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-rw-r--r--resources/tools/presentation/new/jumpavg/BitCountingMetadata.py109
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diff --git a/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py b/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
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--- a/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
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-# Copyright (c) 2018 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 BitCountingMetadata class."""
-
-import math
-
-from AvgStdevMetadata import AvgStdevMetadata
-
-
-class BitCountingMetadata(AvgStdevMetadata):
- """Class for metadata which includes 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.
- """
-
- def __init__(self, max_value, size=0, avg=0.0, stdev=0.0, prev_avg=None):
- """Construct the metadata by computing from the values needed.
-
- 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 max_value: Maximal expected value.
- TODO: This might be more optimal,
- but max-invariant algorithm will be nicer.
- :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 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 max_value: float
- :type size: int
- :type avg: float
- :type stdev: float
- :type prev_avg: float or None
- """
- super(BitCountingMetadata, self).__init__(size, avg, stdev)
- self.max_value = max_value
- self.prev_avg = prev_avg
- self.bits = 0.0
- if self.size < 1:
- return
- # 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)
- 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 "size={size} avg={avg} stdev={stdev} bits={bits}".format(
- size=self.size, avg=self.avg, stdev=self.stdev, bits=self.bits)
-
- def __repr__(self):
- """Return string executable as Python constructor call.
-
- :returns: Executable constructor call.
- :rtype: str
- """
- return ("BitCountingMetadata(max_value={max_value},size={size}," +
- "avg={avg},stdev={stdev},prev_avg={prev_avg})").format(
- max_value=self.max_value, size=self.size, avg=self.avg,
- stdev=self.stdev, prev_avg=self.prev_avg)