def _sample_inference():
r = Inference(problem=_sample_problem(),
starting_parameters=[1, 2, 3, 4, 5, 6, 7],
starting_conditions=[1, 2, 3],
variable_parameters=['c_0', 'c_1'],
observed_trajectories=[
Trajectory([1, 2], [2, 3], Moment([1, 0, 0], 'x'))
],
distance_function_type='gamma',
maxh=0.01) # Some simulation kwargs
return r
def generate_inference_object(self):
parameters = [0.001, 0.5, 330.0]
initial_conditions = [320.0, 0]
optimiser_method = 'sum_of_squares'
variable_parameters = ['c_0', 'c_1', 'c_2', 'y_0']
inference = Inference(self.dimer_problem,
parameters,
initial_conditions,
variable_parameters,
self.observed_trajectories,
distance_function_type=optimiser_method)
return inference
def check_initialisation(variable_parameters,
expected_parameters_with_variability,
expected_initial_conditions_with_variability,
expected_constraints):
p = Inference(
self.dimer_problem, parameters, initial_conditions,
variable_parameters,
[Trajectory([1, 2, 3], [1, 2, 3], Moment([1], symbol='x'))])
self.assertEquals(p.starting_parameters_with_variability,
expected_parameters_with_variability)
self.assertEqual(p.starting_conditions_with_variability,
expected_initial_conditions_with_variability)
self.assertEqual(p.constraints, expected_constraints)
def generate_inference_object(self):
parameters = [0.001, 0.5, 330.0]
initial_conditions = [320.0, 0]
optimiser_method = 'sum_of_squares'
variable_parameters = ['c_0', 'c_1', 'c_2', 'y_0']
inference = Inference(
self.dimer_problem,
parameters,
initial_conditions,
variable_parameters,
# Only means trajectory
[self.observed_trajectories[0]],
distance_function_type=optimiser_method,
# We need to increase rtol here, otherwise wildly different result happens
rtol=1e-4)
return inference
def generate_inference_object(self):
starting_params = [
0.0008721146403084233, 0.34946447118966373, 285.8232870026351
]
starting_conditions = [309.6216092798371, 0]
optimiser_method = 'lognormal'
variable_parameters = {
'c_0': (0.0, 0.001),
'c_1': (0.0, 0.5),
'c_2': (260.0, 330.0),
'y_0': (290.0, 320.0)
}
inference = Inference(
self.dimer_problem,
starting_params,
starting_conditions,
variable_parameters,
# Only means trajectory
[self.observed_trajectories[0]],
distance_function_type=optimiser_method)
return inference
def generate_inference_object(self):
starting_params = [
0.0003553578523702354, 0.29734640303161364, 306.2260484701648
]
starting_conditions = [304.7826314512718, 0]
optimiser_method = 'normal'
variable_parameters = {
'c_0': (0.0, 0.001),
'c_1': (0.0, 0.5),
'c_2': (260.0, 330.0),
'y_0': (290.0, 320.0)
}
inference = Inference(
self.dimer_problem,
starting_params,
starting_conditions,
variable_parameters,
# Only means trajectory
[self.observed_trajectories[0]],
distance_function_type=optimiser_method)
return inference