def test_ordered():
check_vector_transform(tr.ordered, SortedVector(6))
check_jacobian_det(tr.ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False)
vals = get_values(tr.ordered, Vector(R, 3), at.dvector, np.zeros(3))
close_to_logical(np.diff(vals) >= 0, True, tol)
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), at.dvector, [0, 0], elemwise=True)
vals = get_values(tr.log_exp_m1)
close_to_logical(vals > 0, True, tol)
def test_simplex_bounds():
vals = get_values(tr.simplex, 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.simplex, Vector(R, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1])
def test_upperbound():
trans = tr.Interval(None, 0.0)
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_lowerbound():
trans = tr.Interval(0.0, None)
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_logodds():
check_transform(tr.logodds, Unit)
check_jacobian_det(tr.logodds, Unit, elemwise=True)
check_jacobian_det(tr.logodds, Vector(Unit, 2), at.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_circular():
trans = tr.circular
check_transform(trans, Circ)
check_jacobian_det(trans, Circ)
vals = get_values(trans)
close_to_logical(vals > -np.pi, True, tol)
close_to_logical(vals < np.pi, True, tol)
assert isinstance(trans.forward(1, None), TensorConstant)
def test_interval():
for a, b in [(-4, 5.5), (0.1, 0.7), (-10, 4.3)]:
domain = Unit * np.float64(b - a) + np.float64(a)
trans = tr.Interval(a, b)
check_transform(trans, domain)
check_jacobian_det(trans, domain, elemwise=True)
vals = get_values(trans)
close_to_logical(vals > a, True, tol)
close_to_logical(vals < b, True, tol)
def test_upperbound():
def transform_params(*inputs):
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_lowerbound():
def transform_params(*inputs):
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_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)