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refactor: add type hints to maths/softmax.py #14153

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refactor: add type hints to maths/softmax.py #14153

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17 changes: 7 additions & 10 deletions maths/softmax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,25 +13,22 @@
import numpy as np


def softmax(vector):
def softmax(vector: np.ndarray | list | tuple) -> np.ndarray:
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Copilot AI Jan 25, 2026

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The new annotation vector: np.ndarray | list | tuple is very broad and loses element-type information. Consider using a typed protocol/ABC (e.g., Sequence[float] from collections.abc) for list/tuple inputs and/or specifying element types (e.g., list[float] | tuple[float, ...]) to make the type hint more informative and consistent with other modules (e.g., maths/polynomial_evaluation.py).

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def softmax(vector: np.ndarray | list | tuple) -> np.ndarray:
def softmax(vector: np.ndarray | list[float] | tuple[float, ...]) -> np.ndarray:

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"""
Implements the softmax function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
vector (np.array | list | tuple): A numpy array of shape (1,n)
consisting of real values or a similar list, tuple
Returns:
softmax_vec (np.array): The input numpy array after applying
softmax.
np.array: The input numpy array after applying softmax.
Comment on lines +21 to +25
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The docstring uses np.array as a type name in Parameters/Returns, but the function returns an np.ndarray and np.array is the constructor function. Updating the docstring to np.ndarray (and adjusting the described shape to match the 1-D examples) would avoid confusion and keep documentation consistent with the new type hints.

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The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> float(np.ceil(np.sum(softmax([1,2,3,4]))))
The softmax vector adds up to one. We need to ceil to mitigate for precision
>>> float(np.ceil(np.sum(softmax([1, 2, 3, 4]))))
1.0
>>> vec = np.array([5,5])
>>> vec = np.array([5, 5])
>>> softmax(vec)
array([0.5, 0.5])
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