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# D subset |R Domain over which we integrate - sample D = [1,10]
D = (1..10).to_a

# f:|R->|R function - sample x |-> f(x) := x^2 
f = lambda {|x| x*x}

# summation of function values over our domain
summator = lambda {|function, domain| domain.inject(0){|x,y| x += function.call(y)} }

# Integrations Driver (for real valued 1d functions only) - assumption: integration steps equidistant

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# given class Computer: refactor me
class Computer
  def initialize(computer_id, data_source)
    @id = computer_id
    @data_source = data_source
  end

  def mouse
    info = @data_source.get_mouse_info(@id) 
    price = @data_source.get_mouse_price(@id) 

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lambda {
  setups = []
  events = {}
  Kernel.send :define_method, :event do |name, &block| 
    events[name] = block
  end

  Kernel.send :define_method, :setup do |&block| 
    setups << block
  end

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Checkboxes
- [x] A
- [x] B
- [x] C

~~Durchgestrichen~~

FooImage
![NAME](adresse/file.format)

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// a sample class decleration showing javascript closure examples.
// Keep in mind: prototyped properties affect all objects of the 
// same constructor, simultaneously, even if they already exist.
function Knight(hp){  
  // public attribute of Knight
  this.hp = hp; 
  
  // private attribute of Knight
  var secreteHP = hp+1;
  

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# map two lists: e.g sum elementwise
[1,2,3].zip([4,5,6]).map{|a,b| a+b}

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# reset local master
git fetch origin
git reset --hard origin/master

# show code diff of a COMMIT 
git show COMMIT

# rabse last NUMBEROFCOMMITS commits (including head)
git rebase -i HEAD~NUMBEROFCOMMITS

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Ruby
Game of Life in Ruby - my old omnipresent friend <3
Collection of Sorting algorithms 
Own DSL arrogating Matlab
SVM for higherdimensional classifiers
A general Ruby Convas (imagine it would directly make use of OpenGL...)

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% gives a gaussain kernel of size (dim_n x dim_x) having a variance equal to sigma_value.
fspecial('gaussian', dim_n, sigma_value)

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% example determining brightness center from a given image A
% Args to provide: mask, image, threshold (perhaps).
% given a selection mask and image A like the following:

mask =

   1   0   0   0
   1   1   0   0
   1   1   0   0
   0   1   1   1