logistic_regression.rb in svmkit-0.4.1

- old
+ new

@@ -47,11 +47,11 @@
       # @param bias_scale [Float] The scale of the bias term.
       #   If fit_bias is true, the feature vector v becoms [v; bias_scale].
       # @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.
-      #   Nadam is selected automatically on current version.
+      #   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, bias_scale: 1.0,
                      max_iter: 1000, batch_size: 20, optimizer: nil, random_seed: nil)
         check_params_float(reg_param: reg_param, bias_scale: bias_scale)
         check_params_integer(max_iter: max_iter, batch_size: batch_size)
@@ -63,10 +63,11 @@
         @params[:fit_bias] = fit_bias
         @params[:bias_scale] = bias_scale
         @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
         @classes = nil
@@ -173,10 +174,10 @@
         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)