maxframe.tensor.median#

maxframe.tensor.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)[source]#

Compute the median along the specified axis.

Returns the median of the tensor elements.

Parameters:

a (array_like) – Input tensor or object that can be converted to a tensor.
axis ({int, sequence of int, None}, optional) – Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the tensor. A sequence of axes is supported since version 1.9.0.
out (Tensor, optional) – Alternative output tensor in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.
overwrite_input (bool, optional) – Just for compatibility with Numpy, would not take effect.
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 original arr.

Returns:

median – A new tensor holding the result. If the input contains integers or floats smaller than float64, then the output data-type is np.float64. Otherwise, the data-type of the output is the same as that of the input. If out is specified, that tensor is returned instead.

Return type:

Tensor

See also

mean, percentile

Notes

Given a vector V of length N, the median of V is the middle value of a sorted copy of V, V_sorted - i e., V_sorted[(N-1)/2], when N is odd, and the average of the two middle values of V_sorted when N is even.

Examples

>>> import maxframe.tensor as mt
>>> a = mt.array([[10, 7, 4], [3, 2, 1]])
>>> a.execute()
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> mt.median(a).execute()
3.5
>>> mt.median(a, axis=0).execute()
array([6.5, 4.5, 2.5])
>>> mt.median(a, axis=1).execute()
array([7.,  2.])
>>> m = mt.median(a, axis=0)
>>> out = mt.zeros_like(m)
>>> mt.median(a, axis=0, out=m).execute()
array([6.5,  4.5,  2.5])
>>> m.execute()
array([6.5,  4.5,  2.5])