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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.
# 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,
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from numbers import Integral
from typing import List
import numpy as np
from ... import opcodes
from ...core import EntityData
from ...serialization.serializables import Int32Field, KeyField
from ..datasource import tensor as astensor
from ..operators import TensorOperatorMixin
from ..utils import AxisError, gen_random_seeds, validate_axis
from .core import TensorRandomMapReduceOperator
def _permutation_on_axis(ar, axis, rs, xp):
try:
return rs.permutation(ar, axis=axis)
except TypeError:
# numpy starts to support axis from 1.18
if axis == 0:
return rs.permutation(ar)
indices = xp.arange(ar.shape[axis])
rs.shuffle(indices)
slc = (slice(None),) * axis + (indices,)
return ar[slc]
class TensorPermutation(TensorRandomMapReduceOperator, TensorOperatorMixin):
_op_type_ = opcodes.PERMUTATION
input = KeyField("input")
axis = Int32Field("axis")
reduce_size = Int32Field("reduce_size")
@classmethod
def _set_inputs(cls, op: "TensorPermutation", inputs: List[EntityData]):
super()._set_inputs(op, inputs)
op.input = op._inputs[0]
def __call__(self, x):
return self.new_tensor([x], x.shape, order=x.order)
[docs]
def permutation(random_state, x, axis=0, chunk_size=None):
r"""
Randomly permute a sequence, or return a permuted range.
Parameters
----------
x : int or array_like
If `x` is an integer, randomly permute ``mt.arange(x)``.
If `x` is an array, make a copy and shuffle the elements
randomly.
axis : int, optional
The axis which `x` is shuffled along. Default is 0.
chunk_size : : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
Returns
-------
out : Tensor
Permuted sequence or tensor range.
Examples
--------
>>> import maxframe.tensor as mt
>>> rng = mt.random.RandomState()
>>> rng.permutation(10).execute()
array([1, 2, 3, 7, 9, 8, 0, 6, 4, 5]) # random
>>> rng.permutation([1, 4, 9, 12, 15]).execute()
array([ 9, 4, 12, 1, 15]) # random
>>> arr = mt.arange(9).reshape((3, 3))
>>> rng.permutation(arr).execute()
array([[3, 4, 5], # random
[6, 7, 8],
[0, 1, 2]])
>>> rng.permutation("abc")
Traceback (most recent call last):
...
numpy.AxisError: x must be an integer or at least 1-dimensional
"""
if isinstance(x, (Integral, np.integer)):
from ..datasource import arange
x = arange(x, chunk_size=chunk_size)
else:
x = astensor(x, chunk_size=chunk_size)
if x.ndim < 1:
raise AxisError("x must be an integer or at least 1-dimensional")
axis = validate_axis(x.ndim, axis)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorPermutation(seed=seed, axis=axis, dtype=x.dtype, gpu=x.op.gpu)
return op(x)