`scipy.stats._multivariate:_PSD`

source: /scipy/stats/_multivariate.py :169

Signature

   class     _PSD (    M    ,    cond    =  None   ,    rcond    =  None   ,    lower    =  True   ,    check_finite    =  True   ,    allow_singular    =  True     )

Members

Summary

Compute coordinated functions of a symmetric positive semidefinite matrix.

Extended Summary

This class addresses two issues. Firstly it allows the pseudoinverse, the logarithm of the pseudo-determinant, and the rank of the matrix to be computed using one call to eigh instead of three. Secondly it allows these functions to be computed in a way that gives mutually compatible results. All of the functions are computed with a common understanding as to which of the eigenvalues are to be considered negligibly small. The functions are designed to coordinate with scipy.linalg.pinvh() but not necessarily with np.linalg.det() or with np.linalg.matrix_rank().

Parameters

M : array_like: Symmetric positive semidefinite matrix (2-D).
cond, rcond : float, optional: Cutoff for small eigenvalues. Singular values smaller than rcond * largest_eigenvalue are considered zero. If None or -1, suitable machine precision is used.
lower : bool, optional: Whether the pertinent array data is taken from the lower or upper triangle of M. (Default: lower)
check_finite : bool, optional: Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
allow_singular : bool, optional: Whether to allow a singular matrix. (Default: True)

Notes

The arguments are similar to those of scipy.linalg.pinvh().

Aliases

scipy.stats._multivariate._PSD