dwilkie

Build docker image

$ cd /path/to/Dockerfile
$ sudo docker build .

View running processes

jakevdp

mrocklin

napjon

def from_sklearn(docs,vect,lda,**kwargs):
    
    """Create Prepared Data from sklearn's vectorizer and Latent Dirichlet
    Application
    
    Parameters
    ----------
    docs : Pandas Series.
        Documents to be passed as an input.
    vect : Scikit-Learn Vectorizer (CountVectorizer,TfIdfVectorizer).

t-mart

Map	Action
<F1>	Causes Netrw to issue help
<cr>	Netrw will enter the directory or read the file
<del>	Netrw will attempt to remove the file/directory
-	Makes Netrw go up one directory
a	Toggles between normal display, hiding (suppress display of files matching g:netrw_list_hide) showing (display only files which match g:netrw_list_hide)
c	Make browsing directory the current directory
C	Setting the editing window
d	Make a directory

rashmibanthia

#!/usr/bin/env bash
#Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5)

cd ~
#wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh
bash Anaconda3-2.4.1-Linux-x86_64.sh -b
echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc
. .bashrc

hrbrmstr

This hit #rstats today:

Has anyone made a dumbbell dot plot in #rstats, or better yet exported to @plotlygraphs using the API? https://t.co/rWUSpH1rRl

— Ken Davis (@ken_mke) October 23, 2015

<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>

So, I figured it was worth a cpl mins to reproduce.

While the US gov did give the data behind the chart it was all the data and a pain to work with so I used WebPlotDigitizer to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.

It is possible to make the top "dumbbell" legend in ggplot2 (but not by using a guide) and color the "All Metro A

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

sirkkalap

##########################################
# To run: 
# curl -sSL https://gist.githubusercontent.com/sirkkalap/e87cd580a47b180a7d32/raw/d9c9ebae4f5cf64eed4676e8aedac265b5a51bfa/Install-Docker-on-Linux-Mint.sh | bash -x
##########################################

# Check that HTTPS transport is available to APT
if [ ! -e /usr/lib/apt/methods/https ]; then
	sudo apt-get update
	sudo apt-get install -y apt-transport-https
fi

tokestermw

import json
import urlparse
from itertools import chain
flatten = chain.from_iterable

from nltk import word_tokenize

from gensim.corpora import Dictionary
from gensim.models.ldamodel import LdaModel
from gensim.models.tfidfmodel import TfidfModel