Class: Ai4r::Classifiers::SimpleLinearRegression

Inherits:
Classifier
  • Object
show all
Defined in:
lib/ai4r/classifiers/simple_linear_regression.rb

Overview

SimpleLinearRegression performs linear regression on one attribute.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Data::Parameterizable

#get_parameters, included, #set_parameters

Constructor Details

#initialize ⇒ Object 



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 41

def initialize
  super()
  @attribute = nil
  @attribute_index = 0
  @slope = 0
  @intercept = 0
  @selected_attribute = nil
end

Instance Attribute Details

#attribute ⇒ Object (readonly)

Returns the value of attribute attribute.



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 36

def attribute
  @attribute
end

#attribute_index ⇒ Object (readonly)

Returns the value of attribute attribute_index.



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 36

def attribute_index
  @attribute_index
end

#intercept ⇒ Object (readonly)

Returns the value of attribute intercept.



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 36

def intercept
  @intercept
end

#slope ⇒ Object (readonly)

Returns the value of attribute slope.



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 36

def slope
  @slope
end

Instance Method Details

#assign_result(data, result) ⇒ Object 



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 108

def assign_result(data, result)
  raise 'no useful attribute found' if result[:chosen] == -1

  @attribute = data.data_labels[result[:chosen]]
  @attribute_index = result[:chosen]
  @slope = result[:slope]
  @intercept = result[:intercept]
  self
end

#build(data) ⇒ Object

Gets the best attribute and does Linear Regression using it to find out the slope and intercept. Parameter data has to be an instance of DataSet

Parameters:

  • data (Object)

Returns:

  • (Object) 


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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 65

def build(data)
  validate_data(data)

  y_mean = data.get_mean_or_mode[data.num_attributes - 1]
  result = if @selected_attribute
             evaluate_attribute(data, @selected_attribute, y_mean)
           else
             evaluate_all_attributes(data, y_mean)
           end
  assign_result(data, result)
end

#eval(data) ⇒ Object

You can evaluate new data, predicting its category. e.g.

c.eval([1,158,105.8,192.7,71.4,55.7,2844,136,3.19,3.4,8.5,110,5500,19,25])
  => 11876.96774193548

Parameters:

  • data (Object)

Returns:

  • (Object) 


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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 56

def eval(data)
  @intercept + (@slope * data[@attribute_index])
end

#evaluate_all_attributes(data, y_mean) ⇒ Object 



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 94

def evaluate_all_attributes(data, y_mean)
  result = { chosen: -1, msq: Float::MAX }
  data.data_labels.each do |attr_name|
    attr_index = data.get_index attr_name
    next if attr_index == data.num_attributes - 1

    candidate = evaluate_attribute(data, attr_index, y_mean)
    next unless candidate[:msq] < result[:msq]

    result = candidate
  end
  result
end

#evaluate_attribute(data, attr_index, y_mean) ⇒ Object 



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 82

def evaluate_attribute(data, attr_index, y_mean)
  x_mean = data.get_mean_or_mode[attr_index]
  slope, x_diff_sq, y_diff_sq = attribute_sums(data, attr_index, x_mean, y_mean)
  if x_diff_sq.zero?
    { chosen: attr_index, slope: 0, intercept: y_mean, msq: Float::MAX }
  else
    chosen_slope = slope / x_diff_sq
    intercept = y_mean - (chosen_slope * x_mean)
    { chosen: attr_index, slope: chosen_slope, intercept: intercept, msq: y_diff_sq - (chosen_slope * slope) }
  end
end

#get_rules ⇒ Object

Simple Linear Regression classifiers cannot generate human readable rules. This method returns a descriptive string indicating that rule extraction is not supported.



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 121

def get_rules
  'SimpleLinearRegression does not support rule extraction.'
end

#validate_data(data) ⇒ Object 



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# File 'lib/ai4r/classifiers/simple_linear_regression.rb', line 77

def validate_data(data)
  raise 'Error instance must be passed' unless data.is_a?(Ai4r::Data::DataSet)
  raise 'Data should not be empty' if data.data_items.empty?
end