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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 typing import List
import numpy as np
from maxframe import opcodes
from maxframe.core import EntityData
from maxframe.serialization.serializables import KeyField
from maxframe.tensor.datasource import tensor as astensor
from maxframe.tensor.misc.broadcast_to import broadcast_to
from maxframe.tensor.operators import TensorOperator, TensorOperatorMixin
from maxframe.tensor.utils import broadcast_shape
class TensorWhere(TensorOperator, TensorOperatorMixin):
_op_type_ = opcodes.WHERE
condition = KeyField("condition", default=None)
x = KeyField("x", default=None)
y = KeyField("y", default=None)
@classmethod
def _set_inputs(cls, op: "TensorWhere", inputs: List[EntityData]):
super()._set_inputs(op, inputs)
op.condition = op._inputs[0]
op.x = op._inputs[1]
op.y = op._inputs[2]
def __call__(self, condition, x, y, shape=None):
shape = shape or broadcast_shape(condition.shape, x.shape, y.shape)
return self.new_tensor([condition, x, y], shape)
[docs]
def where(condition, x=None, y=None):
"""
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x`, `y` and `condition` need to be
broadcastable to some shape.
Returns
-------
out : Tensor or tuple of Tensors
If both `x` and `y` are specified, the output tensor contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> import maxframe.tensor as mt
>>> mt.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]]).execute()
array([[1, 8],
[3, 4]])
>>> mt.where([[0, 1], [1, 0]]).execute()
(array([0, 1]), array([1, 0]))
>>> x = mt.arange(9.).reshape(3, 3)
>>> mt.where( x > 5 ).execute()
(array([2, 2, 2]), array([0, 1, 2]))
>>> mt.where(x < 5, x, -1).execute() # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
Find the indices of elements of `x` that are in `goodvalues`.
>>> goodvalues = [3, 4, 7]
>>> ix = mt.isin(x, goodvalues)
>>> ix.execute()
array([[False, False, False],
[ True, True, False],
[False, True, False]])
>>> mt.where(ix).execute()
(array([1, 1, 2]), array([0, 1, 1]))
"""
if (x is None) != (y is None):
raise ValueError("either both or neither of x and y should be given")
if x is None and y is None:
return astensor(condition).nonzero()
x, y = astensor(x), astensor(y)
dtype = np.result_type(x.dtype, y.dtype)
shape = broadcast_shape(x.shape, y.shape)
if np.isscalar(condition):
return broadcast_to(x if condition else y, shape).astype(dtype)
else:
condition = astensor(condition)
op = TensorWhere(dtype=dtype, sparse=condition.issparse())
return op(condition, x, y, shape=shape)