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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
# This file is ported from spark/util/StatCounter.scala
import copy
import math
try:
from numpy import maximum, minimum, sqrt
except ImportError:
maximum = max
minimum = min
sqrt = math.sqrt
class StatCounter(object):
def __init__(self, values=None):
if values is None:
values = list()
self.n = 0 # Running count of our values
self.mu = 0.0 # Running mean of our values
self.m2 = 0.0 # Running variance numerator (sum of (x - mean)^2)
self.maxValue = float("-inf")
self.minValue = float("inf")
for v in values:
self.merge(v)
# Add a value into this StatCounter, updating the internal statistics.
def merge(self, value):
delta = value - self.mu
self.n += 1
self.mu += delta / self.n
self.m2 += delta * (value - self.mu)
self.maxValue = maximum(self.maxValue, value)
self.minValue = minimum(self.minValue, value)
return self
# Merge another StatCounter into this one, adding up the internal statistics.
def mergeStats(self, other):
if not isinstance(other, StatCounter):
raise Exception("Can only merge Statcounters!")
if other is self: # reference equality holds
self.merge(copy.deepcopy(other)) # Avoid overwriting fields in a weird order
else:
if self.n == 0:
self.mu = other.mu
self.m2 = other.m2
self.n = other.n
self.maxValue = other.maxValue
self.minValue = other.minValue
elif other.n != 0:
delta = other.mu - self.mu
if other.n * 10 < self.n:
self.mu = self.mu + (delta * other.n) / (self.n + other.n)
elif self.n * 10 < other.n:
self.mu = other.mu - (delta * self.n) / (self.n + other.n)
else:
self.mu = (self.mu * self.n + other.mu * other.n) / (self.n + other.n)
self.maxValue = maximum(self.maxValue, other.maxValue)
self.minValue = minimum(self.minValue, other.minValue)
self.m2 += other.m2 + (delta * delta * self.n * other.n) / (self.n + other.n)
self.n += other.n
return self
# Clone this StatCounter
def copy(self):
return copy.deepcopy(self)
def count(self):
return int(self.n)
def mean(self):
return self.mu
def sum(self):
return self.n * self.mu
def min(self):
return self.minValue
def max(self):
return self.maxValue
# Return the variance of the values.
def variance(self):
if self.n == 0:
return float('nan')
else:
return self.m2 / self.n
#
# Return the sample variance, which corrects for bias in estimating the variance by dividing
# by N-1 instead of N.
#
def sampleVariance(self):
if self.n <= 1:
return float('nan')
else:
return self.m2 / (self.n - 1)
# Return the standard deviation of the values.
def stdev(self):
return sqrt(self.variance())
#
# Return the sample standard deviation of the values, which corrects for bias in estimating the
# variance by dividing by N-1 instead of N.
#
def sampleStdev(self):
return sqrt(self.sampleVariance())
def __repr__(self):
return ("(count: %s, mean: %s, stdev: %s, max: %s, min: %s)" %
(self.count(), self.mean(), self.stdev(), self.max(), self.min()))
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