clean up tests and validate functions - #6
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| # Load the data | ||
| mtcars = pl.read_csv("data/mtcars.csv") | ||
| r_poly = pl.read_csv("data/mtcars_poly_4.csv") |
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Bug: Missing Data Files Cause Test Failures
The new polynomial comparison test and script rely on hardcoded paths to data/mtcars_poly_4.csv and data/mtcars.csv. These data files aren't included in the repository, so tests and the script will fail with a FileNotFoundError if they're missing.
Additional Locations (1)
| beta_k = np.dot(P[:, k], P[:, k]) / np.dot(P[:, k-1], P[:, k-1]) | ||
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| # Three-term recurrence: P_{k+1} = (x - alpha_k) * P_k - beta_k * P_{k-1} | ||
| P[:, k + 1] = (x_data - alpha_k) * P[:, k] - beta_k * P[:, k - 1] |
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Bug: Orthogonal Polynomial Calculation Errors
The orthogonal polynomial calculation has a few issues. Division by zero can occur in alpha_k and beta_k calculations when P vectors become all zeros, particularly with constant input data. The alpha_k calculation uses P[:, k]**2 instead of P[:, k], leading to incorrect orthogonal polynomial coefficients. Empty input data also causes np.mean to return NaN, which propagates through subsequent calculations.
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