[mlpack-git] mlpack-1.0.x: Minor changes to get things to compile. Looks like I didn't do a perfect job of merging... (49d155c)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Thu Mar 5 22:07:35 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : mlpack-1.0.x
Link : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40
>---------------------------------------------------------------
commit 49d155cb6835d953218127cd94a20a2970ae19e4
Author: Ryan Curtin <ryan at ratml.org>
Date: Mon Dec 8 03:47:36 2014 +0000
Minor changes to get things to compile. Looks like I didn't do a perfect job of merging...
>---------------------------------------------------------------
49d155cb6835d953218127cd94a20a2970ae19e4
src/mlpack/core.hpp | 2 +
src/mlpack/methods/amf/init_rules/CMakeLists.txt | 1 +
.../simple_residue_termination.hpp | 26 ++--
.../simple_tolerance_termination.hpp | 43 ++++--
.../methods/decision_stump/decision_stump.hpp | 39 +++---
.../methods/decision_stump/decision_stump_impl.hpp | 144 ++++++++++++++-------
src/mlpack/tests/allknn_test.cpp | 1 -
src/mlpack/tests/gmm_test.cpp | 8 +-
src/mlpack/tests/svd_batch_test.cpp | 35 ++---
9 files changed, 195 insertions(+), 104 deletions(-)
diff --git a/src/mlpack/core.hpp b/src/mlpack/core.hpp
index 08468b4..4800f8f 100644
--- a/src/mlpack/core.hpp
+++ b/src/mlpack/core.hpp
@@ -209,3 +209,5 @@
#undef max
#endif
#endif
+
+#endif
diff --git a/src/mlpack/methods/amf/init_rules/CMakeLists.txt b/src/mlpack/methods/amf/init_rules/CMakeLists.txt
index a31d281..20ef23f 100644
--- a/src/mlpack/methods/amf/init_rules/CMakeLists.txt
+++ b/src/mlpack/methods/amf/init_rules/CMakeLists.txt
@@ -1,6 +1,7 @@
# Define the files we need to compile
# Anything not in this list will not be compiled into MLPACK.
set(SOURCES
+ average_init.hpp
random_init.hpp
random_acol_init.hpp
)
diff --git a/src/mlpack/methods/amf/termination_policies/simple_residue_termination.hpp b/src/mlpack/methods/amf/termination_policies/simple_residue_termination.hpp
index 0e15154..40c6671 100644
--- a/src/mlpack/methods/amf/termination_policies/simple_residue_termination.hpp
+++ b/src/mlpack/methods/amf/termination_policies/simple_residue_termination.hpp
@@ -2,6 +2,8 @@
* @file simple_residue_termination.hpp
* @author Sumedh Ghaisas
*
+ * Termination policy used in AMF (Alternating Matrix Factorization).
+ *
* This file is part of MLPACK 1.0.11.
*
* MLPACK is free software: you can redistribute it and/or modify it under the
@@ -51,18 +53,20 @@ class SimpleResidueTermination
const size_t maxIterations = 10000)
: minResidue(minResidue), maxIterations(maxIterations) { }
+ /**
+ * Initializes the termination policy before stating the factorization.
+ *
+ * @param V Input matrix being factorized.
+ */
template<typename MatType>
void Initialize(const MatType& V)
{
// Initialize the things we keep track of.
residue = DBL_MAX;
iteration = 1;
+ nm = V.n_rows * V.n_cols;
+ // Remove history.
normOld = 0;
-
- const size_t n = V.n_rows;
- const size_t m = V.n_cols;
-
- nm = n * m;
}
/**
@@ -87,9 +91,8 @@ class SimpleResidueTermination
return (residue < minResidue || iteration > maxIterations);
}
- const double& Index() { return residue; }
- const size_t& Iteration() { return iteration; }
- const size_t& MaxIterations() { return maxIterations; }
+ //! Get current value of residue
+ const double& Index() const { return residue; }
//! Get current iteration count
const size_t& Iteration() const { return iteration; }
@@ -102,12 +105,17 @@ class SimpleResidueTermination
const double& MinResidue() const { return minResidue; }
double& MinResidue() { return minResidue; }
- public:
+public:
+ //! residue threshold
double minResidue;
+ //! iteration threshold
size_t maxIterations;
+ //! current value of residue
double residue;
+ //! current iteration count
size_t iteration;
+ //! norm of previous iteration
double normOld;
size_t nm;
diff --git a/src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp b/src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp
index 248ef82..94be641 100644
--- a/src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp
+++ b/src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp
@@ -2,6 +2,8 @@
* @file simple_tolerance_termination.hpp
* @author Sumedh Ghaisas
*
+ * Termination policy used in AMF (Alternating Matrix Factorization).
+ *
* This file is part of MLPACK 1.0.11.
*
* MLPACK is free software: you can redistribute it and/or modify it under the
@@ -39,6 +41,7 @@ template <class MatType>
class SimpleToleranceTermination
{
public:
+ //! empty constructor
SimpleToleranceTermination(const double tolerance = 1e-5,
const size_t maxIterations = 10000,
const size_t reverseStepTolerance = 3)
@@ -46,6 +49,11 @@ class SimpleToleranceTermination
maxIterations(maxIterations),
reverseStepTolerance(reverseStepTolerance) {}
+ /**
+ * Initializes the termination policy before stating the factorization.
+ *
+ * @param V Input matrix to be factorized.
+ */
void Initialize(const MatType& V)
{
residueOld = DBL_MAX;
@@ -62,13 +70,19 @@ class SimpleToleranceTermination
reverseStepCount = 0;
}
+ /**
+ * Check if termination criterio is met.
+ *
+ * @param W Basis matrix of output.
+ * @param H Encoding matrix of output.
+ */
bool IsConverged(arma::mat& W, arma::mat& H)
{
- // Calculate norm of WH after each iteration.
arma::mat WH;
WH = W * H;
+ // compute residue
residueOld = residue;
size_t n = V->n_rows;
size_t m = V->n_cols;
@@ -91,37 +105,43 @@ class SimpleToleranceTermination
residue = sum / count;
residue = sqrt(residue);
-<<<<<<< .working
- iteration++;
-
-=======
+ // increment iteration count
iteration++;
// if residue tolerance is not satisfied
->>>>>>> .merge-right.r17287
if ((residueOld - residue) / residueOld < tolerance && iteration > 4)
{
+ // check if this is a first of successive drops
if (reverseStepCount == 0 && isCopy == false)
{
+ // store a copy of W and H matrix
isCopy = true;
this->W = W;
this->H = H;
+ // store residue values
c_index = residue;
c_indexOld = residueOld;
}
+ // increase successive drop count
reverseStepCount++;
}
+ // if tolerance is satisfied
else
{
+ // initialize successive drop count
reverseStepCount = 0;
+ // if residue is droped below minimum scrap stored values
if(residue <= c_indexOld && isCopy == true)
{
isCopy = false;
}
}
+ // check if termination criterion is met
if(reverseStepCount == reverseStepTolerance || iteration > maxIterations)
{
+ // if stored values are present replace them with current value as they
+ // represent the minimum residue point
if(isCopy)
{
W = this->W;
@@ -133,9 +153,8 @@ class SimpleToleranceTermination
else return false;
}
- const double& Index() { return residue; }
- const size_t& Iteration() { return iteration; }
- const size_t& MaxIterations() { return maxIterations; }
+ //! Get current value of residue
+ const double& Index() const { return residue; }
//! Get current iteration count
const size_t& Iteration() const { return iteration; }
@@ -151,10 +170,13 @@ class SimpleToleranceTermination
private:
//! tolerance
double tolerance;
+ //! iteration threshold
size_t maxIterations;
+ //! pointer to matrix being factorized
const MatType* V;
+ //! current iteration count
size_t iteration;
//! residue values
@@ -162,10 +184,13 @@ class SimpleToleranceTermination
double residue;
double normOld;
+ //! tolerance on successive residue drops
size_t reverseStepTolerance;
//! successive residue drops
size_t reverseStepCount;
+ //! indicates whether a copy of information is available which corresponds to
+ //! minimum residue point
bool isCopy;
//! variables to store information of minimum residue poi
diff --git a/src/mlpack/methods/decision_stump/decision_stump.hpp b/src/mlpack/methods/decision_stump/decision_stump.hpp
index 75f0e9a..bb3e87e 100644
--- a/src/mlpack/methods/decision_stump/decision_stump.hpp
+++ b/src/mlpack/methods/decision_stump/decision_stump.hpp
@@ -69,22 +69,21 @@ class DecisionStump
void Classify(const MatType& test, arma::Row<size_t>& predictedLabels);
/**
- *
- *
- *
- *
+ * Alternate constructor which copies parameters bucketSize and numClass from
+ * an already initiated decision stump, other. It appropriately sets the
+ * weight vector.
+ *
+ * @param other The other initiated Decision Stump object from
+ * which we copy the values.
+ * @param data The data on which to train this object on.
+ * @param D Weight vector to use while training. For boosting purposes.
+ * @param labels The labels of data.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
- DecisionStump(const DecisionStump<>& ds);
-
- /**
- *
- *
- *
- *
- *
- *
- ModifyData(MatType& data, const arma::Row<double>& D);
- */
+ DecisionStump(const DecisionStump<>& other,
+ const MatType& data,
+ const arma::rowvec& weights,
+ const arma::Row<size_t>& labels);
//! Access the splitting attribute.
int SplitAttribute() const { return splitAttribute; }
@@ -123,9 +122,12 @@ class DecisionStump
*
* @param attribute A row from the training data, which might be a
* candidate for the splitting attribute.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
+ template <bool isWeight>
double SetupSplitAttribute(const arma::rowvec& attribute,
- const arma::Row<size_t>& labels);
+ const arma::Row<size_t>& labels,
+ const arma::rowvec& weightD);
/**
* After having decided the attribute on which to split, train on that
@@ -149,7 +151,8 @@ class DecisionStump
* @param subCols The vector in which to find the most frequently
* occurring element.
*/
- template <typename rType> rType CountMostFreq(const arma::Row<rType>& subCols);
+ template <typename rType> rType CountMostFreq(const arma::Row<rType>&
+ subCols);
/**
* Returns 1 if all the values of featureRow are not same.
@@ -163,6 +166,7 @@ class DecisionStump
*
* @param attribute The attribute of which we calculate the entropy.
* @param labels Corresponding labels of the attribute.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
template <typename LabelType, bool isWeight>
double CalculateEntropy(arma::subview_row<LabelType> labels, int begin,
@@ -178,6 +182,7 @@ class DecisionStump
template <bool isWeight>
void Train(const MatType& data, const arma::Row<size_t>& labels,
const arma::rowvec& weightD);
+
};
}; // namespace decision_stump
diff --git a/src/mlpack/methods/decision_stump/decision_stump_impl.hpp b/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
index 6071937..f8190b5 100644
--- a/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
+++ b/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
@@ -26,9 +26,6 @@
// In case it hasn't been included yet.
#include "decision_stump.hpp"
-#include <set>
-#include <algorithm>
-
namespace mlpack {
namespace decision_stump {
@@ -49,11 +46,28 @@ DecisionStump<MatType>::DecisionStump(const MatType& data,
numClass = classes;
bucketSize = inpBucketSize;
+ arma::rowvec weightD;
+
+ Train<false>(data, labels, weightD);
+}
+
+/**
+ * Train the decision stump on the given data and labels.
+ *
+ * @param data Dataset to train on.
+ * @param labels Labels for dataset.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
+ */
+template<typename MatType>
+template <bool isWeight>
+void DecisionStump<MatType>::Train(const MatType& data, const arma::Row<size_t>& labels,
+ const arma::rowvec& weightD)
+{
// If classLabels are not all identical, proceed with training.
- size_t bestAtt = 0;
+ int bestAtt = 0;
double entropy;
- const double rootEntropy = CalculateEntropy<size_t>(
- labels.subvec(0, labels.n_elem - 1));
+ const double rootEntropy = CalculateEntropy<size_t, isWeight>(
+ labels.subvec(0, labels.n_elem - 1), 0, weightD);
double gain, bestGain = 0.0;
for (size_t i = 0; i < data.n_rows; i++)
@@ -63,9 +77,8 @@ DecisionStump<MatType>::DecisionStump(const MatType& data,
{
// For each attribute with non-identical values, treat it as a potential
// splitting attribute and calculate entropy if split on it.
- entropy = SetupSplitAttribute(data.row(i), labels);
+ entropy = SetupSplitAttribute<isWeight>(data.row(i), labels, weightD);
- // Log::Debug << "Entropy for attribute " << i << " is " << entropy << ".\n";
gain = rootEntropy - entropy;
// Find the attribute with the best entropy so that the gain is
// maximized.
@@ -119,48 +132,48 @@ void DecisionStump<MatType>::Classify(const MatType& test,
}
/**
+ * Alternate constructor which copies parameters bucketSize and numClass
+ * from an already initiated decision stump, other. It appropriately
+ * sets the Weight vector.
*
- *
- *
- *
- *
+ * @param other The other initiated Decision Stump object from
+ * which we copy the values from.
+ * @param data The data on which to train this object on.
+ * @param D Weight vector to use while training. For boosting purposes.
+ * @param labels The labels of data.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
template <typename MatType>
-DecisionStump<MatType>::DecisionStump(const DecisionStump<>& ds)
+DecisionStump<MatType>::DecisionStump(
+ const DecisionStump<>& other,
+ const MatType& data,
+ const arma::rowvec& weights,
+ const arma::Row<size_t>& labels
+ )
{
- numClass = ds.numClass;
-
- splitAttribute = ds.splitAttribute;
-
- bucketSize = ds.bucketSize;
+ numClass = other.numClass;
+ bucketSize = other.bucketSize;
- split = ds.split;
+ // weightD = weights;
+ // tempD = weightD;
- binLabels = ds.binLabels;
+ Train<true>(data, labels, weights);
}
/**
- *
- *
- *
- *
- *
- *
-template <typename MatType>
-DecisionStump<MatType>::ModifyData(MatType& data, const arma::Row<double>& D)
- */
-
-/**
* Sets up attribute as if it were splitting on it and finds entropy when
* splitting on attribute.
*
* @param attribute A row from the training data, which might be a candidate for
* the splitting attribute.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
template <typename MatType>
+template <bool isWeight>
double DecisionStump<MatType>::SetupSplitAttribute(
const arma::rowvec& attribute,
- const arma::Row<size_t>& labels)
+ const arma::Row<size_t>& labels,
+ const arma::rowvec& weightD)
{
size_t i, count, begin, end;
double entropy = 0.0;
@@ -175,9 +188,16 @@ double DecisionStump<MatType>::SetupSplitAttribute(
arma::Row<size_t> sortedLabels(attribute.n_elem);
sortedLabels.fill(0);
+ arma::rowvec tempD = arma::rowvec(weightD.n_cols);
+
for (i = 0; i < attribute.n_elem; i++)
+ {
sortedLabels(i) = labels(sortedIndexAtt(i));
+ if(isWeight)
+ tempD(i) = weightD(sortedIndexAtt(i));
+ }
+
i = 0;
count = 0;
@@ -196,8 +216,8 @@ double DecisionStump<MatType>::SetupSplitAttribute(
// Use ratioEl to calculate the ratio of elements in this split.
const double ratioEl = ((double) (end - begin + 1) / sortedLabels.n_elem);
- entropy += ratioEl * CalculateEntropy<size_t>(
- sortedLabels.subvec(begin, end));
+ entropy += ratioEl * CalculateEntropy<size_t, isWeight>(
+ sortedLabels.subvec(begin, end), begin, tempD);
i++;
}
else if (sortedLabels(i) != sortedLabels(i + 1))
@@ -223,8 +243,8 @@ double DecisionStump<MatType>::SetupSplitAttribute(
}
const double ratioEl = ((double) (end - begin + 1) / sortedLabels.n_elem);
- entropy += ratioEl * CalculateEntropy<size_t>(
- sortedLabels.subvec(begin, end));
+ entropy += ratioEl * CalculateEntropy<size_t, isWeight>(
+ sortedLabels.subvec(begin, end), begin, tempD);
i = end + 1;
count = 0;
@@ -368,7 +388,6 @@ rType DecisionStump<MatType>::CountMostFreq(const arma::Row<rType>& subCols)
rType mostFreq = it->first;
size_t mostFreqCount = it->second;
while (it != countMap.end())
->>>>>>> .merge-right.r17318
{
if (it->second >= mostFreqCount)
{
@@ -403,10 +422,13 @@ int DecisionStump<MatType>::IsDistinct(const arma::Row<rType>& featureRow)
*
* @param attribute The attribute for which we calculate the entropy.
* @param labels Corresponding labels of the attribute.
+ * @param isWeight Whether we need to run a weighted Decision Stump.
*/
template<typename MatType>
-template<typename AttType, typename LabelType>
-double DecisionStump<MatType>::CalculateEntropy(arma::subview_row<LabelType> labels)
+template<typename LabelType, bool isWeight>
+double DecisionStump<MatType>::CalculateEntropy(
+ arma::subview_row<LabelType> labels,
+ int begin, const arma::rowvec& tempD)
{
double entropy = 0.0;
size_t j;
@@ -414,19 +436,43 @@ double DecisionStump<MatType>::CalculateEntropy(arma::subview_row<LabelType> lab
arma::Row<size_t> numElem(numClass);
numElem.fill(0);
- // Populate numElem; they are used as helpers to calculate
- // entropy.
- for (j = 0; j < labels.n_elem; j++)
- numElem(labels(j))++;
+ // Variable to accumulate the weight in this subview_row.
+ double accWeight = 0.0;
+ // Populate numElem; they are used as helpers to calculate entropy.
+
+ if (isWeight)
+ {
+ for (j = 0; j < labels.n_elem; j++)
+ {
+ numElem(labels(j)) += tempD(j + begin);
+ accWeight += tempD(j + begin);
+ }
+ // numElem(labels(j))++;
+
+ for (j = 0; j < numClass; j++)
+ {
+ const double p1 = ((double) numElem(j) / accWeight);
- // The equation for entropy uses log2(), but log2() is from C99 and thus
- // Visual Studio will not have it. Therefore, we will use std::log(), and
- // then apply the change-of-base formula at the end of the calculation.
- for (j = 0; j < numClass; j++)
+ // Instead of using log2(), which is C99 and may not exist on some
+ // compilers, use std::log(), then use the change-of-base formula to make
+ // the result correct.
+ entropy += (p1 == 0) ? 0 : p1 * std::log(p1);
+ }
+ }
+ else
{
- const double p1 = ((double) numElem(j) / labels.n_elem);
+ for (j = 0; j < labels.n_elem; j++)
+ numElem(labels(j))++;
- entropy += (p1 == 0) ? 0 : p1 * std::log(p1);
+ for (j = 0; j < numClass; j++)
+ {
+ const double p1 = ((double) numElem(j) / labels.n_elem);
+
+ // Instead of using log2(), which is C99 and may not exist on some
+ // compilers, use std::log(), then use the change-of-base formula to make
+ // the result correct.
+ entropy += (p1 == 0) ? 0 : p1 * std::log(p1);
+ }
}
return entropy / std::log(2.0);
diff --git a/src/mlpack/tests/allknn_test.cpp b/src/mlpack/tests/allknn_test.cpp
index cfe7988..2a178f3 100644
--- a/src/mlpack/tests/allknn_test.cpp
+++ b/src/mlpack/tests/allknn_test.cpp
@@ -1,4 +1,3 @@
-<<<<<<< .working
/**
* @file allknn_test.cpp
*
diff --git a/src/mlpack/tests/gmm_test.cpp b/src/mlpack/tests/gmm_test.cpp
index 7fd1650..8e22360 100644
--- a/src/mlpack/tests/gmm_test.cpp
+++ b/src/mlpack/tests/gmm_test.cpp
@@ -442,7 +442,7 @@ BOOST_AUTO_TEST_CASE(GMMTrainEMMultipleGaussiansWithProbability)
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
- BOOST_REQUIRE_SMALL((g.Component(sortedIndices[0]).Covariance()(row, col)
+ BOOST_REQUIRE_SMALL((g.Covariances()[sortedIndices[0]](row, col)
- d4.Covariance()(row, col)), 0.7); // Big tolerance! Lots of noise.
// Second Gaussian (d1).
@@ -453,7 +453,7 @@ BOOST_AUTO_TEST_CASE(GMMTrainEMMultipleGaussiansWithProbability)
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
- BOOST_REQUIRE_SMALL((g.Component(sortedIndices[1]).Covariance()(row, col)
+ BOOST_REQUIRE_SMALL((g.Covariances()[sortedIndices[1]](row, col)
- d1.Covariance()(row, col)), 0.7); // Big tolerance! Lots of noise.
// Third Gaussian (d2).
@@ -464,7 +464,7 @@ BOOST_AUTO_TEST_CASE(GMMTrainEMMultipleGaussiansWithProbability)
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
- BOOST_REQUIRE_SMALL((g.Component(sortedIndices[2]).Covariance()(row, col)
+ BOOST_REQUIRE_SMALL((g.Covariances()[sortedIndices[2]](row, col)
- d2.Covariance()(row, col)), 0.7); // Big tolerance! Lots of noise.
// Fourth gaussian (d3).
@@ -475,7 +475,7 @@ BOOST_AUTO_TEST_CASE(GMMTrainEMMultipleGaussiansWithProbability)
for (size_t row = 0; row < 3; ++row)
for (size_t col = 0; col < 3; ++col)
- BOOST_REQUIRE_SMALL((g.Component(sortedIndices[3]).Covariance()(row, col)
+ BOOST_REQUIRE_SMALL((g.Covariances()[sortedIndices[3]](row, col)
- d3.Covariance()(row, col)), 0.7);
}
diff --git a/src/mlpack/tests/svd_batch_test.cpp b/src/mlpack/tests/svd_batch_test.cpp
index 3716aaf..bf39c84 100644
--- a/src/mlpack/tests/svd_batch_test.cpp
+++ b/src/mlpack/tests/svd_batch_test.cpp
@@ -1,6 +1,26 @@
+/**
+ * @file svd_batch_test.cpp
+ * @author Sumedh Ghaisas
+ *
+ * This file is part of MLPACK 1.0.11.
+ *
+ * MLPACK is free software: you can redistribute it and/or modify it under the
+ * terms of the GNU Lesser General Public License as published by the Free
+ * Software Foundation, either version 3 of the License, or (at your option) any
+ * later version.
+ *
+ * MLPACK is distributed in the hope that it will be useful, but WITHOUT ANY
+ * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
+ * A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+ * details (LICENSE.txt).
+ *
+ * You should have received a copy of the GNU General Public License along with
+ * MLPACK. If not, see <http://www.gnu.org/licenses/>.
+ */
#include <mlpack/core.hpp>
#include <mlpack/methods/amf/amf.hpp>
#include <mlpack/methods/amf/update_rules/svd_batch_learning.hpp>
+#include <mlpack/methods/amf/init_rules/average_init.hpp>
#include <mlpack/methods/amf/init_rules/random_init.hpp>
#include <mlpack/methods/amf/termination_policies/validation_RMSE_termination.hpp>
#include <mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp>
@@ -17,21 +37,6 @@ using namespace arma;
/**
* Make sure the SVD Batch lerning is converging.
- *
- * This file is part of MLPACK 1.0.11.
- *
- * MLPACK is free software: you can redistribute it and/or modify it under the
- * terms of the GNU Lesser General Public License as published by the Free
- * Software Foundation, either version 3 of the License, or (at your option) any
- * later version.
- *
- * MLPACK is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
- * A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
- * details (LICENSE.txt).
- *
- * You should have received a copy of the GNU General Public License along with
- * MLPACK. If not, see <http://www.gnu.org/licenses/>.
*/
BOOST_AUTO_TEST_CASE(SVDBatchConvergenceElementTest)
{
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