"""
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]