情報量としてのエントロピー増大とは？

entropy.py

# coding: utf-8
import math

def entropy(p_list):
    ans = 0
    for p in p_list:
        ans += (p) * -math.log2(p)
    return ans

p_list = [1.0]
a = entropy(p_list)
print(a)
# 0.0

p_list = [0.5, 0.5]
a = entropy(p_list)
print(a)
# 1.0

p_list = [1/3, 1/3, 1/3]
a = entropy(p_list)
print(a)
# 1.584962500721156

p_list = [0.1, 0.1, 0.1, 0.1, 0.5]
a = entropy(p_list)
print(a)
# 1.828771237954945

p_list = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
a = entropy(p_list)
print(a)
# 3.321928094887362

p_list = [0.1, 0.2, 0.3, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1]
a = entropy(p_list)
print(a)
# 2.946439344671015

p_list = [0.1, 0.2, 0.3, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
a = entropy(p_list)
print(a)
# 3.046439344671015


# 確率0.0001の事象を1000個作成し、そのエントロピーを計算する
p_list = [1/1000 for _ in range(1000)]
a = entropy(p_list)
print(a)
# 9.965784284662018