python类matmul()的实例源码

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)

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_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_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")