* 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.
* Fix DeprecationWarning in local_weighted_learning.py
Fix DeprecationWarning that occurs during build due to converting an
np.ndarray to a scalar implicitly
* DeprecationWarning fix attempt 2
* updating DIRECTORY.md
* Format local_weighted_learning.py doctests for clarity
* Refactor local_weighted_learning.py to use np.array instead of np.mat
The np.matrix class is planned to be eventually depreciated in favor of
np.array, and current use of the class raises warnings in pytest
* Update local_weighted_learning.py documentation
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* The black formatter is no longer beta
* pre-commit autoupdate
* pre-commit autoupdate
* Remove project_euler/problem_145 which is killing our CI tests
* updating DIRECTORY.md
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