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"""

This script demonstrates the implementation of the ReLU function.

It's a kind of activation function defined as the positive part of its argument in the

context of neural network.

The function takes a vector of K real numbers as input and then argmax(x, 0).

After through ReLU, the element of the vector always 0 or real number.

Script inspired from its corresponding Wikipedia article

https://en.wikipedia.org/wiki/Rectifier_(neural_networks)

"""

from __future__ import annotations

import numpy as np

def relu(vector: list[float]):

"""

Implements the relu function

Parameters:

vector (np.array,list,tuple): A numpy array of shape (1,n)

consisting of real values or a similar list,tuple

Returns:

relu_vec (np.array): The input numpy array, after applying

relu.

>>> vec = np.array([-1, 0, 5])

>>> relu(vec)

array([0, 0, 5])

"""

# compare two arrays and then return element-wise maxima.

return np.maximum(0, vector)

if __name__ == "__main__":

print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]

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