In order to measure the information density like proposed in section 3.2 of this paper I need a symmetric positive definite Kernel function. For this purpose I want to use the Gaussian Kernel like suggested in the paper.
Where do I get the value for Sigma?
Is it the standard deviation of something?
Is Sigma calculated from the two input vectors xi and xj or do I need two whole sets of data?
Is the calculated value for sigma then really only raised to the power of -2?
Here is my current Python code for the problem:
def gaussian_kernel(x_i, x_j): # if gamma = sigma negative square then the kernel is known as the # Gaussian kernel of variance sigma square sigma = 0 # how to calculate sigma and sigma negativ squared? gamma = sigma**-2 # <- is this even correct? kernel_result = rbf_kernel(x_i, x_j, gamma) return kernel_result