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-"""MIT License
-
-Copyright (c) 2019 David Luevano Alvarado
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-import math
-import numpy as np
-from lj_matrix.frob_norm import frob_norm
-
-
-def gauss_kernel(X_1, X_2, sigma):
- """
- Calculates the Gaussian Kernel.
- X_1: first representations.
- X_2: second representations.
- sigma: kernel width.
- """
- x1_l = len(X_1)
- x1_range = range(x1_l)
- x2_l = len(X_2)
- x2_range = range(x2_l)
-
- inv_sigma = -0.5 / (sigma*sigma)
-
- K = np.zeros((x1_l, x2_l))
- for i in x1_range:
- for j in x2_range:
- f_norm = frob_norm(X_1[i] - X_2[j])
- # print(f_norm)
- K[i, j] = math.exp(inv_sigma * f_norm)
-
- return K