random correction #139
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random correction #139
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Thanks @EnricaBelfiore , I will have a look. But first I'll wait that you finish the tests |
luca-fiorito-11
requested changes
Feb 20, 2022
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I made these changes without inserting the negative eigenvalues equal to zero because this is done by the to_positive method. I prefer to drop that method and insert the cut here if you agree. |
We agreed the eig method will remove eigenvalues given a tolerance. |
| """ | ||
| Extract eigenvalues and eigenvectors. | ||
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| Parameters | ||
| ---------- | ||
| sort : `bool`, optional, default is `True` | ||
| flag to return sorted eigenvalues and eigenfunctions | ||
| tolerance : `float`, optional, default is `None` | ||
| truncation proportional to the magnitude of the largest positive eigenvalue, | ||
| e.g. 1000 to cut all the eigenvalues 1000 times lower than the largest eigenvalue | ||
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What if you want to remove only negative eigs? What can the user do?
luca-fiorito-11
requested changes
Feb 24, 2022
luca-fiorito-11
approved these changes
Feb 25, 2022
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The random_corr method implemented did not work for matrices with a dimention higher than 30x30 because it returned a matrix not always positive definite nor symettrical (for numerical fluctuations). With this correction the matrix will allow for the creation of a category cov instance up until size 10000x10000 (bigger matrix gives memory issues, in the previous implementation as well), but some eigenvalues could be
-1e-18or in this range (numerical approximation of zero).The method _reduce_size is modified because it did not work for a real covariance matrix.
P.s. even if we will change the decomposition it should be mantained because it is used also for different methods (invert, _gls_G_inv)