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

You may simply gaussian-filter a simple 2D dirac function, the result is then the filter function that was being used:

import numpy as np
import scipy.ndimage.filters as fi

def gkern2(kernlen=21, nsig=3):
    """Returns a 2D Gaussian kernel array."""

    # create nxn zeros
    inp = np.zeros((kernlen, kernlen))
    # set element at the middle to one, a dirac delta
    inp[kernlen//2, kernlen//2] = 1
    # gaussian-smooth the dirac, resulting in a gaussian filter mask
    return fi.gaussian_filter(inp, nsig)