def test_eval_sos(self):
model, options, parameters, data = gf.setup_mcmc()
SOS = SumOfSquares(model = model, data = data, parameters = parameters)
theta = np.array([2., 5.])
ss = SOS.evaluate_sos_function(theta = theta)
self.assertTrue(isinstance(ss, np.ndarray), msg = 'Expect numpy array return')
self.assertEqual(ss.size, 1, msg = 'Size of array is 1')
self.assertTrue(isinstance(ss[0], float), msg = 'Numerical result returned')
self.assertTrue(np.array_equal(SOS.value[SOS.parind], theta), msg = 'Value(s) updated')
def test_eval_sos_model_cs(self):
model, options, parameters, data = gf.setup_mcmc()
model.sos_function = gf.custom_ssfun
SOS = SumOfSquares(model=model, data=data, parameters=parameters)
SOS.sos_style = 1
theta = np.array([2., 5.])
ss = SOS.evaluate_sos_function(theta)
self.assertEqual(ss, None, msg='Expect None')
ss = SOS.evaluate_sos_function(theta, custom=123)
self.assertEqual(ss, 123, msg='Expect 123')
def setup_dr(self):
model, options, parameters, data, covariance, __, __, __, __ = gf.setup_mcmc_case_dr(
)
RDR = covariance._RDR
invR = covariance._invR
old_set = ParameterSet(theta=np.random.rand(2),
ss=np.array([10.2]),
sigma2=np.array([0.5]),
prior=np.array([0.5]))
new_set = ParameterSet(theta=np.random.rand(2),
ss=np.array([8.2]),
sigma2=np.array([0.5]),
prior=np.array([0.5]))
priorobj = PriorFunction(
priorfun=model.prior_function,
mu=parameters._thetamu[parameters._parind[:]],
sigma=parameters._thetasigma[parameters._parind[:]])
sosobj = SumOfSquares(model, data, parameters)
DR = DelayedRejection()
DR._initialize_dr_metrics(options=options)
accept, out_set, outbound = DR.run_delayed_rejection(
old_set=old_set,
new_set=new_set,
RDR=RDR,
ntry=2,
parameters=parameters,
invR=invR,
sosobj=sosobj,
priorobj=priorobj)
return accept, out_set, outbound
def test_init_sos(self):
model, __, parameters, data = gf.setup_mcmc()
SOS = SumOfSquares(model = model, data = data, parameters = parameters)
SOSD = SOS.__dict__
check = {'nbatch': 1, 'parind': parameters._parind, 'value': parameters._value,
'local': parameters._local, 'data': data, 'model_function': model.model_function,
'sos_function': model.sos_function, 'sos_style': 1}
items = ['nbatch', 'sos_style', 'sos_function', 'model_function', 'data']
for ii, ai in enumerate(items):
self.assertEqual(SOSD[ai], check[ai], msg = str('{}: {} != {}'.format(ai, SOSD[ai], check[ai])))
array_items = ['parind', 'value', 'local']
for ii, ai in enumerate(array_items):
self.assertTrue(np.array_equal(SOSD[ai], check[ai]), msg = str('{}: {} != {}'.format(ai, SOSD[ai], check[ai])))
def test_init_sos_model_function_is_none(self):
model, options, parameters, data = gf.setup_mcmc()
model.sos_function = None
model.model_function = None
with self.assertRaises(SystemExit):
SumOfSquares(model = model, data = data, parameters = parameters)