[mlpack-git] mlpack-1.0.x: Backport r17432:17437. (6899506)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Wed Jan 7 11:57:42 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : mlpack-1.0.x
Link : https://github.com/mlpack/mlpack/compare/0000000000000000000000000000000000000000...904762495c039e345beba14c1142fd719b3bd50e
>---------------------------------------------------------------
commit 689950658575ac2b717e92d13967f152d256317b
Author: Ryan Curtin <ryan at ratml.org>
Date: Sun Dec 7 19:47:07 2014 +0000
Backport r17432:17437.
>---------------------------------------------------------------
689950658575ac2b717e92d13967f152d256317b
HISTORY.txt | 3 +++
.../methods/sparse_coding/sparse_coding_impl.hpp | 4 ++--
src/mlpack/tests/cosine_tree_test.cpp | 22 ++++++++--------------
src/mlpack/tests/logistic_regression_test.cpp | 7 +++++--
src/mlpack/tests/sa_test.cpp | 2 +-
src/mlpack/tests/svd_batch_test.cpp | 6 +++---
6 files changed, 22 insertions(+), 22 deletions(-)
diff --git a/HISTORY.txt b/HISTORY.txt
index d0ce8c4..a906829 100644
--- a/HISTORY.txt
+++ b/HISTORY.txt
@@ -18,6 +18,9 @@
* math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo
versions.
+ * Handle Newton method convergence better for
+ SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.
+
2014-08-29 mlpack 1.0.10
* Bugfix for NeighborSearch regression which caused very slow allknn/allkfn.
diff --git a/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp b/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp
index d62e13a..2ffc32d 100644
--- a/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp
+++ b/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp
@@ -270,7 +270,7 @@ double SparseCoding<DictionaryInitializer>::OptimizeDictionary(
<< "." << std::endl;
Log::Debug << " Improvement: " << std::scientific << improvement << ".\n";
- if (improvement < newtonTolerance)
+ if (normGradient < newtonTolerance)
converged = true;
}
@@ -307,7 +307,7 @@ double SparseCoding<DictionaryInitializer>::OptimizeDictionary(
}
}
}
- //printf("final reconstruction error: %e\n", norm(data - dictionary * codes, "fro"));
+
return normGradient;
}
diff --git a/src/mlpack/tests/cosine_tree_test.cpp b/src/mlpack/tests/cosine_tree_test.cpp
index 1e410f1..d4edd14 100644
--- a/src/mlpack/tests/cosine_tree_test.cpp
+++ b/src/mlpack/tests/cosine_tree_test.cpp
@@ -76,7 +76,7 @@ BOOST_AUTO_TEST_CASE(CosineNodeCosineSplit)
nodeStack.push_back(&root);
// While stack is not empty.
- while(nodeStack.size())
+ while (nodeStack.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode, *currentLeft, *currentRight;
@@ -89,7 +89,7 @@ BOOST_AUTO_TEST_CASE(CosineNodeCosineSplit)
currentRight = currentNode->Right();
// If children exist.
- if(currentLeft && currentRight)
+ if (currentLeft && currentRight)
{
// Push the child nodes on to the stack.
nodeStack.push_back(currentLeft);
@@ -112,28 +112,22 @@ BOOST_AUTO_TEST_CASE(CosineNodeCosineSplit)
cosines.zeros(currentNode->NumColumns());
size_t i, j, k;
- for(i = 0; i < leftIndices.size(); i++)
- {
+ for (i = 0; i < leftIndices.size(); i++)
cosines(i) = arma::norm_dot(data.col(leftIndices[i]), splitPoint);
- }
- for(j = 0, k = i; j < rightIndices.size(); j++, k++)
- {
+
+ for (j = 0, k = i; j < rightIndices.size(); j++, k++)
cosines(k) = arma::norm_dot(data.col(rightIndices[j]), splitPoint);
- }
// Check if the columns assigned to the children agree with the splitting
// condition.
double cosineMax = arma::max(cosines % (cosines < 1));
double cosineMin = arma::min(cosines);
- for(i = 0; i < leftIndices.size(); i++)
- {
+ for (i = 0; i < leftIndices.size(); i++)
BOOST_CHECK_LT(cosineMax - cosines(i), cosines(i) - cosineMin);
- }
- for(j = 0, k = i; j < rightIndices.size(); j++, k++)
- {
+
+ for (j = 0, k = i; j < rightIndices.size(); j++, k++)
BOOST_CHECK_GT(cosineMax - cosines(k), cosines(k) - cosineMin);
- }
}
}
}
diff --git a/src/mlpack/tests/logistic_regression_test.cpp b/src/mlpack/tests/logistic_regression_test.cpp
index 1039150..fa1f3ef 100644
--- a/src/mlpack/tests/logistic_regression_test.cpp
+++ b/src/mlpack/tests/logistic_regression_test.cpp
@@ -556,8 +556,11 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionSGDRegularizationSimpleTest)
"1 2 3");
arma::vec responses("1 1 0");
- // Create a logistic regression object using SGD.
- LogisticRegression<SGD> lr(data, responses, 0.001);
+ // Create a logistic regression object using custom SGD with a much smaller
+ // tolerance.
+ LogisticRegressionFunction lrf(data, responses, 0.001);
+ SGD<LogisticRegressionFunction> sgd(lrf, 0.005, 500000, 1e-10);
+ LogisticRegression<SGD> lr(sgd);
// Test sigmoid function.
arma::vec sigmoids = 1 / (1 + arma::exp(-lr.Parameters()[0]
diff --git a/src/mlpack/tests/sa_test.cpp b/src/mlpack/tests/sa_test.cpp
index e73ff8b..8b090fa 100644
--- a/src/mlpack/tests/sa_test.cpp
+++ b/src/mlpack/tests/sa_test.cpp
@@ -58,7 +58,7 @@ BOOST_AUTO_TEST_CASE(GeneralizedRosenbrockTest)
result = sa.Optimize(coordinates);
++iteration;
- BOOST_REQUIRE_LT(iteration, 3); // No more than three tries.
+ BOOST_REQUIRE_LT(iteration, 4); // No more than three tries.
}
// 0.1% tolerance for each coordinate.
diff --git a/src/mlpack/tests/svd_batch_test.cpp b/src/mlpack/tests/svd_batch_test.cpp
index 1e02eea..088047a 100644
--- a/src/mlpack/tests/svd_batch_test.cpp
+++ b/src/mlpack/tests/svd_batch_test.cpp
@@ -174,14 +174,14 @@ BOOST_AUTO_TEST_CASE(SVDBatchNegativeElementTest)
RandomInitialization,
SVDBatchLearning> amf(SimpleToleranceTermination<mat>(),
RandomInitialization(),
- SVDBatchLearning(0.3, 0.001, 0.001, 0));
+ SVDBatchLearning(0.1, 0.001, 0.001, 0));
mat m1, m2;
amf.Apply(test, 3, m1, m2);
arma::mat result = m1 * m2;
- // 2% tolerance on the norm.
- BOOST_REQUIRE_CLOSE(arma::norm(test, "fro"), arma::norm(result, "fro"), 2.0);
+ // 5% tolerance on the norm.
+ BOOST_REQUIRE_CLOSE(arma::norm(test, "fro"), arma::norm(result, "fro"), 5.0);
}
BOOST_AUTO_TEST_SUITE_END();
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