def setUp(self):
init_pdf = pb.UniPdf(np.array([-5.]), np.array([5.]))
p_xt_xtp = pb.MLinGaussCPdf(np.array([[2.]]), np.array([[1.]]),
np.array([0.]))
p_yt_xt = pb.MLinGaussCPdf(np.array([[1.]]), np.array([[1.]]),
np.array([0.]))
self.pf = pb.ParticleFilter(20, init_pdf, p_xt_xtp, p_yt_xt)
def test_eval_log(self):
x = np.array([0.])
cond = np.array([0.])
norm = pb.MLinGaussCPdf(np.array([[1.]]), np.array([[1.]]),
np.array([-1.]))
expected = np.array([
1.48671951473e-06,
0.000133830225765,
0.00443184841194,
0.0539909665132,
0.241970724519,
0.398942280401,
0.241970724519,
0.0539909665132,
0.00443184841194,
0.000133830225765,
1.48671951473e-06,
])
for i in xrange(0, 11):
# cond is set to [1.], which should produce mean = [0.]
x[0] = i - 5.
cond[0] = 1.
res = exp(norm.eval_log(x, cond))
self.assertApproxEqual(res, expected[i])
# cond is set to [456.78], which should produce mean = [455.78]
x[0] = i - 5. + 455.78
cond[0] = 456.78
res = exp(norm.eval_log(x, cond))
self.assertApproxEqual(res, expected[i])
def setUp(self):
# constructor parameters:
self.A = np.array([[1., 0.], [0., 2.], [-1., -1.]])
self.b = np.array([-0.5, -1., -1.5])
self.covariance = np.array([[1., 0., 0.], [0., 2., 0.], [0., 0., 3.]])
# expected values:
self.variance = np.array([1., 2.,
3.]) # diagonal elements of covariance
self.shape = 3 # shape of random variable (and mean)
self.cond_shape = 2
self.test_conds = np.array(
[ # array of test conditions (shared by various tests)
[0., 0.],
[1., 0.],
[0., -1.],
])
self.cond_means = np.array(
[ # array of mean values that match (first n entries of) test_conds
self.b,
np.array([1., 0., -1.]) + self.b, # computed from self.A
np.array([0., -2., 1.]) + self.b, # computed from self.A
])
self.gauss = pb.MLinGaussCPdf(self.covariance, self.A, self.b)
def test_different_base_class(self):
cov = np.array([[1.]])
mean = np.array([-14.568])
condlognorm = pb.MLinGaussCPdf(cov,
np.array([[1.]]),
np.array([0.]),
base_class=pb.LogNormPdf)
lognorm = pb.LogNormPdf(mean, cov)
self.assertEqual(condlognorm.mean(mean), lognorm.mean())
self.assertEqual(condlognorm.variance(mean), lognorm.variance())
for x in np.array([[-0.4], [2.4], [4.5], [12.5]]):
self.assertEqual(condlognorm.eval_log(x, mean),
lognorm.eval_log(x))
for i in range(30):
# only test that samples are positive
self.assertTrue(condlognorm.sample(mean)[0] >= 0)
def test_rvs(self):
self.assertEqual(self.gauss.rv.dimension, 3)
self.assertEqual(self.gauss.cond_rv.dimension, 2)
a, b, c, d = pb.RVComp(2, 'a'), pb.RVComp(1, 'b'), pb.RVComp(
1, 'c'), pb.RVComp(1, 'd')
rv = pb.RV(a, b)
cond_rv = pb.RV(c, d)
gauss = pb.MLinGaussCPdf(self.covariance, self.A, self.b, rv, cond_rv)
self.assertTrue(gauss.rv.contains(a))
self.assertTrue(gauss.rv.contains(b))
self.assertFalse(gauss.rv.contains(c))
self.assertFalse(gauss.rv.contains(d))
self.assertFalse(gauss.cond_rv.contains(a))
self.assertFalse(gauss.cond_rv.contains(b))
self.assertTrue(gauss.cond_rv.contains(c))
self.assertTrue(gauss.cond_rv.contains(d))
def setUp(self):
ide = np.array([[1.]]) # 1x1 identity matrix
self.gauss = pb.MLinGaussCPdf(ide, ide, np.array([0.]))
self.uni = pb.UniPdf(np.array([0.]), np.array([2.]))
self.prod = pb.ProdCPdf((self.gauss, self.uni))