/
kNN.py
executable file
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/
kNN.py
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#! /usr/bin/env python
import math
from mongo import *
from pymongo import *
import dataset
import operator
import numpy as np
def createUserVector(username):
client = MongoClient()
user = queryUser(username, client)
unique_subs = list(subreddits(client))
vector = [0]*len(unique_subs)
for i in range(len(unique_subs)):
if unique_subs[i]['name'] in user['subreddits']:
vector[i] = 10
return vector
def vectorDistance(user1, user2):
vector1 = createUserVector(user1)
vector2 = createUserVector(user2)
# dist = 0
# for i in range(len(vector1)):
# dist += pow(vector1[i] - vector2[i], 2)
# return math.sqrt(dist)
return np.linalg.norm(np.array(vector1) - np.array(vector2))
def getNeighbors(username, k):
client = MongoClient()
distances = []
for user in allUsers(client):
if len(distances) > k:
break
dist = vectorDistance(username, user['username'])
distances.append((user['username'], dist))
distances.sort(key=operator.itemgetter(1))
return distances
def getRecommendedSubreddit(username):
client = MongoClient()
neighbors = getNeighbors(username, 70)
users = allUsersInArray([neighbor[0] for neighbor in neighbors], client)
banned = queryUser(username, client)['subreddits']
subredditFrequency = {}
totalsubs = [sub for user in users for sub in user['subreddits']]
subredditFrequency = {word : totalsubs.count(word) for word in set(totalsubs) if word not in banned}
return max(subredditFrequency, key=subredditFrequency.get)
def main(username):
dataset.getComments(username)
return getRecommendedSubreddit(username)
if __name__ == "__main__":
username = raw_input()
print(main(username))