Class: RubyLinearRegression

Inherits:
Object
  • Object
show all
Defined in:
lib/ruby_linear_regression.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ RubyLinearRegression

Returns a new instance of RubyLinearRegression.



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# File 'lib/ruby_linear_regression.rb', line 7

def initialize
  @mu = 0
  @sigma = 1
end

Instance Attribute Details

#mu ⇒ Object (readonly)

Returns the value of attribute mu.



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# File 'lib/ruby_linear_regression.rb', line 5

def mu
  @mu
end

#sigma ⇒ Object (readonly)

Returns the value of attribute sigma.



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# File 'lib/ruby_linear_regression.rb', line 5

def sigma
  @sigma
end

#theta ⇒ Object (readonly)

Returns the value of attribute theta.



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# File 'lib/ruby_linear_regression.rb', line 5

def theta
  @theta
end

#x ⇒ Object (readonly)

Returns the value of attribute x.



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# File 'lib/ruby_linear_regression.rb', line 5

def x
  @x
end

#y ⇒ Object (readonly)

Returns the value of attribute y.



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# File 'lib/ruby_linear_regression.rb', line 5

def y
  @y
end

Instance Method Details

#compute_cost ⇒ Object 



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# File 'lib/ruby_linear_regression.rb', line 27

def compute_cost
  # Compute the mean squared cost / error function

  # First use matrix multiplication and vector subtracton to find errors

  errors = (@x * @theta) - @y

  # Then square all errors

  errors = errors.map { |e| e * e  }

  # Find the mean of the square errors

  mean_square_error = 0.5 * (errors.inject{ |sum, e| sum + e }.to_f / errors.row_size)

  return mean_square_error
end

#load_training_data(x_data, y_data) ⇒ Object 



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# File 'lib/ruby_linear_regression.rb', line 12

def load_training_data x_data, y_data

      # normalize the x_data

      x_data = normalize_data( x_data )

      # add 1 column to our data

      x_data = x_data.map { |r| [1].concat(r) }

      # build our x Matrix & y Vector

      @x = Matrix.rows( x_data )
      @y = Matrix.rows( y_data.collect { |e| [e] } )

      @theta = Matrix[[0],[0]]
end

#predict(data) ⇒ Object 



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# File 'lib/ruby_linear_regression.rb', line 49

def predict data

  # normalize

  data.each_index do |i|
    data[i] = (data[i] - @mu[i]) / @sigma[i].to_f
  end

  # add 1 column to prediction data

  data = [1].concat( data )

  # perform prediction

  prediction = (Matrix[data] * @theta)[0,0].to_f

  return prediction

end

#train_normal_equation ⇒ Object 



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# File 'lib/ruby_linear_regression.rb', line 41

def train_normal_equation
  # Calculate the optimal theta using the normal equation

  # theta = ( X' * X )^1 * X' * y

  @theta = (@x.transpose * @x).inverse * @x.transpose * @y

  return @theta
end