67 lines
1.7 KiB
Python
67 lines
1.7 KiB
Python
#
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# Copyright 2014 Red Hat, Inc
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#
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# Author: Eoghan Glynn <eglynn@redhat.com>
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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 math
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def mean(s, key=lambda x: x):
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"""Calculate the mean of a numeric list.
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"""
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count = float(len(s))
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if count:
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return math.fsum(map(key, s)) / count
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return 0.0
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def deltas(s, key, m=None):
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"""Calculate the squared distances from mean for a numeric list.
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"""
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m = m or mean(s, key)
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return [(key(i) - m) ** 2 for i in s]
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def variance(s, key, m=None):
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"""Calculate the variance of a numeric list.
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"""
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return mean(deltas(s, key, m))
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def stddev(s, key, m=None):
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"""Calculate the standard deviation of a numeric list.
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"""
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return math.sqrt(variance(s, key, m))
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def outside(s, key, lower=0.0, upper=0.0):
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"""Determine if value falls outside upper and lower bounds.
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"""
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v = key(s)
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return v < lower or v > upper
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def anomalies(s, key, lower=0.0, upper=0.0):
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"""Separate anomalous data points from the in-liers.
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"""
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inliers = []
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outliers = []
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for i in s:
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if outside(i, key, lower, upper):
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outliers.append(i)
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else:
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inliers.append(i)
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return inliers, outliers
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