COO.max¶
-
COO.
max
(axis=None, keepdims=False, out=None)[source]¶ Maximize along the given axes. Uses all axes by default.
Parameters: - axis (Union[int, Iterable[int]], optional) – The axes along which to maximize. Uses all axes by default.
- keepdims (bool, optional) – Whether or not to keep the dimensions of the original array.
- dtype (numpy.dtype) – The data type of the output array.
Returns: The reduced output sparse array.
Return type: See also
numpy.max
- Equivalent numpy function.
scipy.sparse.coo_matrix.max()
- Equivalent Scipy function.
Notes
- This function internally calls
COO.sum_duplicates
to bring the array into canonical form. - The
out
parameter is provided just for compatibility with Numpy and isn’t actually supported.
Examples
You can use
COO.max
to maximize an array across any dimension.>>> x = np.add.outer(np.arange(5), np.arange(5)) >>> x array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) >>> s = COO.from_numpy(x) >>> s2 = s.max(axis=1) >>> s2.todense() array([4, 5, 6, 7, 8])
You can also use the
keepdims
argument to keep the dimensions after the maximization.>>> s3 = s.max(axis=1, keepdims=True) >>> s3.shape (5, 1)
By default, this reduces the array down to one number, maximizing along all axes.
>>> s.max() 8