lib/rumale/ensemble/gradient_boosting_classifier.rb in rumale-ensemble-0.25.0 vs lib/rumale/ensemble/gradient_boosting_classifier.rb in rumale-ensemble-0.26.0

@@ -95,11 +95,11 @@
         ::Rumale::Validation.check_sample_size(x, y)
 
         # initialize some variables.
         n_features = x.shape[1]
         @params[:max_features] = n_features if @params[:max_features].nil?
-        @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
+        @params[:max_features] = [[1, @params[:max_features]].max, n_features].min # rubocop:disable Style/ComparableClamp
         @classes = Numo::Int32[*y.to_a.uniq.sort]
         n_classes = @classes.size
         # train estimator.
         if n_classes > 2
           @base_predictions = multiclass_base_predictions(y)
@@ -185,10 +185,10 @@
 
       def partial_fit(x, y, init_pred)
         # initialize some variables.
         estimators = []
         n_samples = x.shape[0]
-        n_sub_samples = [n_samples, [(n_samples * @params[:subsample]).to_i, 1].max].min
+        n_sub_samples = [n_samples, [(n_samples * @params[:subsample]).to_i, 1].max].min # rubocop:disable Style/ComparableClamp
         whole_ids = Array.new(n_samples) { |v| v }
         y_pred = Numo::DFloat.ones(n_samples) * init_pred
         sub_rng = @rng.dup
         # grow trees.
         @params[:n_estimators].times do |_t|