monk1337

Imaging

• NodeBox - a nice lib to create images of graph networks
• Python Image Library

Scrapping/Parsing

• BeautfulSoup - HTML parser
• mechanize - automated web browsing

monk1337

my_dict = {
    'name1': 'abc', 'job1': 'xyz', 'pos1': 'tak', 'phone1': 12345,
    'name2': 'pqr', 'job2': 'ftr', 'pos2': 'lkj', 'phone2': 27654,
    'name3': 'swq', 'job3': 'nme', 'pos3': 'mnb', 'phone3': 98421,
    'bad': 'bad_data',
}
group_dict=dict()
for key,value in my_dict.items():
    if key[-1] not in group_dict:
        group_dict[key[-1]]=[(key,value)]

monk1337

a=[(2010,2),(2009,4),(1989,8),(2009,7)]

new_dict={}

for i in a:
    if i[0] not in new_dict:
        new_dict[i[0]]=[i[1]]
    else:

        try:

monk1337

original = 'Has two optional arguments which must be specified.'
strings = [{'index': 3, 'string': 'foo'}, {'index': 7, 'string':
'bar'}, {'index': 12, 'string': 'abc'}]


meta_data=[(i['index'],i['string']) for i in strings]

track={}
for i,j in enumerate(original):
    track[i]=j

monk1337

import socket
import threading
from newsapi.articles import Articles

import pickle
import sys
options = ['news', 'jokes', 'life_style']
a=Articles('cff9541f3b5a4558957e63ea59b3743b')

class ThreadedServer(object):

monk1337

import tensorflow as tf



import numpy as np
from tensorflow.contrib import rnn

word_embedding_dim=250

hidden_dim=270

monk1337

import hashlib
import csv

import csv
import random
from pymongo import MongoClient

data_base={}

with open('data.csv','r') as f:

monk1337

def get_last_sunday():
    from datetime import date
    from dateutil.relativedelta import relativedelta, SU
    today = date.today()
    last_monday = today + relativedelta(weekday=SU(-1))
    return last_monday.strftime("%Y-%m-%d")

monk1337

monk1337

import tensorflow as tf
import numpy as np

DropoutWrapper = tf.nn.rnn_cell.DropoutWrapper


class SentimentNetwork(object):

    
    def __init__(self, hdim=25, wdim=25, pdim=25, vocab_size=2000, pos_vocab_size=30,