4.7.8.1.4. statsmodels.tools.linalg.pinv2¶

statsmodels.tools.linalg.pinv2(a, cond=None, rcond=None)[source]¶

Compute the (Moore-Penrose) pseudo-inverse of a matrix.

Calculate a generalized inverse of a matrix using its singular-value decomposition and including all ‘large’ singular values.

Parameters:

a : array, shape (M, N)

Matrix to be pseudo-inverted

cond, rcond : float or None

Cutoff for ‘small’ singular values. Singular values smaller than rcond*largest_singular_value are considered zero.

If None or -1, suitable machine precision is used.

Returns:

B : array, shape (N, M)

Raises LinAlgError if SVD computation does not converge

Examples

>>> from numpy import *
>>> a = random.randn(9, 6)
>>> B = linalg.pinv2(a)
>>> allclose(a, dot(a, dot(B, a)))
True
>>> allclose(B, dot(B, dot(a, B)))
True