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42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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Tencent is pleased to support the open source community by making Metis available.
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Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
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Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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https://opensource.org/licenses/BSD-3-Clause
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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.
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"""
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import numpy as np
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class Statistic(object):
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"""
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In statistics, the 68-95-99.7 rule is a shorthand used to remember the percentage of values
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that lie within a band around the mean in a normal distribution with a width of two, four and
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six standard deviations, respectively; more accurately, 68.27%, 95.45% and 99.73% of the values
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lie within one, two and three standard deviations of the mean, respectively.
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WIKIPEDIA: https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule
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"""
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def __init__(self, index=3):
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"""
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:param index: multiple of standard deviation
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:param type: int or float
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"""
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self.index = index
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def predict(self, X):
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"""
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Predict if a particular sample is an outlier or not.
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:param X: the time series to detect of
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:param type X: pandas.Series
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:return: 1 denotes normal, 0 denotes abnormal
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"""
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if abs(X[-1] - np.mean(X[:-1])) > self.index * np.std(X[:-1]):
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return 0
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return 1
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