> # Clamp a normal distribution outcome > > import random > > class applicant(): > def __init__(self, x, y): > self.randomnum = clamp(random.normalvariate(x, y), 0, 100) > > def clamp(input, min=0, max=100): > """Clamps the input between min and max. > > if input < min, returns min > min < input < max, returns input > input > max, returns max > > Default: min = 0, max = 100.""" > if input < min: > return min > elif input > max: > return max > else: > return input > > if __name__ == "__main__": > for num in range(10): > print applicant(random.randint(0,100), > random.randint(0,100)).randomnum
Why not have the def clamp inside the class? I would prefer to keep everything I need for the class together. 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. 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. Thanks Vincent -- http://mail.python.org/mailman/listinfo/python-list
