cocodrips

def maximum_path(array, n):
    dp = [[0 for __ in xrange(n)] for _ in xrange(n)]
    dp[0][0] = array[0][0]

    for i in xrange(1, n):
        for j in xrange(i + 1):
            if j == 0:
                dp[i][j] = array[i][j] + dp[i - 1][j]
            elif j == i:
                dp[i][j] = array[i][j] + dp[i - 1][j - 1]

cocodrips

def memoize(f):
  memo = {}
  def inner(v):
      if v <0: return memo
      if not v in memo:
        memo[v] = f(v)
      return memo[v]
  return inner

cocodrips

def gcd(a, b):
	while b:
		a, b = b, a % b
	return a

cocodrips

def lcm(a, b):
	return a * b // gcd (a, b)

cocodrips

# -*- coding: utf-8 -*-

uj = u'こんにちは'
ue = u'Hello'
sj = 'こんにちは'
se = 'Hello'

print ue == se # True
print uj == sj # False  Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
print sj.decode('utf-8'), type(sj.decode('utf-8')) #こんにちは

cocodrips

class Clustering:
    def _analyzer(self, text):
        ret = []
        tagger = MeCab.Tagger("-Ochasen")
        if type(text) == UnicodeType:
            text = text.encode("utf-8")
        node = tagger.parseToNode(text)
        while node.next:
            basic = unicode(node.feature.split(',')[-3], 'utf-8')
            if basic != u"*":

cocodrips

# -*- coding: utf-8 -*-
import MeCab
import numpy as np

from types import *
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import Normalizer

cocodrips

import math

memo = []
Ds = []
Ps = []


def table(r, c):
    if r >= N or c >= N:
        return 0

cocodrips

#include <iostream>
#include <map>
#include <string> 
#include <vector>

#define FOR(i,a,b) for(int i=(a);i<(b);++i)
#define REP(i,n)  FOR(i,0,n)

using namespace std;

cocodrips

# -*- coding: utf-8 -*-
import urllib2
import urlparse
import lxml.html

IMAGE_FORMAT = (".jpg", ".png", ".gif")


class ImageExtractor:
    def extract_main_image(self, url):