This should do the trick: WITH CTE AS ( SELECT random() * (SELECT SUM(percent) FROM YOUR_TABLE) R ) SELECT * FROM ( SELECT id, SUM(percent) OVER (ORDER BY id) S, R FROM YOUR_TABLE CROSS JOIN CTE ) Q WHERE S >= R ORDER BY id LIMIT 1; The sub-query Q gives the following result: 1 … Read more
One solution is to generate an array (a “bucket”) with all the values you want to pick, in this case all numbers from 0 to 10. Then you pick one randomly from the array and remove it from the bucket. Note that the example below doesn’t check if the bucket is empty, so if you … Read more
float p = (rand() / static_cast<float>(RAND_MAX)) * total_probability; s* current = &sArray[0]; while ( (p -= current->probability) > 0) ++current; // `current` now points to your chosen target
Why do you want to use a random function? I always assumed that tinyurl used a base 62 (0-9A-Za-z) representation of a sequential Id. No clashes and the urls are always as short as possible. You would have a DB table like Id URL 1 http://google.com 2 … … … 156 … … … and … Read more
The events (p)(1-p) and (1-p)(p) are equiprobable. Taking them as 0 and 1 respectively and discarding the other two pairs of results you get an unbiased random generator. In code this is done as easy as: int UnbiasedRandom() { int x, y; do { x = BiasedRandom(); y = BiasedRandom(); } while (x == y); … Read more
There are probably a lot of different (and good) answers, but in my humble opinion, the common characteristics of probabilistic data structures is that they provide you with approximate, not precise answer. How many items are here? About 1523425 with probability of 99% Update: Quick search produced link to decent article on the issue: https://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/
Use : norm = [float(i)/sum(raw) for i in raw] to normalize against the sum to ensure that the sum is always 1.0 (or as close to as possible). use norm = [float(i)/max(raw) for i in raw] to normalize against the maximum
Yours is a pretty good way already and works well with any range. Just thinking: another possibility is to get rid of the fractions by multiplying with a constant multiplier, and then build an array with the size of this multiplier. Multiplying by 10 you get P(1) = 2 P(2) = 3 P(3) = 5 … Read more
random.random() returns a uniformly distributed pseudo-random floating point number in the range [0, 1). This number is less than a given number p in the range [0,1) with probability p. Thus: def flip(p): return ‘H’ if random.random() < p else ‘T’ Some experiments: >>> N = 100 >>> flips = [flip(0.2) for i in xrange(N)] … Read more