taterbase

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'

taterbase

var express = require('express')
  , app     = express()

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

app.listen(1337);

taterbase

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

taterbase

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) {

taterbase

{"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"}]}}

taterbase

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

taterbase

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));

taterbase

function J = costFunctionJ(X, y, theta)

% X is the "design matrix" containg our training examples
% y is the class labels

m = size(X,1);         % numbedr of training examples
predictions = X*theta; % predictions of hypothesis on all m examples
sqrErrors = (predictions - y) .^2; % squared errors

J= 1/(2*m) * sum(sqrErrors);

taterbase

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

taterbase

    /*              |\       
        .-""-._     \ \.--|  
       /       "-..__) .-'   
     ಠ_______ಠ        /     
      \'-.__,   .__.,'       
       `'----'._\--'  
     * Whale whale whale, what have we here?
     * /