2 Entropy Done
12345678910111213141516171819
import numpy as np
# generate a random data set on [1,10]
data = np.random.randint(low=1,high=10, size=100)
print('data = ' + str(data))
# histogram of the data, number of elements with value 1,2,...,10
hist = np.histogram(data,bins=[1,2,3,4,5,6,7,8,9])
print('hist = ' + str(hist))
# number of elements in the histogram. This should be 100
N = np.sum(d for d in hist[0])
print('N = ' + str(N))
# get the entropy : - sum( d*ln(d) ) for the normalized histogram
H = -((1./N)*np.sum( d*np.log(d) for d in hist[0]) - np.log(N))
print('H = ' + str(H))
# question: does the entropy depend on the number of elements in data ?
Python
INFO