reshape

重塑数组，同时不改变其数据。

参数

名称	类型	描述	默认值
x		要重塑的输入数组。	必需
shape		与原始形状兼容的新形状。允许一个形状维度为-1。当形状维度为-1时，相应的输出数组形状维度必须从数组的长度和剩余维度推断。	必需
copy		是否复制输入数组。如果为True，函数总是复制。如果为False，函数绝不复制。如果为None，函数尽可能避免复制。默认值：None。	None

返回值

名称	类型	描述
out	数组	一个与x具有相同数据类型和元素的输出数组。

引发

类型	描述
ValueError	如果copy=False且需要复制，则会引发ValueError。

示例

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.reshape(a, shape=(1, 4))
>>> o.todense()
array([[0, 1, 2, 0]])

源代码位于 sparse/numba_backend/_common.py

def reshape(x, /, shape, *, copy=None):
    """
    Reshapes an array without changing its data.

    Parameters
    ----------
    x: array
        input array to reshape.
    shape: Tuple[int, ...]
        a new shape compatible with the original shape. One shape dimension is allowed to be ``-1``.
        When a shape dimension is ``-1``, the corresponding output array shape dimension must be inferred
        from the length of the array and the remaining dimensions.
    copy: Optional[bool]
        whether or not to copy the input array.
        If ``True``, the function always copies.
        If ``False``, the function must never copies.
        If ``None``, the function avoids copying, if possible.
        Default: ``None``.

    Returns
    -------
    out: array
        an output array having the same data type and elements as ``x``.

    Raises
    ------
    ValueError
        If ``copy=False`` and a copy would be necessary, a ``ValueError``
        will be raised.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.reshape(a, shape=(1, 4))
    >>> o.todense()
    array([[0, 1, 2, 0]])
    """
    return x.reshape(shape=shape)