def test_numpy_dotproduct():
if not numpy:
skip("numpy not installed")
A = Matrix([x, y, z])
f1 = lambdify([x, y, z], DotProduct(A, A), modules='numpy')
f2 = lambdify([x, y, z], DotProduct(A, A.T), modules='numpy')
f3 = lambdify([x, y, z], DotProduct(A.T, A), modules='numpy')
f4 = lambdify([x, y, z], DotProduct(A, A.T), modules='numpy')
assert f1(1, 2, 3) == \
f2(1, 2, 3) == \
f3(1, 2, 3) == \
f4(1, 2, 3) == \
numpy.array([14])
def test_docproduct():
assert DotProduct(A, B).doit() == 22
assert DotProduct(A.T, B).doit() == 22
assert DotProduct(A, B.T).doit() == 22
assert DotProduct(A.T, B.T).doit() == 22
raises(TypeError, lambda: DotProduct(1, A))
raises(TypeError, lambda: DotProduct(A, 1))
raises(TypeError, lambda: DotProduct(A, D))
raises(TypeError, lambda: DotProduct(D, A))
raises(TypeError, lambda: DotProduct(B, C).doit())
def test_dotproduct_symbolic():
A = MatrixSymbol('A', 3, 1)
B = MatrixSymbol('B', 3, 1)
dot = DotProduct(A, B)
assert dot.is_scalar == True
assert unchanged(Mul, 2, dot)
# XXX Fix forced evaluation for arithmetics with matrix expressions
assert dot * A == (A[0, 0]*B[0, 0] + A[1, 0]*B[1, 0] + A[2, 0]*B[2, 0])*A