I have x = 10 and y = 100. Can I distribute y elements in randomly-sized portions among x 'element holders'? I want to create x categories each with a random number of items; however, the number of items created should be exactly y. I guess it's some
I am generating random sequences of numbers using the same seed: sprev = rng(2,'v5uniform'); for i=1:N %do some operations rndIDX = randperm(sampleSize) ; newdata= data(rndIDX(1:newSampleSize), :) ; if x>y remove=x1; %line 7 end for l=1:M %do some
I have an R script that allows me to select a sample size and take fifty individual random samples with replacement. Below is an example of this code: ## Creates data frame df = as.data.table(data) ## Select sample size sample.size = 5 ## Creates Sam
I am working on an user interface that shows many pins on a map. During the development I am randomly generating 1500 map pins just to be placed on the map to test look/feel/performance etc. issues. The code which does that looks like this: for (var
Good Evening!Still new to programming so bare with me. Simple question I hope! I have data in a list like: L = [(3,4,5),(1,4,5),(1,2,3),(1,2,3)] i need to sample randomly a size of 2 so i wanted to used: import random t1 = random.sample(set(L),2) Now
okay, I forgot what the name for a variable list linked to a single variable already inside of a list is called, but just take a look: function Models(Make) { var cars = { "Acura": ['Integra', 'NSX', 'RSX', ], "BMW": ['M3', ], "Buick": ['Regal', ], "
If I have a large dataset in csv file, in R, how can I take random sample of the data taking in consideration the distribution of the original data. and what if the data skewed and only 1% belong to minor class and I want to take biased sample of the
I am wondering whether this could be worse parallelizing generation of random numbers. I am using Qt and QtConcurrent here. Is it worth using parallelization instead of generating N number in a row? Which could be critical number of generated numbers
I'm a complete nab with python. But now I need a simple storage containing MyObject-objects for some project. Each object contains a few StringProperties nothing fancy. Now I want to get from my list of MyObjects, 10 random objects and store them in
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
I tried using random.randint(0, 100), but some numbers were the same. Is there a method/module to create a list unique random numbers? def getScores(): # open files to read and write f1 = open("page.txt", "r"); p1 = open("pgRes.txt", "a"); gScores =
My goal is to use bootstrapping (1000 reps) to calculate a null distribution, mean, and CI of r (Pearson's correlation coefficient) correlating trait (x) in 20 stimulated random pairs generated from my dataset of 600 unique individuals (ID). I have r
How can I create a stratified sample in R using the "sampling" package? My dataset has 355,000 observations. The code works fine up to the last line. Below is the code I wrote, but I always get the following message: "Error in sort.list(y) : 'x' must
I have a 30 n random sample from a list which I write to a file. I would rather like to tag the lines that went to the sample by writing in the end "in_sample" or "not_in_sample". Now it looks like this: mysample=random.sample(list, 30) for i in mysa
Suppose I have a very large square matrix, M(i, j), such that each element in the matrix represents the probability that the element will be chosen in a weighted random selection. I need to sample n elements from the matrix (by the (i, j) indices) wi
How can I take a sample of n random points from a matrix populated with 1's and 0's ? a=rep(0:1,5) b=rep(0,10) c=rep(1,10) dataset=matrix(cbind(a,b,c),nrow=10,ncol=3) dataset [,1] [,2] [,3] [1,] 0 0 1 [2,] 1 0 1 [3,] 0 0 1 [4,] 1 0 1 [5,] 0 0 1 [6,]
This is the question I was asked some time ago on interview, I could not find answer for. Given some samples S1, S2, ... Sn and their probability distributions(or weights, whatever it is called) P1, P2, .. Pn, design algorith that randomly chooses sa
Ok, it is possible to give weights/probabilities in boost::random::discrete_distribution. e.g. double probabilities[] = { 0.5, 0.1, 0.1, 0.1, 0.1, 0.1 }; boost::random::discrete_distribution<> dist(probabilities); Question: Once the object dist
The radii r is drawn from a cut-off log-normal distribution, which has a following probability density function: pdf=((sqrt(2).*exp(-0.5*((log(r/rch)).^2)))./((sqrt(pi.*(sigma_nd.^2))... .*r).*(erf((log(rmax/rch))./sqrt(2.*(sigma_nd.^2)))-erf((log(rm
I have a large matrix from which I would like to randomly extract a smaller matrix. (I want to do this 1000 times, so ultimately it will be in a for loop.) Say for example that I have this 9x9 matrix: mat=matrix(c(0,0,1,0,1,0,0,0,1,0,0,0,0,1,1,1,0,0,