#!/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.
# 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
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import numpy as np
from ... import opcodes
from ...serialization.serializables import BoolField, Float64Field
from .core import TensorBinOp
class TensorIsclose(TensorBinOp):
_op_type_ = opcodes.ISCLOSE
_func_name = "isclose"
rtol = Float64Field("rtol", default=None)
atol = Float64Field("atol", default=None)
equal_nan = BoolField("equal_nan", default=None)
def __init__(self, casting="same_kind", err=None, sparse=False, **kw):
err = err if err is not None else np.geterr()
super().__init__(casting=casting, err=err, sparse=sparse, **kw)
@classmethod
def _is_sparse(cls, x1, x2):
if (
hasattr(x1, "issparse")
and x1.issparse()
and np.isscalar(x2)
and not np.isclose(x2, 0)
):
return True
if (
hasattr(x2, "issparse")
and x2.issparse()
and np.isscalar(x1)
and not np.isclose(x1, 0)
):
return True
return False
[docs]
def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
"""
Returns a boolean tensor where two tensors are element-wise equal within a
tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
Parameters
----------
a, b : array_like
Input tensors to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output tensor.
Returns
-------
y : array_like
Returns a boolean tensor of where `a` and `b` are equal within the
given tolerance. If both `a` and `b` are scalars, returns a single
boolean value.
See Also
--------
allclose
Notes
-----
For finite values, isclose uses the following equation to test whether
two floating point values are equivalent.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
The above equation is not symmetric in `a` and `b`, so that
`isclose(a, b)` might be different from `isclose(b, a)` in
some rare cases.
Examples
--------
>>> import maxframe.tensor as mt
>>> mt.isclose([1e10,1e-7], [1.00001e10,1e-8]).execute()
array([True, False])
>>> mt.isclose([1e10,1e-8], [1.00001e10,1e-9]).execute()
array([True, True])
>>> mt.isclose([1e10,1e-8], [1.0001e10,1e-9]).execute()
array([False, True])
>>> mt.isclose([1.0, mt.nan], [1.0, mt.nan]).execute()
array([True, False])
>>> mt.isclose([1.0, mt.nan], [1.0, mt.nan], equal_nan=True).execute()
array([True, True])
"""
op = TensorIsclose(rtol=rtol, atol=atol, equal_nan=equal_nan, dtype=np.dtype(bool))
return op(a, b)