Is there a pythonic way to build up a list that contains a running average of some function?
After reading a fun little piece about Martians, black boxes, and the Cauchy distribution, I thought it would be fun to calculate a running average of the Cauchy distribution myself:
import math
import random
def cauchy(location, scale):
p = 0.0
while p == 0.0:
p = random.random()
return location + scale*math.tan(math.pi*(p - 0.5))
# is this next block of code a good way to populate running_avg?
sum = 0
count = 0
max = 10
running_avg = []
while count < max:
num = cauchy(3,1)
sum += num
count += 1
running_avg.append(sum/count)
print running_avg # or do something else with it, besides printing
I think that this approach works, but I'm curious if there might be a more elegant approach to building up that running_avg
list than using loops and counters (e.g. list comprehensions).
There are some related questions, but they address more complicated problems (small window size, exponential weighting) or aren't specific to Python:
You could write a generator:
def running_average():
sum = 0
count = 0
while True:
sum += cauchy(3,1)
count += 1
yield sum/count
Or, given a generator for Cauchy numbers and a utility function for a running sum generator, you can have a neat generator expression:
# Cauchy numbers generator
def cauchy_numbers():
while True:
yield cauchy(3,1)
# running sum utility function
def running_sum(iterable):
sum = 0
for x in iterable:
sum += x
yield sum
# Running averages generator expression (** the neat part **)
running_avgs = (sum/(i+1) for (i,sum) in enumerate(running_sum(cauchy_numbers())))
# goes on forever
for avg in running_avgs:
print avg
# alternatively, take just the first 10
import itertools
for avg in itertools.islice(running_avgs, 10):
print avg