rbf.rb in rumale-0.12.0

- old
+ new

@@ -8,11 +8,11 @@
   # Module for kernel approximation algorithms.
   module KernelApproximation
     # Class for RBF kernel feature mapping.
     #
     # @example
-    #   transformer = Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_coponents: 128, random_seed: 1)
+    #   transformer = Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_components: 128, random_seed: 1)
     #   new_training_samples = transformer.fit_transform(training_samples)
     #   new_testing_samples = transformer.transform(testing_samples)
     #
     # *Refernce*:
     # 1. A. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines," Proc. NIPS'07, pp.1177--1184, 2007.
@@ -61,11 +61,12 @@
       # @return [RBF] The learned transformer itself.
       def fit(x, _y = nil)
         check_sample_array(x)
 
         n_features = x.shape[1]
+        sub_rng = @rng.dup
         @params[:n_components] = 2 * n_features if @params[:n_components] <= 0
-        @random_mat = Rumale::Utils.rand_normal([n_features, @params[:n_components]], @rng) * (2.0 * @params[:gamma])**0.5
+        @random_mat = Rumale::Utils.rand_normal([n_features, @params[:n_components]], sub_rng) * (2.0 * @params[:gamma])**0.5
         n_half_components = @params[:n_components] / 2
         @random_vec = Numo::DFloat.zeros(@params[:n_components] - n_half_components).concatenate(
           Numo::DFloat.ones(n_half_components) * (0.5 * Math::PI)
         )
         self