lib/svmkit/linear_model/pegasos_svc.rb in svmkit-0.1.2 vs lib/svmkit/linear_model/pegasos_svc.rb in svmkit-0.1.3

@@ -42,15 +42,16 @@
 
       # Create a new classifier with Support Vector Machine by the Pegasos algorithm.
       #
       # @overload new(reg_param: 1.0, max_iter: 100, batch_size: 50, random_seed: 1) -> PegasosSVC
       #
-      # @param reg_param   [Float] (defaults to: 1.0) The regularization parameter.
-      # @param fit_bias    [Boolean] (defaults to: false) The flag indicating whether to fit the bias term.
-      # @param bias_scale  [Float] (defaults to: 1.0) The scale of the bias term.
-      # @param max_iter    [Integer] (defaults to: 100) The maximum number of iterations.
-      # @param batch_size  [Integer] (defaults to: 50) The size of the mini batches.
-      # @param random_seed [Integer] (defaults to: nil) The seed value using to initialize the random generator.
+      # @param params [Hash] The parameters for SVC.
+      # @option params [Float]   :reg_param (1.0) The regularization parameter.
+      # @option params [Boolean] :fit_bias (false) The flag indicating whether to fit the bias term.
+      # @option params [Float]   :bias_scale (1.0) The scale of the bias term.
+      # @option params [Integer] :max_iter (100) The maximum number of iterations.
+      # @option params [Integer] :batch_size (50) The size of the mini batches.
+      # @option params [Integer] :random_seed (nil) The seed value using to initialize the random generator.
       def initialize(params = {})
         self.params = DEFAULT_PARAMS.merge(Hash[params.map { |k, v| [k.to_sym, v] }])
         self.params[:random_seed] ||= srand
         @weight_vec = nil
         @bias_term = 0.0