Move frob norm and cholesky solve to math

3 files changed, 30 insertions, 53 deletions

diff --git a/ml_exp/frob_norm.py b/ml_exp/frob_norm.py
deleted file mode 100644
index 4c3a2945d..000000000
--- a/ml_exp/frob_norm.py
+++ /dev/null
@@ -1,51 +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
-
-
-def frob_norm(array):
-    """
-    Calculates the frobenius norm of a given array or matrix.
-    array: array of data.
-    """
-
-    arr_sh_len = len(array.shape)
-    arr_range = range(len(array))
-    fn = 0.0
-
-    # If it is a 'vector'.
-    if arr_sh_len == 1:
-        for i in arr_range:
-            fn += array[i]*array[i]
-
-        return math.sqrt(fn)
-
-    # If it is a matrix.
-    elif arr_sh_len == 2:
-        for i in arr_range:
-            for j in arr_range:
-                fn += array[i, j]*array[i, j]
-
-        return math.sqrt(fn)
-    else:
-        print('Error. Array size greater than 2 ({}).'.format(arr_sh_len))
diff --git a/ml_exp/gauss_kernel.py b/ml_exp/kernels.py
index 834d62408..834d62408 100644
--- a/ml_exp/gauss_kernel.py
+++ b/ml_exp/kernels.py
diff --git a/ml_exp/cholesky_solve.py b/ml_exp/math.py
index bc6a572a3..781985118 100644
--- a/ml_exp/cholesky_solve.py
+++ b/ml_exp/math.py
@@ -21,7 +21,35 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
 import numpy as np
-from numpy.linalg import cholesky
+import math
+
+
+def frob_norm(array):
+    """
+    Calculates the frobenius norm of a given array or matrix.
+    array: array of data.
+    """
+
+    arr_sh_len = len(array.shape)
+    arr_range = range(len(array))
+    fn = 0.0
+
+    # If it is a 'vector'.
+    if arr_sh_len == 1:
+        for i in arr_range:
+            fn += array[i]*array[i]
+
+        return math.sqrt(fn)
+
+    # If it is a matrix.
+    elif arr_sh_len == 2:
+        for i in arr_range:
+            for j in arr_range:
+                fn += array[i, j]*array[i, j]
+
+        return math.sqrt(fn)
+    else:
+        print('Error. Array size greater than 2 ({}).'.format(arr_sh_len))
 
 
 def cholesky_solve(K, y):
@@ -36,7 +64,7 @@ def cholesky_solve(K, y):
     K[np.diag_indices_from(K)] += 1e-8
 
     # Get the Cholesky decomposition of the kernel.
-    L = cholesky(K)
+    L = np.linalg.cholesky(K)
     size = len(L)
 
     # Solve Lx=y for x.