lib/svmkit/linear_model/svc.rb in svmkit-0.2.0 vs lib/svmkit/linear_model/svc.rb in svmkit-0.2.1

@@ -16,20 +16,10 @@
     # 1. S. Shalev-Shwartz and Y. Singer, "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM," Proc. ICML'07, pp. 807--814, 2007.
     class SVC
       include Base::BaseEstimator
       include Base::Classifier
 
-      # @!visibility private
-      DEFAULT_PARAMS = {
-        reg_param: 1.0,
-        fit_bias: false,
-        bias_scale: 1.0,
-        max_iter: 100,
-        batch_size: 50,
-        random_seed: nil
-      }.freeze
-
       # Return the weight vector for SVC.
       # @return [Numo::DFloat] (shape: [n_features])
       attr_reader :weight_vec
 
       # Return the bias term (a.k.a. intercept) for SVC.
@@ -47,21 +37,21 @@
       # @param bias_scale [Float] The scale of the bias term.
       # @param max_iter [Integer] The maximum number of iterations.
       # @param batch_size [Integer] The size of the mini batches.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, max_iter: 100, batch_size: 50, random_seed: nil)
-        self.params = {}
-        self.params[:reg_param] = reg_param
-        self.params[:fit_bias] = fit_bias
-        self.params[:bias_scale] = bias_scale
-        self.params[:max_iter] = max_iter
-        self.params[:batch_size] = batch_size
-        self.params[:random_seed] = random_seed
-        self.params[:random_seed] ||= srand
+        @params = {}
+        @params[:reg_param] = reg_param
+        @params[:fit_bias] = fit_bias
+        @params[:bias_scale] = bias_scale
+        @params[:max_iter] = max_iter
+        @params[:batch_size] = batch_size
+        @params[:random_seed] = random_seed
+        @params[:random_seed] ||= srand
         @weight_vec = nil
         @bias_term = 0.0
-        @rng = Random.new(self.params[:random_seed])
+        @rng = Random.new(@params[:random_seed])
       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.
@@ -71,40 +61,40 @@
         # Generate binary labels
         negative_label = y.to_a.uniq.sort.shift
         bin_y = y.to_a.map { |l| l != negative_label ? 1 : -1 }
         # Expand feature vectors for bias term.
         samples = x
-        if params[:fit_bias]
+        if @params[:fit_bias]
           samples = Numo::NArray.hstack(
-            [samples, Numo::DFloat.ones([x.shape[0], 1]) * params[:bias_scale]]
+            [samples, Numo::DFloat.ones([x.shape[0], 1]) * @params[:bias_scale]]
           )
         end
         # Initialize some variables.
         n_samples, n_features = samples.shape
         rand_ids = [*0..n_samples - 1].shuffle(random: @rng)
         weight_vec = Numo::DFloat.zeros(n_features)
         # Start optimization.
-        params[:max_iter].times do |t|
+        @params[:max_iter].times do |t|
           # random sampling
-          subset_ids = rand_ids.shift(params[:batch_size])
+          subset_ids = rand_ids.shift(@params[:batch_size])
           rand_ids.concat(subset_ids)
           target_ids = subset_ids.map { |n| n if weight_vec.dot(samples[n, true]) * bin_y[n] < 1 }.compact
           n_subsamples = target_ids.size
           next if n_subsamples.zero?
           # update the weight vector.
-          eta = 1.0 / (params[:reg_param] * (t + 1))
+          eta = 1.0 / (@params[:reg_param] * (t + 1))
           mean_vec = Numo::DFloat.zeros(n_features)
           target_ids.each { |n| mean_vec += samples[n, true] * bin_y[n] }
           mean_vec *= eta / n_subsamples
-          weight_vec = weight_vec * (1.0 - eta * params[:reg_param]) + mean_vec
+          weight_vec = weight_vec * (1.0 - eta * @params[:reg_param]) + mean_vec
           # scale the weight vector.
           norm = Math.sqrt(weight_vec.dot(weight_vec))
-          scaler = (1.0 / params[:reg_param]**0.5) / (norm + 1.0e-12)
+          scaler = (1.0 / @params[:reg_param]**0.5) / (norm + 1.0e-12)
           weight_vec *= [1.0, scaler].min
         end
         # Store the learned model.
-        if params[:fit_bias]
+        if @params[:fit_bias]
           @weight_vec = weight_vec[0...n_features - 1]
           @bias_term = weight_vec[n_features - 1]
         else
           @weight_vec = weight_vec[0...n_features]
           @bias_term = 0.0
@@ -140,16 +130,16 @@
       end
 
       # Dump marshal data.
       # @return [Hash] The marshal data about SVC.
       def marshal_dump
-        { params: params, weight_vec: @weight_vec, bias_term: @bias_term, rng: @rng }
+        { params: @params, weight_vec: @weight_vec, bias_term: @bias_term, rng: @rng }
       end
 
       # Load marshal data.
       # @return [nil]
       def marshal_load(obj)
-        self.params = obj[:params]
+        @params = obj[:params]
         @weight_vec = obj[:weight_vec]
         @bias_term = obj[:bias_term]
         @rng = obj[:rng]
         nil
       end