yagays

# for Python 3.6
import unicodedata

# from 0 through 1,114,111 (https://docs.python.org/3.6/library/functions.html#chr)
for unicode_id in range(1114111):
    char = chr(unicode_id)
    normalized_char = unicodedata.normalize("NFKC", char)
    if char != normalized_char:
        if len(normalized_char) == 1:
            code_point = ord(normalized_char)

attakei

Pythonを使ってGoogle Homeに指定したテキストを喋らせる

使い方

1. これをcloneする
2. virtualenvを用意する
3. pip install -r requirements.txtで依存パッケージをインストールする
4. python server.pyでサーバーを起動する
5. ブラウザで、サーバーにアクセスして、フォームにテキストと言語を指定して「喋る」ボタンを押す
6. Google Homeが喋った！

odashi

# usage (single sentence):
#   ref = ['This', 'is', 'a', 'pen', '.']
#   hyp = ['There', 'is', 'a', 'pen', '.']
#   stats = get_bleu_stats(ref, hyp)
#   bleu = calculate_bleu(stats) # => 0.668740
#
# usage (multiple sentences):
#   stats = defaultdict(int)
#   for ref, hyp in zip(refs, hyps):
#     for k, v in get_bleu_stats(ref, hyp).items():

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.

mugenen

#!/usr/bin/env python
# encoding: utf-8
import sys
import sqlite3
from collections import namedtuple

conn = sqlite3.connect("wnjpn.db")

Word = namedtuple('Word', 'wordid lang lemma pron pos')