Find a near correlation matrix that is positive semi-definite
This function clips the eigenvalues, replacing eigenvalues smaller than the threshold by the threshold. The new matrix is normalized, so that the diagonal elements are one. Compared to corr_nearest, the distance between the original correlation matrix and the positive definite correlation matrix is larger, however, it is much faster since it only computes eigenvalues once.
corr_new – corrected correlation matrix
The smallest eigenvalue of the corrected correlation matrix is approximately equal to the
threshold. In examples, the smallest eigenvalue can be by a factor of 10 smaller than the threshold, e.g. threshold 1e-8 can result in smallest eigenvalue in the range between 1e-9 and 1e-8. If the threshold=0, then the smallest eigenvalue of the correlation matrix might be negative, but zero within a numerical error, for example in the range of -1e-16.
Assumes input correlation matrix is symmetric. The diagonal elements of returned correlation matrix is set to ones.
If the correlation matrix is already positive semi-definite given the threshold, then the original correlation matrix is returned.
cov_clipped is 40 or more times faster than
cov_nearest in simple example, but has a slightly larger approximation error.
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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.