def test_underdefined_distribution():
"""
Check if under-defined probability distributions raises errors correctly.
The number of underlying distribution components in a stochastic vector
must always be at least as large as the length of the vector.
"""
with pytest.raises(chaospy.StochasticallyDependentError):
chaospy.Add(2, 2)
dist = chaospy.Uniform(-1, 1)
with pytest.raises(chaospy.StochasticallyDependentError):
chaospy.J(dist, dist)
def test_dist_addition_illegals():
# Too large dim on const:
with raises(chaospy.UnsupportedFeature):
_ = MULTIVARIATE+[[1, 1], [0, 1]]
with raises(chaospy.UnsupportedFeature):
_ = [[1, 1], [0, 1]]+MULTIVARIATE
# 2-D object, 1-D var:
with raises(chaospy.StochasticallyDependentError):
_ = UNIVARIATE+[1, 1]
with raises(chaospy.StochasticallyDependentError):
_ = [1, 1]+UNIVARIATE
# 0-D object:
with raises(chaospy.StochasticallyDependentError):
chaospy.Add(2, 3)
def test_dist_addition_wrappers():
dists = [
chaospy.J(chaospy.Normal(2), chaospy.Normal(2), chaospy.Normal(3)), # ShiftScale
chaospy.J(chaospy.Uniform(1, 3), chaospy.Uniform(1, 3), chaospy.Uniform(1, 5)), # LowerUpper
chaospy.MvNormal([2, 2, 3], numpy.eye(3)), # MeanCovariance
]
for dist in dists:
joint = chaospy.Add([1, 1, 3], dist)
assert numpy.allclose(joint.inv([0.5, 0.5, 0.5]), [3, 3, 6])
assert numpy.allclose(joint.fwd([3, 3, 6]), [0.5, 0.5, 0.5])
density = joint.pdf([2, 2, 2], decompose=True)
assert numpy.isclose(density[0], density[1]), (dist, density)
assert not numpy.isclose(density[0], density[2]), dist
assert numpy.isclose(joint[0].inv([0.5]), 3)
assert numpy.isclose(joint[0].fwd([3]), 0.5)
def __rsub__(self, X):
"""Y.__rsub__(X) <==> Y-X"""
return chaospy.Add(X, -self)
def __sub__(self, X):
"""Y.__sub__(X) <==> X-Y"""
return chaospy.Add(self, -X)
def __radd__(self, X):
"""Y.__radd__(X) <==> Y+X"""
return chaospy.Add(self, X)
def __add__(self, X):
"""Y.__add__(X) <==> X+Y"""
return chaospy.Add(self, X)
