Refactor jumpavg to be more readable and usable

This is the python3 change, the python2 is still used for testing bisect.

+ New version is 0.2.0 due to amount o API changes.
+ Jumpavg is now part of CSIT resource package tree.
+ Perpatch migrated to new jumpavg.
- PAL NOT updated (the update moved to a different Change).

Change-Id: I7d7a8bf8a411196c20c2a40a8c64478d6709bc07
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 169 insertions, 0 deletions

diff --git a/resources/libraries/python/jumpavg/BitCountingStats.py b/resources/libraries/python/jumpavg/BitCountingStats.py
new file mode 100644
index 0000000000..0addec013b
--- /dev/null
+++ b/resources/libraries/python/jumpavg/BitCountingStats.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2019 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