class Rumale::SVM::NuSVC
NuSVC
is a class that provides Kernel Nu-Support Vector Classifier in LIBSVM with Rumale
interface.
@example
estimator = Rumale::SVM::NuSVC.new(nu: 0.5, kernel: 'rbf', gamma: 10.0, random_seed: 1) estimator.fit(training_samples, traininig_labels) results = estimator.predict(testing_samples)
Public Class Methods
Create a new classifier with Kernel Nu-Support Vector Classifier.
@param nu [Float] The regularization parameter. The interval of nu is (0, 1]. @param kernel [String] The type of kernel function ('rbf', 'linear', 'poly', 'sigmoid', and 'precomputed'). @param degree [Integer] The degree parameter in polynomial kernel function. @param gamma [Float] The gamma parameter in rbf/poly/sigmoid kernel function. @param coef0 [Float] The coefficient in poly/sigmoid kernel function. @param shrinking [Boolean] The flag indicating whether to use the shrinking heuristics. @param probability [Boolean] The flag indicating whether to train the parameter for probability estimation. @param cache_size [Float] The cache memory size in MB. @param tol [Float] The tolerance of termination criterion. @param verbose [Boolean] The flag indicating whether to output learning process message @param random_seed [Integer/Nil] The seed value using to initialize the random generator.
# File lib/rumale/svm/nu_svc.rb, line 32 def initialize(nu: 0.5, kernel: 'rbf', degree: 3, gamma: 1.0, coef0: 0.0, shrinking: true, probability: true, cache_size: 200.0, tol: 1e-3, verbose: false, random_seed: nil) check_params_numeric(nu: nu, degree: degree, gamma: gamma, coef0: coef0, cache_size: cache_size, tol: tol) check_params_string(kernel: kernel) check_params_boolean(shrinking: shrinking, probability: probability, verbose: verbose) check_params_numeric_or_nil(random_seed: random_seed) @params = {} @params[:nu] = nu.to_f @params[:kernel] = kernel @params[:degree] = degree.to_i @params[:gamma] = gamma.to_f @params[:coef0] = coef0.to_f @params[:shrinking] = shrinking @params[:probability] = probability @params[:cache_size] = cache_size.to_f @params[:tol] = tol.to_f @params[:verbose] = verbose @params[:random_seed] = random_seed.nil? ? nil : random_seed.to_i end
Public Instance Methods
Calculate confidence scores for samples.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
If the kernel is 'precomputed', the shape of x must be [n_samples, n_training_samples].
@return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence score per sample.
# File lib/rumale/svm/nu_svc.rb, line 72 def decision_function(x) raise "#{self.class.name}\##{__method__} expects to be called after training the model with the fit method." unless trained? x = check_convert_sample_array(x) xx = precomputed_kernel? ? add_index_col(x) : x Numo::Libsvm.decision_function(xx, libsvm_params, @model) end
Return the coefficients of the support vector in decision function. @return [Numo::DFloat] (shape: [n_classes - 1, n_support_vectors])
# File lib/rumale/svm/nu_svc.rb, line 139 def duel_coef @model[:sv_coef] end
Fit the model with given training data.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
If the kernel is 'precomputed', x must be a square distance matrix (shape: [n_samples, n_samples]).
@param y [Numo::Int32] (shape: [n_samples]) The labels to be used for fitting the model. @return [NuSVC] The learned classifier itself.
# File lib/rumale/svm/nu_svc.rb, line 58 def fit(x, y) x = check_convert_sample_array(x) y = check_convert_label_array(y) check_sample_label_size(x, y) xx = precomputed_kernel? ? add_index_col(x) : x @model = Numo::Libsvm.train(xx, y, libsvm_params) self end
Return the intercepts in decision function. @return [Numo::DFloat] (shape: [n_classes * (n_classes - 1) / 2])
# File lib/rumale/svm/nu_svc.rb, line 145 def intercept @model[:rho] end
Dump marshal data. @return [Hash] The marshal data about NuSVC
.
# File lib/rumale/svm/nu_svc.rb, line 106 def marshal_dump { params: @params, model: @model } end
Load marshal data. @return [nil]
# File lib/rumale/svm/nu_svc.rb, line 113 def marshal_load(obj) @params = obj[:params] @model = obj[:model] nil end
Return the number of support vectors for each class. @return [Numo::Int32] (shape: [n_classes])
# File lib/rumale/svm/nu_svc.rb, line 133 def n_support @model[:nSV] end
Predict class labels for samples.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels.
If the kernel is 'precomputed', the shape of x must be [n_samples, n_training_samples].
@return [Numo::Int32] (shape: [n_samples]) Predicted class label per sample.
# File lib/rumale/svm/nu_svc.rb, line 84 def predict(x) raise "#{self.class.name}\##{__method__} expects to be called after training the model with the fit method." unless trained? x = check_convert_sample_array(x) xx = precomputed_kernel? ? add_index_col(x) : x Numo::Int32.cast(Numo::Libsvm.predict(xx, libsvm_params, @model)) end
Predict class probability for samples. This method works correctly only if the probability parameter is true.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the probailities.
If the kernel is 'precomputed', the shape of x must be [n_samples, n_training_samples].
@return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted probability of each class per sample.
# File lib/rumale/svm/nu_svc.rb, line 97 def predict_proba(x) raise "#{self.class.name}\##{__method__} expects to be called after training the model with the fit method." unless trained? x = check_convert_sample_array(x) xx = precomputed_kernel? ? add_index_col(x) : x Numo::Libsvm.predict_proba(xx, libsvm_params, @model) end
Return the probability parameter alpha. @return [Numo::DFloat] (shape: [n_classes * (n_classes - 1) / 2])
# File lib/rumale/svm/nu_svc.rb, line 151 def prob_a @model[:probA] end
Return the probability parameter beta. @return [Numo::DFloat] (shape: [n_classes * (n_classes - 1) / 2])
# File lib/rumale/svm/nu_svc.rb, line 157 def prob_b @model[:probB] end
Return the indices of support vectors. @return [Numo::Int32] (shape: [n_support_vectors])
# File lib/rumale/svm/nu_svc.rb, line 121 def support @model[:sv_indices] end
Return the support_vectors. @return [Numo::DFloat] (shape: [n_support_vectors, n_features])
# File lib/rumale/svm/nu_svc.rb, line 127 def support_vectors precomputed_kernel? ? del_index_col(@model[:SV]) : @model[:SV] end
Private Instance Methods
# File lib/rumale/svm/nu_svc.rb, line 163 def add_index_col(x) idx = Numo::Int32.new(x.shape[0]).seq + 1 Numo::NArray.hstack([idx.expand_dims(1), x]) end
# File lib/rumale/svm/nu_svc.rb, line 168 def del_index_col(x) x[true, 1..-1].dup end
# File lib/rumale/svm/nu_svc.rb, line 176 def libsvm_params res = @params.merge(svm_type: Numo::Libsvm::SvmType::C_SVC) res[:kernel_type] = case res.delete(:kernel) when 'linear' Numo::Libsvm::KernelType::LINEAR when 'poly' Numo::Libsvm::KernelType::POLY when 'sigmoid' Numo::Libsvm::KernelType::SIGMOID when 'precomputed' Numo::Libsvm::KernelType::PRECOMPUTED else Numo::Libsvm::KernelType::RBF end res[:eps] = res.delete(:tol) res end
# File lib/rumale/svm/nu_svc.rb, line 172 def precomputed_kernel? @params[:kernel] == 'precomputed' end
# File lib/rumale/svm/nu_svc.rb, line 194 def trained? !@model.nil? end