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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 maxframe import opcodes
from maxframe.core import EntityData
from maxframe.serialization.serializables import Int32Field, KeyField
from maxframe.tensor.datasource import tensor as astensor
from maxframe.tensor.operators import TensorOperatorMixin
from maxframe.tensor.random.core import TensorRandomMapReduceOperator
from maxframe.tensor.utils import AxisError, gen_random_seeds, validate_axis
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 maxframe.tensor.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)