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Diffstat (limited to 'resources/libraries/python/jumpavg/BitCountingStats.py')
| -rw-r--r-- | resources/libraries/python/jumpavg/BitCountingStats.py | 149 |
1 files changed, 0 insertions, 149 deletions
diff --git a/resources/libraries/python/jumpavg/BitCountingStats.py b/resources/libraries/python/jumpavg/BitCountingStats.py deleted file mode 100644 index 524ac952c8..0000000000 --- a/resources/libraries/python/jumpavg/BitCountingStats.py +++ /dev/null @@ -1,149 +0,0 @@ -# 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 |