WIP: Add GED transformer

[Genuster]

What does this implement/fix?

Adds transformer for generalized eigenvalue decomposition (or approximate joint diagonalization) of covariance matrices.
It generalizes xdawn, csp, ssd, and spoc algorithms.

Additional information

Currently testing that it outputs identical filters and patterns as child classes for all tests.
Next step will be to clean up redundant code from the child classes, and unify linear algebra, e.g.:
(1) choose between mne's pinv or np.linalg.pinv for all classes
(2) remove hacky _mult_order from _smart_ged (it's currently there as, numerically, change of multiplication order breaks np.testing.assert_allclose for ssd)
(3) perhaps ssd should use mne.cov.compute_whitener() instead of its own whitener implementation. It won't be identical, but conceptually seems to do the same thing.
(4) add feature to perform GED in the principal subspace for xdawn

[larsoner]

Already have a failure but fortunately it's just a tol issue I think:

mne/decoding/tests/test_csp.py:444: in test_spoc
    spoc.fit(X, y)
mne/decoding/csp.py:985: in fit
    np.testing.assert_allclose(old_filters, self.filters_)
E   AssertionError: 
E   Not equal to tolerance rtol=1e-07, atol=0
E   
E   Mismatched elements: 1 / 100 (1%)
E   Max absolute difference among violations: 9.04019248e-09
E   Max relative difference among violations: 1.11806536e-07
E    ACTUAL: array([[  2.037415,   1.424886,   2.718162,  -3.07798 ,  -3.862132,
E             1.412549,  -3.821452,   1.276637,   1.899782,  -2.389858],
E          [ 11.534231, -22.178034, -12.321628, -52.410096,  62.876084,...
E    DESIRED: array([[  2.037415,   1.424886,   2.718162,  -3.07798 ,  -3.862132,
E             1.412549,  -3.821452,   1.276637,   1.899782,  -2.389858],
E          [ 11.534231, -22.178034, -12.321628, -52.410096,  62.876084,...

I would just bump the rtol a bit here to 1e-6, and if you know the magnitudes are in the single/double digits then an atol=1e-7 would also be reasonable (could do both).

[Genuster]

Thanks!
Interesting how it passed macos-13/mamba/3.12, but didn't pass macos-latest/mamba/3.12

It might be that the small difference between filters_ will propagate and increase in patterns_, so rtol/atol won't be much of help for patterns_. But let's see

[larsoner]

Different architectures, macos-13 is Intel x86_64 and macos-latest is ARM / M1. And Windows also failed, could be use of MKL there or something. I'm cautiously optimistic it's just floating point errors...