pinv2(a, cond=None, rcond=None, return_rank=False, check_finite=True)
scipy.linalg.pinv2
is deprecated since SciPy 1.7.0, use scipy.linalg.pinv
instead for better tolerance control.
Calculate a generalized inverse of a matrix using its singular-value decomposition and including all 'large' singular values.
Matrix to be pseudo-inverted.
Cutoff for 'small' singular values; singular values smaller than this value are considered as zero. If both are omitted, the default value max(M,N)*largest_singular_value*eps
is used where eps
is the machine precision value of the datatype of a
.
Previously the default cutoff value was just eps*f
where f
was 1e3
for single precision and 1e6
for double precision.
If True, return the effective rank of the matrix.
Whether to check that the input matrix contains 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.
If SVD computation does not converge.
The pseudo-inverse of matrix a.
The effective rank of the matrix. Returned if :None:None:`return_rank` is True.
Compute the (Moore-Penrose) pseudo-inverse of a matrix.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._basic.pinv2
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them