From 487bf8840846b5d4d694b38985268c308aadb36e Mon Sep 17 00:00:00 2001
From: David Luevano <55825613+luevano@users.noreply.github.com>
Date: Wed, 18 Dec 2019 07:21:35 -0700
Subject: Refactor files

---
 lj_matrix/gauss_kernel.py | 49 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 lj_matrix/gauss_kernel.py

(limited to 'lj_matrix/gauss_kernel.py')

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
-- 
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