def test_hypercube_regression_dimer(self): param_ranges = [(0.001, 0.0), (0.5, 0.0), (330.0, 260.0)] initial_conditions_ranges = [(320.0, 290.0)] ans = hypercube(5, param_ranges + initial_conditions_ranges) expected_ans = [[ 0.0003553578523702354, 0.29734640303161364, 306.2260484701648, 304.7826314512718 ], [ 5.270575716719754e-05, 0.4781362025196397, 318.7185304743407, 296.16130541612375 ], [ 0.0008721146403084233, 0.34946447118966373, 285.8232870026351, 309.6216092798371 ], [ 0.0005449941363261761, 0.03501155622204766, 260.59901698910505, 293.7287937367499 ], [ 0.0007949978489554667, 0.1801162349313351, 297.2364927687481, 315.1572303603537 ]] assert_array_almost_equal(ans, expected_ans, decimal=12)
def test_hypercube_regression_dimer(self): param_ranges = [(0.001, 0.0), (0.5, 0.0), (330.0, 260.0)] initial_conditions_ranges = [(320.0, 290.0)] ans = hypercube(5, param_ranges + initial_conditions_ranges) expected_ans = [[0.0003553578523702354, 0.29734640303161364, 306.2260484701648, 304.7826314512718], [5.270575716719754e-05, 0.4781362025196397, 318.7185304743407, 296.16130541612375], [0.0008721146403084233, 0.34946447118966373, 285.8232870026351, 309.6216092798371], [0.0005449941363261761, 0.03501155622204766, 260.59901698910505, 293.7287937367499], [0.0007949978489554667, 0.1801162349313351, 297.2364927687481, 315.1572303603537]] assert_array_almost_equal(ans, expected_ans, decimal=12)
def _inference_objects(self): full_list_of_ranges = self.starting_parameter_ranges[:] + self.starting_conditions_ranges[:] variables_collection = hypercube(self.number_of_samples, full_list_of_ranges) inference_objects = [] for variables in variables_collection: starting_parameters = variables[:len(self.starting_parameter_ranges)] starting_conditions = variables[len(self.starting_parameter_ranges):] inference_objects.append(Inference(self.problem, starting_parameters, starting_conditions, self.variable_parameters, self.observed_trajectories, distance_function_type=self.distance_function_type, )) return inference_objects