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
