Jeffrey Barish wrote: > Robert Kern wrote: > >> Ivan Voras wrote: >>> Jeffrey Barish wrote: >>> >>>> If you take the difference between two uniformly distributed random >>>> variables, the probability density function forms an isosceles triangle >>>> centered at 0. Take the absolute value of that variable and the pdf is >>>> a >>>> straight line with maximum value at 0 tapering to 0 at max. Thus, >>>> >>>> z = abs(randint(0, max) - randint(0, max)) >>>> >>>> ought to do the trick. >>> It's elegant :) >>> >>> I've noticed something interesting in my test: the value 0 appears less >>> often than other values (which behave as they should). >> The distribution of the difference (before the abs()) looks like this >> (max=4): >> >> # >> ### >> ##### >> ####### >> ---0+++ >> 321 123 >> >> Taking the absolute value doubles up the non-zero masses, but there's no >> "negative 0" to add to the 0s stack. >> >> # >> # >> ### >> ### >> #### >> #### >> 0123 >> >> The method does not work because of that. >> > The math says that it works, so we must not be implementing it correctly.
"The math" says nothing of the kind about the method that was stated. > I > suspect that our mistake is quantizing the random variable first and then > taking the difference and absolute value. What result do you get when you > quantize once? That is, obtain two random values (floats) with uniform > pdf. Take the difference. Abs. Round to int. This way, everything in the > band from (-0.5, +0.5) goes to 0, and that's the highest part of the > triangle. That's a very different "it". The difference is not just implementation. If you change "round" to "truncate", that method should work, though. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
