Alex Tu adtu

## min-char-rnn.py
"""
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)

## Assignment-3.py

# coding: utf-8

# ---
#
# _You are currently looking at **version 1.5** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
#
# ---

# # Assignment 3 - More Pandas

## pagerank.py
import numpy as np
from scipy.sparse import csc_matrix

def pageRank(G, s = .85, maxerr = .001):
    """
    Computes the pagerank for each of the n states.

    Used in webpage ranking and text summarization using unweighted
    or weighted transitions respectively.

## gist:5f889852c0d12ea084ce

      
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              Last active
              August 29, 2015 14:24
                — forked from jonathanmoore/gist:2640302
            
          
    Share Counts

I have always struggled with getting all the various share buttons from Facebook, Twitter, Google Plus, Pinterest, etc to align correctly and to not look like a tacky explosion of buttons.  Seeing a number of sites rolling their own share buttons with counts, for example The Next Web I decided to look into the various APIs on how to simply return the share count.
If you want to roll up all of these into a single jQuery plugin check out Sharrre
Many of these API calls and methods are undocumented, so anticipate that they will change in the future.  Also, if you are planning on rolling these out across a site I would recommend creating a simple endpoint that periodically caches results from all of the APIs so that you are not overloading the services will requests.
Twitter

  
## json_to_csv.html
<html>

<head>
<style>
textarea {
	height: 300px;
	width: 100%;
}
</style>
<script type="text/javascript">
	"""
	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)

	# coding: utf-8

	# ---
	#
	# _You are currently looking at version 1.5 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
	#
	# ---

	# # Assignment 3 - More Pandas
	import numpy as np
	from scipy.sparse import csc_matrix

	def pageRank(G, s = .85, maxerr = .001):
	"""
	Computes the pagerank for each of the n states.

	Used in webpage ranking and text summarization using unweighted
	or weighted transitions respectively.
	<html>

	<head>
	<style>
	textarea {
	height: 300px;
	width: 100%;
	}
	</style>
	<script type="text/javascript">