Use chosolve from scipy

2 files changed, 2 insertions, 63 deletions

diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py
index 5efd13690..480ca9b8f 100644
--- a/ml_exp/do_ml.py
+++ b/ml_exp/do_ml.py
@@ -22,9 +22,9 @@ SOFTWARE.
 """
 import time
 import numpy as np
+from scipy import linalg as LA
 from ml_exp.misc import printc
 from ml_exp.kernels import gaussian_kernel
-from ml_exp.math import cholesky_solve
 from ml_exp.qm7db import qm7db
 
 
@@ -82,8 +82,7 @@ def simple_ml(descriptors,
                                  X_training,
                                  sigma,
                                  opt=opt)
-    alpha = cholesky_solve(K_training,
-                           Y_training)
+    alpha = LA.cho_solve(LA.cho_factor(K_training), Y_training)
 
     X_test = descriptors[-test_size:]
     Y_test = energies[-test_size:]
diff --git a/ml_exp/math.py b/ml_exp/math.py
deleted file mode 100644
index 253abe5d2..000000000
--- a/ml_exp/math.py
+++ /dev/null
@@ -1,60 +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
-
-
-def cholesky_solve(K,
-                   y):
-    """
-    Applies Cholesky decomposition to solve Ka=y. Where a are the alpha
-        coeficients.
-    K: kernel.
-    y: known parameters.
-    """
-    # Add a small lambda value.
-    K[np.diag_indices_from(K)] += 1e-8
-
-    # Get the Cholesky decomposition of the kernel.
-    L = np.linalg.cholesky(K)
-    size = K.shape[0]
-
-    # Solve Lx=y for x.
-    x = np.zeros(size, dtype=np.float64)
-    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, dtype=np.float64)
-    a[size - 1] = x[size - 1] / L2[size - 1, size - 1]
-    for i in range(0, size - 1)[::-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