text: import os
  import random
  import numpy as np
  
  from slam.NFiSAM import NFiSAM_empirial_study
  
  
  if __name__ == '__main__':
      random.seed(0)
      np.random.seed(0)
  
      knots = [9]
      iters = [2000]
      training_samples = [2000]
      learning_rates = [.025]
      hidden_dims = [8]
  
      run_file_dir = os.path.dirname(__file__)
      case_folder = 'journal_paper/case1'
      case_dir = os.path.join(run_file_dir, case_folder)
      data_file = 'factor_graph.fg'
      data_format = 'fg'
      NFiSAM_empirial_study(knots, iters, training_samples, learning_rates, hidden_dims, case_dir, data_file, data_format,
                            incremental_step=1, traj_plot=True,
                            plot_args={'xlim': (-100, 100), 'ylim': (-100, 100), 'fig_size': (8, 8),
                                       'truth_label_offset': (3, -3), 'show_plot': False}, check_root_transform=False, cuda_training=True,
                            elimination_method='pose_first', training_set_frac=1.0, loss_delta_tol=.01, posterior_sample_num=1000)