Adding the SoftMax Function

Summary of change

This pull request introduces an implementation of the SoftMax algorithm. The SoftMax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes.

Definition

The SoftMax function takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.

Motivation

The SoftMax function is a critical component in many machine learning models, particularly in classification tasks within neural networks.

Time Complexity

The time complexity for the SoftMax algorithm is O(n), where n is the number of elements in the input array.

[x] I have performed a self-review of my code
[X] My code follows the style guidelines of this project
[X] I have added tests that prove my fix is effective or that my feature works
[X] New and existing unit tests pass locally with my changes
[X] Comments in areas I changed are up to date
[X] I have added comments to hard-to-understand areas of my code
[X] I have made corresponding changes to the README.md

TheAlgorithms / C-Sharp