Class: SvmToolkit::Svm

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

Overview

Extends the Java SVM class.

Defined Under Namespace

Classes: ContourDisplay, CrossValidationSearch, SvmTrainer

Class Method Summary collapse

Class Method Details

.cross_validation(problem, parameters, n_folds, target) ⇒ Object

Using given Problem and Parameter instances, builds and trains SVM models using n-fold cross-validation.



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# File 'lib/svm_toolkit/svm.rb', line 14

def self.cross_validation(problem, parameters, n_folds, target)
  Svm.svm_cross_validation(problem, parameters, n_folds, target)
end

.cross_validation_search(training_set, cross_valn_set, costs = [-2,-1,0,1,2,3].collect {|i| 2**i}, gammas = [-2,-1,0,1,2,3].collect {|i| 2**i}, params = {}) ⇒ Object

Perform cross validation search on given gamma/cost values, using an RBF kernel, returning the best performing model and optionally displaying a contour map of performance.

  • training_set - instance of Problem, used for training
  • cross_valn_set - instance of Problem, used for evaluating models
  • costs - array of cost values to search across
  • gammas - array of gamma values to search across
  • params - Optional parameters include:
    • :evaluator => Evaluator::OverallAccuracy, the name of the class to use for computing performance
    • :show_plot => false, whether to display contour plot

Returns an instance of Model, the best performing model.



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# File 'lib/svm_toolkit/svm.rb', line 34

def Svm.cross_validation_search(training_set, cross_valn_set, 
                                 costs = [-2,-1,0,1,2,3].collect {|i| 2**i}, 
                                 gammas = [-2,-1,0,1,2,3].collect {|i| 2**i}, 
                                 params = {})
  evaluator = params.fetch :evaluator, Evaluator::OverallAccuracy
  show_plot = params.fetch :show_plot, false

  fjp = ForkJoinPool.new
  task = CrossValidationSearch.new gammas, costs, training_set, cross_valn_set, evaluator
  results, best_model = fjp.invoke task

  if show_plot
    ContourDisplay.new(costs.collect {|n| Math.log2(n)}, 
                       gammas.collect {|n| Math.log2(n)}, 
                       results)
  end

  return best_model
end

.train(problem, parameters) ⇒ Object

Using given Problem and Parameter instances, builds and trains an SVM model.



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# File 'lib/svm_toolkit/svm.rb', line 8

def self.train(problem, parameters)
  Svm.svm_train(problem, parameters)
end