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executable file 72 lines (49 sloc) 1.34 KB
#!/usr/bin/env python
import math
from blb import *
def mean(sample):
return sum(sample)*1.0/len(sample)
def stddev(sample):
mn = mean(sample)
return math.sqrt(sum([(x-mn)*(x-mn) for x in sample])*1.0/(len(sample)-1))
class MeanMean_BLB(BLB):
def compute_estimate(self, sample):
return mean(sample)
def reduce_bootstraps(self, sample):
return mean(sample)
def average(self, sample):
return mean(sample)
class SDMean_BLB(BLB):
def compute_estimate(self, sample):
return mean(sample)
def reduce_bootstraps(self, sample):
return stddev(sample)
def average(self, sample):
return mean(sample)
class MeanSD_BLB(BLB):
def compute_estimate(self, sample):
return stddev(sample)
def reduce_bootstraps(self, sample):
return mean(sample)
def average(self, sample):
return mean(sample)
class SDSD_BLB(BLB):
def compute_estimate(self, sample):
return stddev(sample)
def reduce_bootstraps(self, sample):
return stddev(sample)
def average(self, sample):
return mean(sample)
data = range(10000)
blb = MeanMean_BLB()
result = blb.run(data)
print ("Mean of Mean: ", result)
blb = SDMean_BLB()
result = blb.run(data)
print ("SD of Mean: ", result)
blb = MeanSD_BLB()
result = blb.run(data)
print ("Mean of SD: ", result)
blb = SDSD_BLB()
result = blb.run(data)
print ("SD of SD: ", result)