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priya-sundaram-dev
df3a091184
numpy.linalg.inv can return platform-dependent float representations (e.g. 0.6 vs 0.6000000000000001), which made the doctest fragile across BLAS/LAPACK backends. Round the results in the doctests so the expected output is deterministic, and add a second invertible-matrix example.
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import numpy as np
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def invert_matrix(matrix: list[list[float]]) -> list[list[float]]:
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"""
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Returns the inverse of a square matrix using NumPy.
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Parameters:
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matrix (list[list[float]]): A square matrix.
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Returns:
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list[list[float]]: Inverted matrix if invertible, else raises error.
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The exact floating-point representation returned by ``numpy.linalg.inv``
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can vary slightly across platforms and BLAS/LAPACK backends
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(e.g. ``0.6`` vs ``0.6000000000000001``), so the doctests below round the
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result to make the expected output deterministic.
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>>> [[round(x, 6) for x in row] for row in invert_matrix([[4.0, 7.0], [2.0, 6.0]])]
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[[0.6, -0.7], [-0.2, 0.4]]
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>>> [[round(x, 6) for x in row] for row in invert_matrix([[1.0, 0.0], [0.0, 2.0]])]
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[[1.0, 0.0], [0.0, 0.5]]
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>>> invert_matrix([[1.0, 2.0], [0.0, 0.0]])
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Traceback (most recent call last):
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...
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ValueError: Matrix is not invertible
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"""
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np_matrix = np.array(matrix)
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try:
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inv_matrix = np.linalg.inv(np_matrix)
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except np.linalg.LinAlgError:
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raise ValueError("Matrix is not invertible")
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return inv_matrix.tolist()
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if __name__ == "__main__":
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mat = [[4.0, 7.0], [2.0, 6.0]]
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print("Original Matrix:")
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print(mat)
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print("Inverted Matrix:")
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print(invert_matrix(mat))
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