kurahaupo

#!/bin/bash
# (replace the preceding line with the path to Bash on your system)

# Takes the list of items as command-line parameters
# Interacts using stdin+stderr
# Writes the answer to stdout

# Use like:
#   result=$( bash dialog.bash Foo Bar Zot Baz ) || exit
# or

rkitover

diff -ruN Python-2.7.14.orig/setup.py Python-2.7.14.new/setup.py
--- Python-2.7.14.orig/setup.py	2017-09-16 10:38:35.000000000 -0700
+++ Python-2.7.14.new/setup.py	2017-11-16 07:48:31.368080272 -0800
@@ -808,10 +808,15 @@
                                depends=['socketmodule.h'],
                                libraries=math_libs) )
         # Detect SSL support for the socket module (via _ssl)
+        openssl_root = os.getenv('OPENSSL_ROOT')
+
         search_for_ssl_incs_in = [

roadrunner2

Introduction

This is about documenting getting Linux running on the late 2016 and mid 2017 MPB's; the focus is mostly on the MacBookPro13,3 and MacBookPro14,3 (15inch models), but I try to make it relevant and provide information for MacBookPro13,1, MacBookPro13,2, MacBookPro14,1, and MacBookPro14,2 (13inch models) too. I'm currently using Fedora 27, but most the things should be valid for other recent distros even if the details differ. The kernel version is 4.14.x (after latest update).

The state of linux on the MBP (with particular focus on MacBookPro13,2) is also being tracked on https://github.com/Dunedan/mbp-2016-linux . And for Ubuntu users there are a couple tutorials (here and here) focused on that distro and the MacBook.

Note: For those who have followed these instructions ealier, and in particular for those who have had problems with the custom DSDT, modifying the DSDT is not necessary anymore - se

agnanachandran

import math
import random

def get_random_neighbour(state):
    neighbour = [house[:] for house in state] # Deep copy

    i, j = random.sample(xrange(5), 2)
    attr_idx = random.randint(0, 4)

    neighbour[i][attr_idx], neighbour[j][attr_idx] = neighbour[j][attr_idx], neighbour[i][attr_idx]

glamp

import pandas as pd
from ggplot import *
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import roc_curve

# vectorizer
from sklearn.feature_extraction.text import HashingVectorizer

# our classifiers
from sklearn.naive_bayes import BernoulliNB, MultinomialNB

cigrainger

from urllib2 import urlopen
from json import load 
import re, nltk
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet, stopwords
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', 
    level=logging.INFO)
from gensim import corpora, models, similarities, matutils
import numpy as np

jvilledieu

Credit Card Fraud Detection

This interactive Neo4j graph tutorial covers a common credit card fraud detection scenario.

---

phil-pedruco

mrflix

/**
 * @author qiao / https://github.com/qiao
 * @author mrdoob / http://mrdoob.com
 * @author alteredq / http://alteredqualia.com/
 * @author WestLangley / http://github.com/WestLangley
 * @author erich666 / http://erichaines.com
 * @author mrflix / http://felixniklas.de
 * 
 * released under MIT License (MIT)
 */

todgru

#!/bin/sh
#
# Setup a work space called `work` with two windows
# first window has 3 panes. 
# The first pane set at 65%, split horizontally, set to api root and running vim
# pane 2 is split at 25% and running redis-server 
# pane 3 is set to api root and bash prompt.
# note: `api` aliased to `cd ~/path/to/work`
#
session="work"