lib/svmkit/linear_model/ridge.rb in svmkit-0.4.0 vs lib/svmkit/linear_model/ridge.rb in svmkit-0.4.1

@@ -37,10 +37,12 @@
       #
       # @param reg_param [Float] The regularization parameter.
       # @param fit_bias [Boolean] The flag indicating whether to fit the bias term.
       # @param max_iter [Integer] The maximum number of iterations.
       # @param batch_size [Integer] The size of the mini batches.
+      # @param optimizer [Optimizer] The optimizer to calculate adaptive learning rate.
+      #   If nil is given, Nadam is used.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(reg_param: 1.0, fit_bias: false, max_iter: 1000, batch_size: 10, optimizer: nil, random_seed: nil)
         check_params_float(reg_param: reg_param)
         check_params_integer(max_iter: max_iter, batch_size: batch_size)
         check_params_boolean(fit_bias: fit_bias)
@@ -50,10 +52,11 @@
         @params[:reg_param] = reg_param
         @params[:fit_bias] = fit_bias
         @params[:max_iter] = max_iter
         @params[:batch_size] = batch_size
         @params[:optimizer] = optimizer
+        @params[:optimizer] ||= Optimizer::Nadam.new
         @params[:random_seed] = random_seed
         @params[:random_seed] ||= srand
         @weight_vec = nil
         @bias_term = nil
         @rng = Random.new(@params[:random_seed])
@@ -73,15 +76,11 @@
         _n_samples, n_features = x.shape
 
         if n_outputs > 1
           @weight_vec = Numo::DFloat.zeros(n_outputs, n_features)
           @bias_term = Numo::DFloat.zeros(n_outputs)
-          n_outputs.times do |n|
-            weight, bias = single_fit(x, y[true, n])
-            @weight_vec[n, true] = weight
-            @bias_term[n] = bias
-          end
+          n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = single_fit(x, y[true, n]) }
         else
           @weight_vec, @bias_term = single_fit(x, y)
         end
 
         self
@@ -122,10 +121,10 @@
         samples = @params[:fit_bias] ? expand_feature(x) : x
         # Initialize some variables.
         n_samples, n_features = samples.shape
         rand_ids = [*0...n_samples].shuffle(random: @rng)
         weight_vec = Numo::DFloat.zeros(n_features)
-        optimizer = Optimizer::Nadam.new
+        optimizer = @params[:optimizer].dup
         # Start optimization.
         @params[:max_iter].times do |_t|
           # Random sampling.
           subset_ids = rand_ids.shift(@params[:batch_size])
           rand_ids.concat(subset_ids)