morningstar.R

require(RCurl)
require(jsonlite)

myticker<-"FB"
url.histprice<-function(x){ return(paste0("http://globalquote.morningstar.com/globalcomponent/RealtimeHistoricalStockData.ashx?ticker=",x,"&showVol=true&dtype=his&f=d&curry=USD&range=1900-1-1|2014-10-10&isD=true&isS=true&hasF=true&ProdCode=DIRECT"))}
url.keyratios<-function(x){return(paste0("http://financials.morningstar.com/ajax/exportKR2CSV.html?t=",x))}

#Retrieve historical prices
json.histprice<-getURL(url.histprice(myticker))
json.histprice<-sub("NaN","\"NA\"",json.histprice)

radames

import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 640)  # WIDTH
cap.set(4, 480)  # HEIGHT

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")

while(True):

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

tianchaijz

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import re
import time
import json
import logging
import requests

fadere

import csv
import requests
import bs4
import argparse

# parser = argparse.ArgumentParser(description='Process a list of search terms.')
# parser.add_argument('terms', metavar='N', type=str, nargs='+',
#                    help='comma separated list of terms to search for')

# args = parser.parse_args()

timelyportfolio

lattice

examples inspired by this book

ggplot2

examples inspired by this book

debasishg

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

ericjang

import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
import sys

def currentCPI:
	url = 'http://www.tradingeconomics.com/country-list/consumer-price-index-(cpi)'
	r = requests.get(url)
	soup = BeautifulSoup(r.text)

ericjang

import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
import sys

def globalValues:
	url = 'http://www.starcapital.de/research/stockmarketvaluation?SortBy=Shiller_PE'
	r = requests.get(url)
	if r.status_code != 200: