max

Calculates the maximum value of the input array x.

Parameters:

Name	Type	Description	Default
x		input array of a real-valued data type.	required
axis		axis or axes along which maximum values are computed. By default, the maximum value are computed over the entire array. If a tuple of integers, maximum values are computed over multiple axes. Default: None.	None
keepdims		If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Accordingly, the result is compatible with the input array. Otherwise, if False, the reduced axes (dimensions) must not be included in the result. Default: False.	False

Returns:

Name	Type	Description
out	array	if the maximum value was computed over the entire array, a zero-dimensional array containing the maximum value. Otherwise, a non-zero-dimensional array containing the maximum values. The returned array has the same data type as x.

Special Cases

For floating-point operands, if x_i is NaN, the maximum value is NaN (i.e., NaN values propagate).

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.max(a, axis=1)
>>> o.todense()
array([1, 2])

Source code in sparse/numba_backend/_common.py

def max(x, /, *, axis=None, keepdims=False):
    """
    Calculates the maximum value of the input array ``x``.

    Parameters
    ----------
    x: array
        input array of a real-valued data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which maximum values are computed.
        By default, the maximum value are computed over the entire array.
        If a tuple of integers, maximum values are computed over multiple axes. Default: ``None``.
    keepdims: bool
        If ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions.
        Accordingly, the result is compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) must not be included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the maximum value was computed over the entire array, a zero-dimensional array containing the maximum value.
        Otherwise, a non-zero-dimensional array containing the maximum values.
        The returned array has the same data type as ``x``.

    Special Cases
    -------------
    For floating-point operands, if ``x_i`` is ``NaN``, the maximum value is ``NaN`` (i.e., ``NaN`` values propagate).

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