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 tried using numpy only. Here is the code

def get_gauss_kernel(size=3,sigma=1):
    center=(int)(size/2)
    kernel=np.zeros((size,size))
    for i in range(size):
       for j in range(size):
          diff=np.sqrt((i-center)**2+(j-center)**2)
          kernel[i,j]=np.exp(-(diff**2)/(2*sigma**2))
    return kernel/np.sum(kernel)

You can visualise the result using:

plt.imshow(get_gauss_kernel(5,1))