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# Copyright (c) 2024 Cisco and/or its affiliates.
# 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
# limitations under the License.
"""Module holding AvgStdevStats class."""
import dataclasses
import math
import typing
@dataclasses.dataclass
class AvgStdevStats:
"""Class for statistics which include average and stdev of a group.
Contrary to other stats types, adding values to the group
is computationally light without any caching.
Instances are only statistics, the data itself is stored elsewhere.
"""
size: int = 0
"""Number of scalar values (samples) participating in this group."""
avg: float = 0.0
"""Population average of the participating sample values."""
stdev: float = 0.0
"""Population standard deviation of the sample values."""
@classmethod
def for_runs(
cls,
runs: typing.Iterable[typing.Union[float, "AvgStdevStats"]],
) -> "AvgStdevStats":
"""Return new stats instance describing the sequence of runs.
If you want to append data to existing stats object,
you can simply use the stats object as the first run.
Instead of a verb, "for" is used to start this method name,
to signify the result contains less information than the input data.
Here, run is a hypothetical abstract class, an union of float and cls.
Defining that as a real abstract class in Python is too much hassle.
:param runs: Sequence of data to describe by the new metadata.
:type runs: Iterable[Union[float, cls]]
:returns: The new stats instance.
:rtype: cls
"""
# Using Welford method to be more resistant to rounding errors.
# Adapted from code for sample standard deviation at:
# https://www.johndcook.com/blog/standard_deviation/
# The logic of plus operator is taken from
# https://www.johndcook.com/blog/skewness_kurtosis/
total_size = 0
total_avg = 0.0
moment_2 = 0.0
for run in runs:
if isinstance(run, (float, int)):
run_size = 1
run_avg = run
run_stdev = 0.0
else:
run_size = run.size
run_avg = run.avg
run_stdev = run.stdev
if run_size < 1:
continue
old_total_size = total_size
delta = run_avg - total_avg
total_size += run_size
total_avg += delta * run_size / total_size
moment_2 += run_stdev * run_stdev * run_size
moment_2 += delta * delta * old_total_size * run_size / total_size
if total_size < 1:
# Avoid division by zero.
return cls(size=0)
# TODO: Is it worth tracking moment_2 instead, and compute and cache
# stdev on demand, just to possibly save some sqrt calls?
total_stdev = math.sqrt(moment_2 / total_size)
ret_obj = cls(size=total_size, avg=total_avg, stdev=total_stdev)
return ret_obj
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