lib/rumale/decomposition/nmf.rb in rumale-0.20.0 vs lib/rumale/decomposition/nmf.rb in rumale-0.20.1

@@ -75,11 +75,11 @@
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The data to be transformed with the learned model.
       # @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed data.
       def transform(x)
         x = check_convert_sample_array(x)
-        partial_fit(x, false)
+        partial_fit(x, update_comps: false)
       end
 
       # Inverse transform the given transformed data with the learned model.
       #
       # @param z [Numo::DFloat] (shape: [n_samples, n_components]) The data to be restored into original space with the learned model.
@@ -89,10 +89,10 @@
         z.dot(@components)
       end
 
       private
 
-      def partial_fit(x, update_comps = true)
+      def partial_fit(x, update_comps: true)
         # initialize some variables.
         n_samples, n_features = x.shape
         scale = Math.sqrt(x.mean / @params[:n_components])
         sub_rng = @rng.dup
         @components = Rumale::Utils.rand_uniform([@params[:n_components], n_features], sub_rng) * scale if update_comps