From 6e9b439d4b3d303b246d9e66b3ed3852e3fad9a5 Mon Sep 17 00:00:00 2001
From: David Luevano <55825613+luevano@users.noreply.github.com>
Date: Thu, 23 Jan 2020 18:59:34 -0700
Subject: Rename lj_matrix to ml_exp

---
 lj_matrix/gauss_kernel.py | 49 -----------------------------------------------
 1 file changed, 49 deletions(-)
 delete 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
deleted file mode 100644
index 5dd8e6406..000000000
--- a/lj_matrix/gauss_kernel.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""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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