Fix pca_numpy conditioning: SVD the centered data matrix, not the covariance matrix

[mengxihex]

Fixes #1522.

What

pca_numpy formed the covariance matrix C = Y.T @ Y / (n - 1) and SVD'd it. Forming C squares the condition number, so for near-degenerate inputs (an almost collinear point cloud whose smallest extent is ~1e-8 of its largest — still an exactly planar, perfectly posed fit) the smallest principal direction drowns at machine epsilon and comes back wrong: bestfit_plane_numpy normals were off by 11.6° (1e-8 aspect) to 37.3° (1e-9 aspect) on the reproduction in #1522.

This PR runs the SVD on the centered data matrix Y directly. The right-singular vectors of Y are exactly the eigenvectors of C, and the singular values are rescaled (s**2 / (n - 1)) so the returned eigenvalues keep their meaning (variance along each principal direction). Well-conditioned inputs return the same results as before; only the ill-conditioned ones improve. Cost is equivalent (both routes are O(n·dim²)).

Checks

• New tests/compas/geometry/test_pca_numpy.py: (1) well-conditioned parity with the covariance route (eigenvalues match eigvalsh(C), eigenvectors diagonalize C), (2) the near-collinear cloud recovers its smallest direction to <1e-4°, (3) bestfit_plane_numpy regression on the same cloud (fails on the previous implementation with ~11.6° error).
• Full tests/compas/geometry suite: 519 passed (includes the pca_numpy consumers bestfit_numpy, bbox_numpy, icp_numpy).
• CHANGELOG entry added under Unreleased → Changed.

One behavioural note for review: SVD sign indeterminacy means individual principal directions may flip sign relative to the old implementation on some inputs (the sign was already unspecified in both routes).

[mengxihex]

Fixed the windows-ironpython failure: the new test file used the @ matmul operator, which is a parse-time SyntaxError under IronPython 2.7. It now follows the test_bbox.py idiom — py2-parseable, if compas.IPY: return guards, numpy imported inside the guarded test bodies. Verified locally: 3/3 new tests + the full tests/compas/geometry suite pass on CPython. Workflows on the new commit are awaiting approval.

[gonzalocasas]

@tomvanmele ping ;)

[tomvanmele]

nice, thanks for correcting this!
LGTM

[tomvanmele]

@mengxihex perhaps add yourself to the author list :)

[mengxihex]

thanks a lot @tomvanmele! Long-time admirer of compas, the abstraction layer AEC has been missing to scale. Glad to contribute.

[tomvanmele]

btw @mengxihex i am currently modernizing the codebase in this PR #1512
a big part of this will be the improved integration of numpy/scipy in the core algorithms, without having to care about ipy and/or py2. if you have opinions there, feel free to weigh in...