[mlpack-git] master: Make Newton tolerance larger so the method actually converges most of the time... (a50bd2a)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/a6b57a4e3c6c63bdd3412cd2480afbd3f560bdcd...84b9d42aae2975eae93f890a9861ce9963bd1916

>---------------------------------------------------------------

commit a50bd2a5fbe0df96ba991b866eac986176aa56d7
Author: ryan <ryan at ratml.org>
Date:   Wed Dec 23 15:02:03 2015 -0500

    Make Newton tolerance larger so the method actually converges most of the time...


>---------------------------------------------------------------

a50bd2a5fbe0df96ba991b866eac986176aa56d7
 src/mlpack/tests/sparse_coding_test.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlpack/tests/sparse_coding_test.cpp b/src/mlpack/tests/sparse_coding_test.cpp
index ef91c58..cae4a31 100644
--- a/src/mlpack/tests/sparse_coding_test.cpp
+++ b/src/mlpack/tests/sparse_coding_test.cpp
@@ -119,7 +119,7 @@ BOOST_AUTO_TEST_CASE(SparseCodingTestDictionaryStep)
   for (uword i = 0; i < nPoints; ++i)
     X.col(i) /= norm(X.col(i), 2);
 
-  SparseCoding sc(nAtoms, lambda1, 0.0, 0, 0.01, 1e-15);
+  SparseCoding sc(nAtoms, lambda1, 0.0, 0, 0.01, 1e-8);
   mat Z;
   DataDependentRandomInitializer::Initialize(X, 25, sc.Dictionary());
   sc.Encode(X, Z);