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Pull request overview
This PR implements two skeleton functions in a NumPy assignment: max_index to find the maximum element's position in a 2D array, and wallis_product to approximate π using the Wallis product formula.
Key changes:
- Implemented
max_indexwith input validation and nested loop traversal to find the maximum value's indices - Implemented
wallis_productusing the mathematical formula π/2 = ∏(4n²)/(4n²-1)
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| for row in range(X.shape[0]): | ||
| for col in range(X.shape[1]): | ||
| if X[row, col] > max_val: | ||
| max_val = X[row, col] | ||
| i, j = row, col |
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The nested loop implementation is inefficient for finding the maximum index in a numpy array. NumPy provides built-in optimized functions like np.argmax() or np.unravel_index() that are significantly faster and more idiomatic. Using these would avoid the O(n*m) Python loop overhead and leverage NumPy's vectorized operations.
| raise ValueError("Input must be a numpy array") | ||
| if X.ndim != 2: | ||
| raise ValueError("Input must be 2D") | ||
| max_val = X[0, 0] |
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This implementation assumes the array has at least one element by accessing X[0, 0]. If an empty array or an array with shape (0, n) or (n, 0) is passed, this will raise an IndexError. The function should handle empty arrays gracefully, either by checking if the array has elements first or by catching this edge case.
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