对角化

将 COO 数组对角化。新维度追加在末尾。

警告

sparse.diagonalize 不兼容 numpy，因为 NumPy 没有直接的等效项。API 在未来可能会发生变化。

参数

名称	类型	描述	默认值
a	Union[COO, ndarray, spmatrix]	要对角化的数组。	必需
axis	int	要对角化的轴。默认为第一个轴（0）。	0

示例

>>> import sparse
>>> x = sparse.as_coo(np.arange(1, 4))
>>> sparse.diagonalize(x).todense()
array([[1, 0, 0],
       [0, 2, 0],
       [0, 0, 3]])

>>> x = sparse.as_coo(np.arange(24).reshape((2, 3, 4)))
>>> x_diag = sparse.diagonalize(x, axis=1)
>>> x_diag.shape
(2, 3, 4, 3)

sparse.diagonalize 是 sparse.diagonal 的逆操作。

>>> a = sparse.random((3, 3, 3, 3, 3), density=0.3)
>>> a_diag = sparse.diagonalize(a, axis=2)
>>> (sparse.diagonal(a_diag, axis1=2, axis2=5) == a.transpose([0, 1, 3, 4, 2])).all()
np.True_

返回值

名称	类型	描述
out	COO	操作的结果。

另请参阅

numpy.diag : 一维数组的 NumPy 等效项

源代码位于 sparse/numba_backend/_coo/common.py

def diagonalize(a, axis=0):
    """
    Diagonalize a COO array. The new dimension is appended at the end.

    !!! warning

        [`sparse.diagonalize`][] is not [numpy][] compatible as there is no direct [numpy][] equivalent. The
        API may change in the future.

    Parameters
    ----------
    a : Union[COO, np.ndarray, scipy.sparse.spmatrix]
        The array to diagonalize.
    axis : int, optional
        The axis to diagonalize. Defaults to first axis (0).

    Examples
    --------
    >>> import sparse
    >>> x = sparse.as_coo(np.arange(1, 4))
    >>> sparse.diagonalize(x).todense()
    array([[1, 0, 0],
           [0, 2, 0],
           [0, 0, 3]])

    >>> x = sparse.as_coo(np.arange(24).reshape((2, 3, 4)))
    >>> x_diag = sparse.diagonalize(x, axis=1)
    >>> x_diag.shape
    (2, 3, 4, 3)

    [`sparse.diagonalize`][] is the inverse of [`sparse.diagonal`][]

    >>> a = sparse.random((3, 3, 3, 3, 3), density=0.3)
    >>> a_diag = sparse.diagonalize(a, axis=2)
    >>> (sparse.diagonal(a_diag, axis1=2, axis2=5) == a.transpose([0, 1, 3, 4, 2])).all()
    np.True_

    Returns
    -------
    out: COO
        The result of the operation.

    See Also
    --------
    [`numpy.diag`][] : NumPy equivalent for 1D array
    """
    from .core import COO, as_coo

    a = as_coo(a)

    diag_shape = a.shape + (a.shape[axis],)
    diag_coords = np.vstack([a.coords, a.coords[axis]])

    return COO(diag_coords, a.data, diag_shape)