我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用operator.matmul()。
def test_exceptions(self): dims = [ ((1,), (2,)), # mismatched vector vector ((2, 1,), (2,)), # mismatched matrix vector ((2,), (1, 2)), # mismatched vector matrix ((1, 2), (3, 1)), # mismatched matrix matrix ((1,), ()), # vector scalar ((), (1)), # scalar vector ((1, 1), ()), # matrix scalar ((), (1, 1)), # scalar matrix ((2, 2, 1), (3, 1, 2)), # cannot broadcast ] for dt, (dm1, dm2) in itertools.product(self.types, dims): a = np.ones(dm1, dtype=dt) b = np.ones(dm2, dtype=dt) assert_raises(ValueError, self.matmul, a, b)
def test_shapes(self): dims = [ ((1, 1), (2, 1, 1)), # broadcast first argument ((2, 1, 1), (1, 1)), # broadcast second argument ((2, 1, 1), (2, 1, 1)), # matrix stack sizes match ] for dt, (dm1, dm2) in itertools.product(self.types, dims): a = np.ones(dm1, dtype=dt) b = np.ones(dm2, dtype=dt) res = self.matmul(a, b) assert_(res.shape == (2, 1, 1)) # vector vector returns scalars. for dt in self.types: a = np.ones((2,), dtype=dt) b = np.ones((2,), dtype=dt) c = self.matmul(a, b) assert_(np.array(c).shape == ())
def test_numpy_ufunc_override(self): # 2016-01-29: NUMPY_UFUNC_DISABLED return class A(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs): return "A" class B(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs): return NotImplemented a = A([1, 2]) b = B([1, 2]) c = np.ones(2) assert_equal(self.matmul(a, b), "A") assert_equal(self.matmul(b, a), "A") assert_raises(TypeError, self.matmul, b, c)
def test_matmul(self): if not TEST_MATMUL: pytest.skip("matmul is only tested in Python 3.5+") D = {'shape': self.A.shape, 'matvec': lambda x: np.dot(self.A, x).reshape(self.A.shape[0]), 'rmatvec': lambda x: np.dot(self.A.T.conj(), x).reshape(self.A.shape[1]), 'matmat': lambda x: np.dot(self.A, x)} A = interface.LinearOperator(**D) B = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) b = B[0] assert_equal(operator.matmul(A, b), A * b) assert_equal(operator.matmul(A, B), A * B) assert_raises(ValueError, operator.matmul, A, 2) assert_raises(ValueError, operator.matmul, 2, A)
def test_numpy_ufunc_override(self): # Temporarily disable __numpy_ufunc__ for 1.10; see gh-5844 return class A(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs): return "A" class B(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs): return NotImplemented a = A([1, 2]) b = B([1, 2]) c = np.ones(2) assert_equal(self.matmul(a, b), "A") assert_equal(self.matmul(b, a), "A") assert_raises(TypeError, self.matmul, b, c)
def test_result_types(self): mat = np.ones((1,1)) vec = np.ones((1,)) for dt in self.types: m = mat.astype(dt) v = vec.astype(dt) for arg in [(m, v), (v, m), (m, m)]: res = self.matmul(*arg) assert_(res.dtype == dt) # vector vector returns scalars res = self.matmul(v, v) assert_(type(res) is np.dtype(dt).type)
def test_vector_vector_values(self): vec = np.array([1, 2]) tgt = 5 for dt in self.types[1:]: v1 = vec.astype(dt) res = self.matmul(v1, v1) assert_equal(res, tgt) # boolean type vec = np.array([True, True], dtype='?') res = self.matmul(vec, vec) assert_equal(res, True)
def test_vector_matrix_values(self): vec = np.array([1, 2]) mat1 = np.array([[1, 2], [3, 4]]) mat2 = np.stack([mat1]*2, axis=0) tgt1 = np.array([7, 10]) tgt2 = np.stack([tgt1]*2, axis=0) for dt in self.types[1:]: v = vec.astype(dt) m1 = mat1.astype(dt) m2 = mat2.astype(dt) res = self.matmul(v, m1) assert_equal(res, tgt1) res = self.matmul(v, m2) assert_equal(res, tgt2) # boolean type vec = np.array([True, False]) mat1 = np.array([[True, False], [False, True]]) mat2 = np.stack([mat1]*2, axis=0) tgt1 = np.array([True, False]) tgt2 = np.stack([tgt1]*2, axis=0) res = self.matmul(vec, mat1) assert_equal(res, tgt1) res = self.matmul(vec, mat2) assert_equal(res, tgt2)
def test_out_arg(self): a = np.ones((2, 2), dtype=np.float) b = np.ones((2, 2), dtype=np.float) tgt = np.full((2,2), 2, dtype=np.float) # test as positional argument msg = "out positional argument" out = np.zeros((2, 2), dtype=np.float) self.matmul(a, b, out) assert_array_equal(out, tgt, err_msg=msg) # test as keyword argument msg = "out keyword argument" out = np.zeros((2, 2), dtype=np.float) self.matmul(a, b, out=out) assert_array_equal(out, tgt, err_msg=msg) # test out with not allowed type cast (safe casting) # einsum and cblas raise different error types, so # use Exception. msg = "out argument with illegal cast" out = np.zeros((2, 2), dtype=np.int32) assert_raises(Exception, self.matmul, a, b, out=out) # skip following tests for now, cblas does not allow non-contiguous # outputs and consistency with dot would require same type, # dimensions, subtype, and c_contiguous. # test out with allowed type cast # msg = "out argument with allowed cast" # out = np.zeros((2, 2), dtype=np.complex128) # self.matmul(a, b, out=out) # assert_array_equal(out, tgt, err_msg=msg) # test out non-contiguous # msg = "out argument with non-contiguous layout" # c = np.zeros((2, 2, 2), dtype=np.float) # self.matmul(a, b, out=c[..., 0]) # assert_array_equal(c, tgt, err_msg=msg)
def test_array_priority_override(self): class A(object): __array_priority__ = 1000 def __matmul__(self, other): return "A" def __rmatmul__(self, other): return "A" a = A() b = np.ones(2) assert_equal(self.matmul(a, b), "A") assert_equal(self.matmul(b, a), "A")
def test_matmul_inplace(): # It would be nice to support in-place matmul eventually, but for now # we don't have a working implementation, so better just to error out # and nudge people to writing "a = a @ b". a = np.eye(3) b = np.eye(3) assert_raises(TypeError, a.__imatmul__, b) import operator assert_raises(TypeError, operator.imatmul, a, b) # we avoid writing the token `exec` so as not to crash python 2's # parser exec_ = getattr(builtins, "exec") assert_raises(TypeError, exec_, "a @= b", globals(), locals())
def test_operator_matmul(self, xp, dtype1, dtype2): x1 = testing.shaped_arange(self.shape_pair[0], xp, dtype1) x2 = testing.shaped_arange(self.shape_pair[1], xp, dtype2) return operator.matmul(x1, x2)
def test_cupy_matmul(self, xp, dtype1, dtype2): x1 = testing.shaped_arange(self.shape_pair[0], xp, dtype1) x2 = testing.shaped_arange(self.shape_pair[1], xp, dtype2) return xp.matmul(x1, x2)
def test_operator_matmul(self, xp, dtype1, dtype2): if ((dtype1, dtype2) in self.skip_dtypes or (dtype2, dtype1) in self.skip_dtypes): return xp.array([]) x1 = testing.shaped_arange(self.shape_pair[0], xp, dtype1) x2 = testing.shaped_arange(self.shape_pair[1], xp, dtype2) return operator.matmul(x1, x2)
def test_cupy_matmul(self, xp, dtype1, dtype2): if ((dtype1, dtype2) in self.skip_dtypes or (dtype2, dtype1) in self.skip_dtypes): return xp.array([]) shape1, shape2 = self.shape_pair x1 = testing.shaped_arange(shape1, xp, dtype1) x2 = testing.shaped_arange(shape2, xp, dtype2) return xp.matmul(x1, x2)
def test_invalid_shape(self, xp): shape1, shape2 = self.shape_pair x1 = testing.shaped_arange(shape1, xp, numpy.float32) x2 = testing.shaped_arange(shape2, xp, numpy.float32) xp.matmul(x1, x2)
