Nicole He nicolehe

## index.html
<!DOCTYPE html>
<html>

<head>
    <title>cheaters by religiousness - by nicole he</title>
    <link rel="stylesheet" type="text/css" href="style.css">
    <link href='https://fonts.googleapis.com/css?family=Droid+Sans+Mono' rel='stylesheet' type='text/css'>
</head>

<body>

## index.html


d3.csv('/claims-data-2015.csv', function(data) {

    var claimsDataByAirline = _.groupBy(data, 'Airline Name');
    //console.log(claimsDataByAirline);

    var eachClaimByAirline = {};

    _.each(claimsDataByAirline, function(claimsDataForOneAirline, airline) {

## run_length.py
import sys

# split the input string into a list of characters
input_split = list(sys.argv[1])

def encode(i):
	result = list() # storing the result in a list of tuples
	previous = None # for the first letter, the 'previous' character is null
	matches = 0 # no matches to begin with
	for letter in i: # for each letter in the input...

## _traceroute-commute.html
<!DOCTYPE html>
<html>

<head>
    <meta charset="utf-8">
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
    <link rel="stylesheet" href="//fonts.googleapis.com/css?family=Baloo+Bhaina" />
    <title>Directly accessing Street View data</title>
    <style>
    html,

## _recommender.py
import numpy as np
import sys
from data import critics as people

# euclidian distance method:
# get the shared items between each person, and then find the distance
def similarity(person1, person2):
	# if they share a rated item, add it to the shared list
	shared = [item for item in people[person1] if item in people[person2]]

## mlp.py
import numpy as np

class MultilayerPerceptron():
    'making a multilayer preceptron'

    def __init__(self, sample_size, hidden_size, output_size, hidden_layers, total_epochs, learning_rate, report_freq):
        self.sample_size = sample_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.hidden_layers = hidden_layers

## _mlp_income.py
#!/usr/bin/python

# Learning Machines
# Taught by Patrick Hebron at NYU ITP

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D

import numpy as np
# np.set_printoptions(threshold=np.nan)

## index.html
<!DOCTYPE html>
<html>

<head>
    <meta charset="utf-8">
    <title>relax :)</title>

    <script type="text/javascript">
        var image = 0;
        var background = ['0.jpg', '1.jpg', '2.jpg', '3.jpg', '4.jpg'];

## shuffler.js
                // shuffle
                function shuffle(array) {
                    var currentIndex = array.length,
                        temporaryValue, randomIndex;

                    while (0 !== currentIndex) {

                        randomIndex = Math.floor(Math.random() * currentIndex);
                        currentIndex -= 1;

## freshdirect.py
      """
      So far, this script logs in to FreshDirect and then lists all products that you search
      for (like "bacon"). What I'm trying to do is get it to add all of those items to your cart.

      The part I'm struggling with is getting the "Add To Cart" buttons "visible" on the page.
      I'm trying to use this ActionChains thing, but I don't think it's working, maybe I have
      the wrong selector? (Also, it doesn't work with Firefox's webdriver, and I couldn't get
      the Chromedriver upgraded to work. But with PhantomJS it's hard to tell what's going on...

      """
	<!DOCTYPE html>
	<html>

	<head>
	<title>cheaters by religiousness - by nicole he</title>
	<link rel="stylesheet" type="text/css" href="style.css">
	<link href='https://fonts.googleapis.com/css?family=Droid+Sans+Mono' rel='stylesheet' type='text/css'>
	</head>

	<body>


	d3.csv('/claims-data-2015.csv', function(data) {

	var claimsDataByAirline = _.groupBy(data, 'Airline Name');
	//console.log(claimsDataByAirline);

	var eachClaimByAirline = {};

	_.each(claimsDataByAirline, function(claimsDataForOneAirline, airline) {
	import sys

	# split the input string into a list of characters
	input_split = list(sys.argv[1])

	def encode(i):
	result = list() # storing the result in a list of tuples
	previous = None # for the first letter, the 'previous' character is null
	matches = 0 # no matches to begin with
	for letter in i: # for each letter in the input...
	import numpy as np
	import sys
	from data import critics as people

	# euclidian distance method:
	# get the shared items between each person, and then find the distance
	def similarity(person1, person2):
	# if they share a rated item, add it to the shared list
	shared = [item for item in people[person1] if item in people[person2]]
	import numpy as np

	class MultilayerPerceptron():
	'making a multilayer preceptron'

	def __init__(self, sample_size, hidden_size, output_size, hidden_layers, total_epochs, learning_rate, report_freq):
	self.sample_size = sample_size
	self.hidden_size = hidden_size
	self.output_size = output_size
	self.hidden_layers = hidden_layers
	#!/usr/bin/python

	# Learning Machines
	# Taught by Patrick Hebron at NYU ITP

	import matplotlib.pyplot as plt
	from matplotlib.lines import Line2D

	import numpy as np
	# np.set_printoptions(threshold=np.nan)
	// shuffle
	function shuffle(array) {
	var currentIndex = array.length,
	temporaryValue, randomIndex;

	while (0 !== currentIndex) {

	randomIndex = Math.floor(Math.random() * currentIndex);
	currentIndex -= 1;
	"""
	So far, this script logs in to FreshDirect and then lists all products that you search
	for (like "bacon"). What I'm trying to do is get it to add all of those items to your cart.

	The part I'm struggling with is getting the "Add To Cart" buttons "visible" on the page.
	I'm trying to use this ActionChains thing, but I don't think it's working, maybe I have
	the wrong selector? (Also, it doesn't work with Firefox's webdriver, and I couldn't get
	the Chromedriver upgraded to work. But with PhantomJS it's hard to tell what's going on...

	"""