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import scipy | |
import numpy as np | |
from collections import Counter | |
# Kullback–Leibler divergence | |
# https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence | |
# http://scipy.github.io/devdocs/generated/scipy.stats.entropy.html | |
def kl(p, q): | |
# compute common elements | |
set_p = set(p) | |
set_q = set(q) | |
intersection = set(p).intersection(set(q)) | |
# similarity is 0 when there are no common elements | |
if len(intersection) == 0: | |
return 0 | |
# count occurences of common elements | |
intersection_p = Counter(sorted([e for e in p if e in intersection])).values() | |
intersection_q = Counter(sorted([e for e in q if e in intersection])).values() | |
# calculate probability distribution | |
sum_p = float(sum(intersection_p)) | |
sum_q = float(sum(intersection_q)) | |
intersection_p = [e/sum_p for e in intersection_p] | |
intersection_q = [e/sum_q for e in intersection_q] | |
# common elements similarity | |
intersection_similarity = 1. - scipy.stats.entropy(intersection_p, intersection_q) | |
# ratio of common elements | |
area_ratio = float(len(intersection)**2) / (len(set_p)*len(set_q)) | |
# similarity | |
return intersection_similarity * area_ratio | |
lines = open('user_content_1_count.txt').readlines() | |
d = np.ndarray((len(lines),3), np.int32) | |
for i, line in enumerate(lines): | |
d[i] = map(int, line.strip().split()) | |
requests = {} | |
for i in range(d.shape[0]): | |
key = str(d[i,1]) | |
if not key in requests: | |
requests[key] = [] | |
for j in range(d[i,0]): | |
requests[key].append(d[i,2]) | |
similarities = [] | |
for key in requests.keys(): | |
for key2 in requests.keys(): | |
if key != key2: | |
similarities.append((key, key2, kl(requests[key], requests[key2]))) | |
with open('result.txt', 'w') as f: | |
for ret in similarities: | |
f.write(str(ret) + '\n') |
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