distribution

问题 I wonder how it's possible to perform distributional sampling in Node.js. In particular, I have the number of elements, where the i'th value of the arrays is the probability of the element i'th, like following [0.2 0.3 0.5] Now I need to perform sampling, and the result of the sampling in half of the samples should be 2 in 0.2 of the samples is 0 and in 0.3 of the samples is 1. 回答1: Trivial method: function distribute(probs) { return function() { var r = Math.random(); var i = 0, acc = 0;

问题 I've been working on generating Perlin noise for a map generator of mine. The problem I've run into is that the random noise is not distributed normally, and is more likely a normal distribution of kinds. Given two integers X and Y, and a seed value, I do the following: Use MurmurHash2 to generate a random number (-1,1). This is uniformly distributed. Interpolate points between integer values with cubic interpolation. Values now fall in the range (-2.25, 2.25) because the interpolation can

问题 I am trying to program in Matlab a conditional expectation of the form: E[x|A<=x<=B] where X~N(u,s^2) (sorry, apparently the math editing here isn't what I am used to) In Matlab, I have written up the following code: Y=u+s*(normpdf(A,u,s)-normpdf(B,u,s))/(normcdf(B,u,s)-normcdf(A,u,s)) the problem is that it breaks down at higher values of A and B. For example, let u=0, s=1, A=10 and B=11. Simple logic says the answer should be between 10 and 11, but Matlab gives me back Inf because the

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . I have a small framework that I would like to put out there. I would love to export these classes as a single file. I know there is a SWC format, but there is not much recent information on how to export build this and I have a feeling this format might be outdated. 回答1: A SWC file is just a ZIP file (with the

问题 I am working on Spike Trains and my code to get a spike train like this: for 20 trials is written below. The image is representational for 5 trials. fr = 100 dt = 1/1000 #dt in milisecond duration = 2 #no of duration in s nBins = 2000 #10msSpikeTrain nTrials = 20 #NumberOfSimulations MyPoissonSpikeTrain = function(p, fr= 100) { p = runif(nBins) q = ifelse(p < fr*dt, 1, 0) return(q) } set.seed(1) SpikeMat <- t(replicate(nTrials, MyPoissonSpikeTrain())) plot(x=-1,y=-1, xlab="time (s)", ylab=

问题 We'd like to change the App ID that we currently use without changing the seed ID- we want to change the bundle ID to a GUID. I know that the app bundle ID is used: During installation to check that the distribution profile app ID (Second part) matches the bundle ID in the info.plist. So we have to generate a new provisioning profile. By iTunes to segregate apps and identify upgrades for apps. I guess this means this will come out with iTunes treating it as a new app? Are there any

问题 I'm trying to create a build for a small group of non-developer testers to do some beta testing on my App before I submit it to iTunes Connect. I have read the Developer's Guide on "Distributing Your App for Testing (you may need to be registered with Apple to read this) as well as Technical Note TN2250 on Ad Hoc distribution, and innumerable blog posts and Stack Overflow articles, but with no success thus far. Steps I've taken: (I'm running Xcode 3.2.5.) I've created a Development

问题 I'm trying to fit an exponential distribution to a dataset I have. Strangely, no matter what I do I can't seem to scale the histogram so it fits the fitted exponential distribution. param=expon.fit(data) pdf_fitted=norm.pdf(x,loc=param[0],scale=param[1]) plot(x,pdf_fitted,'r-') hist(constraint1N55, normed=1,alpha=.3,histtype='stepfilled') For some reason, the histogram takes up much more space than the probability distribution, even though I have normed=1. Is there something I can do to make

问题 Background: Normally, R gives quantiles for well-known distributions. Out of these quantiles, the lower 2.5% up to the upper 97.5% covers 95% of the area under these distributions. Question: Suppose I have a F distribution (df1 = 10, df2 = 90). In R, how can I determine the 95% of the area under this distribution such that this 95% only covers the HIGH DENSITY area, not the 95% that R normally gives (see my R code Below )? Note: Clearly, the highest density is the "mode" (dashed line in the

问题 I am trying to generate random numbers from a custom distribution, i already found this question: Simulate from an (arbitrary) continuous probability distribution but unfortunatly it does not help me since the approach suggested there requires a formula for the distribution function. My distribution is a combination of multiple uniform distributions, basically the distribution function looks like a histogram. An example would be: f(x) = { 0 for x < 1 0.5 for 1 <= x < 2 0.25 for 2 <= x < 4 0