HarshadRanganathan

def getProjectName() {
    return 'JenkinsPipeline'
}

def getJDKVersion() {
    return 'jdk1.8.0_101'
}

def getMavenConfig() {
    return 'maven-config'

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

ghoulaymen

/* ==================================================================== */
/* STEPHANE TUFFERY
/* MODELISATION PREDICTIVE ET APPRENTISSAGE STATISTIQUE  AVEC R
/* ==================================================================== */



# ---------------------------------------------------------------------------------------------------------
# Installation des packages
# ---------------------------------------------------------------------------------------------------------

ognis1205

import numpy
import sys
import scipy.stats as stats
import matplotlib.pyplot as plotter
from gensim import corpora, models, similarities, matutils


# Defines dictionary from the specified corpus.
dictionary = corpora.Dictionary(
    line.lower().split() for line in open('corpus_train.txt', 'rb')

leesmith

Simple Git Workflow For Continuous Delivery

Workflow guidelines:

• master branch is always production-ready, deployable, 100% green test suite
• New development is done on feature branches, with frequent rebasing onto master
• Clean commit history by preferring to rebase instead of merge (git pull is configured to automatically rebase)

Workflow

rocarvaj

" VIM Configuration File
" Description: Optimized for C/C++ development, but useful also for other things.
" Author: Gerhard Gappmeier
"

" set UTF-8 encoding
set enc=utf-8
set fenc=utf-8
set termencoding=utf-8
" disable vi compatibility (emulation of old bugs)

mrorii

# refer to http://staff.science.uva.nl/~tsagias/?p=185
import re, math, collections
 
def tokenize(_str):
    stopwords = ['and', 'for', 'if', 'the', 'then', 'be', 'is', 'are', 'will', 'in', 'it', 'to', 'that']
    tokens = collections.defaultdict(lambda: 0.)
    for m in re.finditer(r"(\w+)", _str, re.UNICODE):
        m = m.group(1).lower()
        if len(m) < 2: continue