gaussian

Given the start and the end of an integer range, how do I calculate a normally distributed random integer between this range? I realize that the normal distribution goes into -+ infinity. I guess the tails can be cutoff, so when a random gets computed outside the range, recompute. This elevates the probability of integers in the range, but as long as the this effect is tolerable (<5%), it's fine. public class Gaussian { private static bool uselast = true; private static double next_gaussian = 0.0; private static Random random = new Random(); public static double BoxMuller() { if (uselast) {

I want to apply a Gaussian filter of dimension 5x5 pixels on an image of 512x512 pixels. I found a scipy function to do that: scipy.ndimage.filters.gaussian_filter(input, sigma, truncate=3.0) How I choose the parameter of sigma to make sure that my Gaussian window is 5x5 pixels? Check out the source code here: https://github.com/scipy/scipy/blob/master/scipy/ndimage/filters.py You'll see that gaussian_filter calls gaussian_filter1d for each axis. In gaussian_filter1d , the width of the filter is determined implicitly by the values of sigma and truncate . In effect, the width w is w = 2*int

I can generate Gaussian data with random.gauss(mu, sigma) function, but how can I generate 2D gaussian? Is there any function like that? Since the standard 2D Gaussian distribution is just the product of two 1D Gaussian distribution, if there are no correlation between the two axes (i.e. the covariant matrix is diagonal), just call random.gauss twice. def gauss_2d(mu, sigma): x = random.gauss(mu, sigma) y = random.gauss(mu, sigma) return (x, y) If you can use numpy , there is numpy.random.multivariate_normal(mean, cov[, size]) . For example, to get 10,000 2D samples: np.random.multivariate

I've been hunting for a convenient way to sample from a multivariate normal distribution. Does anyone know of a readily available code snippet to do that? For matrices/vectors, I'd prefer to use Boost or Eigen or another phenomenal library I'm not familiar with, but I could use GSL in a pinch. I'd also like it if the method accepted nonnegative -definite covariance matrices rather than requiring positive-definite (e.g., as with the Cholesky decomposition). This exists in MATLAB, NumPy, and others, but I've had a hard time finding a ready-made C/C++ solution. If I have to implement it myself, I

My question is very close to this question: How do I gaussian blur an image without using any in-built gaussian functions? The answer to this question is very good, but it doesn't give an example of actually calculating a real Gaussian filter kernel. The answer gives an arbitrary kernel and shows how to apply the filter using that kernel but not how to calculate a real kernel itself. I am trying to implement a Gaussian blur in C++ or Matlab from scratch, so I need to know how to calculate the kernel from scratch. I'd appreciate it if someone could calculate a real Gaussian filter kernel using

I want some data to fit the corresponding Gaussian distribution. The data is meant to be Gaussian already, but for some filtering reasons, they will not perfectly match the prescribed and expected Gaussian distribution. I therefore aim to reduce the existing scatter between data and desired distribution. For example, my data fit the Gaussian distribution as follows (the expected mean value is 0 and the standard deviation 0.8): The approximation is already decent, but I really want to crunch the still tangible scatter between simulated data and expected distribution. How can I achieve this?