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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.
+
+import math
+
+from AvgStdevMetadata import AvgStdevMetadata
+
+
+class BitCountingMetadata(AvgStdevMetadata):
+ """Class for metadata which includes information content."""
+
+ 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)