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:

ericjang

ticker = 'USSPX';  % s&p500 index
periods = 1:100; 
nP = numel(periods);
c = yahoo;
dates = {'Jan 1 2000','Sep 6 2014'};
prices = fetch(c,ticker,'Adj Close',dates{1},dates{2}); % should I use Close instead?
nDays = size(prices,1);
nTrials = 50;
returns = zeros(nTrials,nP);
tP = 50*nDays; % total principal invested over time

ericjang

% feigenbaum diagram
r = 1:.01:4; % r values
nr = length(r); % number of r values
nI = 600; % number of iterations to perform for each r
converge = 300; 
p = nan(converge,nr);
x = zeros(1,nI);
for rx=1:nr
    x(1)=.5;
    for i=2:nI

ericjang

# testing the automatic differentiation capabilities
using Cairo;
using DataFrames;
using Gadfly;
using ForwardDiff;

f(x) = sin(x[1]);
x = [-pi:.1:pi];
outf=[f(i) for i in x];

ericjang

//note that this is just basic IP address routing. XSS exploit needed for more accurate location.
var http = require('http');
var server = http.createServer(function(req,res){
	res.writeHead(200);
	res.end('Hello HTTP');
	console.log('client connected! ip address is ' + req.socket.remoteAddress);
});
server.listen(8080);
console.log('server listening at localhost:8080. Send your victim over!');

ericjang

#script for parsing Mrs. Cao's file.

import csv

original = csv.reader(open('/Users/Eric/Desktop/destinations.csv','rU'))
f = open('/Users/Eric/Desktop/formattedList.txt', 'w')

current = ""
for row in original:
	college = row[2]

ericjang

import requests
from bs4 import BeautifulSoup
import re
for v in range(1,18): # update chapter numbers as needed
    fname = 'Volume_%02d.jpg' % v
    url = 'http://prison-school.wikia.com/wiki/Category:Volume_Cover_Images?file=%s' % fname
    res = requests.get(url)
    soup = BeautifulSoup(res.text, 'html.parser')
    pattern = r'http:\/\/vignette\d.wikia.nocookie.net\/prison-school\/images\/\w*\/\w*\/' + fname + '\/revision\/latest\/'
    r = re.compile(pattern)

ericjang

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
import pdb

N_max = int(1e3)  							# numbers to try out
T_max = int(200)								# max iterations

sequence = np.zeros((N_max, T_max), dtype=int)

ericjang

ex = quote
  s = string(ex)
	o = s	
	array = uint8(collect(s))
	for i = 1:length(array), bit = 0:7
	  if rand() < 1/(length(array)*8)
	    array[i] $= (0x01 << bit)
	  end
	end
	s = bytestring(array)

ericjang

for i in range(TRAIN_ITERS):
    x= np.random.normal(mu,sigma,M) # sample minibatch from p_data
    z= np.linspace(-5.0,5.0,M)+np.random.random(M)*0.01  # sample minibatch from noise prior
    sess.run(opt_d, {x_node: x, z_node: z}) # update discriminator D
    z= np.linspace(-5.0,5.0,M)+np.random.random(M)*0.01 # sample noise prior
    sess.run(opt_g, {z_node: z}) # update generator G