def test_ordered():
check_vector_transform(tr.ordered, SortedVector(6))
check_jacobian_det(tr.ordered, Vector(R, 2), aet.dvector, np.array([0, 0]), elemwise=False)
vals = get_values(tr.ordered, Vector(R, 3), aet.dvector, np.zeros(3))
close_to_logical(np.diff(vals) >= 0, True, tol)
def test_chain():
chain_tranf = tr.Chain([tr.logodds, tr.ordered])
check_vector_transform(chain_tranf, UnitSortedVector(3))
check_jacobian_det(chain_tranf, Vector(R, 4), aet.dvector, np.zeros(4), elemwise=False)
vals = get_values(chain_tranf, Vector(R, 5), aet.dvector, np.zeros(5))
close_to_logical(np.diff(vals) >= 0, True, tol)
def test_mv_t(self):
for n in [2, 3]:
pymc3_random(MvStudentT, {'nu': Domain([5, 10, 25, 50]), 'Sigma': PdMatrix(n),
'mu':Vector(R,n)}, size=100,
valuedomain=Vector(R,n),
ref_rand=(lambda nu=None, Sigma=None, mu=None, size=None:
mu + np.sqrt(nu)
* (st.multivariate_normal.rvs(cov=Sigma, size=size).T
/ st.chi2.rvs(df=nu, size=size)).T))
def test_stickbreaking_bounds():
vals = get_values(tr.stick_breaking, Vector(R, 2), at.dvector,
np.array([0, 0]))
close_to(vals.sum(axis=1), 1, tol)
close_to_logical(vals > 0, True, tol)
close_to_logical(vals < 1, True, tol)
check_jacobian_det(tr.stick_breaking, Vector(R, 2), at.dvector,
np.array([0, 0]), lambda x: x[:-1])
def test_dirichlet(self):
for n in [2, 3]:
pymc3_random(Dirichlet, {'a': Vector(Rplus, n)},
valuedomain=Simplex(n),
size=100,
ref_rand=lambda a=None, size=None: st.dirichlet.rvs(
a, size=size))
def test_sum_to_1():
check_vector_transform(tr.sum_to_1, Simplex(2))
check_vector_transform(tr.sum_to_1, Simplex(4))
check_jacobian_det(
tr.sum_to_1, Vector(Unit, 2), aet.dvector, np.array([0, 0]), lambda x: x[:-1]
)
def test_chain_jacob_det():
chain_tranf = tr.Chain([tr.logodds, tr.ordered])
check_jacobian_det(chain_tranf,
Vector(R, 4),
at.dvector,
np.zeros(4),
elemwise=False)
def test_log_exp_m1():
check_transform(tr.log_exp_m1, Rplusbig)
check_jacobian_det(tr.log_exp_m1, Rplusbig, elemwise=True)
check_jacobian_det(tr.log_exp_m1, Vector(Rplusbig, 2), aet.dvector, [0, 0], elemwise=True)
vals = get_values(tr.log_exp_m1)
close_to_logical(vals > 0, True, tol)
def test_upperbound():
trans = tr.upperbound(0.0)
check_transform(trans, Rminusbig)
check_jacobian_det(trans, Rminusbig, elemwise=True)
check_jacobian_det(trans, Vector(Rminusbig, 2), aet.dvector, [-1, -1], elemwise=True)
vals = get_values(trans)
close_to_logical(vals < 0, True, tol)
def test_lowerbound():
trans = tr.lowerbound(0.0)
check_transform(trans, Rplusbig)
check_jacobian_det(trans, Rplusbig, elemwise=True)
check_jacobian_det(trans, Vector(Rplusbig, 2), aet.dvector, [0, 0], elemwise=True)
vals = get_values(trans)
close_to_logical(vals > 0, True, tol)
def test_logodds():
check_transform(tr.logodds, Unit)
check_jacobian_det(tr.logodds, Unit, elemwise=True)
check_jacobian_det(tr.logodds, Vector(Unit, 2), aet.dvector, [0.5, 0.5], elemwise=True)
vals = get_values(tr.logodds)
close_to_logical(vals > 0, True, tol)
close_to_logical(vals < 1, True, tol)
def test_quad_limb_dark_transform():
values = get_values(
tr.quad_limb_dark,
Vector(R, 2),
constructor=tt.vector,
test=np.array([0.0, 0.0]),
)
domain = namedtuple("Domain", ["vals"])(values)
check_transform(
tr.quad_limb_dark,
domain,
constructor=tt.vector,
test=np.array([0.0, 0.0]),
)
def test_lowerbound():
def transform_params(rv_var):
return 0.0, None
trans = tr.interval(transform_params)
check_transform(trans, Rplusbig)
check_jacobian_det(trans, Rplusbig, elemwise=True)
check_jacobian_det(trans,
Vector(Rplusbig, 2),
at.dvector, [0, 0],
elemwise=True)
vals = get_values(trans)
close_to_logical(vals > 0, True, tol)
def test_upperbound():
def transform_params(rv_var):
return None, 0.0
trans = tr.interval(transform_params)
check_transform(trans, Rminusbig)
check_jacobian_det(trans, Rminusbig, elemwise=True)
check_jacobian_det(trans,
Vector(Rminusbig, 2),
at.dvector, [-1, -1],
elemwise=True)
vals = get_values(trans)
close_to_logical(vals < 0, True, tol)
def test_multinomial(self):
for n in [2, 3]:
pymc3_random_discrete(Multinomial, {'p': Simplex(n), 'n' : Nat},
valuedomain=Vector(Nat, n), size=100,
ref_rand=lambda n=None, p=None, size=None: \
nr.multinomial(n, p, size=size))
def test_mv_normal(self):
for n in [2, 3]:
pymc3_random(MvNormal, {'mu':Vector(R,n), 'tau': PdMatrix(n)}, size=100,
valuedomain=Vector(R,n),
ref_rand=lambda mu=None, tau=None, size=None: \
st.multivariate_normal.rvs(mean=mu, cov=tau, size=size))
def test_chain_values():
chain_tranf = tr.Chain([tr.logodds, tr.ordered])
vals = get_values(chain_tranf, Vector(R, 5), at.dvector, np.zeros(5))
close_to_logical(np.diff(vals) >= 0, True, tol)