onyxfish

import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()
     
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)

lrvick

from geventwebsocket.handler import WebSocketHandler
from gevent.pywsgi import WSGIServer
from flask import Flask, request, render_template

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

jsmecham

(function() {

  //
  // Iterates over an array of numbers and returns the sum. Example:
  //
  //    _.sum([1, 2, 3]) => 6
  //
  _.sum = function(obj) {
    if (!$.isArray(obj) || obj.length == 0) return 0;
    return _.reduce(obj, function(sum, n) {

turicas

test:
	clear
	nosetests --with-coverage --cover-package name_utils test_name_utils.py

clean:
	find -regex '.*\.pyc' -exec rm {} \;
	find -regex '.*~' -exec rm {} \;

.PHONY: test clean

jtratner

import os

PATH = path/to/my/blueprints/directory
BLUEPRINT = 'the_blueprint'

def import_file(path, name=None):
    """ imports a file with given name and path """
    # use the imp module to do actual imports
    import imp
    name = name or os.path.split(path)[-1].replace(".", "_")

dergachev

// works on selected text
var selection = window.getSelection().toString();

if (selection) {
  selection = selection.match(/[^\r\n]+/g)
    .map(function(a) { return "* " + a; }).
    join("\n"); 
    prompt("Here's your markdown:", selection);
}

kmike

import numpy as np
import scipy.sparse as sp
import hat_trie
from sklearn.feature_extraction.text import CountVectorizer, _make_int_array


class HatTrieCountVectorizer(CountVectorizer):

    def _count_vocab(self, raw_documents, fixed_vocab):
        """Create sparse feature matrix, and vocabulary where fixed_vocab=False

ngpestelos

May 8, 2018

I wrote this four years ago, so instead use this command:

$ docker rmi $(docker images -q -f dangling=true)

anildigital

docker rmi $(docker images -q -f dangling=true)

konradkonrad

import elasticsearch
from math import log


def tfidf_matrix(es, index, doc_type, fields, size=10, bulk=500, query=dict(match_all=[])):
    """Generate tfidf for `size` documents of `index`/`doc_type`.
    All `fields` need to have the mapping "term_vector": "yes".
    This is the consuming version (i.e. get everything at once).

    :param es: elasticsearch client