Class: SvmToolkit::Parameter

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

Overview

Extends the Java Parameter class with some additional methods.

Parameter holds values determining the kernel type and training process.

svm_type

The type of SVM problem being solved.

  • C_SVC, the usual classification task.

  • NU_SVC

  • ONE_CLASS

  • EPSILON_SVR

  • NU_SVR

kernel_type

The type of kernel to use.

  • LINEAR

  • POLY

  • RBF

  • SIGMOID

  • PRECOMPUTED

degree

A parameter in polynomial kernels.

gamma

A parameter in poly/rbf/sigmoid kernels.

coef0

A parameter for poly/sigmoid kernels.

cache_size

For training, in MB.

eps

For training, stopping criterion.

C

For training with C_SVC, EPSILON_SVR, NU_SVR: the cost parameter.

nr_weight

For training with C_SVC.

weight_label

For training with C_SVC.

weight

For training with C_SVC.

nu

For training with NU_SVR, ONE_CLASS, NU_SVC.

p

For training with EPSILON_SVR.

shrinking

training, whether to use shrinking heuristics.

probability

For training, whether to use probability estimates.

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(args) ⇒ Parameter

Constructor sets up values of attributes based on provided map. Valid keys with their default values:

  • :svm_type = Parameter::C_SVC, for the type of SVM

  • :kernel_type = Parameter::LINEAR, for the type of kernel

  • :cost = 1.0, for the cost or C parameter

  • :gamma = 0.0, for the gamma parameter in kernel

  • :degree = 1, for polynomial kernel

  • :coef0 = 0.0, for polynomial/sigmoid kernels

  • :eps = 0.001, for stopping criterion

  • :nr_weight = 0, for C_SVC

  • :nu = 0.5, used for NU_SVC, ONE_CLASS and NU_SVR. Nu must be in (0,1]

  • :p = 0.1, used for EPSILON_SVR

  • :shrinking = 1, use the shrinking heuristics

  • :probability = 0, use the probability estimates



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

def initialize args
  super()
  self.svm_type    = args.fetch(:svm_type, Parameter::C_SVC)
  self.kernel_type = args.fetch(:kernel_type, Parameter::LINEAR)
  self.C           = args.fetch(:cost, 1.0)
  self.gamma       = args.fetch(:gamma, 0.0)
  self.degree      = args.fetch(:degree, 1)
  self.coef0       = args.fetch(:coef0, 0.0)
  self.eps         = args.fetch(:eps, 0.001)
  self.nr_weight   = args.fetch(:nr_weight, 0)
  self.nu          = args.fetch(:nu, 0.5)
  self.p           = args.fetch(:p, 0.1)
  self.shrinking   = args.fetch(:shrinking, 1)
  self.probability = args.fetch(:probability, 0)

  unless self.nu > 0.0 and self.nu <= 1.0
    raise ArgumentError "Invalid value of nu #{self.nu}, should be in (0,1]"
  end
end

Class Method Details

.kernel_name(kernel) ⇒ Object

Return a printable name for the given kernel.



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# File 'lib/svm_toolkit/parameter.rb', line 100

def self.kernel_name kernel
  case kernel
  when Parameter::LINEAR
    "Linear"
  when Parameter::POLY
    "Polynomial"
  when Parameter::RBF
    "Radial basis function"
  when Parameter::SIGMOID
    "Sigmoid"
  else
    "Unknown"
  end
end

.kernels ⇒ Object

Return a list of the available kernels.



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# File 'lib/svm_toolkit/parameter.rb', line 95

def self.kernels
  [Parameter::LINEAR, Parameter::POLY, Parameter::RBF, Parameter::SIGMOID]
end

Instance Method Details

#cost ⇒ Object

A more readable accessor for the C parameter



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# File 'lib/svm_toolkit/parameter.rb', line 85

def cost
  self.C
end

#cost=(val) ⇒ Object

A more readable mutator for the C parameter



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

def cost= val
  self.C = val
end