'y_noise_std': [0.1], }, } simulators_params = {'LinearStudentT': {'ndim_x': [10]}} observations = 100 * np.logspace(2, 6, num=8, base=2.0, dtype=np.int32) return estimator_params, simulators_params, observations if __name__ == '__main__': estimator_params, simulators_params, observations = question4() load = base_experiment.launch_experiment(estimator_params, simulators_params, observations, EXP_PREFIX, n_mc_samples=N_MC_SAMPLES, tail_measures=False) if load: logger.configure(config.DATA_DIR, EXP_PREFIX) results_from_pkl_file = dict(logger.load_pkl_log(RESULTS_FILE)) gof_result = GoodnessOfFitResults( single_results_dict=results_from_pkl_file) results_df = gof_result.generate_results_dataframe( base_experiment.KEYS_OF_INTEREST) gof_result = ConfigRunner.load_dumped_estimators(gof_result)
simulators_params = { 'EconDensity': { 'std': [1], 'heteroscedastic': [True], }, 'GaussianMixture': { 'n_kernels': [10], 'ndim_x': [1], 'ndim_y': [1], 'means_std': [1.5] }, 'ArmaJump': { 'c': [0.1], 'arma_a1': [0.9], 'std': [0.05], 'jump_prob': [0.05], }, 'SkewNormal': {} } observations = 100 * np.logspace(0, 6, num=7, base=2.0, dtype=np.int32) return estimator_params, simulators_params, observations if __name__ == '__main__': estimator_params, simulators_params, observations = question1() load = base_experiment.launch_experiment(estimator_params, simulators_params, observations, EXP_PREFIX)