maheshkkumar

import numpy as np

class Perceptron(object):
	""" Perceptron Classifier

	Parameters
	------------
	rate : float
		Learning rate (ranging from 0.0 to 1.0)
	number_of_iteration : int

maheshkkumar

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

maheshkkumar

% my training data.
% so if x > 3 || x < 7, y = 1, otherwise y = 0.
x = 1:100;
y = [0, 0, 0, 1, 1, 1, 1, zeros(1, 93)];

% instead of theta' * x, I'm trying to create
% a non-linear decision boundary.
% So instead of y = theta_0 + theta_1 * x, I use:
function result = h(x, theta)
  result = sigmoid(theta(1) + theta(2) * x + theta(3) * ((x - theta(4))^2));

maheshkkumar

% data
x = [1, 2, 3, 4, 5, 6];
y = [0, 0, 0, 1, 1, 1];

% function to calculate the predicted value
function result = h(x, t0, t1)
  result = sigmoid(t0 + t1 * x);
end

% sigmoid function

maheshkkumar

import numpy as np

class Perceptron(object):
	""" Perceptron Classifier

	Parameters
	------------
	rate : float
		Learning rate (ranging from 0.0 to 1.0)
	number_of_iteration : int

maheshkkumar

import urllib2
from lxml import html
from bs4 import BeautifulSoup
import re
from pymongo import MongoClient
from pymongo.errors import ConnectionFailure, DuplicateKeyError
import datetime
import time

DB_NAME = 'news_aggregator'

maheshkkumar

import argparse
import re
from multiprocessing.pool import ThreadPool as Pool
import requests
import bs4

root_url = 'http://pyvideo.org'
index_url = root_url + '/category/50/pycon-us-2014'