+++ /dev/null
-# Copyright (c) 2022 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 BitCountingStats class."""
-
-import dataclasses
-import math
-import typing
-
-from .AvgStdevStats import AvgStdevStats
-
-
-@dataclasses.dataclass
-class BitCountingStats(AvgStdevStats):
- """Class for statistics which include information content of a group.
-
- The information content is based on an assumption that the data
- consists of independent random values from a normal distribution.
-
- Instances are only statistics, the data itself is stored elsewhere.
-
- The coding needs to know the previous average, and a maximal value
- so both values are required as inputs.
-
- This is a subclass of AvgStdevStats, even though all methods are overriden.
- Only for_runs method calls the parent implementation, without using super().
- """
-
- max_value: float = None
- """Maximal sample value (real or estimated).
- Default value is there just for argument ordering reasons,
- leaving None leads to exceptions."""
- prev_avg: typing.Optional[float] = None
- """Population average of the previous group (if any)."""
- bits: float = None
- """The computed information content of the group.
- It is formally an argument to init function, just to keep repr string
- a valid call. ut the init value is ignored and always recomputed.
- """
-
- def __post_init__(self):
- """Construct the stats object by computing from the values needed.
-
- The None values are allowed for stats for zero size data,
- but such stats can report arbitrary avg and max_value.
- Stats for nonzero size data cannot contain None,
- else ValueError is raised.
-
- The max_value needs to be numeric for nonzero size,
- but its relations to avg and prev_avg are not examined.
-
- The bit count is not real, as that would depend on numeric precision
- (number of significant bits in values).
- The difference is assumed to be constant per value,
- which is consistent with Gauss distribution
- (but not with floating point mechanic).
- The hope is the difference will have
- no real impact on the classification procedure.
- """
- # Zero size should in principle have non-zero bits (coding zero size),
- # but zero allows users to add empty groups without affecting bits.
- self.bits = 0.0
- if self.size < 1:
- return
- if self.max_value <= 0.0:
- raise ValueError(f"Invalid max value: {self!r}")
- # Length of the sequence must be also counted in bits,
- # otherwise the message would not be decodable.
- # Model: probability of k samples is 1/k - 1/(k+1) == 1/k/(k+1)
- # This is compatible with zero size leading to zero bits.
- self.bits += math.log(self.size * (self.size + 1), 2)
- if self.prev_avg is None:
- # Avg is considered to be uniformly distributed
- # from zero to max_value.
- self.bits += math.log(self.max_value + 1.0, 2)
- else:
- # Opposite triangle distribution with minimum.
- self.bits += math.log(
- (self.max_value * (self.max_value + 1))
- / (abs(self.avg - self.prev_avg) + 1),
- 2,
- )
- if self.size < 2:
- return
- # Stdev is considered to be uniformly distributed
- # from zero to max_value. That is quite a bad expectation,
- # but resilient to negative samples etc.
- self.bits += math.log(self.max_value + 1.0, 2)
- # Now we know the samples lie on sphere in size-1 dimensions.
- # So it is (size-2)-sphere, with radius^2 == stdev^2 * size.
- # https://en.wikipedia.org/wiki/N-sphere
- sphere_area_ln = math.log(2)
- sphere_area_ln += math.log(math.pi) * ((self.size - 1) / 2.0)
- sphere_area_ln -= math.lgamma((self.size - 1) / 2.0)
- sphere_area_ln += math.log(self.stdev + 1.0) * (self.size - 2)
- sphere_area_ln += math.log(self.size) * ((self.size - 2) / 2.0)
- self.bits += sphere_area_ln / math.log(2)
-
- # TODO: Rename, so pylint stops complaining about signature change.
- @classmethod
- def for_runs(
- cls,
- runs: typing.Iterable[typing.Union[float, AvgStdevStats]],
- max_value: float,
- prev_avg: typing.Optional[float] = None,
- ):
- """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.
-
- The two optional values can come from outside of the runs provided.
-
- The max_value cannot be None for non-zero size data.
- The implementation does not check if no datapoint exceeds max_value.
-
- TODO: Document the behavior for zero size result.
-
- :param runs: Sequence of data to describe by the new metadata.
- :param max_value: Maximal expected value.
- :param prev_avg: Population average of the previous group, if any.
- :type runs: Iterable[Union[float, AvgStdevStats]]
- :type max_value: Union[float, NoneType]
- :type prev_avg: Union[float, NoneType]
- :returns: The new stats instance.
- :rtype: cls
- """
- asd = AvgStdevStats.for_runs(runs)
- ret_obj = cls(
- size=asd.size,
- avg=asd.avg,
- stdev=asd.stdev,
- max_value=max_value,
- prev_avg=prev_avg,
- )
- return ret_obj
Code Review / csit.git / blobdiff