Re: Generate a sequence of random numbers that sum up to 1?

Discussion in 'Python' started by Anthony Liu, Apr 22, 2006.

  1. Anthony Liu

    Anthony Liu Guest

    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

    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 <nobody@> 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).
    > --

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    Anthony Liu, Apr 22, 2006
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  2. Paul Rubin

    Paul Rubin Guest

    Anthony Liu <> writes:
    > OK, I actually just want to "manually" create Hidden
    > Markov Models by randomly generating the initial state
    > probabilities PI,

    OK, this sounds like you actually want to divide the unit interval up
    into pieces of varying sizes. Example (untested):

    n = 10 # number of pieces desired

    # Take the unit interval [0,1] and sprinkle in n-1 random points
    s = sorted([0,1] + [random() for i in xrange(n-1)])

    # now find the size of each sub-interval and make a list
    r = [(s[i+1] - s) for i in xrange(n)]

    print sum(r) # should be 1.0 within rounding error
    Paul Rubin, Apr 30, 2006
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