OK, I actually just want to "manually" create Hidden Markov Models by randomly generating the initial state probabilities PI, the transition probabilities A and the emission probabilities B, instead of learning such statistics from a corpus. They have to be subject the constraint that
sum(PI) = 1.0 sum(each row of A) = 1.0 sum(each row of B) = 1.0 Got an idea? Thank you for your help. I guess I can use random() instead of uniform(0,1) given your comments about uniform. I did not know the uniform function until a moment ago when I checked the python.org documentation. 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. --- Edward Elliott <[EMAIL PROTECTED]> wrote: > Anthony Liu wrote: > > But, I want the random numbers just generated sum > up > > to 1 . > > This seems like an odd request. Might I ask what > it's for? > > Generating random numbers in [0,1) that are both > uniform and sum to 1 looks > like an unsatisfiable task. Each number you > generate restricts the > possibilities for future numbers. E.g. if the first > number is 0.5, all > future numbers must be < 0.5 (indeed, must *sum* to > 0.5). You'll end up > with a distribution increasingly skewed towards > smaller numbers the more > you generate. I can't imagine what that would be > useful for. > > If that's not a problem, do this: generate the > numbers, add them up, and > divide each by the sum. > > nums = [random.uniform(0,1) for x in range(0,100)] > sum = reduce(lambda x,y: x+y, nums) > norm = [x/sum for x in nums] > > Of course now the numbers aren't uniform over [0,1) > anymore. > > Also note that the sum of the normalized numbers > will be very close to 1, > but slightly off due to representation issues. If > that level of accuracy > matters, you might consider generating your rands as > integers and then > fp-dividing by the sum (or just store them as > integers/fractions). > -- > 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
