'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)
