Rename lj_matrix to ml_exp

1 files changed, 0 insertions, 64 deletions

diff --git a/lj_matrix/cholesky_solve.py b/lj_matrix/cholesky_solve.py
deleted file mode 100644
index bc6a572a3..000000000
--- a/lj_matrix/cholesky_solve.py
+++ /dev/null
@@ -1,64 +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 numpy as np
-from numpy.linalg import cholesky
-
-
-def cholesky_solve(K, y):
-    """
-    Applies Cholesky decomposition to obtain the 'alpha coeficients'.
-    K: kernel.
-    y: known parameters.
-    """
-    # The initial mathematical problem is to solve Ka=y.
-
-    # First, add a small lambda value.
-    K[np.diag_indices_from(K)] += 1e-8
-
-    # Get the Cholesky decomposition of the kernel.
-    L = cholesky(K)
-    size = len(L)
-
-    # Solve Lx=y for x.
-    x = np.zeros(size)
-    x[0] = y[0] / L[0, 0]
-    for i in range(1, size):
-        temp_sum = 0.0
-        for j in range(i):
-            temp_sum += L[i, j] * x[j]
-        x[i] = (y[i] - temp_sum) / L[i, i]
-
-    # Now, solve LTa=x for a.
-    L2 = L.T
-    a = np.zeros(size)
-    a_ms = size - 1
-    a[a_ms] = x[a_ms] / L2[a_ms, a_ms]
-    # Because of the form of L2 (upper triangular matriz), an inversion of
-    # range() needs to be done.
-    for i in range(0, a_ms)[::-1]:
-        temp_sum = 0.0
-        for j in range(i, size)[::-1]:
-            temp_sum += L2[i, j] * a[j]
-        a[i] = (x[i] - temp_sum) / L2[i, i]
-
-    return a