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diff --git a/PyPI/jumpavg/jumpavg/BitCountingClassifier.py b/PyPI/jumpavg/jumpavg/BitCountingClassifier.py
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+++ b/PyPI/jumpavg/jumpavg/BitCountingClassifier.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 BitCountingClassifier class.
+
+This is the main class to be used by callers."""
+
+from AbstractGroupClassifier import AbstractGroupClassifier
+from BitCountingGroup import BitCountingGroup
+from BitCountingGroupList import BitCountingGroupList
+from BitCountingMetadataFactory import BitCountingMetadataFactory
+from ClassifiedMetadataFactory import ClassifiedMetadataFactory
+
+
+class BitCountingClassifier(AbstractGroupClassifier):
+    """Classifier using Minimal Description Length principle."""
+
+    def classify(self, values):
+        """Return the values in groups of optimal bit count.
+
+        The current implementation could be a static method,
+        but we might support options in later versions,
+        for example for chosing encodings.
+
+        :param values: Sequence of runs to classify.
+        :type values: Iterable of float or of AvgStdevMetadata
+        :returns: Classified group list.
+        :rtype: BitCountingGroupList
+        """
+        max_value = BitCountingMetadataFactory.find_max_value(values)
+        factory = BitCountingMetadataFactory(max_value)
+        opened_at = []
+        closed_before = [BitCountingGroupList()]
+        for index, value in enumerate(values):
+            singleton = BitCountingGroup(factory, [value])
+            newly_opened = closed_before[index].with_group_appended(singleton)
+            opened_at.append(newly_opened)
+            record_group_list = newly_opened
+            for previous in range(index):
+                previous_opened_list = opened_at[previous]
+                still_opened = (
+                    previous_opened_list.with_value_added_to_last_group(value))
+                opened_at[previous] = still_opened
+                if still_opened.bits < record_group_list.bits:
+                    record_group_list = still_opened
+            closed_before.append(record_group_list)
+        partition = closed_before[-1]
+        previous_average = partition[0].metadata.avg
+        for group in partition:
+            if group.metadata.avg == previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "normal")
+            elif group.metadata.avg < previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "regression")
+            elif group.metadata.avg > previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "progression")
+            previous_average = group.metadata.avg
+        return partition