Adding the SoftMax Function

[RicardoRibeiroRodrigues]

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.

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

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 94.99%. Comparing base (cb45f4a) to head (62ac483).
Report is 1 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #469      +/-   ##
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- Coverage   95.00%   94.99%   -0.02%     
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  Files         238      239       +1     
  Lines       10091    10108      +17     
  Branches     1429     1432       +3     
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+ Hits         9587     9602      +15     
  Misses        389      389              
- Partials      115      117       +2     

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[siriak]

Looks good, thanks!