best_benchmark_statistic.py

import random

# rand = lambda: random.normalvariate(0, 1)
rand = lambda: random.lognormvariate(0, 1)
# rand = lambda: random.expovariate(1)
def rand():
    t = 0.0
    N=1000
    P=0.1
    for i in xrange(N):
        if random.random() < P:
            t += 1.0/(N*P)
    return t

# print dir(random)
def gen():
    l = [rand() for i in xrange(3)]
    return (min(l), sum(l) / len(l))

mins = []
avgs = []
for i in xrange(10000):
    m, a = gen()
    mins.append(m)
    avgs.append(a)

def stddev(l):
    s = 0
    ssq = 0
    for x in l:
        s += x
        ssq += x * x

    s /= len(l)
    ssq /= len(l)
    return (ssq - s * s) ** 0.5

print "min:", stddev(mins)
print "avg:", stddev(avgs)