🎨 Add max_iter parameter to NCA

lib/rumale/metric_learning/neighbourhood_component_analysis.rb

@@ -35,17 +35,19 @@ class NeighbourhoodComponentAnalysis
       #
       # @param n_components [Integer] The number of components.
       # @param init [String] The initialization method for components ('random' or 'pca').
+      # @param max_iter [Integer] The maximum number of iterations.
       # @param tol [Float] The tolerance of termination criterion.
       # @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(n_components: nil, init: 'random', tol: 1e-6, verbose: false, random_seed: nil)
+      def initialize(n_components: nil, init: 'random', max_iter: 100, tol: 1e-6, verbose: false, random_seed: nil)
         check_params_numeric_or_nil(n_components: n_components, random_seed: random_seed)
-        check_params_numeric(tol: tol)
+        check_params_numeric(max_iter: max_iter, tol: tol)
         check_params_string(init: init)
         check_params_boolean(verbose: verbose)
         @params = {}
         @params[:n_components] = n_components
         @params[:init] = init
+        @params[:max_iter] = max_iter
         @params[:tol] = tol
         @params[:verbose] = verbose
         @params[:random_seed] = random_seed
@@ -114,7 +116,9 @@ def optimize_components(x, y, n_features, n_components)
         res[:n_iter] = 0
         # perform optimization.
         optimizer = Mopti::ScaledConjugateGradient.new(
-          fnc: method(:nca_loss), jcb: method(:nca_dloss), x_init: comp_init, args: [x, y], ftol: @params[:tol]
+          fnc: method(:nca_loss), jcb: method(:nca_dloss),
+          x_init: comp_init, args: [x, y],
+          max_iter: @params[:max_iter], ftol: @params[:tol]
         )
         fold = 0.0
         dold = 0.0