How to calculate a Gaussian kernel matrix efficiently in numpy?

Question

def GaussianMatrix(X,sigma):
    row,col=X.shape
    GassMatrix=np.zeros(shape=(row,row))
    X=np.asarray(X)
    i=0
    for v_i in X:
        j=0
        for v_j i         


        

Answer 1

I myself used the accepted answer for my image processing, but I find it (and the other answers) too dependent on other modules. Therefore, here is my compact solution:

import numpy as np

def gkern(l=5, sig=1.):
    """\
    creates gaussian kernel with side length l and a sigma of sig
    """

    ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)
    xx, yy = np.meshgrid(ax, ax)

    kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))

    return kernel / np.sum(kernel)

Edit: Changed arange to linspace to handle even side lengths