text: from run_experiments import plot_from_res, plot_from_res_gain
  """
  Gain experiments
  """
  n = 500
  k = 10
  dim_l = [3, 5, 7, 10, 15]
  ampl_l = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5]
  # ampl_l += [2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
  n_avrg = 100
  # type_matrix_l = ['CircularStrongDecrease']
  type_matrix_l = ['CircularStrongDecrease', 'LinearStrongDecrease',
                   'CircularBanded', 'LinearBanded']
  scaled = True
  
  save_res_dir = '/home/thomas/Dropbox/RobustSeriationEmbeddingBiblio/results_cluster_new'
  # Make the gain
  for type_matrix in type_matrix_l:
      fig_name = "kendall-tau-vs-noise-for-several-dims-typematrix_{}.pdf" \
                 "".format(type_matrix)
      fig_name = save_res_dir + '/' + fig_name
  
      plot_from_res_gain(n, k, dim_l, ampl_l, type_matrix, scaled,
                         type_lap_l='random_walk', n_avrg=n_avrg,
                         save_res_dir=save_res_dir,
                         save_fig_path=fig_name)
  
  """
  Scaling experiments
  """
  n = 500
  k = 10
  dim_l = [20]
  ampl_l = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
  n_avrg = 100
  # type_matrix_l = ['CircularStrongDecrease']
  type_matrix_l = ['CircularStrongDecrease', 'LinearStrongDecrease',
                   'CircularBanded', 'LinearBanded']
  scaled_l = [True, 'CTD', False]
  
  save_res_dir = '/home/thomas/Dropbox/RobustSeriationEmbeddingBiblio/results_cluster_new'
  for type_matrix in type_matrix_l:
      fig_name = "kendall-tau-vs-noise-for-several-dims-typematrix_{}_scaling.pdf"\
                 "".format(type_matrix)
      fig_name = save_res_dir + '/' + fig_name
  
      plot_from_res(n, k, dim_l[0], ampl_l, type_matrix, scaled_l,
                    type_lap_l='random_walk', n_avrg=n_avrg,
                    save_res_dir=save_res_dir,
                    save_fig_path=fig_name)
  