Class: LModel

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

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(arg1, arg2 = nil) ⇒ LModel

Returns a new instance of LModel.



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# File 'lib/linear.rb', line 205

def initialize(arg1,arg2=nil)
  if arg2 == nil
    # create model from file
    filename = arg1
    @model = load_model(filename)
  else
    # create model from problem and parameter
    prob,param = arg1,arg2
    @prob = prob
    msg = check_parameter(prob.prob,param.param)
    raise "ValueError", msg if msg
    @model = Liblinear::train(prob.prob,param.param)
  end
  #setup some classwide variables
  @nr_class = Liblinear::get_nr_class(@model)
  #create labels(classes)
  intarr = new_int(@nr_class)
  Liblinear::get_labels(@model,intarr)
  @labels = _int_array_to_list(intarr, @nr_class)
  delete_int(intarr)
end

Instance Attribute Details

#model ⇒ Object

Returns the value of attribute model.



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# File 'lib/linear.rb', line 203

def model
  @model
end

#probability ⇒ Object

Returns the value of attribute probability.



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# File 'lib/linear.rb', line 203

def probability
  @probability
end

Instance Method Details

#destroy ⇒ Object 



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# File 'lib/linear.rb', line 319

def destroy
  destroy_model(@model)
end

#get_labels ⇒ Object 



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# File 'lib/linear.rb', line 239

def get_labels
  return @labels
end

#get_nr_class ⇒ Object 



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# File 'lib/linear.rb', line 235

def get_nr_class
  return @nr_class
end

#predict(x) ⇒ Object 



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# File 'lib/linear.rb', line 227

def predict(x)
  data = _convert_to_feature_node_array(x, @model.nr_feature, @model.bias)
  ret = Liblinear::predict(@model,data)
  feature_node_array_destroy(data)
  return ret
end

#predict_probability(x) ⇒ Object 



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# File 'lib/linear.rb', line 281

def predict_probability(x)
#    if not @probability
#      raise TypeError, "model does not support probabiliy estimates"
#    end
  
  #convert x into feature_node, alloc a double array to receive probabilities
  data = _convert_to_feature_node_array(x, @model.nr_feature, @model.bias)
  dblarr = new_double(@nr_class)
  pred = Liblinear::predict_probability(@model, data, dblarr)
  pv = _double_array_to_list(dblarr, @nr_class)
  delete_double(dblarr)
  feature_node_array_destroy(data)
  p = {}
  for i in (0..@labels.size-1)
    p[@labels[i]] = pv[i]
  end
  return pred, p
end

#predict_values(x) ⇒ Object 



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# File 'lib/linear.rb', line 255

def predict_values(x)
  v=predict_values_raw(x)
  #puts v.inspect
  if false
    #if @svm_type == NU_SVR or @svm_type == EPSILON_SVR or @svm_type == ONE_CLASS
    return v[0]
  else #self.svm_type == C_SVC or self.svm_type == NU_SVC
    count = 0
    
    # create a width x height array
    width = @labels.size
    height = @labels.size
    d = Array.new(width)
    d.map! { Array.new(height) }
    
    for i in (0..@labels.size-1)
      for j in (i+1..@labels.size-1)
        d[@labels[i]][@labels[j]] = v[count]
        d[@labels[j]][@labels[i]] = -v[count]
        count += 1
      end
    end
    return  d
  end
end

#predict_values_raw(x) ⇒ Object 



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# File 'lib/linear.rb', line 243

def predict_values_raw(x)
  #convert x into feature_node, allocate a double array for return
  n = (@nr_class*(@nr_class-1)/2).floor
  data = _convert_to_feature_node_array(x, @model.nr_feature, @model.bias)
  dblarr = new_double(n)
  Liblinear::predict_values(@model, data, dblarr)
  ret = _double_array_to_list(dblarr, n)
  delete_double(dblarr)
  feature_node_array_destroy(data)
  return ret
end

#save(filename) ⇒ Object

def get_svr_pdf

#get_svr_probability will handle error checking
sigma = get_svr_probability()
return Proc.new{|z| exp(-z.abs/sigma)/(2*sigma)}  # TODO: verify this works

end



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# File 'lib/linear.rb', line 315

def save(filename)
  save_model(filename,@model)
end