def test_functional_scale():
r3 = odl.Rn(3)
Aop = SumFunctional(r3)
x = r3.element([1, 2, 3])
y = 1
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
scalars = [-1.432, -1, 0, 1, 3.14]
for scale in scalars:
C = OperatorRightScalarMult(Aop, scale)
assert C.is_linear
assert C.adjoint.is_linear
assert C(x) == scale * np.sum(x)
assert all_almost_equal(C.adjoint(y), scale * y * np.ones(3))
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert (scale * Aop)(x) == scale * np.sum(x)
assert (Aop * scale)(x) == scale * np.sum(x)
assert all_almost_equal((scale * Aop).adjoint(y),
scale * y * np.ones(3))
assert all_almost_equal((Aop * scale).adjoint(y),
scale * y * np.ones(3))
def test_functional_scale():
r3 = odl.Rn(3)
Aop = SumFunctional(r3)
x = r3.element([1, 2, 3])
y = 1
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
scalars = [-1.432, -1, 0, 1, 3.14]
for scale in scalars:
C = OperatorRightScalarMult(Aop, scale)
assert C.is_linear
assert C.adjoint.is_linear
assert C(x) == scale * np.sum(x)
assert all_almost_equal(C.adjoint(y), scale * y * np.ones(3))
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert (scale * Aop)(x) == scale * np.sum(x)
assert (Aop * scale)(x) == scale * np.sum(x)
assert all_almost_equal((scale * Aop).adjoint(y),
scale * y * np.ones(3))
assert all_almost_equal((Aop * scale).adjoint(y),
scale * y * np.ones(3))
def test_linear_scale():
A = np.random.rand(4, 3)
x = np.random.rand(3)
y = np.random.rand(4)
Aop = MatVecOperator(A)
xvec = Aop.domain.element(x)
yvec = Aop.range.element(y)
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
scalars = [-1.432, -1, 0, 1, 3.14]
for scale in scalars:
C = OperatorRightScalarMult(Aop, scale)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(xvec), scale * np.dot(A, x))
assert all_almost_equal(C.adjoint(yvec), scale * np.dot(A.T, y))
# Using operator overloading
assert all_almost_equal((scale * Aop)(xvec), scale * np.dot(A, x))
assert all_almost_equal((Aop * scale)(xvec), np.dot(A, scale * x))
assert all_almost_equal((scale * Aop).adjoint(yvec),
scale * np.dot(A.T, y))
assert all_almost_equal((Aop * scale).adjoint(yvec),
np.dot(A.T, scale * y))
def test_linear_scale():
A = np.random.rand(4, 3)
x = np.random.rand(3)
y = np.random.rand(4)
Aop = MatVecOperator(A)
xvec = Aop.domain.element(x)
yvec = Aop.range.element(y)
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
scalars = [-1.432, -1, 0, 1, 3.14]
for scale in scalars:
C = OperatorRightScalarMult(Aop, scale)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(xvec), scale * np.dot(A, x))
assert all_almost_equal(C.adjoint(yvec), scale * np.dot(A.T, y))
# Using operator overloading
assert all_almost_equal((scale * Aop)(xvec),
scale * np.dot(A, x))
assert all_almost_equal((Aop * scale)(xvec),
np.dot(A, scale * x))
assert all_almost_equal((scale * Aop).adjoint(yvec),
scale * np.dot(A.T, y))
assert all_almost_equal((Aop * scale).adjoint(yvec),
np.dot(A.T, scale * y))