fix(machine_learning): call features.any() in linear_discriminant_analysis

[hardikkaurani]

Describe your change

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Problem & Context

In machine_learning/dimensionality_reduction.py, line 146 in linear_discriminant_analysis wrote:

    if features.any:

instead of calling the method if features.any(): (compare with line 107 in principal_component_analysis, which correctly uses if features.any():).

Because an ndarray.any bound method object is always truthy, ty check emitted:

warning[redundant-condition]: Method `ndarray.any` is always truthy
   --> machine_learning/dimensionality_reduction.py:146:8
    |
146 |       if features.any:
    |          ^^^^^^^^^^^^ Did you mean to call this method?

As a result, the else branch handling empty datasets was unreachable dead code, and passing an empty dataset would bypass the check and fail with an unexpected error inside covariance_between_classes.

Fix

• Call features.any() in linear_discriminant_analysis, matching principal_component_analysis.
• Add a test case in test_linear_discriminant_analysis verifying that an empty dataset raises AssertionError.

Validation

• uvx ty check machine_learning/dimensionality_reduction.py -> All checks passed (0 diagnostics).
• uvx --with scipy --with scikit-learn --with numpy pytest machine_learning/dimensionality_reduction.py --doctest-modules -> 9 passed.
• uvx ruff check machine_learning/dimensionality_reduction.py -> All checks passed.
• uvx ruff format --check machine_learning/dimensionality_reduction.py -> 1 file already formatted.

Checklist

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.

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[hardikkaurani]

Hi @cclauss,

I've opened this PR to resolve an uncalled method in linear_discriminant_analysis within machine_learning/dimensionality_reduction.py.

Overview:

• Line 146 previously checked if features.any: instead of calling the method if features.any(): (matching line 107 in principal_component_analysis).
• Because a bound method object is always truthy, ty check flagged warning[redundant-condition]: Method ndarray.any is always truthy, making the else block dead code.
• Calling features.any() ensures empty arrays correctly hit the else branch, and a test case was added to test_linear_discriminant_analysis to verify this behavior.

Validation:

• uvx ty check machine_learning/dimensionality_reduction.py: All checks passed (0 diagnostics).
• uvx --with scipy --with scikit-learn --with numpy pytest machine_learning/dimensionality_reduction.py --doctest-modules: 9 passed.
• uvx ruff check / format: Clean.

Whenever you have a moment, could you please take a look? Thank you!