mars.tensor.amax(a, axis=None, out=None, keepdims=None, combine_size=None)#

Return the maximum of an array or maximum along an axis.

  • a (array_like) – Input data.

  • axis (None or int or tuple of ints, optional) –

    Axis or axes along which to operate. By default, flattened input is used.

    If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before.

  • out (Tensor, optional) – Alternative output tensor in which to place the result. Must be of the same shape and buffer length as the expected output. See doc.ufuncs (Section “Output arguments”) for more details.

  • keepdims (bool, optional) –

    If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

    If the default value is passed, then keepdims will not be passed through to the amax method of sub-classes of ndarray, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.

  • combine_size (int, optional) – The number of chunks to combine.


amax – Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is a tensor of dimension a.ndim - 1.

Return type

Tensor or scalar

See also


The minimum value of a tensor along a given axis, propagating any NaNs.


The maximum value of a tensor along a given axis, ignoring any NaNs.


Element-wise maximum of two tensors, propagating any NaNs.


Element-wise maximum of two tensors, ignoring any NaNs.


Return the indices of the maximum values.

nanmin, minimum, fmin


NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

Don’t use amax for element-wise comparison of 2 arrays; when a.shape[0] is 2, maximum(a[0], a[1]) is faster than amax(a, axis=0).


>>> import mars.tensor as mt
>>> a = mt.arange(4).reshape((2,2))
>>> a.execute()
array([[0, 1],
       [2, 3]])
>>> mt.amax(a).execute()           # Maximum of the flattened array
>>> mt.amax(a, axis=0).execute()   # Maxima along the first axis
array([2, 3])
>>> mt.amax(a, axis=1).execute()   # Maxima along the second axis
array([1, 3])
>>> b = mt.arange(5, dtype=float)
>>> b[2] = mt.NaN
>>> mt.amax(b).execute()
>>> mt.nanmax(b).execute()