drjwbaker

#I have a list of strings in a text file (`mylist.txt`). I want to search for these strings in a tsv file (`somestuff.tsv`) and make a new file that contains only the lines in which the strings appear. Some strings in the text file will not appear in the tsv file.

#see https://gist.github.com/MartinPaulEve/c0610fa89da4df4d546a

#!/usr/bin/env python
output = []

# use a "with" block to automatically close I/O streams
with open('mylist.txt') as word_list:

soodoku

"
Basic Text Classifier 		 
	- Takes a csv with a text column, and column of labels
	- Splits into train and test
	- Preprocesses text using tm/bag-of-words, 1/2-order Markov
	- Uses SVM and Lasso

@author: Gaurav Sood								 
 	
"

findsimilar.py

from whoosh.index import open_dir
from whoosh.index import create_in
from whoosh.fields import *
from whoosh.qparser import QueryParser
import glob
import os


# USER SET PARAMETERS ############

darinwilson

# Ambient experiment for Sonic Pi (http://sonic-pi.net/)
#
# The piece consists of three long loops, each of which plays one of
# two randomly selected pitches. Each note has different attack,
# release and sleep values, so that they move in and out of phase
# with each other. This can play for quite awhile without
# repeating itself :)

live_loop :note1 do
  use_synth :hollow

jabranham

Export from evernote

You’ll have to open up the evernote application on either Mac or Windows (they don’t have a linux client), right click on the notebook you want to export, and select “Export.” Select the option to export to html (either one page or several pages, depending on your preference. I went with one html page for each note).

Clean filenames

quadrismegistus

def smart_procrustes_align_gensim(base_embed, other_embed, words=None):
	"""Procrustes align two gensim word2vec models (to allow for comparison between same word across models).
	Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <[email protected]>.
		(With help from William. Thank you!)

	First, intersect the vocabularies (see `intersection_align_gensim` documentation).
	Then do the alignment on the other_embed model.
	Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.
	Return other_embed.

quadrismegistus

"""
Code to make a network out of the shortest N cosine-distances (or, equivalently, the strongest N associations)
between a set of words in a gensim word2vec model.

To use:
Set the filenames for the word2vec model.
Set `my_words` to be a list of your own choosing.
Set `num_top_dists` to be a number or a factor of the length of `my_words.`
Choose between the two methods below to produce distances, and comment-out the other one.
"""

aparrish

aparrish

aparrish