cocodrips

def prime_decomposition(n):
  i = 2
  table = []
  while i * i <= n:
    while n % i == 0:
      n /= i
      table.append(i)
    i += 1
  if n > 1:
    table.append(n)

cocodrips

def is_prime(n): 
  i = 2
  while i * i <=n:
    if n % i == 0:
      return False
    i += 1
  return True

cocodrips

def prime_table(n):
  list = [True for _ in xrange(n + 1)]
  i = 2
  while i * i <= n:
    if list[i]:
      j = i + i
      while j <= n:
        list[j] = False
        j += i
    i += 1

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