elastic_net.rb in rumale-0.21.0

- old
+ new

@@ -8,11 +8,11 @@
     # ElasticNet is a class that implements Elastic-net Regression
     # with stochastic gradient descent (SGD) optimization.
     #
     # @example
     #   estimator =
-    #     Rumale::LinearModel::ElasticNet.new(reg_param: 0.1, l1_ratio: 0.5, max_iter: 200, batch_size: 50, random_seed: 1)
+    #     Rumale::LinearModel::ElasticNet.new(reg_param: 0.1, l1_ratio: 0.5, max_iter: 1000, batch_size: 50, random_seed: 1)
     #   estimator.fit(training_samples, traininig_values)
     #   results = estimator.predict(testing_samples)
     #
     # *Reference*
     # - Shalev-Shwartz, S., and Singer, Y., "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM," Proc. ICML'07, pp. 807--814, 2007.
@@ -57,10 +57,10 @@
       #   This parameter is ignored if the Parallel gem is not loaded.
       # @param verbose [Boolean] The flag indicating whether to output loss during iteration.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(learning_rate: 0.01, decay: nil, momentum: 0.9,
                      reg_param: 1.0, l1_ratio: 0.5, fit_bias: true, bias_scale: 1.0,
-                     max_iter: 200, batch_size: 50, tol: 1e-4,
+                     max_iter: 1000, batch_size: 50, tol: 1e-4,
                      n_jobs: nil, verbose: false, random_seed: nil)
         check_params_numeric(learning_rate: learning_rate, momentum: momentum,
                              reg_param: reg_param, l1_ratio: l1_ratio, bias_scale: bias_scale,
                              max_iter: max_iter, batch_size: batch_size, tol: tol)
         check_params_boolean(fit_bias: fit_bias, verbose: verbose)