tfoldi

# CoffeeScript version of Google Spreadsheet Driver for Tableau Data Web Connector

init = ->
  if !tableau
    alert 'init- tableau NOT defined!'
    return
  tableau.scriptVersion = '1.0'
  tableau.log 'init'
  tableau.initCallback()

BrikerMan

from gensim.models import KeyedVectors

# Load gensim word2vec
w2v_path = '<Gensim File Path>'
w2v = KeyedVectors.load_word2vec_format(w2v_path)

import io

# Vector file, `\t` seperated the vectors and `\n` seperate the words
"""

aneesha

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
import numpy as np

def display_topics(H, W, feature_names, documents, no_top_words, no_top_documents):
    for topic_idx, topic in enumerate(H):
        print "Topic %d:" % (topic_idx)
        print " ".join([feature_names[i]
                        for i in topic.argsort()[:-no_top_words - 1:-1]])
        top_doc_indices = np.argsort( W[:,topic_idx] )[::-1][0:no_top_documents]

benrules2

import random
import sys

def build_chain(text, chain = {}):
    words = text.split(' ')
    index = 1
    for word in words[index:]:
        key = words[index - 1]
        if key in chain:
            chain[key].append(word)