Thanks a lot, Alex and Gerard. I am actually not very concerned about the inter-dependency of the floating numbers generated randomly. They are good enough if they are subject to the constraint of summing up to 1.
It is simply not worth the time to get an HMM by training it on a large corpus. My sole purpose is to test the predicting power of an HMM, given a set of parameter values. I will definitely try out your snippet and see if it works. Thanks a lot! --- Alex Martelli <[EMAIL PROTECTED]> wrote: > Anthony Liu <[EMAIL PROTECTED]> wrote: > ... > > As a matter of fact, given that we have to specify > the > > number of states for an HMM, I would like to > create a > > specified number of random floating numbers whose > sum > > is 1.0. > > def forAL(N): > N_randoms = [random.random() for x in > xrange(N)] > total = sum(N_randoms) > return [x/total for x in N_randoms] > > > Does this do what you want? Of course, the > resulting numbers are not > independent, but then the constraints you pose would > contradict that. > > > Alex > -- > http://mail.python.org/mailman/listinfo/python-list > __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com -- http://mail.python.org/mailman/listinfo/python-list
