hns

// First argument (or array element for nested arrays) is tag name.
// Second can optionally be an object, in which case it is used to 
// supply attributes. All others are added to the tag's body.
html("p", {"class": "foo"}, "hello world", "!")
'<p class="foo">hello world!</p>'

// Shortcuts for denoting id and class attributes
html("p#foo.bar.baz", "hello world!")
'<p id="foo" class="bar baz">hello world!</p>'

yuku

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

import logging
import sys
import os.path
import bz2

from gensim.corpora import WikiCorpus
from gensim.corpora.wikicorpus import filterWiki

kiyukuta

#coding: utf8
"""
1. Download this gist.
2. Get the MNIST data. 
    wget http://deeplearning.net/data/mnist/mnist.pkl.gz 
3. Run this code.
    python autoencoder.py 100 -e 1 -b 20 -v 
"""
import numpy
import argparse

YOwatari

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

# パラメータの目安
# alpha k/50
# beta 0.01

import csv
from random import randint
from itertools import chain

elben

(ns my-transducers.core
  (:require [clojure.core.async :as async]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Understanding Transducers
;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;
;; This is the source code for the blog post Understanding Transducers, found
;; here: http://elbenshira.com/blog/understanding-transducers
;;

yasaichi

"""
以下の論文で提案された改良x-means法の実装

クラスター数を自動決定するk-meansアルゴリズムの拡張について
http://www.rd.dnc.ac.jp/~tunenori/doc/xmeans_euc.pdf
"""

import numpy as np
from scipy import stats
from sklearn.cluster import KMeans

aidiary

#coding: utf-8
import time
import numpy as np
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
import gzip
import cPickle

class Autoencoder(object):

etienne87

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

from chainer import cuda
import cupy as cp
import time, threading

#backend