#!/usr/bin/env python
# -*- coding: utf-8 -*-
from math import sqrt

def wordlist(text):
    text = text.lower()
    return text.split()

class Collection(dict):
   def __init__(self, *args, **kargs):
       dict.__init__(self, *args, **kargs)
       for k, v in self.items(): 
        dict.__setitem__(self, k, [v])
   def __setitem__(self, item, value):
      if item in self: 
        self[item].append(value)
      else: 
        dict.__setitem__(self, item, [value])

def normalize(n, maximum, k=100):
    return float(n)/maximum * k + 1

def distribution(words, k=100):
    vec = Collection()
    numwords = len(words)
    for i, word in enumerate(words):
        vec[word] = int(normalize(i, numwords, k))
    return vec 

def freq(seq):
    fq = {}
    for e in seq: 
        if e not in fq: fq[e] = 1
        else: fq[e] += 1
    return fq

def scalar(vec):
    total = 0 
    for elem in vec:
        total += vec[elem] * vec[elem]
    return sqrt(total)

def sim(v, w):
    total = 0 
    for elem in v:
        if elem in w:
            total += v[elem] * w[elem]
    return float(total) / (scalar(v) * scalar(w))

def compare(query, source, target, k=100):
    queryvector = source[query]
    comparisons = []
    for word in target:
        targetvector = target[word]
        comparisons.append(
            (sim(freq(queryvector), freq(targetvector)), query, word)
        )
    return sorted(comparisons)
        
if __name__ == "__main__":
    import sys
    sourcefile, targetfile, q  = unicode(sys.argv[1]), sys.argv[2], sys.argv[3]
    source = unicode(open(sourcefile, 'U').read())
    target = unicode(open(targetfile, 'U').read())
    sourcewords = wordlist(source)
    targetwords = wordlist(target)
    sourcedist = distribution(sourcewords)
    targetdist = distribution(targetwords)
    print q
    for similarity, query, matched, in compare(q, sourcedist, targetdist, k=100)[-10:]:
        print "%.2f %s" % (similarity, matched),
    print