zhengjia

=Navigating=
    visit('/projects')
    visit(post_comments_path(post))

=Clicking links and buttons=
    click_link('id-of-link')
    click_link('Link Text')
    click_button('Save')
    click('Link Text') # Click either a link or a button
    click('Button Value')

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

turadg

# Support for Rspec / Capybara subdomain integration testing
# Make sure this file is required by spec_helper.rb
# (e.g. save as spec/support/subdomains.rb)
 
def switch_to_subdomain(subdomain)
  # lvh.me always resolves to 127.0.0.1
  hostname = subdomain ? "#{subdomain}.lvh.me" : "lvh.me"
  Capybara.app_host = "http://#{hostname}"
end

crmccreary

from Crypto.Cipher import AES
from Crypto import Random

BS = 16
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS) 
unpad = lambda s : s[0:-ord(s[-1])]

class AESCipher:
    def __init__( self, key ):
        """

yono

前提

• [local] -> [admin] -> [prod]
• [admin] -> [prod] の鍵は事前に配置済み
• $HOME/.ssh/config で設定
• 踏み台サーバ(admin)、目的のサーバ(prod) どちらにも鍵認証でログイン

結果

• $HOME/.ssh/config だけだと実行終了時の DONE で固まる

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

steerapi

BOOST_INC_DIR = []                                                              
BOOST_LIB_DIR = []                                                              
BOOST_COMPILER = 'gcc43'                                                        
USE_SHIPPED_BOOST = True                                                        
BOOST_PYTHON_LIBNAME = ['boost_python-py27']                                    
BOOST_THREAD_LIBNAME = ['boost_thread']                                         
CUDA_TRACE = False                                                              
CUDA_ROOT = '/usr/local/cuda'                                                   
CUDA_ENABLE_GL = False                                                          
CUDA_ENABLE_CURAND = True                                                       

parmentf

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

dannyvassallo

# db/migrate/filename.rb
# generate with >> rails g model image alt:string hint:string file:string

class CreateImages < ActiveRecord::Migration[5.0]
  def change
    create_table :images do |t|
      t.string :alt
      t.string :hint
      t.string :file

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats