Class: SvmToolkit::Model

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

Overview

Holds information for a trained SVM model.

Class Method Summary collapse

Instance Method Summary collapse

Class Method Details

.load(filename) ⇒ Object

Load model from given filename. Raises IOError on any error.



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# File 'lib/svm_toolkit/model.rb', line 90

def self.load filename
  begin
    Svm.svm_load_model(filename)
  rescue java.io.IOException
    raise IOError.new "Error in loading SVM model from file"
  end
end

Instance Method Details

#cost ⇒ Object

Return the value of the cost parameter



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# File 'lib/svm_toolkit/model.rb', line 69

def cost
  self.param.cost
end

#degree ⇒ Object

Return the value of the degree parameter



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# File 'lib/svm_toolkit/model.rb', line 59

def degree
  self.param.degree
end

#evaluate_dataset(data, params = {}) ⇒ Object

Evaluate model on given data set (an instance of Problem), returning the number of errors made. Optional parameters include:

  • :evaluator => Evaluator::OverallAccuracy, the name of the class to use for computing performance

  • :print_results => false, whether to print the result for each instance



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# File 'lib/svm_toolkit/model.rb', line 11

def evaluate_dataset(data, params = {})
  evaluator = params.fetch(:evaluator, Evaluator::OverallAccuracy)
  print_results = params.fetch(:print_results, false)
  performance = evaluator.new
  data.l.times do |i|
    pred = Svm.svm_predict(self, data.x[i])
    performance.add_result(data.y[i], pred)
    if print_results
      puts "Instance #{i}, Prediction: #{pred}, True label: #{data.y[i]}"
    end
  end
  return performance
end

#gamma ⇒ Object

Return the value of the gamma parameter



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# File 'lib/svm_toolkit/model.rb', line 64

def gamma
  self.param.gamma
end

#kernel_type ⇒ Object

Return the kernel type for this model



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# File 'lib/svm_toolkit/model.rb', line 54

def kernel_type
  self.param.kernel_type
end

#number_classes ⇒ Object

Return the number of classes handled by this model.



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# File 'lib/svm_toolkit/model.rb', line 74

def number_classes
  self.nr_class
end

#predict(problem, instance_number) ⇒ Object

Predict the class of given instance number in given problem.



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# File 'lib/svm_toolkit/model.rb', line 101

def predict(problem, instance_number)
  Svm.svm_predict(self, problem.x[instance_number])
end

#predict_values(problem, instance_number) ⇒ Object

Return the values of given instance number of given problem against each decision boundary. (This is the distance of the instance from each boundary.)

Return value is an array if more than one decision boundary.



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# File 'lib/svm_toolkit/model.rb', line 111

def predict_values(problem, instance_number)
  dist = Array.new(number_classes*(number_classes-1)/2, 0).to_java(:double)
  Svm.svm_predict_values(self, problem.x[instance_number], dist)
  if dist.size == 1
    return dist[0]
  else
    return dist.to_a
  end
end

#save(filename) ⇒ Object

Save model to given filename. Raises IOError on any error.



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# File 'lib/svm_toolkit/model.rb', line 80

def save filename
  begin
    Svm.svm_save_model(filename, self)
  rescue java.io.IOException
    raise IOError.new "Error in saving SVM model to file"
  end
end

#support_vector_indices ⇒ Object

Return an array of indices of the training instances used as support vectors.



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# File 'lib/svm_toolkit/model.rb', line 37

def support_vector_indices
  result = []
  unless sv_indices.nil?
    sv_indices.size.times do |i|
      result << sv_indices[i]
    end
  end

  return result
end

#svm_type ⇒ Object

Return the SVM problem type for this model



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# File 'lib/svm_toolkit/model.rb', line 49

def svm_type
  self.param.svm_type
end

#w_squared ⇒ Object

Return the value of w squared for the hyperplane. – returned as an array if there is not just one value.



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# File 'lib/svm_toolkit/model.rb', line 27

def w_squared
  if self.w_2.size == 1
    self.w_2[0]
  else
    self.w_2.to_a
  end
end