Kent Johnson posted this to Tutor list Aug 8, 2007
(<http://mail.python.org/pipermail/tutor/2007-August/056194.html>):
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> Python provides you with a pseudo random number generator whose output
> values are uniformly distributed between the input parameters. What you
> are dealing with in fish weights or test scores or other natural
> phenomena is most likely a normal distribution. Check out Wikipedia's
> normal distribution entry. The math is really juicy. You may end up
> with a recipe for the Python Cookbook.
No need for all that, use random.gauss()
Kent
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I hadn't noticed gauss was there in the Random module. I got to
wondering if I could graph the distribution. This code produces a
nice bell-curve-seeming curve (on its side). Takes about 80 secs to
run on my computer. To fit your situation, the length of the bars can
be shortened or lengthened by decreasing or increasing, respectively,
the divisor of gaussCalls in line 5, "barLengthAdjuster = gaussCalls//2600".
Dick Moores
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from random import gauss
mean = 100
std = 10
gaussCalls = 10000000
barLengthAdjuster = gaussCalls//2600
d = []
for k in range(200):
d.append([k, 0])
for k in xrange(gaussCalls):
n = int(gauss(mean, std))
d[n][1] += 1
for c in d:
barLength = c[1]//barLengthAdjuster
print barLength, "=", c[0], c[1]
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