class SynLikelihoodTests(unittest.TestCase):
def setUp(self):
self.mu = Uniform([[-5.0], [5.0]], name='mu')
self.sigma = Uniform([[5.0], [10.0]], name='sigma')
self.model = Normal([self.mu, self.sigma])
self.stat_calc = Identity(degree=2, cross=False)
self.likfun = SynLikelihood(self.stat_calc)
# create fake simulated data
self.mu._fixed_values = [1.1]
self.sigma._fixed_values = [1.0]
self.y_sim = self.model.forward_simulate(self.model.get_input_values(), 100, rng=np.random.RandomState(1))
def test_likelihood(self):
# Checks whether wrong input type produces error message
self.assertRaises(TypeError, self.likfun.loglikelihood, 3.4, [2, 1])
self.assertRaises(TypeError, self.likfun.loglikelihood, [2, 4], 3.4)
# create observed data
y_obs = [1.8]
# calculate the statistics of the observed data
comp_likelihood = self.likfun.loglikelihood(y_obs, self.y_sim)
expected_likelihood = 0.20963610211945238
# This checks whether it computes a correct value and dimension is right
self.assertAlmostEqual(comp_likelihood, np.log(expected_likelihood))
def test_likelihood_multiple_observations(self):
y_obs = [1.8, 0.9]
comp_likelihood = self.likfun.loglikelihood(y_obs, self.y_sim)
print(comp_likelihood)
expected_likelihood = 0.04457899184856649
# This checks whether it computes a correct value and dimension is right
self.assertAlmostEqual(comp_likelihood, np.log(expected_likelihood))
class SynLikelihoodTests(unittest.TestCase):
def setUp(self):
self.mu = Uniform([[-5.0], [5.0]], name='mu')
self.sigma = Uniform([[5.0], [10.0]], name='sigma')
self.model = Normal([self.mu, self.sigma])
self.stat_calc = Identity(degree=2, cross=False)
self.likfun = SynLikelihood(self.stat_calc)
# create fake simulated data
self.mu._fixed_values = [1.1]
self.sigma._fixed_values = [1.0]
self.y_sim = self.model.forward_simulate(self.model.get_input_values(),
100,
rng=np.random.RandomState(1))
def test_likelihood(self):
# Checks whether wrong input type produces error message
self.assertRaises(TypeError, self.likfun.loglikelihood, 3.4, [2, 1])
self.assertRaises(TypeError, self.likfun.loglikelihood, [2, 4], 3.4)
# create observed data
y_obs = [1.8]
# calculate the statistics of the observed data
comp_loglikelihood = self.likfun.loglikelihood(y_obs, self.y_sim)
expected_loglikelihood = -0.6434435652263701
# This checks whether it computes a correct value and dimension is right
self.assertAlmostEqual(comp_loglikelihood, expected_loglikelihood)
def test_likelihood_multiple_observations(self):
y_obs = [1.8, 0.9]
comp_loglikelihood = self.likfun.loglikelihood(y_obs, self.y_sim)
expected_loglikelihood = -1.2726154993040115
# This checks whether it computes a correct value and dimension is right
self.assertAlmostEqual(comp_loglikelihood, expected_loglikelihood)
def test_loglikelihood_additive(self):
y_obs = [1.8, 0.9]
comp_loglikelihood_a = self.likfun.loglikelihood([y_obs[0]],
self.y_sim)
comp_loglikelihood_b = self.likfun.loglikelihood([y_obs[1]],
self.y_sim)
comp_loglikelihood_two = self.likfun.loglikelihood(y_obs, self.y_sim)
self.assertAlmostEqual(comp_loglikelihood_two,
comp_loglikelihood_a + comp_loglikelihood_b)