def test_functional_right_vector_mult():
r3 = odl.Rn(3)
Aop = SumFunctional(r3)
vec = r3.element([1, 2, 3])
x = r3.element([4, 5, 6])
y = 2.0
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
C = OperatorRightVectorMult(Aop, vec)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(x), np.sum(vec * x))
assert all_almost_equal(C.adjoint(y), vec * y)
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert all_almost_equal((Aop * vec)(x), np.sum(vec * x))
assert all_almost_equal((Aop * vec).adjoint(y), vec * y)
def test_linear_right_vector_mult():
A = np.random.rand(4, 3)
Aop = MatVecOperator(A)
vec = Aop.domain.element([1, 2, 3])
x = Aop.domain.element([4, 5, 6])
y = Aop.range.element([5, 6, 7, 8])
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
C = OperatorRightVectorMult(Aop, vec)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(x), np.dot(A, vec * x))
assert all_almost_equal(C.adjoint(y), vec * np.dot(A.T, y))
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert all_almost_equal((Aop * vec)(x), np.dot(A, vec * x))
assert all_almost_equal((Aop * vec).adjoint(y), vec * np.dot(A.T, y))
def test_functional_right_vector_mult():
r3 = odl.Rn(3)
Aop = SumFunctional(r3)
vec = r3.element([1, 2, 3])
x = r3.element([4, 5, 6])
y = 2.0
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
C = OperatorRightVectorMult(Aop, vec)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(x), np.sum(vec * x))
assert all_almost_equal(C.adjoint(y), vec * y)
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert all_almost_equal((Aop * vec)(x),
np.sum(vec * x))
assert all_almost_equal((Aop * vec).adjoint(y),
vec * y)
def test_linear_right_vector_mult():
A = np.random.rand(4, 3)
Aop = MatVecOperator(A)
vec = Aop.domain.element([1, 2, 3])
x = Aop.domain.element([4, 5, 6])
y = Aop.range.element([5, 6, 7, 8])
# Test a range of scalars (scalar multiplication could implement
# optimizations for (-1, 0, 1).
C = OperatorRightVectorMult(Aop, vec)
assert C.is_linear
assert C.adjoint.is_linear
assert all_almost_equal(C(x), np.dot(A, vec * x))
assert all_almost_equal(C.adjoint(y), vec * np.dot(A.T, y))
assert all_almost_equal(C.adjoint.adjoint(x), C(x))
# Using operator overloading
assert all_almost_equal((Aop * vec)(x),
np.dot(A, vec * x))
assert all_almost_equal((Aop * vec).adjoint(y),
vec * np.dot(A.T, y))
