kzinmr

import re
import unicodedata
_address_re = re.compile(r'(京都府|東京都|.+?[道府県])?(.+郡)?(.+?[市町村])?(.+?区)?(.*)')
 
def transduce_jpaddress(addrstr):
	addrstr_n = kansuji_to_arabic_numeral(addrstr)
    m = _address_re.match(addrstr_n)
    if m is not None:
        (prefecture, county, city, ward, address) = m.groups()
        address = address.strip()

kzinmr

import re

tt_ksuji = str.maketrans('一二三四五六七八九〇壱弐参', '1234567890123')

re_suji = re.compile(r'[十拾百千万億兆\d]+')
re_kunit = re.compile(r'[十拾百千]|\d+')
re_manshin = re.compile(r'[万億兆]|[^万億兆]+')

TRANSUNIT = {'十': 10,
             '拾': 10,

kzinmr

import math


def round_float(n, precision=3):
    factor = 10**precision
    return math.ceil(v * factor) / factor)

kzinmr

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in range(0, len(l), n):
        yield l[i:i + n]

kzinmr

dict_merge = lambda a,b: a.update(b) or a
dict_merge({'a':1, 'b':3},{'c':5})

kzinmr

from itertools import chain

def flatten(l):
    return list(chain.from_iterable(l))

kzinmr

import matplotlib.pyplot as plt
import numpy as np

# x: array of 1-d scalars
print(f'{np.mean(x):.03}, {np.std(x):.03}, {np.median(x)}')
plt.hist(x, bins=100)
plt.axvline(x.mean(), color='r')
plt.axvline(np.median(x), color='y')
plt.show()

kzinmr

# char-ngram jaccard sim
from nltk import ngrams
from nltk.metrics import jaccard_distance


def ngram(sequence_list, n=3):
    return [''.join(grams) for i in range(1, n) for grams in ngrams(sequence_list, i)]


def jaccard_similarity(s1, s2):

kzinmr

def ordered_uniq(seq):
    seen = set()
    seen_add = seen.add
    return [x for x in seq if x not in seen and not seen_add(x)]


def ordered_uniq_unhashable(seq):
    seq_s = [str(l) for l in seq]
    return [eval(s) for s in ordered_uniq(seq_s)]