nubela

def get_mongo(db=[]):
    """
    This function employs a good ol` gotcha with using mutable objects as a default value for an argument to cache
    the database object.
    
    If the `db` arg is an empty list, populate it with the object.
    Every other call to this function will skip the if clause and return the cached `db` object.
    Win.
    """
    if db == []:

nubela

def generate_login_form():
    """
    Generates a traditional login form, bootstrap-styled.

    DEV USE: Look at this function to figure out how to generate
    forms

    :return form_skeleton_obj:
    """
    form = [

nubela

from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import smtplib
from threading import Thread

from base_cfg import STMP_SERVER_HOST, SMTP_PASSWD, SMTP_USERNAME, FROM_EMAIL_NAME, FROM_EMAIL_ADDR


class Mail:
    """

nubela

1) Join the Javelin Google+ community at https://plus.google.com/communities/106470412179443137104
2) Go to https://play.google.com/apps/testing/com.nubelacorp.javelin and accept being a beta tester
3) Wait about an hour, and check Google Play for a new update

nubela

import datetime
import random

from BeautifulSoup import BeautifulSoup
from base.util import generate_uuid
from homepage.app import homepage_db
from homepage.model import UnsplashImage
from homepage_cfg import EXPIRE_UNSPLASH_DAYS, UNSPLASH_IMAGE_DIR
import os
import requests

nubela

import json

from BeautifulSoup import BeautifulSoup
import requests


FILE_NAME = "result.txt"
BASE_URL = "http://www.propertyguru.com.sg/"
URL = "http://www.propertyguru.com.sg/singapore-property-listing/property-for-rent/%d?property_type=H" \
      "&property_type_code[]=HDB&minprice=1500&maxprice=2500&minsize=1000&distance=0.5&center_lat=1" \

nubela

from random import randint
import numpy as np
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.models import mlp
from pylearn2.termination_criteria import EpochCounter
from pylearn2.training_algorithms import sgd
import theano


def gen_xor_data():

nubela

"""
This trains a simple 2-layered MLP for classifying handwritten digits (MNIST dataset)
For this script to work, you need to have a ./mnist folder that contains the MNIST dataset.

The dataset can be downloaded using the script in the following pylearn2 folder:
<workspace>/pylearn2/pylearn2/scripts/datasets/download_mnist.py

After training the model (neural network containing layers), it will save trained model to disk.
"""

nubela

import struct
import numpy as np
from pylearn2.utils import serial
import theano


def read_mnist_images():
    path = "mnist/"
    label_path = path + 't10k-images-idx3-ubyte'
    dtype = 'float32'

nubela

// Sign requests using:
// https://tools.ietf.org/html/draft-cavage-http-signatures-09

import hmacSHA256 from 'crypto-js/hmac-sha256';
import sha256 from 'crypto-js/sha256';
import Base64 from 'crypto-js/enc-base64';

// Signed string be MUST be built from headers in this specific order
const SIGNATURE_HEADERS = [
    "(request-target)", "host", "accept", "content-type", "x-original-length",