random-sample

问题 I am using the following code which generates random number between 0 to Totalfriends, I would like to get the random numbers but they should not be repeated. Any idea how? This is the code I am using FB.getLoginStatus(function(response) { var profilePicsDiv = document.getElementById('profile_pics'); FB.api({ method: 'friends.get' }, function(result) { // var result =resultF.data; // console.log(result); var user_ids="" ; var totalFriends = result.length; // console.log(totalFriends); var

问题 I would like to sample from a multinomial distribution. I would do this by using sample and specifying some probabilites. E.g: I have 3 categories, and I want to sample 10 times. > my_prob = c(0.2, 0.3, 0.5) > x = sample(c(0:2), 100, replace = T, prob = my_prob) > head(x) [1] 2 0 2 1 1 2 My setting is now only different in the following aspect: I want to sample a lot (e.g. 1e09) numbers. And actually I am only interested in the frequency of each category. So in the above mentioned example

问题 Does anyone have some ideas on how to generate a bootstrapped sample of rows in T-SQL? The problem with using something like NEWID() or RAND() then just sorting is that you end up generating a random sample without replacement. Bootstrapping should be done by sampling with replacement. 回答1: Here's one solution. It starts by assigning a row identifier to each row in the population table. Next, a uniform random integer between 1 and the rowcount of the population, inclusive, is generated using

问题 I have two columns in a large file, say pro1 lig1 pro2 lig2 pro3 lig3 pro4 lig1 ..... Second is column redundant. I want new random combinations of double size which should not match given combination, for example pro1 lig2 pro1 lig4 pro2 lig1 pro2 lig3 pro3 lig4 pro3 lig2 pro4 lig2 pro4 lig3 ..... Thanks. 回答1: If you want exactly two results for each value in column one, I'd brute force the non-matching part, with something like this: import random def gen_random_data(inputfile): with open

问题 This question already has an answer here : How to efficiently get 10% of random numbers, then 10% of remaining 90 etc untill all points allocated (1 answer) Closed 6 years ago . I have a set of points xyz8, I want to randomly get 10% of points. Then I want to randomly get 10% of the remaining 90% Then I want to randomly get 10% of the remaining 70% etc until all points done How can I go about doing this? Any advice is hugely appreciated 回答1: something like: import random l = [1,2,3,4] random

问题 I have a 2-d numpy array populated with integers [-1, 0, +1]. I need to choose a random element that is not zero from it and calculate the sum of its adjacent elements. Is there a way to get the index of a numpy.random.choice ? lattice=np.zeros(9,dtype=numpy.int) lattice[:2]=-1 lattice[2:4]=1 random.shuffle(lattice) lattice=lattice.reshape((3,3)) random.choice(lattice[lattice!=0]) This gives the draw from the right sample, but I would need the index of the choice to be able to identify its

问题 I'm trying to select a random subset of records using either rand() or newid(), but any time I run the following, I always get the same results back: SELECT *,rand() as rid INTO #mytable FROM othertable select top 10 * from #mytable order by rid I've tried every variation of this, but it seems that sybase server iq always returns the same rand() value when I do it like the above. When I try to do SELECT * FROM othertable order by newid() I get an error saying it's illegal to use newid() in

问题 I would like to generate some large random multivariate (more than 6 dimensions) normal samples. In R, many packages can do this such as rmnorm, rmvn... But the problem is the speed! So I tried to write some C code through Rcpp. I went through some tutorial online but it seems there is no "sugar" for multivariate distribution, neither in STL library. Any help is appreciated! Thanks! 回答1: I'm not sure that Rcpp will help unless you find a good algorithm to approximate your multivariate

问题 I would like to generate some large random multivariate (more than 6 dimensions) normal samples. In R, many packages can do this such as rmnorm, rmvn... But the problem is the speed! So I tried to write some C code through Rcpp. I went through some tutorial online but it seems there is no "sugar" for multivariate distribution, neither in STL library. Any help is appreciated! Thanks! 回答1: I'm not sure that Rcpp will help unless you find a good algorithm to approximate your multivariate

问题 I would like to generate some large random multivariate (more than 6 dimensions) normal samples. In R, many packages can do this such as rmnorm, rmvn... But the problem is the speed! So I tried to write some C code through Rcpp. I went through some tutorial online but it seems there is no "sugar" for multivariate distribution, neither in STL library. Any help is appreciated! Thanks! 回答1: I'm not sure that Rcpp will help unless you find a good algorithm to approximate your multivariate