def test_find_best_parametric_model_works_for_interval_censoring(): T_1 = np.random.exponential(2, 100) T_2 = T_1 + 1 model, score = utils.find_best_parametric_model((T_1, T_2), censoring_type="interval", show_progress=True) assert True
def test_find_best_parametric_model_works_with_weights_and_entry(): T = np.random.exponential(5, 100) W = np.random.randint(1, 5, size=100) entry = np.random.exponential(0.01, 100) model, score = utils.find_best_parametric_model(T, weights=W, entry=entry, show_progress=True) assert True
def test_find_best_parametric_model_with_BIC(): T = np.random.exponential(2, 1000) model, score = utils.find_best_parametric_model(T, scoring_method="BIC") assert True
def test_find_best_parametric_model_can_accept_other_models(): T = np.random.exponential(2, 1000) model, score = utils.find_best_parametric_model( T, additional_models=[ExponentialFitter(), ExponentialFitter()]) assert True
def test_find_best_parametric_model(): T = np.random.exponential(2, 1000) E = np.ones_like(T) model, score = utils.find_best_parametric_model(T, E) assert True
def test_find_best_parametric_model_works_for_left_censoring(): T = np.random.exponential(2, 100) model, score = utils.find_best_parametric_model(T, censoring_type="left", show_progress=True) assert True