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#!/usr/bin/env python | |
import time | |
import threading | |
import urllib | |
import httplib2 | |
import re | |
sum = 25000 | |
concurrent = 1000 |
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#include <stdio.h> | |
#include <stdlib.h> | |
#include <string.h> | |
#include <ctype.h> | |
#include <memory.h> | |
#include <errno.h> | |
#include <assert.h> | |
#include <sys/types.h> | |
#include <stdarg.h> | |
#include <fcntl.h> |
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#!/usr/bin/env python | |
# Time-stamp: <2011-12-25 16:34:05 Sunday by devil> | |
# @version 1.0 | |
# @author ahei | |
import httplib | |
import os.path | |
import urllib2 |
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#!/usr/bin/env python | |
import time | |
import threading | |
import urllib | |
import httplib2 | |
import re | |
sum = 10 | |
concurrent = 1 |
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#!/bin/sh | |
# | |
# Simple control script for Juniper Network Connect VPN clients | |
# | |
C='Copyright 2008-2011 Paul D. Smith <[email protected]>' | |
V='Version 1.16' | |
D='31 Oct 2011' | |
U='http://mad-scientist.net/juniper.html' | |
# | |
# This script is free software; you can redistribute it and/or modify it under |
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#include <baseapi.h> | |
#include <iostream> | |
#include <allheaders.h> | |
Pix *pix; | |
int offset; | |
float slope; | |
int main(int argc, char *argv[]) { | |
tesseract::TessBaseAPI api; |
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#!/bin/sh | |
cd $OPENSHIFT_TMP_DIR | |
wget http://python.org/ftp/python/2.7.3/Python-2.7.3.tar.bz2 | |
tar jxf Python-2.7.3.tar.bz2 | |
cd Python-2.7.3 | |
./configure --prefix=$OPENSHIFT_RUNTIME_DIR | |
make install | |
cd $OPENSHIFT_TMP_DIR | |
wget http://pypi.python.org/packages/source/s/setuptools/setuptools-0.6c11.tar.gz |
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''' | |
Created on 2012-9-19 | |
@author: Xiang Wang | |
''' | |
import ctypes | |
import logging | |
import os | |
class ColorizingStreamHandler(logging.StreamHandler): |
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#!/usr/bin/env python | |
# Time-stamp: <2012-11-01 22:36:50 Thursday by devil> | |
# @version 1.0 | |
# @author Xiang Wang ([email protected]) | |
import urllib2,sys |
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class AttentionLSTM(LSTM): | |
"""LSTM with attention mechanism | |
This is an LSTM incorporating an attention mechanism into its hidden states. | |
Currently, the context vector calculated from the attended vector is fed | |
into the model's internal states, closely following the model by Xu et al. | |
(2016, Sec. 3.1.2), using a soft attention model following | |
Bahdanau et al. (2014). | |
The layer expects two inputs instead of the usual one: |