I have an open PR which lets users control the checks on the input covariance matrix. The matrix is required to be symmetric and positve semi-definite (PSD). The current behavior is that NumPy raises a warning if the matrix is not PSD, and does not even check for symmetry.
I added a symmetry check, which raises a warning when the input is not symmetric. And added two keyword args which users can use to turn off the checks/warnings when the matrix is ill formed. So this would only cause another new warning to be raised in existing code. This is needed because sometimes the covariance matrix is only *almost* symmetric or PSD due to roundoff error. Thoughts? PR: https://github.com/numpy/numpy/pull/5726
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