annoviko / pyclustering

        ax.set_xlabel("Iteration");

        ax.set_ylabel("Fitness function");

        ax.legend([line_best, line_current, line_mean], ["The best pop.", "Cur. best pop.", "Average"], prop={'size': 10});

        ax.grid();

        print(start_iteration, stop_iteration);

        if (display is True):

            plt.show();

    @staticmethod

    def show_clusters(data, observer, marker = '.', markersize = None):

        figure = plt.figure();

        ax1 = figure.add_subplot(121);

        clusters = ga_math.get_clusters_representation(observer.get_global_best()['chromosome'][-1]);

        visualizer = cluster_visualizer(1, 2);

        visualizer.append_clusters(clusters, data, 0, marker, markersize);

        visualizer.show(figure, display = False);

        ga_visualizer.show_evolution(observer, 0, None, ax1, True);

    @staticmethod

    def animate_cluster_allocation(data, observer, animation_velocity = 75, save_movie = None):

        figure = plt.figure();

    @staticmethod

    def animate_cluster_allocation(data, observer, animation_velocity = 75, save_movie = None):

        figure = plt.figure();

        def init_frame():

            return frame_generation(0);

        def frame_generation(index_iteration):

            figure.clf();

            figure.suptitle("Clustering genetic algorithm (iteration: " + str(index_iteration) +")", fontsize = 20, fontweight = 'bold');

            visualizer = cluster_visualizer(4, 2, ["Population #" + str(index_iteration), "The best population"]);

            local_minimum_clusters = ga_math.get_clusters_representation(observer.get_population_best()['chromosome'][index_iteration]);

            visualizer.append_clusters(local_minimum_clusters, data, 0);

            global_minimum_clusters = ga_math.get_clusters_representation(observer.get_global_best()['chromosome'][index_iteration]);

            visualizer.append_clusters(global_minimum_clusters, data, 1);

            ax1 = plt.subplot2grid((2, 2), (1, 0), colspan = 2);

            ga_visualizer.show_evolution(observer, 0, index_iteration + 1, ax1, False);

            visualizer.show(figure, shift = 0, display = False);

            figure.subplots_adjust(top = 0.85);

            return [ figure.gca() ];