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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.
from BitCountingGroup import BitCountingGroup
from BitCountingGroupList import BitCountingGroupList
from BitCountingMetadataFactory import BitCountingMetadataFactory
from ClassifiedMetadataFactory import ClassifiedMetadataFactory
class BitCountingClassifier(object):
@staticmethod
def classify(values):
"""Return the values in groups of optimal bit count.
TODO: Could we return BitCountingGroupList and let caller process it?
:param values: Sequence of runs to classify.
:type values: Iterable of float or of AvgStdevMetadata
:returns: Classified group list.
:rtype: list of BitCountingGroup
"""
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.group_list
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