maths.softmax
=============

.. py:module:: maths.softmax

.. autoapi-nested-parse::

   This script demonstrates the implementation of the Softmax function.

   Its a function that takes as input a vector of K real numbers, and normalizes
   it into a probability distribution consisting of K probabilities proportional
   to the exponentials of the input numbers. After softmax, the elements of the
   vector always sum up to 1.

   Script inspired from its corresponding Wikipedia article
   https://en.wikipedia.org/wiki/Softmax_function



Functions
---------

.. autoapisummary::

   maths.softmax.softmax


Module Contents
---------------

.. py:function:: softmax(vector)

   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


   Returns:
       softmax_vec (np.array): The input numpy array  after applying
       softmax.

   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])
   >>> softmax(vec)
   array([0.5, 0.5])

   >>> softmax([0])
   array([1.])