75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2016 Hewlett Packard Enterprise Development Company, L.P.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import abc
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from monasca_analytics.component import base
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class BaseSML(base.BaseComponent):
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"""Base SML, to be extended by statistical or machine learning classes"""
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def __init__(self, _id, _config):
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super(BaseSML, self).__init__(_id, _config)
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self._voter = None
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@abc.abstractmethod
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def learn_structure(self, samples):
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"""Learn structure based on the provided data
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Abstract method to be implemented by subclasses.
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It should learn over those samples a structure such as
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a causality matrix or a decision tree that is then
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going to be suggested to a voter which then
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forward it to a LDP.
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:type samples: numpy.ndarray
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:param samples: the list of samples to be processed
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:rtype: numpy.ndarray
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:returns: The information learned with the training set.
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"""
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pass
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@abc.abstractmethod
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def number_of_samples_required(self):
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"""This function returns the number of samples required
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Algorithm typically requires a number of samples before
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being able to produce a result. You can specify this constraint
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with this function.
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:rtype: int
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:returns: the number of samples required.
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"""
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pass
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def learn(self, samples):
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"""Learning phase
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This method is called by the aggregator owning this sml.
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:type samples: numpy.ndarray
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:param samples: the list of samples that can be
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processed by the data.
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"""
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self._voter.suggest_structure(self, self.learn_structure(samples))
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def set_voter(self, voter):
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if self._voter is not None:
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self._voter.remove_sml(self)
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self._voter = voter
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self._voter.append_sml(self)
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