140am

import pdb
import time
import threading

class Event(object):
    pass

class Observable(object):
    def __init__(self):
        self.callbacks = []

enjoylife

# -*- coding: utf-8 -*-

"""
SCAN: A Structural Clustering Algorithm for Networks
As described in http://ualr.edu/nxyuruk/publications/kdd07.pdf
"""

from collections import deque
import numpy as np
from scipy.sparse import csr_matrix

stevedonovan

#!/usr/bin/env lua

--[[  -- installing LDoc
function install()
--    requires('pl','Penlight')
--    install_script 'ldoc'
end
--]]

-- --[[

blmarket

#/usr/bin/env python
# vim: set fileencoding=utf-8

from ghost import Ghost
from config import COOKIE_FILE, LOGIN_ID, LOGIN_PW
import urllib2
import cookielib
import Cookie

class NaverCrawler:

jegger

# Replace 'venv' with the foldername of your virtualenv.
# This script links the gst module and it's dependecies into the virtualenv.
# Tested on ubuntu 12.04

sudo apt-get install python-gst0.10
cd venv/lib/python2.7/site-packages
ln -s /usr/lib/python2.7/dist-packages/glib
ln -s /usr/lib/python2.7/dist-packages/gobject
ln -s /usr/lib/python2.7/dist-packages/gst-0.10
ln -s /usr/lib/python2.7/dist-packages/gstoption.so

elizabrock

Great Papers in Computer Science

19th Century

Lovelace, Ada Augusta (1842) "Notes on the Analytical Engine"

1930's

Aiken, Howard (1937) "Proposed Automatic Calculating Machine"

declank

package main

import (
    "code.google.com/p/portaudio-go/portaudio"
    "encoding/binary"
    "log"
    "io"
    "os"
    "os/exec"
)

allieus

네이버 블로그 크롤링

• 파이썬3 에서 동작합니다.
• requests, beautifulsoup4 라이브러리가 필요합니다.

pip install requests beautifulsoup4

AskDjango

kanhua

This gist demonstrates how to setup a python project that process a numpy array from C language.

To compile the project, run

make all

To test it, run

make test

InnerPeace-Wu

import tensorflow as tf

#aplly exponential decay on learning rate
global_step = tf.Variable(0, trainable=False)
stater_learning_rate = lr #for start
learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 
                                           decay_steps, decay_rate, staircase=True)

optimizer = tf.train.AdamOptimizer(learning_rate)