J. Clifford Dyer wrote:
> My understanding of what you are looking for is that on each individual
> selection,
> the probability of picking a given name is that name's prob value, divided by
> the
> sum of all prob values. That is, in the following case:
> items = [('Mary', 96),('Shimon', 1)
On 2007-11-17, Bruza <[EMAIL PROTECTED]> wrote:
> OOPS. I pressed the Send too fast.
>
> The problem w/ Boris's solution is that after repeated calling
> of randomPick(3,items), 'Jane' is not the most "frequent
> appearing" member in all the out list of 3 member lists...
How does this solution fai
On Sat, 17 Nov 2007 13:51:16 -0800, [EMAIL PROTECTED] wrote:
> I am not guaranteeing this even works. I am seeing that there is some
> collision among the numbers, but it will work for the most part.
"Work for the most part" -- is that another way of saying "Apart from the
bugs, this is bug-free
Knuth says to pick N distinct records from a collection where the
probability is equal you should:
first fill up N records by chosing the first seen.
if less than N were in the collection, quit.
otherwise, t = (the number of items looked at) or N to start.
while your not at the end of the colle
On Fri, 2007-11-16 at 16:47 -0800, Bruza wrote:
I think I need to explain on the probability part: the "prob" is a
> relative likelihood that the object will be included in the output
> list. So, in my example input of
>
> items = [('Mary',30), ('John', 10), ('Tom', 45), ('Jane', 15)]
>
> So, f
How about this variation on your intial attempt?
# Untested!
def randomPick(n, items):
def pickOne():
index = random.randint(0, 99)
currentP = 0
for (obj, p) in items:
currentP += p
if currentP > index:
return obj
selection = set()
while len(selection) < n:
On Fri, 16 Nov 2007 16:47:16 -0800, Bruza wrote:
> I think I need to explain on the probability part: the "prob" is a
> relative likelihood that the object will be included in the output list.
> So, in my example input of
>
> items = [('Mary',30), ('John', 10), ('Tom', 45), ('Jane', 15)]
>
> S
Bruza wrote:
> On Nov 16, 4:47 pm, Bruza <[EMAIL PROTECTED]> wrote:
>> On Nov 16, 6:58 am, duncan smith <[EMAIL PROTECTED]>
>> wrote:
>>
>>
>>
>>> Bruza wrote:
I need to implement a "random selection" algorithm which takes a list
of [(obj, prob),...] as input. Each of the (obj, prob) repr
Maybe it would help to make your problem statement a litte rigorous so
we can get a clearer idea of whats required.
One possible formulation:
Given a list L of pairs of values, weightings: [ (v_0, w_0), (v_1,
w_1), ], and some N between 1 and length(L)
you would like to randomly select a set
On Nov 16, 4:47 pm, Bruza <[EMAIL PROTECTED]> wrote:
> On Nov 16, 6:58 am, duncan smith <[EMAIL PROTECTED]>
> wrote:
>
>
>
> > Bruza wrote:
> > > I need to implement a "random selection" algorithm which takes a list
> > > of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> > > l
On Nov 16, 6:58 am, duncan smith <[EMAIL PROTECTED]>
wrote:
> Bruza wrote:
> > I need to implement a "random selection" algorithm which takes a list
> > of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> > likely an object, "obj", should be selected based on its probability
> >
Bruza wrote:
> I need to implement a "random selection" algorithm which takes a list
> of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> likely an object, "obj", should be selected based on its probability
> of
> "prob".To simplify the problem, assuming "prob" are integers, an
Boris Borcic wrote:
> Bruza wrote:
>> No. That does not solve the problem. What I want is a function
>>
>> def randomPick(n, the_items):
>>
>> which will return n DISTINCT items from "the_items" such that
>> the n items returned are according to their probabilities specified
>> in the (item, pro)
Bruza wrote:
> No. That does not solve the problem. What I want is a function
>
> def randomPick(n, the_items):
>
> which will return n DISTINCT items from "the_items" such that
> the n items returned are according to their probabilities specified
> in the (item, pro) elements inside "the_items
Bruza <[EMAIL PROTECTED]> writes:
> But how about the general case, for N > 1 and N < len(items)? Is there
> some clever algorithm using Python standard "random" package
Yeah, I'm not sure what the name for it is, but there'ss a well known
algorithm that's sort of an online verison of random.choic
This recipe of mine may help:
http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/498229
Bye,
bearophile
--
http://mail.python.org/mailman/listinfo/python-list
Bruza wrote:
> No. That does not solve the problem. What I want is a function
>
> def randomPick(n, the_items):
>
> which will return n DISTINCT items from "the_items" such that
> the n items returned are according to their probabilities specified
> in the (item, pro) elements inside "the_items
On Nov 15, 9:32 pm, "[EMAIL PROTECTED]" <[EMAIL PROTECTED]> wrote:
> On Nov 15, 10:40�pm, Bruza <[EMAIL PROTECTED]> wrote:
>
>
>
> > I need to implement a "random selection" algorithm which takes a list
> > of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> > likely an object,
On Nov 15, 10:40�pm, Bruza <[EMAIL PROTECTED]> wrote:
> I need to implement a "random selection" algorithm which takes a list
> of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> likely an object, "obj", should be selected based on its probability
> of
> "prob".To simplify the
Bruza wrote:
> I need to implement a "random selection" algorithm which takes a list
> of [(obj, prob),...] as input. Each of the (obj, prob) represents how
> likely an object, "obj", should be selected based on its probability
> of
> "prob".To simplify the problem, assuming "prob" are integers, an
I need to implement a "random selection" algorithm which takes a list
of [(obj, prob),...] as input. Each of the (obj, prob) represents how
likely an object, "obj", should be selected based on its probability
of
"prob".To simplify the problem, assuming "prob" are integers, and the
sum of all "prob"
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