Return minimum of a tensor or minimum along an axis, ignoring any NaNs.
When all-NaN slices are encountered a RuntimeWarning is raised and
Nan is returned for that slice.
a (array_like) – Tensor containing numbers whose minimum is desired. If a is not an
tensor, a conversion is attempted.
axis (int, optional) – Axis along which the minimum is computed. The default is to compute
the minimum of the flattened tensor.
out (Tensor, optional) – Alternate output tensor in which to place the result. The default
is None; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
doc.ufuncs for 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 original a.
If the value is anything but the default, then
keepdims will be passed through to the min method
of sub-classes of Tensor. If the sub-classes methods
does not implement keepdims any exceptions will be raised.
combine_size (int, optional) – The number of chunks to combine.
nanmin – An tensor with the same shape as a, with the specified axis
removed. If a is a 0-d tensor, or if axis is None, a tensor
scalar is returned. The same dtype as a is returned.
The maximum value of an array along a given axis, ignoring any NaNs.
The minimum value of an array along a given axis, propagating any NaNs.
Element-wise minimum of two arrays, ignoring any NaNs.
Element-wise minimum of two arrays, propagating any NaNs.
Shows which elements are Not a Number (NaN).
Shows which elements are neither NaN nor infinity.
amax, fmax, maximum
Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to mt.min.
>>> import mars.tensor as mt
>>> a = mt.array([[1, 2], [3, mt.nan]])
>>> mt.nanmin(a, axis=0).execute()
array([ 1., 2.])
>>> mt.nanmin(a, axis=1).execute()
array([ 1., 3.])
When positive infinity and negative infinity are present:
>>> mt.nanmin([1, 2, mt.nan, mt.inf]).execute()
>>> mt.nanmin([1, 2, mt.nan, mt.NINF]).execute()