sigmoid.py

import numpy as np

def softmax(L):
    expL = np.exp(L)
    sumExpL = sum(expL)
    result = []
    for i in expL:
        result.append(i*1.0/sumExpL)
    return result
    
    # Note: The function np.divide can also be used here, as follows:
    # def softmax(L):
    #     expL(np.exp(L))
    #     return np.divide (expL, expL.sum())