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

Building up on Teddy Hartanto's answer. You can just calculate your own one dimensional Gaussian functions and then use np.outer to calculate the two dimensional one. Very fast and efficient way.

With the code below you can also use different Sigmas for every dimension

import numpy as np
def generate_gaussian_mask(shape, sigma, sigma_y=None):
    if sigma_y==None:
        sigma_y=sigma
    rows, cols = shape

    def get_gaussian_fct(size, sigma):
        fct_gaus_x = np.linspace(0,size,size)
        fct_gaus_x = fct_gaus_x-size/2
        fct_gaus_x = fct_gaus_x**2
        fct_gaus_x = fct_gaus_x/(2*sigma**2)
        fct_gaus_x = np.exp(-fct_gaus_x)
        return fct_gaus_x

    mask = np.outer(get_gaussian_fct(rows,sigma), get_gaussian_fct(cols,sigma_y))
    return mask