Python operator.matmul方法代碼示例

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def check_forward(self, x_data, y_data):
        if self.left_const:
            x = x_data
        else:
            x = chainer.Variable(x_data)
        if self.right_const:
            y = y_data
        else:
            y = chainer.Variable(y_data)
        z = operator.matmul(x, y)
        if self.dtype == numpy.float16:
            options = {'atol': 2e-3, 'rtol': 2e-3}
        else:
            options = {'atol': 2e-7, 'rtol': 2e-7}
        testing.assert_allclose(
            self._get_forward_answer(self.x, self.y), z.data, **options) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def check_backward(self, x_data, y_data, z_grad):
        if self.right_const:
            def op(x):
                return operator.matmul(x, y_data)
            data = x_data,
        elif self.left_const:
            def op(y):
                return operator.matmul(x_data, y)
            data = y_data,
        else:
            op = operator.matmul
            data = x_data, y_data

        if self.dtype == numpy.float16:
            options = {'atol': 1e-3, 'rtol': 1e-2}
        else:
            options = {'atol': 1e-4, 'rtol': 1e-4}
        gradient_check.check_backward(
            op, data, z_grad, dtype=numpy.float64, **options) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def check_double_backward(
            self, x_data, y_data, z_grad, x_grad_grad, y_grad_grad):
        if self.right_const:
            def op(x):
                return operator.matmul(x, y_data.astype(x.dtype))
            data = x_data,
            grad_grad = x_grad_grad,
        elif self.left_const:
            def op(y):
                return operator.matmul(x_data.astype(y.dtype), y)
            data = y_data,
            grad_grad = y_grad_grad,
        else:
            op = operator.matmul
            data = x_data, y_data
            grad_grad = x_grad_grad, y_grad_grad

        if self.dtype == numpy.float16:
            options = {'atol': 1e-3, 'rtol': 1e-2}
        else:
            options = {'atol': 1e-4, 'rtol': 1e-4}
        gradient_check.check_double_backward(
            op, data, z_grad, grad_grad, dtype=numpy.float64, **options) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_matmul(self):
        if not TEST_MATMUL:
            pytest.skip("matmul is only tested in Python 3.5+")

        M = self.spmatrix(matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
        B = self.spmatrix(matrix([[0,1],[1,0],[0,2]],'d'))
        col = matrix([1,2,3]).T

        # check matrix-vector
        assert_array_almost_equal(operator.matmul(M, col),
                                  M.todense() * col)

        # check matrix-matrix
        assert_array_almost_equal(operator.matmul(M, B).todense(),
                                  (M * B).todense())
        assert_array_almost_equal(operator.matmul(M.todense(), B),
                                  (M * B).todense())
        assert_array_almost_equal(operator.matmul(M, B.todense()),
                                  (M * B).todense())

        # check error on matrix-scalar
        assert_raises(ValueError, operator.matmul, M, 1)
        assert_raises(ValueError, operator.matmul, 1, M) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
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) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_matmul(a_shape, b_shape):
    sa = sparse.random(a_shape, density=0.5)
    sb = sparse.random(b_shape, density=0.5)

    a = sa.todense()
    b = sb.todense()

    assert_eq(np.matmul(a, b), sparse.matmul(sa, sb))
    assert_eq(sparse.matmul(sa, b), sparse.matmul(a, sb))
    assert_eq(np.matmul(a, b), sparse.matmul(sa, sb))

    if a.ndim == 2 or b.ndim == 2:
        assert_eq(
            np.matmul(a, b),
            sparse.matmul(
                scipy.sparse.coo_matrix(a) if a.ndim == 2 else sa,
                scipy.sparse.coo_matrix(b) if b.ndim == 2 else sb,
            ),
        )

    if hasattr(operator, "matmul"):
        assert_eq(operator.matmul(a, b), operator.matmul(sa, sb)) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_dot(a_shape, b_shape):
    sa = sparse.random(a_shape, density=0.5)
    sb = sparse.random(b_shape, density=0.5)

    a = sa.todense()
    b = sb.todense()

    assert_eq(a.dot(b), sa.dot(sb))
    assert_eq(np.dot(a, b), sparse.dot(sa, sb))
    assert_eq(sparse.dot(sa, b), sparse.dot(a, sb))
    assert_eq(np.dot(a, b), sparse.dot(sa, sb))

    if hasattr(operator, "matmul"):
        # Basic equivalences
        assert_eq(operator.matmul(a, b), operator.matmul(sa, sb))
        # Test that SOO's and np.array's combine correctly
        # Not possible due to https://github.com/numpy/numpy/issues/9028
        # assert_eq(eval("a @ sb"), eval("sa @ b")) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def __init__(self, factors, n_terms=None, complex=False, fixed_order=False, softmax_fn='softmax'):
        super().__init__()
        self.factors = nn.ModuleList(factors)
        if n_terms is None:
            n_terms = len(factors)
        self.n_terms = n_terms
        self.complex = complex
        self.matmul_op = complex_matmul if complex else operator.matmul
        self.fixed_order = fixed_order
        if not self.fixed_order:
            assert softmax_fn in ['softmax', 'sparsemax']
            self.logit = nn.Parameter(torch.randn((self.n_terms, len(factors))))
            if softmax_fn == 'softmax':
                self.softmax_fn = lambda logit: nn.functional.softmax(logit, dim=-1)
            else:
                self.softmax_fn = sparsemax 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_matmul(self):
        array = np.array([1, 2], dtype=np.float64)
        array_like = ArrayLike(array)
        expected = ArrayLike(np.float64(5))
        _assert_equal_type_and_value(expected, np.matmul(array_like, array))
        if not PY2:
            _assert_equal_type_and_value(
                expected, operator.matmul(array_like, array))
            _assert_equal_type_and_value(
                expected, operator.matmul(array, array_like)) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def _get_forward_answer(self, x, y):
        if x.ndim <= 2 or y.ndim == 1:
            return numpy.dot(x, y)
        elif hasattr(numpy, 'matmul'):
            # Note: NumPy 1.14.0 has a bug in einsum (numpy/numpy#10343),
            # so we use matmul if available to avoid it
            return numpy.matmul(x, y)
        else:
            return numpy.einsum('...ij,...jk->...ik', x, y) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_invalid_type(self):
        x = chainer.Variable(self.x)
        y = chainer.Variable(self.y)
        with pytest.raises(type_check.InvalidType):
            operator.matmul(x, y) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def assert_in(member, collection, msg=None):
    assert_(member in collection, msg=msg if msg is not None else "%r not found in %r" % (member, collection))


# Only test matmul operator (A @ B) when available (Python 3.5+) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
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) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
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) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import matmul [as 別名]
def test_invalid_shape(self):
        for xp in (numpy, cupy):
            shape1, shape2 = self.shape_pair
            x1 = testing.shaped_arange(shape1, xp, numpy.float32)
            x2 = testing.shaped_arange(shape2, xp, numpy.float32)
            with pytest.raises(ValueError):
                xp.matmul(x1, x2)