Class: Treat::Workers::Learners::Classifiers::SVM

Inherits:
Object
  • Object
show all
Defined in:
lib/treat/workers/learners/classifiers/svm.rb

Constant Summary collapse

DefaultOptions = 
{
  cache_size: 1, # in MB
  eps: 0.001,
  c: 10
}
@@classifiers = 
{}

Class Method Summary collapse

Class Method Details

.classify(entity, options = {}) ⇒ Object

  • (Numeric) :cache_size => cache size in MB.

  • (Numeric) :eps => tolerance of termination criterion

  • (Numeric) :c => C parameter



16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
 
# File 'lib/treat/workers/learners/classifiers/svm.rb', line 16

def self.classify(entity, options = {})
  options = DefaultOptions.merge(options)
  dset = options[:training]
  prob, items = dset.problem, dset.items
  if !@@classifiers[prob]
    lprob = Libsvm::Problem.new
    lparam = Libsvm::SvmParameter.new
    lparam.cache_size = options[:cache_size]
    lparam.eps = options[:eps]
    lparam.c = options[:c]
    llabels = items.map { |it| it[:features][-1] }
    lexamples = items.map { |it| it[:features][0..-2] }.
    map { |ary| Libsvm::Node.features(ary) }
    lprob.set_examples(llabels, lexamples)
    model = Libsvm::Model.train(lprob, lparam)
    @@classifiers[prob] = model
  end
  features = prob.export_features(entity, false)
  @@classifiers[prob].predict(
  Libsvm::Node.features(features))
end