mirror of
https://github.com/TheAlgorithms/Python.git
synced 2026-09-30 01:17:25 +08:00
priya-sundaram-dev
659b468cab
* ci: un-ignore local_weighted_learning doctests in build.yml machine_learning/local_weighted_learning/local_weighted_learning.py only imports numpy and matplotlib (both already project dependencies) and its 5 doctests pass headlessly. Removing it from the pytest --ignore list so the module is covered by CI again. * fix(local_weighted_learning): use a well-conditioned bandwidth in doctests The doctests used tau=0.6 on data with feature values ~17-25, so the Gaussian weights underflowed to ~0 (e.g. 8e-118, 1e-177). That made X\u1d40WX numerically singular (cond ~5.6e18), so its inverse - and the resulting predictions - were nondeterministic across numpy/BLAS builds. That is why the module was on the pytest --ignore list; on the CI numpy the first prediction came out 0.0 instead of the documented 1.07. Switch the doctests to tau=5 (cond ~2e2), matching the bandwidth the module's own main() already uses, and round the outputs so they are stable across platforms. Deterministic now; removed from --ignore.