#
#
#
# if __name__ == '__main__':
#     # Parameters
#     scenarios = []
#     nb_obs_list = []
#     nb_fit = 1000
#
#     # Load the object that will handle the simulation
#     simu = Simulations(nb_fit, scenarios, nb_obs_list)
#
#     # Fit many estimators to this simulation
#     estimator_types = []
#     for estimator_type in estimator_types:
#         simu.fit(estimator_type)
#
#     # Comparison of the diverse estimator
#
#     # Compare all the estimator on a global graph (one graph per scenario)
#     # On each graph the X axis should be the number of obs
#     # the Y graph should the error
#     simu.visualize_mean_test_error_graph(estimator_types, scenarios, nb_obs_list)
#     # the other possible view, is to have one graph per number of observations
#     # on the X axis should the name of the different estimator
#     # on the y axis their error
#
#
#     # Plot the same graph for the train/test error
#     # For a single scenario, and a single obs (we give a plot detailing all the estimation steps that enabled to get
#     # the result)
#     simu.visualize_comparison_graph(estimator_types, scenario, nb_obs)
#
#     # Analyse the result of a single estimator
#
#     # Or all the result could be recorded in a matrix, with scenario as line, and nb_observaitons as columns
#     # with the mean value (and the std in parenthesis)
#     # (on the border on this matrix we should have the mean value)
#     # for example, the first columns should be the mean of the other column for the same line
#     simu.visualize_mean_test_error_matrix(estimator_type, scenarios, nb_obs_list)
#
#
#     #
#     simu.visualize
#
#
#