CSIT-1110: Use jumpavg library from pip

+ Move the jumpavg library code to separate directory.
- Bump to 0.1.2 has to be done later.

Change-Id: I9722ede48f00e99eeb68ca3f91e0bdeee2937973
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 0 insertions, 109 deletions

diff --git a/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py b/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
deleted file mode 100644
index d25d355cab..0000000000
--- a/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# 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)