def test_student_t_log_likelihood_single(self):
# Single-output test for Student-t noise log-likelihood methods
model = pints.toy.ConstantModel(1)
parameters = [0]
times = np.asarray([1, 2, 3])
model.simulate(parameters, times)
values = np.asarray([1.0, -10.7, 15.5])
problem = pints.SingleOutputProblem(model, times, values)
log_likelihood = pints.StudentTLogLikelihood(problem)
# Test Student-t_logpdf(values|mean=0, df = 3, scale = 10) = -11.74..
self.assertAlmostEqual(log_likelihood([0, 3, 10]), -11.74010919785115)
def test_student_t_log_likelihood_multi(self):
# Multi-output test for Student-t noise log-likelihood methods
model = pints.toy.ConstantModel(4)
parameters = [0, 0, 0, 0]
times = np.arange(1, 4)
model.simulate(parameters, times)
values = np.asarray([[3.5, 7.6, 8.5, 3.4], [1.1, -10.3, 15.6, 5.5],
[-10, -30.5, -5, 7.6]])
problem = pints.MultiOutputProblem(model, times, values)
log_likelihood = pints.StudentTLogLikelihood(problem)
# Test Student-t_logpdf((3.5,1.1,-10)|mean=0, df=2, scale=13) +
# Student-t_logpdf((7.6,-10.3,-30.5)|mean=0, df=1, scale=8) +
# Student-t_logpdf((8.5,15.6,-5)|mean=0, df=2.5, scale=13.5) +
# Student-t_logpdf((3.4,5.5,7.6)|mean=0, df=3.4, scale=10.5)
# = -47.83....
self.assertAlmostEqual(
log_likelihood(parameters + [2, 13, 1, 8, 2.5, 13.5, 3.4, 10.5]),
-47.83720347766945)