix(*args)

Correlation Matrix.

Available constructors:

CorrelationMatrix(dim)

CorrelationMatrix(dim, values)

Parameters:
dimint

The dimension of the correlation matrix (square matrix with dim rows and dim columns).

valuessequence of float

Collection of scalar values to put in the correlation matrix, filled by rows. When not specified, the correlation matrix is initialized to the identity matrix.

Notes

In the first usage, the correlation matrix is the identity matrix.

In the second usage, the correlation matrix contains the specified values, filled by rows.

Warning

No check is made on the values, in particular the diagonal elements are not forced to be equal to 1 and the positiveness of the matrix is not checked.

Methods

 Check if the internal representation is really symmetric. clean(threshold) Set elements smaller than a threshold to zero. Compute the Cholesky factor. Compute the Cholesky factor in place. Compute the determinant. Compute the determinant in place. Compute the eigenvalues decomposition (EVD). Compute the eigenvalues decomposition (EVD) in place. Compute eigenvalues. Compute eigenvalues in place. computeGram([transpose]) Compute the associated Gram matrix. Compute the Hadamard product matrix. Compute the largest eigenvalue module. Compute the logarithm of the absolute value of the determinant. Compute the determinant in place. computeQR([fullQR]) Compute the QR factorization. computeQRInPlace([fullQR]) Compute the QR factorization in place. Compute the regularized Cholesky factor. computeSVD([fullSVD]) Compute the singular values decomposition (SVD). computeSVDInPlace([fullSVD]) Compute the singular values decomposition (SVD). Compute the singular values. Compute the singular values in place. Compute the sum of the matrix elements. Compute the trace of the matrix. Frobenius norm accessor. Accessor to the object's name. Get the k-th diagonal of the matrix. Get the k-th diagonal of the matrix. Accessor to the dimension (the number of rows). Accessor to the object's id. Accessor to the underlying implementation. Accessor to the object's name. Accessor to the number of columns. Accessor to the number of rows. Compute the inverse of the matrix. Test whether the matrix is diagonal or not. Tell if the matrix is empty. Test whether the matrix is positive definite or not. reshape(newRowDim, newColDim) Reshape the matrix. reshapeInPlace(newRowDim, newColDim) Reshape the matrix, in place. setDiagonal(*args) Set the k-th diagonal of the matrix. setName(name) Accessor to the object's name. solveLinearSystem(*args) Solve a square linear system whose the present matrix is the operator. Solve a rectangular linear system whose the present matrix is the operator. Square the Matrix, ie each element of the matrix is squared. Transpose the matrix.
__init__(*args)
checkSymmetry()

Check if the internal representation is really symmetric.

clean(threshold)

Set elements smaller than a threshold to zero.

Parameters:
thresholdfloat

Threshold for zeroing elements.

Returns:
cleaned_matrixMatrix

Input matrix with elements smaller than the threshold set to zero.

computeCholesky()

Compute the Cholesky factor.

The Cholesky factor of a covariance (real symmetric positive definite) matrix is the lower triangular matrix such that:

Returns:
cholesky_factorTriangularMatrix

The left (lower) Cholesky factor.

Notes

This uses LAPACK’s DPOTRF.

computeCholeskyInPlace()

Compute the Cholesky factor in place.

Similar to computeCholesky() but modifies the matrix in place to avoid a copy.

computeDeterminant()

Compute the determinant.

Returns:
determinantfloat

The square matrix determinant.

Examples

>>> import openturns as ot
>>> A = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> A.computeDeterminant()
-2.0

computeDeterminantInPlace()

Compute the determinant in place.

Similar to computeDeterminant() but modifies the matrix in place to avoid copy.

computeEV()

Compute the eigenvalues decomposition (EVD).

The eigenvalues decomposition of a square matrix with size reads:

where is an diagonal matrix and is an orthogonal matrix.

Returns:
eigenvaluesPoint

The vector of eigenvalues with size that form the diagonal of the matrix of the EVD.

PhiSquareComplexMatrix

The left matrix of the EVD.

Notes

This uses LAPACK’S DSYEV.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.SymmetricMatrix([[1.0, 2.0], [2.0, -4.0]])
>>> eigen_values, Phi = M.computeEV()
>>> Lambda = ot.SquareMatrix(M.getDimension())
>>> for i in range(eigen_values.getSize()):
...     Lambda[i, i] = eigen_values[i]
>>> np.testing.assert_array_almost_equal(Phi * Lambda * Phi.transpose(), M)

computeEVInPlace()

Compute the eigenvalues decomposition (EVD) in place.

Similar to computeEV() but the matrix is modified in place to avoid copy.

computeEigenValues()

Compute eigenvalues.

Returns:
eigenvaluesPoint

Eigenvalues.

Examples

>>> import openturns as ot
>>> M = ot.SymmetricMatrix([[1.0, 2.0], [2.0, -4.0]])
>>> print(M.computeEigenValues())
[-4.70156,1.70156]

computeEigenValuesInPlace()

Compute eigenvalues in place.

Similar to computeEigenValues() but the matrix is modified in place to avoid copy.

computeGram(transpose=True)

Compute the associated Gram matrix.

Parameters:
transposedbool

Tells if matrix is to be transposed or not. Default value is True

Returns:
MMTMatrix

The Gram matrix.

Notes

When transposed is True, compute . Otherwise, compute .

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> MtM = M.computeGram()
>>> print(MtM)
[[ 35 44 ]
[ 44 56 ]]
>>> MMt = M.computeGram(False)
>>> print(MMt)
[[  5 11 17 ]
[ 11 25 39 ]
[ 17 39 61 ]]


Parameters:
MatMatrix

The right hand matrix

Returns:
CMatrix

Notes

The matrix resulting from the Hadamard product ( also known as the elementwise product) of the matrices and is:

for any and .

Examples

>>> import openturns as ot
>>> A = ot.Matrix([[1.0, 2.0], [3.0, 4.0]])
>>> B = ot.Matrix([[1.0, 2.0], [3.0, 4.0]])
>>> print(C)
[[  1  4 ]
[  9 16 ]]
[[  1  4 ]
[  9 16 ]]

computeLargestEigenValueModule(*args)

Compute the largest eigenvalue module.

Parameters:
maximumIterationsint, optional

The maximum number of power iterations to perform to get the approximation. Default is given by the ‘Matrix-LargestEigenValueIterations’ key in the ResourceMap.

epsilonfloat, optional

The target relative error. Default is given by the ‘Matrix-LargestEigenValueRelativeError’ key in the ResourceMap.

Returns:
largestEigenvalueModulefloat

The largest eigenvalue module.

Examples

>>> import openturns as ot
>>> M = ot.SymmetricMatrix([[1.0, 3.0], [3.0, 4.0]])
>>> M.computeLargestEigenValueModule()
5.8541...

computeLogAbsoluteDeterminant()

Compute the logarithm of the absolute value of the determinant.

Returns:
determinantfloat

The logarithm of the absolute value of the square matrix determinant.

signfloat

The sign of the determinant.

Examples

>>> import openturns as ot
>>> A = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> A.computeLogAbsoluteDeterminant()
[0.693147..., -1.0]

computeLogAbsoluteDeterminantInPlace()

Compute the determinant in place.

Similar to computeLogAbsoluteDeterminant() but modifies the matrix in place to avoid copy.

computeQR(fullQR=False)

Compute the QR factorization.

By default, it is the economic decomposition which is computed. The economic QR factorization of a rectangular matrix with (more rows than columns) is defined as follows:

where is an upper triangular matrix, is , is , and and both have orthogonal columns.

Parameters:
full_qrbool, optional

A flag telling whether Q, R or Q1, R1 are returned. Default is False and returns Q1, R1.

Returns:
Q1Matrix

The orthogonal matrix of the economic QR factorization.

R1TriangularMatrix

The right (upper) triangular matrix of the economic QR factorization.

QMatrix

The orthogonal matrix of the full QR factorization.

RTriangularMatrix

The right (upper) triangular matrix of the full QR factorization.

Notes

The economic QR factorization is often used for solving overdetermined linear systems (where the operator has ) in the least-square sense because it implies solving a (simple) triangular system:

This uses LAPACK’s DGEQRF and DORGQR.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> Q1, R1 = M.computeQR()
>>> np.testing.assert_array_almost_equal(Q1 * R1, M)

computeQRInPlace(fullQR=False)

Compute the QR factorization in place.

Similar to computeQR()

computeRegularizedCholesky()

Compute the regularized Cholesky factor.

Similar to computeCholesky() but with a regularization loop according to the largest eigenvalue and keys Matrix-StartingScaling and Matrix-MaximalScaling.

Returns:
cholesky_factorTriangularMatrix

The left (lower) Cholesky factor.

computeSVD(fullSVD=False)

Compute the singular values decomposition (SVD).

The singular values decomposition of a rectangular matrix with size reads:

where is an orthogonal matrix, is an diagonal matrix and is an orthogonal matrix.

Parameters:
fullSVDbool, optional

Whether the null parts of the orthogonal factors are explicitly stored or not. Default is False and computes a reduced SVD.

Returns:
singular_valuesPoint

The vector of singular values with size that form the diagonal of the matrix of the SVD.

USquareMatrix

The left orthogonal matrix of the SVD.

VTSquareMatrix

The transposed right orthogonal matrix of the SVD.

Notes

This uses LAPACK’s DGESDD.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> singular_values, U, VT = M.computeSVD(True)
>>> Sigma = ot.Matrix(M.getNbRows(), M.getNbColumns())
>>> for i in range(singular_values.getSize()):
...     Sigma[i, i] = singular_values[i]
>>> np.testing.assert_array_almost_equal(U * Sigma * VT, M)

computeSVDInPlace(fullSVD=False)

Compute the singular values decomposition (SVD).

Unlike computeSVD, this modifies the matrix in place and avoids a copy.

computeSingularValues()

Compute the singular values.

Parameters:
fullSVDbool, optional

Whether the null parts of the orthogonal factors are explicitly stored or not. Default is False and computes a reduced SVD.

Returns:
singular_valuesPoint

The vector of singular values with size that form the diagonal of the matrix of the SVD decomposition.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M.computeSingularValues())
[9.52552,0.514301]

computeSingularValuesInPlace()

Compute the singular values in place.

Similar to computeSingularValues() but the matrix is modified in place to avoid copy.

computeSumElements()

Compute the sum of the matrix elements.

Returns:
suma float

The sum of the elements.

Notes

Compute the sum of elements of the matrix :

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> s = M.computeSumElements()
>>> print(s)
21.0

computeTrace()

Compute the trace of the matrix.

Returns:
tracefloat

The trace of the matrix.

Examples

>>> import openturns as ot
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> M.computeTrace()
5.0

frobeniusNorm()

Frobenius norm accessor.

Returns:
normfloat

The Frobenius norm .

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getDiagonal(k=0)

Get the k-th diagonal of the matrix.

Parameters:
kint

The k-th diagonal to extract Default value is 0

Returns:
D: Matrix

The k-th diagonal.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
>>> diag = M.getDiagonal()
>>> print(diag)
[[ 1 ]
[ 5 ]
[ 9 ]]
>>> print(M.getDiagonal(1))
[[ 2 ]
[ 6 ]]

getDiagonalAsPoint(k=0)

Get the k-th diagonal of the matrix.

Parameters:
kint

The k-th diagonal to extract Default value is 0

Returns:
ptPoint

The k-th digonal.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
>>> pt = M.getDiagonalAsPoint()
>>> print(pt)
[1,5,9]

getDimension()

Accessor to the dimension (the number of rows).

Returns:
dimensionint
getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:
implImplementation

A copy of the underlying implementation object.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getNbColumns()

Accessor to the number of columns.

Returns:
n_columnsint
getNbRows()

Accessor to the number of rows.

Returns:
n_rowsint
inverse()

Compute the inverse of the matrix.

Returns:
inverseMatrixSymmetricMatrix

The inverse of the matrix.

Examples

>>> import openturns as ot
>>> M = ot.SymmetricMatrix([[4.0, 2.0, 1.0], [2.0, 5.0, 3.0], [1.0, 3.0, 6.0]])
>>> print(67.0 * M.inverse())
[[  21  -9   1 ]
[  -9  23 -10 ]
[   1 -10  16 ]]

isDiagonal()

Test whether the matrix is diagonal or not.

Returns:
testbool

isEmpty()

Tell if the matrix is empty.

Returns:
is_emptybool

True if the matrix contains no element.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[]])
>>> M.isEmpty()
True

isPositiveDefinite()

Test whether the matrix is positive definite or not.

A matrix is positive definite if is positive for every compatible non-zero column vector .

Returns:
testbool

Notes

This uses LAPACK’s DPOTRF.

reshape(newRowDim, newColDim)

Reshape the matrix.

Parameters:
newRowDimint

The row dimension of the reshaped matrix.

newColDimint

The column dimension of the reshaped matrix.

Returns:
MTMatrix

The reshaped matrix.

Notes

If the size of the reshaped matrix is smaller than the size of the matrix to be reshaped, only the first elements are kept (in a column-major storage sense). If the size is greater, the new elements are set to zero.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M)
[[ 1 2 ]
[ 3 4 ]
[ 5 6 ]]
>>> print(M.reshape(1, 6))
1x6
[[ 1 3 5 2 4 6 ]]
>>> print(M.reshape(2, 2))
[[ 1 5 ]
[ 3 2 ]]
>>> print(M.reshape(2, 6))
2x6
[[ 1 5 4 0 0 0 ]
[ 3 2 6 0 0 0 ]]

reshapeInPlace(newRowDim, newColDim)

Reshape the matrix, in place.

Parameters:
newRowDimint

The row dimension of the reshaped matrix.

newColDimint

The column dimension of the reshaped matrix.

Notes

If the size of the reshaped matrix is smaller than the size of the matrix to be reshaped, only the first elements are kept (in a column-major storage sense). If the size is greater, the new elements are set to zero. If the size is unchanged, no copy of data is done.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M)
[[ 1 2 ]
[ 3 4 ]
[ 5 6 ]]
>>> M.reshapeInPlace(1, 6)
>>> print(M)
1x6
[[ 1 3 5 2 4 6 ]]
>>> M.reshapeInPlace(2, 2)
>>> print(M)
[[ 1 5 ]
[ 3 2 ]]
>>> M.reshapeInPlace(2, 6)
>>> print(M)
2x6
[[ 1 5 0 0 0 0 ]
[ 3 2 0 0 0 0 ]]

setDiagonal(*args)

Set the k-th diagonal of the matrix.

Parameters:
diagPoint or Matrix or a float

Value(s) used to fill the diagonal of the matrix

kint

The k-th diagonal to fill Default value is 0

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
>>> M.setDiagonal([-1, 23, 9])
>>> print(M)
[[ -1  2  3 ]
[  4 23  6 ]
[  7  8  9 ]]
>>> M.setDiagonal(1.0)
>>> print(M)
[[ 1 2 3 ]
[ 4 1 6 ]
[ 7 8 1 ]]
>>> M.setDiagonal([2, 6, 9])
>>> print(M)
[[ 2 2 3 ]
[ 4 6 6 ]
[ 7 8 9 ]]

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

solveLinearSystem(*args)

Solve a square linear system whose the present matrix is the operator.

Parameters:
rhssequence of float or Matrix with values or rows, respectively

The right hand side member of the linear system.

Returns:
solution

The solution of the square linear system.

Notes

This will handle both matrices and vectors. Note that you’d better type explicitly the matrix if it has some properties that could simplify the resolution (see TriangularMatrix).

This uses LAPACK’S DGESV for matrices and DGELSY for vectors.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> b = [1.0] * 2
>>> x = M.solveLinearSystem(b)
>>> np.testing.assert_array_almost_equal(M * x, b)

solveLinearSystemInPlace(*args)

Solve a rectangular linear system whose the present matrix is the operator.

Similar to solveLinearSystem() except the matrix is modified in-place during the resolution avoiding the need to allocate an extra copy if the original copy is not re-used.

squareElements()

Square the Matrix, ie each element of the matrix is squared.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> M.squareElements()
>>> print(M)
[[  1  4 ]
[  9 16 ]
[ 25 36 ]]

transpose()

Transpose the matrix.

Returns:
MTSquareMatrix

The transposed matrix.

Examples

>>> import openturns as ot
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> print(M)
[[ 1 2 ]
[ 3 4 ]]
>>> print(M.transpose())
[[ 1 3 ]
[ 2 4 ]]


## Examples using the class¶

Estimate Wilks and empirical quantile

Estimate Wilks and empirical quantile

Estimate a multivariate distribution

Estimate a multivariate distribution

Test independence

Test independence

Fit a parametric copula

Fit a parametric copula

Fit a non parametric copula

Fit a non parametric copula

Visualize pairs

Visualize pairs

Visualize clouds

Visualize clouds

Create and draw multivariate distributions

Create and draw multivariate distributions

Quick start guide to distributions

Quick start guide to distributions

Draw minimum volume level sets

Draw minimum volume level sets

Create a copula

Create a copula

Extract the copula from a distribution

Extract the copula from a distribution

Assemble copulas

Assemble copulas

Create a functional basis process

Create a functional basis process

Create a parametric spectral density function

Create a parametric spectral density function

Export a field to VTK

Export a field to VTK

Create a stationary covariance model

Create a stationary covariance model

Create a normal process

Create a normal process

Draw a field

Draw a field

Sample trajectories from a Gaussian Process with correlated outputs

Sample trajectories from a Gaussian Process with correlated outputs

Draw fields

Draw fields

Mixture of experts

Mixture of experts

Kriging: configure the optimization solver

Kriging: configure the optimization solver

Estimate moments from Taylor expansions

Estimate moments from Taylor expansions

Use the post-analytical importance sampling algorithm

Use the post-analytical importance sampling algorithm

Subset Sampling

Subset Sampling

Create unions or intersections of events

Create unions or intersections of events

Create an event based on a process

Create an event based on a process

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

Parallel coordinates graph as sensitivity tool

Parallel coordinates graph as sensitivity tool

Use the ANCOVA indices

Use the ANCOVA indices

Create mixed deterministic and probabilistic designs of experiments

Create mixed deterministic and probabilistic designs of experiments

Calibration without observed inputs

Calibration without observed inputs

Iterated Functions System

Iterated Functions System

Control algorithm termination

Control algorithm termination

A quick start guide to contours

A quick start guide to contours

A quick start guide to graphs

A quick start guide to graphs