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Diffstat (limited to 'resources/libraries/python/jumpavg/BitCountingStats.py')
-rw-r--r-- | resources/libraries/python/jumpavg/BitCountingStats.py | 169 |
1 files changed, 0 insertions, 169 deletions
diff --git a/resources/libraries/python/jumpavg/BitCountingStats.py b/resources/libraries/python/jumpavg/BitCountingStats.py deleted file mode 100644 index 7b5e659214..0000000000 --- a/resources/libraries/python/jumpavg/BitCountingStats.py +++ /dev/null @@ -1,169 +0,0 @@ -# Copyright (c) 2021 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 math - -from .AvgStdevStats import AvgStdevStats - - -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(). - """ - - def __init__( - self, size=0, avg=None, stdev=0.0, max_value=None, prev_avg=None): - """Construct the stats object by computing from the values needed. - - The values are not sanitized, faulty callers can cause math errors. - - 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. - - :param size: Number of values participating in this group. - :param avg: Population average of the participating sample values. - :param stdev: Population standard deviation of the sample values. - :param max_value: Maximal expected value. - TODO: This might be more optimal, - but max-invariant algorithm will be nicer. - :param prev_avg: Population average of the previous group. - If None, no previous average is taken into account. - If not None, the given previous average is used to discourage - consecutive groups with similar averages - (opposite triangle distribution is assumed). - :type avg: float - :type size: int - :type stdev: float - :type max_value: Union[float, NoneType] - :type prev_avg: Union[float, NoneType] - """ - self.avg = avg - self.size = size - self.stdev = stdev - self.max_value = max_value - self.prev_avg = prev_avg - # 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 avg is None: - raise ValueError(f"Avg is None: {self!r}") - if max_value is None or 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(size * (size + 1), 2) - if prev_avg is None: - # Avg is considered to be uniformly distributed - # from zero to max_value. - self.bits += math.log(max_value + 1.0, 2) - else: - # Opposite triangle distribution with minimum. - self.bits += math.log( - max_value * (max_value + 1) / (abs(avg - 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(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) + math.log(math.pi) * ((size - 1) / 2.0) - sphere_area_ln -= math.lgamma((size - 1) / 2.0) - sphere_area_ln += math.log(stdev + 1.0) * (size - 2) - sphere_area_ln += math.log(size) * ((size - 2) / 2.0) - self.bits += sphere_area_ln / math.log(2) - - def __str__(self): - """Return string with human readable description of the group. - - :returns: Readable description. - :rtype: str - """ - return ( - f"size={self.size} avg={self.avg} stdev={self.stdev}" - f" bits={self.bits}" - ) - - def __repr__(self): - """Return string executable as Python constructor call. - - :returns: Executable constructor call. - :rtype: str - """ - return ( - f"BitCountingStats(size={self.size!r},avg={self.avg!r}" - f",stdev={self.stdev!r},max_value={self.max_value!r}" - f",prev_avg={self.prev_avg!r})" - ) - - @classmethod - def for_runs(cls, runs, max_value=None, prev_avg=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 |