davidrichards

=begin
  One of my favorite equations from business school: the learning curve.  
  Human experience has shown that every time our experience with a single 
  task doubles, our efficiency increases by 20%.  Our learning curve is
  actually around 20%, with a 95% confidence rate around 10% and 30%.

  This was the equation that founded BCG as they measured everything 
  related to the then-new aviation industry.  It's been used to measure
  all sorts of human tasks and has proven pretty resilient for people of
  all kinds of backgrounds and aptitudes.  

davidrichards

gem 'jquery-rails'
gem 'coffee-rails', '~> 3.1.1'
gem "haml-rails"
gem 'ruby-haml-js'

davidrichards

require 'active_support/core_ext'

class Hash
  
  def recursive_symbolize_keys!
    symbolize_keys!
    values.each do |h|
      case h
      when Array
        h.each{|vv| vv.recursive_symbolize_keys! if Hash === vv }

davidrichards

group :development, :test do
  gem "growl_notify"
  gem "guard-jasmine-headless-webkit"
  # Can bring this back in when we're ready to go with 3.1
  # gem "jasmine-rails"
  gem "jasmine-headless-webkit"
  gem "rb-fsevent"
  gem "guard-jammit", :git => "git://github.com/johnbintz/guard-jammit.git"
end

davidrichards

## Rails App Template
## Useful for Rails 3.0.x and Ruby 1.9.2
## Run using $ rails new [appname] -JT -m tpl-basicapp.rb

# ========
# = Gems =
# ========

# pre-install spork, dydram and elastic_searchable
run "gem install spork -v 0.9.0.rc --pre"

davidrichards

module Confusion
  module MatrixBehavior
    # A matrix of the values (actual: rows, predicted: columns)
    attr_reader :values
  
    protected
      def assert_matrix_behavior(opts)
        assert_values(opts)
      end

davidrichards

# ============================
# = Load Server Dependencies =
# ============================
require 'rack-flash'
require 'warden'
require 'sinatra'
require 'haml'
require 'sinatra_more/markup_plugin'

# ===============================

davidrichards

data_frame (master): plain_frame 
Loading Data Frame version: 0.1.8
>> df = DataFrame.from_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv')
=> DataFrame rows: 517 labels: [:x, :y, :month, :day, :ffmc, :dmc, :dc, :isi, :temp, :rh, :wind, :rain, :area]
>> df.month.categories                                                                                             
=> ["apr", "aug", "dec", "feb", "jan", "jul", "jun", "mar", "may", "nov", "oct", "sep"]
>> df.wind.categories                                                                                              
=> [0.4, 0.9, 1.3, 1.8, 2.2, 2.7, 3.1, 3.6, 4, 4.5, 4.9, 5.4, 5.8, 6.3, 6.7, 7.2, 7.6, 8, 8.5, 8.9, 9.4]
>> df.wind.add_category(0) {|e| e <= 5}                                                                            
=> {0=>#<Proc:0x0259cdf4@(irb):4>}

davidrichards

# This is used when the data set's true max and min can't be calculated.
# It provides approximate values for normalization. 
class PseudoNormalize
  require 'mathn'
  class << self
    def process(opts={})
      sample = opts.delete(:sample)
      opts = {:sample => sample} if opts.empty
      pn = new(opts)
      pn.process(*sample)

davidrichards

require 'rubygems'
require 'mlp'
require 'data_frame'

IRIS = {1 => 'Iris Setosa', 2 => 'Iris Versicolour', 3 => 'Iris Virginica'}.freeze
KEYS = {'Iris-setosa' => 1, 'Iris-versicolor' => 2, 'Iris-virginica' => 3}.freeze

@mlp = MLP.new(:hidden_layers => [4], :output_nodes => 3, :inputs => 4)

@df = DataFrame.new(:sepal_length, :sepal_width, :petal_length, :petal_width, :class)