George Shank taterbase

## client.html
<!DOCTYPE html>
<html>
  <head>
    <script src='//polysocket.io/polysocket.js'></script>
    <script>
      var ps = new PolySocket('ws://echo.websocket.org')
      ps.onmessage = function(e) {
        console.log(e.data)
      }
      ps.onopen = function() {

## postFind.coffee
mongoose = require 'mongoose'
Model    = mongoose.Model
Query    = mongoose.Query

setHooks = (hooks) ->
  modifyCallback = ->
    args = Array.prototype.slice.call arguments

    args.forEach (arg, index) ->
      if typeof arg is 'function'

## server.js
var express = require('express')
  , app     = express()

app.get('*', function(req, res, next) {
  res.sendfile(req.url.replace(/^\//, ''))
})

app.listen(1337);

## es.sh
cd ~
sudo apt-get update
sudo apt-get install openjdk-7-jre-headless -y


### Check http://www.elasticsearch.org/download/ for latest version of ElasticSearch and replace wget link below

# NEW WAY / EASY WAY
wget https://download.elasticsearch.org/elasticsearch/elasticsearch/elasticsearch-0.90.3.deb
sudo dpkg -i elasticsearch-0.90.3.deb

## test.js
var five = require("johnny-five")
  , board = new five.Board()
  , leds  = [2, 3, 4, 5, 6, 7, 8, 9]

board.on("ready", function() {
  var led = new five.Led(6)
  this.repl.inject({unlockDoor: unlockDoor.bind(this, led)})
})

function unlockDoor(led) {

## echonest-error.json
{"response": {"status": {"version": "4.2", "code": 0, "message": "Success"}, "songs": [{"message": "query code length is too small", "error": "need co
des in query for fingerprint matching"}]}}

## featureNormalize.m
function [X_norm, mu, sigma] = featureNormalize(X)
%FEATURENORMALIZE Normalizes the features in X
%   FEATURENORMALIZE(X) returns a normalized version of X where
%   the mean value of each feature is 0 and the standard deviation
%   is 1. This is often a good preprocessing step to do when
%   working with learning algorithms.

% You need to set these values correctly
X_norm = X;
mu = zeros(1, size(X, 2));

## gradientDescent.m
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
%   theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
%   taking num_iters gradient steps with learning rate alpha

% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);

for iter = 1:num_iters

## es.sh
cd ~
sudo apt-get update
sudo apt-get install openjdk-7-jre-headless -y

# Download the compiled elasticsearch rather than the source.
wget http://download.elasticsearch.org/elasticsearch/elasticsearch/elasticsearch-0.20.2.tar.gz -O elasticsearch.tar.gz
tar -xf elasticsearch.tar.gz
rm elasticsearch.tar.gz
sudo mv elasticsearch-* elasticsearch
sudo mv elasticsearch /usr/local/share

## gist:4515802
    /*              |\
        .-""-._     \ \.--|
       /       "-..__) .-'
     ಠ_______ಠ        /
      \'-.__,   .__.,'
       `'----'._\--'
     * Whale whale whale, what have we here?
     * /
	<!DOCTYPE html>
	<html>
	<head>
	<script src='//polysocket.io/polysocket.js'></script>
	<script>
	var ps = new PolySocket('ws://echo.websocket.org')
	ps.onmessage = function(e) {
	console.log(e.data)
	}
	ps.onopen = function() {
	mongoose = require 'mongoose'
	Model = mongoose.Model
	Query = mongoose.Query

	setHooks = (hooks) ->
	modifyCallback = ->
	args = Array.prototype.slice.call arguments

	args.forEach (arg, index) ->
	if typeof arg is 'function'
	var express = require('express')
	, app = express()

	app.get('*', function(req, res, next) {
	res.sendfile(req.url.replace(/^\//, ''))
	})

	app.listen(1337);
	cd ~
	sudo apt-get update
	sudo apt-get install openjdk-7-jre-headless -y


	### Check http://www.elasticsearch.org/download/ for latest version of ElasticSearch and replace wget link below

	# NEW WAY / EASY WAY
	wget https://download.elasticsearch.org/elasticsearch/elasticsearch/elasticsearch-0.90.3.deb
	sudo dpkg -i elasticsearch-0.90.3.deb
	var five = require("johnny-five")
	, board = new five.Board()
	, leds = [2, 3, 4, 5, 6, 7, 8, 9]

	board.on("ready", function() {
	var led = new five.Led(6)
	this.repl.inject({unlockDoor: unlockDoor.bind(this, led)})
	})

	function unlockDoor(led) {
	{"response": {"status": {"version": "4.2", "code": 0, "message": "Success"}, "songs": [{"message": "query code length is too small", "error": "need co
	des in query for fingerprint matching"}]}}
	function [X_norm, mu, sigma] = featureNormalize(X)
	%FEATURENORMALIZE Normalizes the features in X
	% FEATURENORMALIZE(X) returns a normalized version of X where
	% the mean value of each feature is 0 and the standard deviation
	% is 1. This is often a good preprocessing step to do when
	% working with learning algorithms.

	% You need to set these values correctly
	X_norm = X;
	mu = zeros(1, size(X, 2));
	function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
	%GRADIENTDESCENT Performs gradient descent to learn theta
	% theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
	% taking num_iters gradient steps with learning rate alpha

	% Initialize some useful values
	m = length(y); % number of training examples
	J_history = zeros(num_iters, 1);

	for iter = 1:num_iters
	/* \|\
	.-""-._ \ \.--\|
	/ "-..__) .-'
	ಠ_______ಠ /
	\'-.__, .__.,'
	`'----'._\--'
	* Whale whale whale, what have we here?
	* /