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Forked from pims/runningCorrelation.py
Created November 6, 2017 16:23
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#/usr/bin/env python
import math
class RunningCorrelation:
def __init__(self):
self.n = 0.0
self.mean_x = 0.0
self.mean_y = 0.0
self.m2_x = 0.0
self.m2_y = 0.0
self.cov = 0.0
self.variance_x = 0.0
self.variance_y = 0.0
self.old_mean_x = 0.0
self.old_mean_y = 0.0
def __online_variance(self,x,y):
delta_x = x - self.mean_x
delta_y = y - self.mean_y
self.mean_x = self.mean_x + delta_x / self.n
self.mean_y = self.mean_y + delta_y / self.n
self.m2_x = self.m2_x + delta_x * (x - self.mean_x) # This expression uses the new value of mean
self.m2_y = self.m2_y + delta_y * (y - self.mean_y) # This expression uses the new value of mean
self.variance_x = self.m2_x/(self.n - 1)
self.variance_y = self.m2_y/(self.n - 1)
def __online_covariance(self,x,y):
self.cov = self.cov + ((y - self.old_mean_y) * (x - self.old_mean_x)) * (self.n - 1.0) / self.n
def push(self,x,y):
self.n = self.n + 1
if self.n == 1:
self.mean_x = x
self.mean_y = y
else:
self.old_mean_x = self.mean_x
self.old_mean_y = self.mean_y
self.__online_variance(x,y)
self.__online_covariance(x,y)
@property
def r(self):
if self.cov > 0:
return self.cov / (math.sqrt(self.variance_x) * math.sqrt(self.variance_y))
return 0
@property
def variance(self):
return (self.variance_x,self.variance_y)
def main():
rc = RunningCorrelation()
v1 = [2.5,3.5,3.0,3.5,2.5,3.0]
v2 = [3.0,3.5,1.5,5.0,3.5,3.0]
for x,y in zip(v1,v2):
rc.push(x,y)
print rc.r #value of r is wrong, and is twice the correct value on the first pass > 1
if __name__ == '__main__':
main()
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