I am having trouble trying to use scipy.stats.multivariate_normal
, hopefully one of you might be able to help.
I have a 2x2 matrix which is possible to find the inverse of using numpy.linalg.inv()
, however when I attempt to use it as the covariance matrix in multivariate_normal
I receive a LinAlgError
stating that it is a singular matrix:
In [89]: cov = np.array([[3.2e5**2, 3.2e5*0.103*-0.459],[3.2e5*0.103*-0.459, 0.103**2]])
In [90]: np.linalg.inv(cov)
Out[90]:
array([[ 1.23722158e-11, 1.76430200e-05],
[ 1.76430200e-05, 1.19418880e+02]])
In [91]: multivariate_normal([0,0], cov)
---------------------------------------------------------------------------
LinAlgError Traceback (most recent call last)
<ipython-input-91-44a6625beda5> in <module>()
----> 1 multivariate_normal([0,0], cov)
/mnt/ssd/Enthought_jli199/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/_multivariate.pyc in __call__(self, mean, cov, allow_singular, seed)
421 return multivariate_normal_frozen(mean, cov,
422 allow_singular=allow_singular,
--> 423 seed=seed)
424
425 def _logpdf(self, x, mean, prec_U, log_det_cov, rank):
/mnt/ssd/Enthought_jli199/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/_multivariate.pyc in __init__(self, mean, cov, allow_singular, seed)
591 """
592 self.dim, self.mean, self.cov = _process_parameters(None, mean, cov)
--> 593 self.cov_info = _PSD(self.cov, allow_singular=allow_singular)
594 self._dist = multivariate_normal_gen(seed)
595
/mnt/ssd/Enthought_jli199/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/_multivariate.pyc in __init__(self, M, cond, rcond, lower, check_finite, allow_singular)
217 d = s[s > eps]
218 if len(d) < len(s) and not allow_singular:
--> 219 raise np.linalg.LinAlgError('singular matrix')
220 s_pinv = _pinv_1d(s, eps)
221 U = np.multiply(u, np.sqrt(s_pinv))
LinAlgError: singular matrix
By default multivariate_normal
checks whether any of the eigenvalues of the covariance matrix are less than some tolerance chosen based on its dtype and the magnitude of its largest eigenvalue (take a look at the source code for scipy.stats._multivariate._PSD
and scipy.stats._multivariate._eigvalsh_to_eps
for the full details).
As @kazemakase mentioned above, whilst your covariance matrix may be invertible according to the criteria used by np.linalg.inv
, it is still very ill-conditioned and fails the more stringent test used by multivariate_normal
.
You could pass allow_singular=True
to multivariate_normal
to skip this test, but in general it would be better to rescale your data to avoid passing such an ill-conditioned covariance matrix in the first place.
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