From d1b84b91f4b94b717de477e47b22946fd42be0db Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Tue, 25 Feb 2020 20:52:08 -0700
Subject: Refactor gauss kernel, more natural syntax

---
 ml_exp/kernels.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

(limited to 'ml_exp/kernels.py')

diff --git a/ml_exp/kernels.py b/ml_exp/kernels.py
index 834d62408..7a61a1e1e 100644
--- a/ml_exp/kernels.py
+++ b/ml_exp/kernels.py
@@ -22,27 +22,23 @@ SOFTWARE.
 """
 import math
 import numpy as np
-from ml_exp.frob_norm import frob_norm
 
 
-def gauss_kernel(X_1, X_2, sigma):
+def gaussian_kernel(X1,
+                    X2,
+                    sigma):
     """
     Calculates the Gaussian Kernel.
-    X_1: first representations.
-    X_2: second representations.
+    X1: first representations.
+    X2: 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])
+    K = np.zeros((X1.shape[0], X2.shape[0]), dtype=float)
+    for i in X1:
+        for j in X2:
+            f_norm = np.linalg.norm(i - j)
             # print(f_norm)
             K[i, j] = math.exp(inv_sigma * f_norm)
 
-- 
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