class GaussianTests(unittest.TestCase):
def setUp(self):
self.prior = Uniform([-1.0, 0.0], [1.0, 1.0], seed=1)
self.model = Gaussian(self.prior, 0, 1, seed=1)
def test_simulate(self):
samples = self.model.simulate(10)
self.assertIsInstance(samples, list)
expected_output = [1.6243453636632417, -0.61175641365007538, -0.5281717522634557, \
-1.0729686221561705, 0.86540762932467852, -2.3015386968802827, \
1.74481176421648, -0.76120690089510279, 0.31903909605709857, \
-0.24937037547741009]
self.assertEqual(samples, expected_output)
def test_get_parameters(self):
self.model.sample_from_prior()
params = self.model.get_parameters()
# test shape of parameters
param_len = len(params)
self.assertEqual(param_len, 2)
def test_set_parameters(self):
self.assertRaises(TypeError, self.model.set_parameters, 3.4)
self.assertFalse(self.model.set_parameters([1, 3, 2]))
self.assertFalse(self.model.set_parameters([2, -1]))
class PenLogRegTests(unittest.TestCase):
def setUp(self):
self.prior = Uniform([-5.0, 5.0], [5.0, 10.0], seed=1)
self.model = Gaussian(self.prior, mu=1.1, sigma=1.0, seed=1)
self.stat_calc = Identity(degree=2, cross=0)
self.likfun = PenLogReg(self.stat_calc,
self.model,
n_simulate=100,
n_folds=10)
def test_likelihood(self):
#Checks whether wrong input type produces error message
self.assertRaises(TypeError, self.likfun.likelihood, 3.4, [2, 1])
self.assertRaises(TypeError, self.likfun.likelihood, [2, 4], 3.4)
# create observed data
y_obs = self.model.simulate(1)
# create fake simulated data
self.model.set_parameters(np.array([1.1, 1.0]))
y_sim = self.model.simulate(100)
comp_likelihood = self.likfun.likelihood(y_obs, y_sim)
expected_likelihood = 4.39965563272
# This checks whether it computes a correct value and dimension is right
self.assertLess(comp_likelihood - expected_likelihood, 10e-2)
