#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import AnyField, TupleField
from .core import TensorReduction, TensorArgReductionMixin
class TensorNanArgmin(TensorReduction, TensorArgReductionMixin):
_op_type_ = OperandDef.NANARGMIN
_func_name = 'nanargmin'
_agg_func_name = 'nanmin'
_offset = AnyField('offset')
_total_shape = TupleField('total_shape')
def __init__(self, axis=None, dtype=None, combine_size=None,
offset=None, total_shape=None, stage=None, **kw):
if dtype is None:
dtype = np.dtype(int)
stage = self._rewrite_stage(stage)
super().__init__(_axis=axis, _combine_size=combine_size,
_offset=offset, _total_shape=total_shape,
dtype=dtype, stage=stage, **kw)
@property
def offset(self):
return getattr(self, '_offset', None)
@property
def total_shape(self):
return getattr(self, '_total_shape', None)
[docs]def nanargmin(a, axis=None, out=None, combine_size=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
index_array : Tensor
A tensor of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([[mt.nan, 4], [2, 3]])
>>> mt.argmin(a).execute()
0
>>> mt.nanargmin(a).execute()
2
>>> mt.nanargmin(a, axis=0).execute()
array([1, 1])
>>> mt.nanargmin(a, axis=1).execute()
array([1, 0])
"""
op = TensorNanArgmin(axis=axis, dtype=np.dtype(int), combine_size=combine_size)
return op(a, out=out)