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from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from .core import TensorReduction, TensorReductionMixin
class TensorNanMin(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.NANMIN
_func_name = 'nanmin'
def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
stage = self._rewrite_stage(stage)
_combine_size=combine_size, stage=stage, **kw)
[docs]def nanmin(a, axis=None, out=None, keepdims=None, combine_size=None):
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 : Tensor
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()
a = astensor(a)
op = TensorNanMin(axis=axis, dtype=a.dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)