def setup_class(cls):
covx = np.array([[1.0, 0.5], [0.5, 1.0]])
mu3 = [-1, 0., 2.]
cov3 = np.array([[1., 0.5, 0.75], [0.5, 1.5, 0.6], [0.75, 0.6, 2.]])
cls.mu3 = mu3
cls.cov3 = cov3
mvn3 = MVNormal(mu3, cov3)
mvn3c = MVNormal(np.array([0, 0, 0]), cov3)
cls.mvn3 = mvn3
cls.mvn3c = mvn3c
def __init__(self):
covx = np.array([[1.0, 0.5], [0.5, 1.0]])
mu3 = [-1, 0., 2.]
cov3 = np.array([[ 1. , 0.5 , 0.75],
[ 0.5 , 1.5 , 0.6 ],
[ 0.75, 0.6 , 2. ]])
self.mu3 = mu3
self.cov3 = cov3
mvn3 = MVNormal(mu3, cov3)
mvn3c = MVNormal(np.array([0,0,0]), cov3)
self.mvn3 = mvn3
self.mvn3c = mvn3c
def test_mvn_pdf(self):
cov3 = self.cov3
mvn3 = self.mvn3
mvn3c = self.mvn3c
r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155]
assert_array_almost_equal( mvn3.logpdf(cov3), r_val, decimal = 14)
#decimal 18
r_val = [0.000467562492721686, 0.000989829804859273, 0.005696077243833402]
assert_array_almost_equal( mvn3.pdf(cov3), r_val, decimal = 17)
#cheating new mean, same cov, too dangerous, got wrong instance in tests
#mvn3.mean = np.array([0,0,0])
mvn3b = MVNormal(np.array([0,0,0]), cov3)
r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269]
assert_array_almost_equal( mvn3b.pdf(cov3), r_val, decimal = 16)
def test_mvn_pdf(self):
cov3 = self.cov3
mvn3 = self.mvn3
r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155]
assert_allclose(mvn3.logpdf(cov3), r_val, rtol=1e-13)
r_val = [
0.000467562492721686, 0.000989829804859273, 0.005696077243833402
]
assert_allclose(mvn3.pdf(cov3), r_val, rtol=1e-13)
mvn3b = MVNormal(np.array([0, 0, 0]), cov3)
r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269]
assert_allclose(mvn3b.pdf(cov3), r_val, rtol=1e-13)
def test_mvn_pdf(self):
cov3 = self.cov3
mvn3 = self.mvn3
mvn3c = self.mvn3c
r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155]
assert_array_almost_equal( mvn3.logpdf(cov3), r_val, decimal = 14)
#decimal 18
r_val = [0.000467562492721686, 0.000989829804859273, 0.005696077243833402]
assert_array_almost_equal( mvn3.pdf(cov3), r_val, decimal = 17)
#cheating new mean, same cov, too dangerous, got wrong instance in tests
#mvn3.mean = np.array([0,0,0])
mvn3b = MVNormal(np.array([0,0,0]), cov3)
r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269]
assert_array_almost_equal( mvn3b.pdf(cov3), r_val, decimal = 16)
def gen_data(mu, cov, n, lowd, highd):
lams = MVNormal(mu, cov).rvs(n)
ps = np.empty_like(lams)
ps = di.poisson.rvs(
di.uniform.rvs(lowd, highd - lowd, size=lams.shape) * np.exp(lams))
return ps