envp

1. When I load c9.io for the first time, no styles are loaded from cdn.c9.io and I get a screen [like this](http://puu.sh/nBEwg/b9cf73fe0e.png).

2. After going to status.c9.io, which needs me to fill out a [captcha](http://puu.sh/nBEGI/92762b043c.jpg). I am able to see my dashboard with proper styling, but is still missing some resources all of which are from cdn.c9.io.

3. [See screenshot of above](http://puu.sh/nBEN9/3b9a868896.png) Note the missing glyphicons in this picture. [And a dropbox link to the console logs](https://www.dropbox.com/s/stn8rl62ndz50v1/c9.io-1457627113298.log?dl=0)

4. Now when I click on "Open" to open my workspace. It gets [stuck loading](http://puu.sh/nBF5n/427dde3458.png). Here's the [dropbox log for that](https://www.dropbox.com/s/jcujwnsvb4uvc9a/ide.c9.io-1457627242366_stuck_Loading.log?dl=0)

envp

describe Statistical::Distribution::DistNameGoesHere do
  describe '.new' do
    context 'when called with no arguments'
    context 'when upper and lower bounds are specified'
  end
  
  describe '#pdf' do
    context 'when called with x < lower'
    context 'when called with x > upper'
    context 'when called with x in [lower, upper]'

envp

# require all the rngs defined in lib/statistical/rng
require 'statistical/rng/uniform'

module Statistical
  module Rng
    
    # :nodoc:
    # Dynamically add constant RNG_TYPES when called
    def self.const_missing(cname)
      if cname == 'RNG_TYPES'.to_sym

envp

state = 1
counter = 0

# Customary: "Here be evil"
# For a live version, see: https://repl.it/BiiG/1
loop do
  puts [state, counter].inspect

  case state
  when 1

envp

module Distribution
  module Binomial
    module Ruby_
      class << self
        # Returns a `Proc`object that yields a random integer `k` <= `n`
        # which is binomially distributed with mean np and variance np(1-p)
        # This method uses  Luc Devroye's "Rejection Method" from page 533
        # of his text: "Non-Uniform Random Variate Generation."
        #
        # @param n [Fixnum] the total number of trials

envp

# When the mean is still small, but the probability of success
# is not miniscule. Covers n > 20
# Taken from "The Generation of Binomial Random Variates"
# Wolfgang Hormann (http://epub.wu.ac.at/1242/1/document.pdf)
# This is algorithm BTRD from the text

# This is a precalculated table for correction terms in
# stirling's approximation to log(k!) each index corresponds
# to a seperate value of k
fc_table = [0.08106146679532726, 0.04134069595540929,

envp

        # Returns the inverse-CDF or the quantile given the probability `prob`,
        # the total number of trials `n` and the number of successes `k`
        # Note: This is a binary search under the hood and is a candidate for
        #   updates once more stable techniques are found.
        #   This still needs to be optimized and benchmarked against 
        #   the vanilla quantile  
        #
        # @paran qn [Float] the cumulative function value to be inverted
        # @param n [Fixnum, Bignum] total number of trials
        # @param prob [Float] probabilty of success in a single independant trial

envp

def rb_rand_entropy(num_trials)
	a = Array.new(num_trials) {rand(100)}
	p = []
	0.upto(99) do |i|
		p[i] = a.count(i) / num_trials.to_f
	end

	return p.map {|c| -c * Math.log(c)}.reduce(:+)
end

envp

module GoodnessOfFit
  # All goodness of fit helpers go here
  
  ## Pearson's chi squared test for goodness of fit
  # == References
  # * http://stattrek.com/chi-square-test/goodness-of-fit.aspx?Tutorial=AP
  # * https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)#Residuals
  #
  # The null hypothesis is always that the +candidate+ does belong to 
  # the +target+ distribution.

envp

Pending: (Failures listed here are expected and do not affect your suite's status)

  1) Distribution::Uniform singleton it should behave like uniform engine .rng should generate sequences with the right mean & variance
     # This method is very innacurrate due to the low convergence rate
     # ./spec/uniform_spec.rb:8

  2) Distribution::Uniform Distribution::Uniform::Ruby_ it should behave like uniform engine .rng should generate sequences with the right mean & variance
     # This method is very innacurrate due to the low convergence rate
     # ./spec/uniform_spec.rb:8