otknoy

#!/usr/bin/env python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import Normalizer

def tf(doc):
    vectorizer = CountVectorizer(token_pattern=u'(?u)\\b\\w+\\b')
    features = vectorizer.fit_transform(doc)
    terms = vectorizer.get_feature_names()

otknoy

#!/usr/bin/env python
from sklearn.cluster import KMeans

def kmeans(features, k=10):
    km = KMeans(n_clusters=k, init='k-means++', n_init=1, verbose=True)
    km.fit(features)
    return km.labels_

def plot(features, labels):
    import matplotlib.pyplot as plt

otknoy

Sketch.prototype.setStrokeStyle =  function(color) {
    this.context.strokeStyle = color;
};

otknoy

// モジュールの読み込み
var Sketch = require('./sketch.js');

// スケッチを作る
var sketch = new Sketch('sketch');

// スケッチの機能を使う
// (x, y) に円を描く
sketch.drawCircle(point.x, point.y);
// スケッチを白紙にする

otknoy

ArrayList<PVector> points;

boolean isDrawing;

void setup() {
  size(640, 480);

  points = new ArrayList<PVector>();
}

otknoy

var loadFile = function(filename) {
    var dfd = Promise.defer();

    var xhr = new XMLHttpRequest();
    xhr.onload = function() {
	dfd.resolve(xhr.responseText);
    };

    xhr.open("GET", filename, true);
    xhr.send(null);

otknoy

#!/usr/bin/env python
import fitbit

CONSUMER_KEY = ''
CONSUMER_SECRET = ''

def init_fitbit(token=None):
    client = fitbit.FitbitOauthClient(CONSUMER_KEY, CONSUMER_SECRET)

    if not token:

otknoy

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from dmm import DMM
api_id = 'hogehoge'
affiliate_id = 'hugehuge'
dmm = DMM(api_id, affiliate_id)

print dmm.search_by_keyword(u'つぼみ')

otknoy

import json

data = {"hoge": 1, "hige": 2, "huge" 3}
json_data = json.dumps(data, ensure_ascii=False, indent=2)
print json_data

otknoy

print sum(1 for _ in re.finditer(pattern, text))