Source code for maxframe.tensor.reduction.argmax
#!/usr/bin/env python
# -*- coding: utf-8 -*-
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
from ... import opcodes
from ...serialization.serializables import AnyField, TupleField
from ..utils import validate_axis
from .core import TensorArgReductionMixin, TensorReduction
class TensorArgmax(TensorReduction, TensorArgReductionMixin):
_op_type_ = opcodes.ARGMAX
_func_name = "argmax"
_agg_func_name = "max"
offset = AnyField("offset", default=None)
total_shape = TupleField("total_shape", default=None)
def __init__(self, dtype=None, **kw):
if dtype is None:
dtype = np.dtype(int)
super().__init__(dtype=dtype, **kw)
[docs]
def argmax(a, axis=None, out=None):
"""
Returns the indices of the maximum values along an axis.
Parameters
----------
a : array_like
Input tensor.
axis : int, optional
By default, the index is into the flattened tensor, otherwise
along the specified axis.
out : Tensor, optional
If provided, the result will be inserted into this tensor. It should
be of the appropriate shape and dtype.
Returns
-------
index_array : Tensor of ints
Tensor of indices into the tensor. It has the same shape as `a.shape`
with the dimension along `axis` removed.
See Also
--------
Tensor.argmax, argmin
amax : The maximum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
Notes
-----
In case of multiple occurrences of the maximum values, the indices
corresponding to the first occurrence are returned.
Examples
--------
>>> import maxframe.tensor as mt
>>> a = mt.arange(6).reshape(2,3)
>>> a.execute()
array([[0, 1, 2],
[3, 4, 5]])
>>> mt.argmax(a).execute()
5
>>> mt.argmax(a, axis=0).execute()
array([1, 1, 1])
>>> mt.argmax(a, axis=1).execute()
array([2, 2])
Indexes of the maximal elements of a N-dimensional tensor:
>>> ind = mt.unravel_index(mt.argmax(a, axis=None), a.shape)
>>> ind.execute()
(1, 2)
>>> a[ind].execute() # TODO(jisheng): accomplish when fancy index on tensor is supported
>>> b = mt.arange(6)
>>> b[1] = 5
>>> b.execute()
array([0, 5, 2, 3, 4, 5])
>>> mt.argmax(b).execute() # Only the first occurrence is returned.
1
"""
axis = validate_axis(a.ndim, axis) if axis is not None else None
op = TensorArgmax(axis=axis, dtype=np.dtype(int))
return op(a, out=out)