Vincent Davis wrote: >> # Clamp a normal distribution outcome
I don't know who you are quoting -- you should give attribution to them. >> def clamp(input, min=0, max=100): ... >> if input < min: >> return min >> elif input > max: >> return max >> else: >> return input An easier way to do this: return min(100, max(0, input)) but again, I stress that this will strongly distort the random distribution. It's probably not what you want. > Why not have the def clamp inside the class? Why bother? > I would prefer to keep > everything I need for the class together. But you don't. You have the random.normalvariate in a completely different module. I'm sure you have other operations like +, - etc as built-in functions. Not everything is inside the class. > I am new to classes but I have to say things like if __name__ == > "__main__": have no intuitive meaning to me. It is true I don't know > what __name__ and __main__ do and I can look it up but I don't even > have a guess based on the names and usage. When you import a module with the line: import mymodule Python automatically creates a variable mymodule.__name__ and sets it to the string "__mymodule__". When you run a module as a script, by calling it from the shell, using (for example): $ python mymodule.py Python automatically creates the variable mymodule.__name__ as before, but this time sets its value to the string "__main__". So the construction: if __name__ == "__main__": do_stuff_here() is a way to include code that will only be executed when running the module as a script, not when it is imported as a module. > I am Now not sure if that is what I want or If I want to redraw from > the distribution. I am wanting to simulate test scores. My option see > to be to draw from a normal (I don't want linear) distribution and > scale it to 0-100 or clamp it as you (Xavier) suggested or draw from > the distribution again (this is what I was thinking) I think this is > still what I want but I should look up the implications of each. The > problem I have with the clamp is that the tails in the sample could be > large. Strictly speaking, you want a different distribution, not normal. Possibly the F-distribution? Anyway, whatever it is, unless you need very accurate results, a truncated normal distribution shouldn't be *too* far off: close enough for government work. Clamping will not be very good: it will result in an excess of 0 and 100 scores. Imagine a graph that looks vaguely like this: 000: ********** 010: * 020: ** 030: **** 040: ******* 050: ********* 060: ********** 070: ******** 080: ***** 090: ** 100: ************* That's what you'll get by clamping (possibly exaggerated for effect). Truncating with a while loop will result in something closer to this: 000: * 010: * 020: ** 030: **** 040: ******* 050: ********* 060: ********** 070: ******** 080: ***** 090: ** 100: * which is far less distorted. -- Steven -- http://mail.python.org/mailman/listinfo/python-list
