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author | David Luevano Alvarado <55825613+luevano@users.noreply.github.com> | 2020-01-23 18:29:21 -0700 |
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committer | GitHub <noreply@github.com> | 2020-01-23 18:29:21 -0700 |
commit | 7f122fdb38cd34916820d6ff4fb0e3a49fde80fc (patch) | |
tree | 47efddb979957029945a473fde6ed2cde2c2b196 /lj_matrix/gauss_kernel.py | |
parent | bd4fb4d77919bc75d3d181e124c3c5752a74dff3 (diff) | |
parent | 4704314c9b4d1066383da5c3d6ca87bba9067c8d (diff) |
Merge pull request #1 from luevano/add_parallel
Add parallel
Diffstat (limited to 'lj_matrix/gauss_kernel.py')
-rw-r--r-- | lj_matrix/gauss_kernel.py | 49 |
1 files changed, 49 insertions, 0 deletions
diff --git a/lj_matrix/gauss_kernel.py b/lj_matrix/gauss_kernel.py new file mode 100644 index 000000000..5dd8e6406 --- /dev/null +++ b/lj_matrix/gauss_kernel.py @@ -0,0 +1,49 @@ +"""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 |