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Merge pull request #684 from MarcosPividori/approx-knn

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Approximate Neighbor Search for Dual tree algorithms.
This commit is contained in:
sumedhghaisas
2016-06-23 02:17:55 +05:30
committed by GitHub
parent a9f5622c8a fd33fd5ca6
commit 37fda23945
97 changed files with 922 additions and 126 deletions
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src/mlpack/methods/neighbor_search/kfn_main.cpp
+27 -1
View File
@@ -72,6 +72,12 @@ PARAM_INT("seed", "Random seed (if 0, std::time(NULL) is used).", "s", 0);
PARAM_FLAG("naive", "If true, O(n^2) naive mode is used for computation.", "N");
PARAM_FLAG("single_mode", "If true, single-tree search is used (as opposed to "
"dual-tree search).", "s");
PARAM_DOUBLE("epsilon", "If specified, will do approximate furthest neighbor "
"search with given relative error. Must be in the range [0,1).", "e", 0);
PARAM_DOUBLE("percentage", "If specified, will do approximate furthest neighbor"
" search. Must be in the range (0,1] (decimal form). Resultant neighbors "
"will be at least (p*100) % of the distance as the true furthest neighbor.",
"p", 1);
// Convenience typedef.
typedef NSModel<FurthestNeighborSort> KFNModel;
@@ -138,6 +144,24 @@ int main(int argc, char *argv[])
Log::Fatal << "Invalid leaf size: " << lsInt << ". Must be greater than 0."
<< endl;
// Sanity check on epsilon.
double epsilon = CLI::GetParam<double>("epsilon");
if (epsilon < 0 || epsilon >= 1)
Log::Fatal << "Invalid epsilon: " << epsilon << ". Must be in the range "
<< "[0,1)." << endl;
// Sanity check on percentage.
const double percentage = CLI::GetParam<double>("percentage");
if (percentage <= 0 || percentage > 1)
Log::Fatal << "Invalid percentage: " << percentage << ". Must be in the "
<< "range (0,1] (decimal form)." << endl;
if (CLI::HasParam("percentage") && CLI::HasParam("epsilon"))
Log::Fatal << "Cannot provide both epsilon and percentage." << endl;
if (CLI::HasParam("percentage"))
epsilon = 1 - percentage;
// We either have to load the reference data, or we have to load the model.
NSModel<FurthestNeighborSort> kfn;
const bool naive = CLI::HasParam("naive");
@@ -175,7 +199,8 @@ int main(int argc, char *argv[])
Log::Info << "Loaded reference data from '" << referenceFile << "' ("
<< referenceSet.n_rows << "x" << referenceSet.n_cols << ")." << endl;
kfn.BuildModel(std::move(referenceSet), size_t(lsInt), naive, singleMode);
kfn.BuildModel(std::move(referenceSet), size_t(lsInt), naive, singleMode,
epsilon);
}
else
{
@@ -191,6 +216,7 @@ int main(int argc, char *argv[])
kfn.SingleMode() = CLI::HasParam("single_mode");
kfn.Naive() = CLI::HasParam("naive");
kfn.LeafSize() = size_t(lsInt);
kfn.Epsilon() = epsilon;
}
// Perform search, if desired.
src/mlpack/methods/neighbor_search/knn_main.cpp
+11 -3
View File
@@ -74,6 +74,8 @@ PARAM_INT("seed", "Random seed (if 0, std::time(NULL) is used).", "s", 0);
PARAM_FLAG("naive", "If true, O(n^2) naive mode is used for computation.", "N");
PARAM_FLAG("single_mode", "If true, single-tree search is used (as opposed to "
"dual-tree search).", "S");
PARAM_DOUBLE("epsilon", "If specified, will do approximate nearest neighbor "
"search with given relative error.", "e", 0);
// Convenience typedef.
typedef NSModel<NearestNeighborSort> KNNModel;
@@ -137,10 +139,14 @@ int main(int argc, char *argv[])
// Sanity check on leaf size.
const int lsInt = CLI::GetParam<int>("leaf_size");
if (lsInt < 1)
{
Log::Fatal << "Invalid leaf size: " << lsInt << ". Must be greater "
"than 0." << endl;
}
// Sanity check on epsilon.
const double epsilon = CLI::GetParam<double>("epsilon");
if (epsilon < 0)
Log::Fatal << "Invalid epsilon: " << epsilon << ". Must be non-negative. "
<< endl;
// We either have to load the reference data, or we have to load the model.
NSModel<NearestNeighborSort> knn;
@@ -180,7 +186,8 @@ int main(int argc, char *argv[])
<< referenceSet.n_rows << " x " << referenceSet.n_cols << ")."
<< endl;
knn.BuildModel(std::move(referenceSet), size_t(lsInt), naive, singleMode);
knn.BuildModel(std::move(referenceSet), size_t(lsInt), naive, singleMode,
epsilon);
}
else
{
@@ -196,6 +203,7 @@ int main(int argc, char *argv[])
knn.SingleMode() = CLI::HasParam("single_mode");
knn.Naive() = CLI::HasParam("naive");
knn.LeafSize() = size_t(lsInt);
knn.Epsilon() = epsilon;
}
// Perform search, if desired.
src/mlpack/methods/neighbor_search/neighbor_search.hpp
+15
View File
@@ -84,11 +84,13 @@ class NeighborSearch
* dual-tree search). This overrides singleMode (if it is set to true).
* @param singleMode If true, single-tree search will be used (as opposed to
* dual-tree search).
* @param epsilon Relative approximate error (non-negative).
* @param metric An optional instance of the MetricType class.
*/
NeighborSearch(const MatType& referenceSet,
const bool naive = false,
const bool singleMode = false,
const double epsilon = 0,
const MetricType metric = MetricType());
/**
@@ -108,11 +110,13 @@ class NeighborSearch
* dual-tree search). This overrides singleMode (if it is set to true).
* @param singleMode If true, single-tree search will be used (as opposed to
* dual-tree search).
* @param epsilon Relative approximate error (non-negative).
* @param metric An optional instance of the MetricType class.
*/
NeighborSearch(MatType&& referenceSet,
const bool naive = false,
const bool singleMode = false,
const double epsilon = 0,
const MetricType metric = MetricType());
/**
@@ -138,10 +142,12 @@ class NeighborSearch
* @param referenceSet Set of reference points corresponding to referenceTree.
* @param singleMode Whether single-tree computation should be used (as
* opposed to dual-tree computation).
* @param epsilon Relative approximate error (non-negative).
* @param metric Instantiated distance metric.
*/
NeighborSearch(Tree* referenceTree,
const bool singleMode = false,
const double epsilon = 0,
const MetricType metric = MetricType());
/**
@@ -152,10 +158,12 @@ class NeighborSearch
* @param naive Whether to use naive search.
* @param singleMode Whether single-tree computation should be used (as
* opposed to dual-tree computation).
* @param epsilon Relative approximate error (non-negative).
* @param metric Instantiated metric.
*/
NeighborSearch(const bool naive = false,
const bool singleMode = false,
const double epsilon = 0,
const MetricType metric = MetricType());
@@ -270,6 +278,11 @@ class NeighborSearch
//! Modify whether or not search is done in single-tree mode.
bool& SingleMode() { return singleMode; }
//! Access the relative error to be considered in approximate search.
double Epsilon() const { return epsilon; }
//! Modify the relative error to be considered in approximate search.
double& Epsilon() { return epsilon; }
//! Access the reference dataset.
const MatType& ReferenceSet() const { return *referenceSet; }
@@ -294,6 +307,8 @@ class NeighborSearch
bool naive;
//! Indicates if single-tree search is being used (as opposed to dual-tree).
bool singleMode;
//! Indicates the relative error to be considered in approximate search.
double epsilon;
//! Instantiation of metric.
MetricType metric;
src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp
+24 -8
View File
@@ -75,6 +75,7 @@ NeighborSearch<SortPolicy, MetricType, MatType, TreeType, TraversalType>::
NeighborSearch(const MatType& referenceSetIn,
const bool naive,
const bool singleMode,
const double epsilon,
const MetricType metric) :
referenceTree(naive ? NULL :
BuildTree<MatType, Tree>(referenceSetIn, oldFromNewReferences)),
@@ -83,12 +84,14 @@ NeighborSearch(const MatType& referenceSetIn,
setOwner(false),
naive(naive),
singleMode(!naive && singleMode), // No single mode if naive.
epsilon(epsilon),
metric(metric),
baseCases(0),
scores(0),
treeNeedsReset(false)
{
// Nothing to do.
if (epsilon < 0)
throw std::invalid_argument("epsilon must be non-negative");
}
// Construct the object.
@@ -103,6 +106,7 @@ NeighborSearch<SortPolicy, MetricType, MatType, TreeType, TraversalType>::
NeighborSearch(MatType&& referenceSetIn,
const bool naive,
const bool singleMode,
const double epsilon,
const MetricType metric) :
referenceTree(naive ? NULL :
BuildTree<MatType, Tree>(std::move(referenceSetIn),
@@ -113,12 +117,14 @@ NeighborSearch(MatType&& referenceSetIn,
setOwner(naive),
naive(naive),
singleMode(!naive && singleMode),
epsilon(epsilon),
metric(metric),
baseCases(0),
scores(0),
treeNeedsReset(false)
{
// Nothing to do.
if (epsilon < 0)
throw std::invalid_argument("epsilon must be non-negative");
}
// Construct the object.
@@ -132,6 +138,7 @@ template<typename SortPolicy,
NeighborSearch<SortPolicy, MetricType, MatType, TreeType, TraversalType>::
NeighborSearch(Tree* referenceTree,
const bool singleMode,
const double epsilon,
const MetricType metric) :
referenceTree(referenceTree),
referenceSet(&referenceTree->Dataset()),
@@ -139,12 +146,14 @@ NeighborSearch(Tree* referenceTree,
setOwner(false),
naive(false),
singleMode(singleMode),
epsilon(epsilon),
metric(metric),
baseCases(0),
scores(0),
treeNeedsReset(false)
{
// Nothing else to initialize.
if (epsilon < 0)
throw std::invalid_argument("epsilon must be non-negative");
}
// Construct the object without a reference dataset.
@@ -158,6 +167,7 @@ template<typename SortPolicy,
NeighborSearch<SortPolicy, MetricType, MatType, TreeType, TraversalType>::
NeighborSearch(const bool naive,
const bool singleMode,
const double epsilon,
const MetricType metric) :
referenceTree(NULL),
referenceSet(new MatType()), // Empty matrix.
@@ -165,11 +175,14 @@ NeighborSearch<SortPolicy, MetricType, MatType, TreeType, TraversalType>::
setOwner(true),
naive(naive),
singleMode(singleMode),
epsilon(epsilon),
metric(metric),
baseCases(0),
scores(0),
treeNeedsReset(false)
{
if (epsilon < 0)
throw std::invalid_argument("epsilon must be non-negative");
// Build the tree on the empty dataset, if necessary.
if (!naive)
{
@@ -364,7 +377,8 @@ Search(const MatType& querySet,
if (naive)
{
// Create the helper object for the tree traversal.
RuleType rules(*referenceSet, querySet, *neighborPtr, *distancePtr, metric);
RuleType rules(*referenceSet, querySet, *neighborPtr, *distancePtr, metric,
epsilon);
// The naive brute-force traversal.
for (size_t i = 0; i < querySet.n_cols; ++i)
@@ -376,7 +390,8 @@ Search(const MatType& querySet,
else if (singleMode)
{
// Create the helper object for the tree traversal.
RuleType rules(*referenceSet, querySet, *neighborPtr, *distancePtr, metric);
RuleType rules(*referenceSet, querySet, *neighborPtr, *distancePtr, metric,
epsilon);
// Create the traverser.
typename Tree::template SingleTreeTraverser<RuleType> traverser(rules);
@@ -402,7 +417,7 @@ Search(const MatType& querySet,
// Create the helper object for the tree traversal.
RuleType rules(*referenceSet, queryTree->Dataset(), *neighborPtr,
*distancePtr, metric);
*distancePtr, metric, epsilon);
// Create the traverser.
TraversalType<RuleType> traverser(rules);
@@ -527,7 +542,8 @@ Search(Tree* queryTree,
// Create the helper object for the traversal.
typedef NeighborSearchRules<SortPolicy, MetricType, Tree> RuleType;
RuleType rules(*referenceSet, querySet, *neighborPtr, distances, metric);
RuleType rules(*referenceSet, querySet, *neighborPtr, distances, metric,
epsilon);
// Create the traverser.
TraversalType<RuleType> traverser(rules);
@@ -598,7 +614,7 @@ Search(const size_t k,
// Create the helper object for the traversal.
typedef NeighborSearchRules<SortPolicy, MetricType, Tree> RuleType;
RuleType rules(*referenceSet, *referenceSet, *neighborPtr, *distancePtr,
metric, true /* don't return the same point as nearest neighbor */);
metric, epsilon, true /* don't return the same point as nearest neighbor */);
if (naive)
{
src/mlpack/methods/neighbor_search/neighbor_search_rules.hpp
+4
View File
@@ -22,6 +22,7 @@ class NeighborSearchRules
arma::Mat<size_t>& neighbors,
arma::mat& distances,
MetricType& metric,
const double epsilon = 0,
const bool sameSet = false);
/**
* Get the distance from the query point to the reference point.
@@ -120,6 +121,9 @@ class NeighborSearchRules
//! Denotes whether or not the reference and query sets are the same.
bool sameSet;
//! Relative error to be considered in approximate search.
const double epsilon;
//! The last query point BaseCase() was called with.
size_t lastQueryIndex;
//! The last reference point BaseCase() was called with.
src/mlpack/methods/neighbor_search/neighbor_search_rules_impl.hpp
+10 -4
View File
@@ -1,8 +1,8 @@
/**
* @file nearest_neighbor_rules_impl.hpp
* @file neighbor_search_rules_impl.hpp
* @author Ryan Curtin
*
* Implementation of NearestNeighborRules.
* Implementation of NeighborSearchRules.
*/
#ifndef MLPACK_METHODS_NEIGHBOR_SEARCH_NEAREST_NEIGHBOR_RULES_IMPL_HPP
#define MLPACK_METHODS_NEIGHBOR_SEARCH_NEAREST_NEIGHBOR_RULES_IMPL_HPP
@@ -20,6 +20,7 @@ NeighborSearchRules<SortPolicy, MetricType, TreeType>::NeighborSearchRules(
arma::Mat<size_t>& neighbors,
arma::mat& distances,
MetricType& metric,
const double epsilon,
const bool sameSet) :
referenceSet(referenceSet),
querySet(querySet),
@@ -27,6 +28,7 @@ NeighborSearchRules<SortPolicy, MetricType, TreeType>::NeighborSearchRules(
distances(distances),
metric(metric),
sameSet(sameSet),
epsilon(epsilon),
lastQueryIndex(querySet.n_cols),
lastReferenceIndex(referenceSet.n_cols),
baseCases(0),
@@ -112,7 +114,8 @@ inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Score(
}
// Compare against the best k'th distance for this query point so far.
const double bestDistance = distances(distances.n_rows - 1, queryIndex);
double bestDistance = distances(distances.n_rows - 1, queryIndex);
bestDistance = SortPolicy::Relax(bestDistance, epsilon);
return (SortPolicy::IsBetter(distance, bestDistance)) ? distance : DBL_MAX;
}
@@ -128,7 +131,8 @@ inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Rescore(
return oldScore;
// Just check the score again against the distances.
const double bestDistance = distances(distances.n_rows - 1, queryIndex);
double bestDistance = distances(distances.n_rows - 1, queryIndex);
bestDistance = SortPolicy::Relax(bestDistance, epsilon);
return (SortPolicy::IsBetter(oldScore, bestDistance)) ? oldScore : DBL_MAX;
}
@@ -419,6 +423,8 @@ inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::
queryNode.Stat().SecondBound() = bestDistance;
queryNode.Stat().AuxBound() = auxDistance;
worstDistance = SortPolicy::Relax(worstDistance, epsilon);
if (SortPolicy::IsBetter(worstDistance, bestDistance))
return worstDistance;
else
src/mlpack/methods/neighbor_search/ns_model.hpp
+21 -2
View File
@@ -69,7 +69,11 @@ class MonoSearchVisitor : public boost::static_visitor<void>
MonoSearchVisitor(const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances);
arma::mat& distances) :
k(k),
neighbors(neighbors),
distances(distances)
{};
};
/**
@@ -177,6 +181,16 @@ class NaiveVisitor : public boost::static_visitor<bool&>
bool& operator()(NSType *ns) const;
};
/**
* EpsilonVisitor exposes the Epsilon method of the given NSType.
*/
class EpsilonVisitor : public boost::static_visitor<double&>
{
public:
template<typename NSType>
double& operator()(NSType *ns) const;
};
/**
* ReferenceSetVisitor exposes the referenceSet of the given NSType.
*/
@@ -266,6 +280,10 @@ class NSModel
bool Naive() const;
bool& Naive();
//! Expose Epsilon.
double Epsilon() const;
double& Epsilon();
//! Expose leafSize.
size_t LeafSize() const { return leafSize; }
size_t& LeafSize() { return leafSize; }
@@ -282,7 +300,8 @@ class NSModel
void BuildModel(arma::mat&& referenceSet,
const size_t leafSize,
const bool naive,
const bool singleMode);
const bool singleMode,
const double epsilon = 0);
//! Perform neighbor search. The query set will be reordered.
void Search(arma::mat&& querySet,
src/mlpack/methods/neighbor_search/ns_model_impl.hpp
+40 -18
View File
@@ -18,15 +18,6 @@
namespace mlpack {
namespace neighbor {
//! Save parameters for monochromatic neighbor search.
MonoSearchVisitor::MonoSearchVisitor(const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances) :
k(k),
neighbors(neighbors),
distances(distances)
{}
//! Monochromatic neighbor search on the given NSType instance.
template<typename NSType>
void MonoSearchVisitor::operator()(NSType *ns) const
@@ -185,6 +176,15 @@ bool& NaiveVisitor::operator()(NSType* ns) const
throw std::runtime_error("no neighbor search model initialized");
}
//! Expose the Epsilon method of the given NSType.
template<typename NSType>
double& EpsilonVisitor::operator()(NSType* ns) const
{
if (ns)
return ns->Epsilon();
throw std::runtime_error("no neighbor search model initialized");
}
//! Expose the referenceSet of the given NSType.
template<typename NSType>
const arma::mat& ReferenceSetVisitor::operator()(NSType* ns) const
@@ -293,12 +293,25 @@ bool& NSModel<SortPolicy>::Naive()
return boost::apply_visitor(NaiveVisitor(), nSearch);
}
template<typename SortPolicy>
double NSModel<SortPolicy>::Epsilon() const
{
return boost::apply_visitor(EpsilonVisitor(), nSearch);
}
template<typename SortPolicy>
double& NSModel<SortPolicy>::Epsilon()
{
return boost::apply_visitor(EpsilonVisitor(), nSearch);
}
//! Build the reference tree.
template<typename SortPolicy>
void NSModel<SortPolicy>::BuildModel(arma::mat&& referenceSet,
const size_t leafSize,
const bool naive,
const bool singleMode)
const bool singleMode,
const double epsilon)
{
// Initialize random basis if necessary.
if (randomBasis)
@@ -348,23 +361,26 @@ void NSModel<SortPolicy>::BuildModel(arma::mat&& referenceSet,
switch (treeType)
{
case KD_TREE:
nSearch = new NSType<SortPolicy, tree::KDTree>(naive, singleMode);
nSearch = new NSType<SortPolicy, tree::KDTree>(naive, singleMode,
epsilon);
break;
case COVER_TREE:
nSearch = new NSType<SortPolicy, tree::StandardCoverTree>(naive,
singleMode);
singleMode, epsilon);
break;
case R_TREE:
nSearch = new NSType<SortPolicy, tree::RTree>(naive, singleMode);
nSearch = new NSType<SortPolicy, tree::RTree>(naive, singleMode, epsilon);
break;
case R_STAR_TREE:
nSearch = new NSType<SortPolicy, tree::RStarTree>(naive, singleMode);
nSearch = new NSType<SortPolicy, tree::RStarTree>(naive, singleMode,
epsilon);
break;
case BALL_TREE:
nSearch = new NSType<SortPolicy, tree::BallTree>(naive, singleMode);
nSearch = new NSType<SortPolicy, tree::BallTree>(naive, singleMode,
epsilon);
break;
case X_TREE:
nSearch = new NSType<SortPolicy, tree::XTree>(naive, singleMode);
nSearch = new NSType<SortPolicy, tree::XTree>(naive, singleMode, epsilon);
break;
}
@@ -389,13 +405,16 @@ void NSModel<SortPolicy>::Search(arma::mat&& querySet,
if (randomBasis)
querySet = q * querySet;
Log::Info << "Searching for " << k << " nearest neighbors with ";
Log::Info << "Searching for " << k << " neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree " << TreeName() << " search..." << std::endl;
else if (!Naive())
Log::Info << "single-tree " << TreeName() << " search..." << std::endl;
else
Log::Info << "brute-force (naive) search..." << std::endl;
if (Epsilon() != 0 && !Naive())
Log::Info << "Maximum of " << Epsilon() * 100 << "% relative error."
<< std::endl;
BiSearchVisitor<SortPolicy> search(querySet, k, neighbors, distances,
leafSize);
@@ -408,13 +427,16 @@ void NSModel<SortPolicy>::Search(const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
Log::Info << "Searching for " << k << " nearest neighbors with ";
Log::Info << "Searching for " << k << " neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree " << TreeName() << " search..." << std::endl;
else if (!Naive())
Log::Info << "single-tree " << TreeName() << " search..." << std::endl;
else
Log::Info << "brute-force (naive) search..." << std::endl;
if (Epsilon() != 0 && !Naive())
Log::Info << "Maximum of " << Epsilon() * 100 << "% relative error."
<< std::endl;
MonoSearchVisitor search(k, neighbors, distances);
boost::apply_visitor(search, nSearch);
src/mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.cpp
+2 -2
View File
@@ -1,5 +1,5 @@
/***
* @file nearest_neighbor_sort.cpp
* @file furthest_neighbor_sort.cpp
* @author Ryan Curtin
*
* Implementation of the simple FurthestNeighborSort policy class.
@@ -12,7 +12,7 @@ size_t FurthestNeighborSort::SortDistance(const arma::vec& list,
const arma::Col<size_t>& indices,
double newDistance)
{
// The first element in the list is the nearest neighbor. We only want to
// The first element in the list is the furthest neighbor. We only want to
// insert if the new distance is greater than the last element in the list.
if (newDistance < list[list.n_elem - 1])
return (size_t() - 1); // Do not insert.
src/mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.hpp
+17
View File
@@ -145,6 +145,23 @@ class FurthestNeighborSort
*/
static inline double CombineWorst(const double a, const double b)
{ return std::max(a - b, 0.0); }
/**
* Return the given value relaxed.
*
* @param value Value to relax.
* @param epsilon Relative error (non-negative).
*
* @return double Value relaxed.
*/
static inline double Relax(const double value, const double epsilon)
{
if (value == 0)
return 0;
if (value == DBL_MAX || epsilon >= 1)
return DBL_MAX;
return (1 / (1 - epsilon)) * value;
}
};
} // namespace neighbor
src/mlpack/methods/neighbor_search/sort_policies/nearest_neighbor_sort.hpp
+15
View File
@@ -150,6 +150,21 @@ class NearestNeighborSort
return DBL_MAX;
return a + b;
}
/**
* Return the given value relaxed.
*
* @param value Value to relax.
* @param epsilon Relative error (non-negative).
*
* @return double Value relaxed.
*/
static inline double Relax(const double value, const double epsilon)
{
if (value == DBL_MAX)
return DBL_MAX;
return (1 / (1 + epsilon)) * value;
}
};
} // namespace neighbor
src/mlpack/tests/CMakeLists.txt
+2
View File
@@ -28,9 +28,11 @@ add_executable(mlpack_test
kernel_pca_test.cpp
kernel_traits_test.cpp
kfn_test.cpp
akfn_test.cpp
kmeans_test.cpp
knn_test.cpp
krann_search_test.cpp
aknn_test.cpp
lars_test.cpp
lbfgs_test.cpp
lin_alg_test.cpp
src/mlpack/tests/activation_functions_test.cpp
+1 -1
View File
@@ -25,7 +25,7 @@
#include <mlpack/methods/ann/layer/hard_tanh_layer.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/ada_delta_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/logistic_regression/logistic_regression.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace arma;
using namespace mlpack::optimization;
src/mlpack/tests/adaboost_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/adaboost/adaboost.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
using namespace arma;
src/mlpack/tests/adam_test.cpp
+1 -1
View File
@@ -11,7 +11,7 @@
#include <mlpack/methods/logistic_regression/logistic_regression.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace arma;
using namespace mlpack::optimization;
src/mlpack/tests/akfn_test.cpp
+240
View File
@@ -0,0 +1,240 @@
/**
* @file akfn_test.cpp
*
* Tests for KFN (k-furthest-neighbors) with different values of epsilon.
*/
#include <mlpack/core.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <mlpack/core/tree/cover_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
using namespace mlpack::tree;
using namespace mlpack::metric;
using namespace mlpack::bound;
BOOST_AUTO_TEST_SUITE(AKFNTest);
/**
* Test the dual-tree furthest-neighbors method with different values for
* epsilon. This uses both a query and reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(AproxVsExact1)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
for (size_t c = 0; c < 4; c++)
{
KFN* akfn;
double epsilon;
switch (c)
{
case 0: // Use the dual-tree method with e=0.02.
epsilon = 0.02;
break;
case 1: // Use the dual-tree method with e=0.05.
epsilon = 0.05;
break;
case 2: // Use the dual-tree method with e=0.10.
epsilon = 0.10;
break;
case 3: // Use the dual-tree method with e=0.20.
epsilon = 0.20;
break;
}
// Now perform the actual calculation.
akfn = new KFN(dataset, false, false, epsilon);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
akfn->Search(dataset, 15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox(i), distancesExact(i), epsilon);
// Clean the memory.
delete akfn;
}
}
/**
* Test the dual-tree furthest-neighbors method with the exact method. This
* uses only a reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(AproxVsExact2)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
KFN akfn(dataset, false, false, 0.05);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
akfn.Search(15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox[i], distancesExact[i], 0.05);
}
/**
* Test the single-tree furthest-neighbors method with the exact method. This
* uses only a reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleTreeVsExact)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
KFN akfn(dataset, false, true, 0.05);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
akfn.Search(15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox[i], distancesExact[i], 0.05);
}
/**
* Test the cover tree single-tree furthest-neighbors method against the exact
* method. This uses only a random reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleCoverTreeTest)
{
arma::mat dataset;
dataset.randu(75, 1000); // 75 dimensional, 1000 points.
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
StandardCoverTree<EuclideanDistance, NeighborSearchStat<FurthestNeighborSort>,
arma::mat> tree(dataset);
NeighborSearch<FurthestNeighborSort, LMetric<2>, arma::mat, StandardCoverTree>
coverTreeSearch(&tree, true, 0.05);
arma::Mat<size_t> neighborsCoverTree;
arma::mat distancesCoverTree;
coverTreeSearch.Search(dataset, 15, neighborsCoverTree, distancesCoverTree);
for (size_t i = 0; i < neighborsCoverTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesCoverTree[i], distancesExact[i], 0.05);
}
/**
* Test the cover tree dual-tree furthest neighbors method against the exact
* method.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(DualCoverTreeTest)
{
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
StandardCoverTree<EuclideanDistance, NeighborSearchStat<FurthestNeighborSort>,
arma::mat> referenceTree(dataset);
NeighborSearch<FurthestNeighborSort, LMetric<2>, arma::mat, StandardCoverTree>
coverTreeSearch(&referenceTree, false, 0.05);
arma::Mat<size_t> neighborsCoverTree;
arma::mat distancesCoverTree;
coverTreeSearch.Search(dataset, 15, neighborsCoverTree, distancesCoverTree);
for (size_t i = 0; i < neighborsCoverTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesCoverTree[i], distancesExact[i], 0.05);
}
/**
* Test the ball tree single-tree furthest-neighbors method against the exact
* method. This uses only a random reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleBallTreeTest)
{
arma::mat dataset;
dataset.randu(75, 1000); // 75 dimensional, 1000 points.
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
NeighborSearch<FurthestNeighborSort, EuclideanDistance, arma::mat, BallTree>
ballTreeSearch(dataset, false, true, 0.05);
arma::Mat<size_t> neighborsBallTree;
arma::mat distancesBallTree;
ballTreeSearch.Search(dataset, 15, neighborsBallTree, distancesBallTree);
for (size_t i = 0; i < neighborsBallTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesBallTree(i), distancesExact(i), 0.05);
}
/**
* Test the ball tree dual-tree furthest neighbors method against the exact
* method.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(DualBallTreeTest)
{
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
KFN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
NeighborSearch<FurthestNeighborSort, EuclideanDistance, arma::mat, BallTree>
ballTreeSearch(dataset, false, false, 0.05);
arma::Mat<size_t> neighborsBallTree;
arma::mat distancesBallTree;
ballTreeSearch.Search(15, neighborsBallTree, distancesBallTree);
for (size_t i = 0; i < neighborsBallTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesBallTree(i), distancesExact(i), 0.05);
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/aknn_test.cpp
+400
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@@ -0,0 +1,400 @@
/**
* @file aknn_test.cpp
*
* Test file for KNN class with different values of epsilon.
*/
#include <mlpack/core.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <mlpack/methods/neighbor_search/unmap.hpp>
#include <mlpack/methods/neighbor_search/ns_model.hpp>
#include <mlpack/core/tree/cover_tree.hpp>
#include <mlpack/core/tree/example_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
using namespace mlpack::tree;
using namespace mlpack::metric;
using namespace mlpack::bound;
BOOST_AUTO_TEST_SUITE(AKNNTest);
/**
* Test the dual-tree nearest-neighbors method with different values for
* epsilon. This uses both a query and reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(AproxVsExact1)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
for (size_t c = 0; c < 4; c++)
{
KNN* aknn;
double epsilon;
switch (c)
{
case 0: // Use the dual-tree method with e=0.02.
epsilon = 0.02;
break;
case 1: // Use the dual-tree method with e=0.05.
epsilon = 0.05;
break;
case 2: // Use the dual-tree method with e=0.10.
epsilon = 0.10;
break;
case 3: // Use the dual-tree method with e=0.20.
epsilon = 0.20;
break;
}
// Now perform the actual calculation.
aknn = new KNN(dataset, false, false, epsilon);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
aknn->Search(dataset, 15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox(i), distancesExact(i), epsilon);
// Clean the memory.
delete aknn;
}
}
/**
* Test the dual-tree nearest-neighbors method with the exact method. This uses
* only a reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(AproxVsExact2)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
KNN aknn(dataset, false, false, 0.05);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
aknn.Search(15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox(i), distancesExact(i), 0.05);
}
/**
* Test the single-tree nearest-neighbors method with the exact method. This
* uses only a reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleTreeAproxVsExact)
{
arma::mat dataset;
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
KNN aknn(dataset, false, true, 0.05);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
aknn.Search(15, neighborsAprox, distancesAprox);
for (size_t i = 0; i < neighborsAprox.n_elem; i++)
REQUIRE_RELATIVE_ERR(distancesAprox[i], distancesExact[i], 0.05);
}
/**
* Test the cover tree single-tree nearest-neighbors method against the exact
* method. This uses only a random reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleCoverTreeTest)
{
arma::mat dataset;
dataset.randu(75, 1000); // 75 dimensional, 1000 points.
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
StandardCoverTree<EuclideanDistance, NeighborSearchStat<NearestNeighborSort>,
arma::mat> tree(dataset);
NeighborSearch<NearestNeighborSort, LMetric<2>, arma::mat, StandardCoverTree>
coverTreeSearch(&tree, true, 0.05);
arma::Mat<size_t> neighborsCoverTree;
arma::mat distancesCoverTree;
coverTreeSearch.Search(dataset, 15, neighborsCoverTree, distancesCoverTree);
for (size_t i = 0; i < neighborsCoverTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesCoverTree[i], distancesExact[i], 0.05);
}
/**
* Test the cover tree dual-tree nearest neighbors method against the exact
* method.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(DualCoverTreeTest)
{
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
StandardCoverTree<EuclideanDistance, NeighborSearchStat<NearestNeighborSort>,
arma::mat> referenceTree(dataset);
NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat,
StandardCoverTree> coverTreeSearch(&referenceTree, false, 0.05);
arma::Mat<size_t> neighborsCoverTree;
arma::mat distancesCoverTree;
coverTreeSearch.Search(&referenceTree, 15, neighborsCoverTree,
distancesCoverTree);
for (size_t i = 0; i < neighborsCoverTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesCoverTree[i], distancesExact[i], 0.05);
}
/**
* Test the ball tree single-tree nearest-neighbors method against the exact
* method. This uses only a random reference dataset.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(SingleBallTreeTest)
{
arma::mat dataset;
dataset.randu(50, 300); // 50 dimensional, 300 points.
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(dataset, 15, neighborsExact, distancesExact);
NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat, BallTree>
ballTreeSearch(dataset, false, true, 0.05);
arma::Mat<size_t> neighborsBallTree;
arma::mat distancesBallTree;
ballTreeSearch.Search(dataset, 15, neighborsBallTree, distancesBallTree);
for (size_t i = 0; i < neighborsBallTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesBallTree(i), distancesExact(i), 0.05);
}
/**
* Test the ball tree dual-tree nearest neighbors method against the exact
* method.
*
* Errors are produced if the results are not according to relative error.
*/
BOOST_AUTO_TEST_CASE(DualBallTreeTest)
{
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
KNN exact(dataset);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(15, neighborsExact, distancesExact);
NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat, BallTree>
ballTreeSearch(dataset, false, false, 0.05);
arma::Mat<size_t> neighborsBallTree;
arma::mat distancesBallTree;
ballTreeSearch.Search(15, neighborsBallTree, distancesBallTree);
for (size_t i = 0; i < neighborsBallTree.n_elem; ++i)
REQUIRE_RELATIVE_ERR(distancesBallTree(i), distancesExact(i), 0.05);
}
/**
* Make sure sparse nearest neighbors works with kd trees.
*/
BOOST_AUTO_TEST_CASE(SparseKNNKDTreeTest)
{
// The dimensionality of these datasets must be high so that the probability
// of a completely empty point is very low. In this case, with dimensionality
// 70, the probability of all 70 dimensions being zero is 0.8^70 = 1.65e-7 in
// the reference set and 0.9^70 = 6.27e-4 in the query set.
arma::sp_mat queryDataset;
queryDataset.sprandu(70, 200, 0.2);
arma::sp_mat referenceDataset;
referenceDataset.sprandu(70, 500, 0.1);
arma::mat denseQuery(queryDataset);
arma::mat denseReference(referenceDataset);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::sp_mat,
KDTree> SparseKNN;
SparseKNN aknn(referenceDataset, false, false, 0.05);
arma::mat distancesSparse;
arma::Mat<size_t> neighborsSparse;
aknn.Search(queryDataset, 10, neighborsSparse, distancesSparse);
KNN exact(denseReference);
arma::mat distancesExact;
arma::Mat<size_t> neighborsExact;
exact.Search(denseQuery, 10, neighborsExact, distancesExact);
for (size_t i = 0; i < neighborsExact.n_cols; ++i)
for (size_t j = 0; j < neighborsExact.n_rows; ++j)
REQUIRE_RELATIVE_ERR(distancesSparse(j, i), distancesExact(j, i), 0.05);
}
/**
* Ensure that we can build an NSModel<NearestNeighborSearch> and get correct
* results.
*/
BOOST_AUTO_TEST_CASE(KNNModelTest)
{
typedef NSModel<NearestNeighborSort> KNNModel;
arma::mat queryData = arma::randu<arma::mat>(10, 50);
arma::mat referenceData = arma::randu<arma::mat>(10, 200);
// Build all the possible models.
KNNModel models[12];
models[0] = KNNModel(KNNModel::TreeTypes::KD_TREE, true);
models[1] = KNNModel(KNNModel::TreeTypes::KD_TREE, false);
models[2] = KNNModel(KNNModel::TreeTypes::COVER_TREE, true);
models[3] = KNNModel(KNNModel::TreeTypes::COVER_TREE, false);
models[4] = KNNModel(KNNModel::TreeTypes::R_TREE, true);
models[5] = KNNModel(KNNModel::TreeTypes::R_TREE, false);
models[6] = KNNModel(KNNModel::TreeTypes::R_STAR_TREE, true);
models[7] = KNNModel(KNNModel::TreeTypes::R_STAR_TREE, false);
models[8] = KNNModel(KNNModel::TreeTypes::X_TREE, true);
models[9] = KNNModel(KNNModel::TreeTypes::X_TREE, false);
models[10] = KNNModel(KNNModel::TreeTypes::BALL_TREE, true);
models[11] = KNNModel(KNNModel::TreeTypes::BALL_TREE, false);
for (size_t j = 0; j < 3; ++j)
{
// Get a baseline.
KNN aknn(referenceData);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
aknn.Search(queryData, 3, neighborsExact, distancesExact);
for (size_t i = 0; i < 12; ++i)
{
// We only have std::move() constructors so make a copy of our data.
arma::mat referenceCopy(referenceData);
arma::mat queryCopy(queryData);
if (j == 0)
models[i].BuildModel(std::move(referenceCopy), 20, false, false, 0.05);
if (j == 1)
models[i].BuildModel(std::move(referenceCopy), 20, false, true, 0.05);
if (j == 2)
models[i].BuildModel(std::move(referenceCopy), 20, true, false);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
models[i].Search(std::move(queryCopy), 3, neighborsAprox, distancesAprox);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_rows, neighborsExact.n_rows);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_cols, neighborsExact.n_cols);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_elem, neighborsExact.n_elem);
BOOST_REQUIRE_EQUAL(distancesAprox.n_rows, distancesExact.n_rows);
BOOST_REQUIRE_EQUAL(distancesAprox.n_cols, distancesExact.n_cols);
BOOST_REQUIRE_EQUAL(distancesAprox.n_elem, distancesExact.n_elem);
for (size_t k = 0; k < distancesAprox.n_elem; ++k)
REQUIRE_RELATIVE_ERR(distancesAprox[k], distancesExact[k], 0.05);
}
}
}
/**
* Ensure that we can build an NSModel<NearestNeighborSearch> and get correct
* results, in the case where the reference set is the same as the query set.
*/
BOOST_AUTO_TEST_CASE(KNNModelMonochromaticTest)
{
typedef NSModel<NearestNeighborSort> KNNModel;
arma::mat referenceData = arma::randu<arma::mat>(10, 200);
// Build all the possible models.
KNNModel models[12];
models[0] = KNNModel(KNNModel::TreeTypes::KD_TREE, true);
models[1] = KNNModel(KNNModel::TreeTypes::KD_TREE, false);
models[2] = KNNModel(KNNModel::TreeTypes::COVER_TREE, true);
models[3] = KNNModel(KNNModel::TreeTypes::COVER_TREE, false);
models[4] = KNNModel(KNNModel::TreeTypes::R_TREE, true);
models[5] = KNNModel(KNNModel::TreeTypes::R_TREE, false);
models[6] = KNNModel(KNNModel::TreeTypes::R_STAR_TREE, true);
models[7] = KNNModel(KNNModel::TreeTypes::R_STAR_TREE, false);
models[8] = KNNModel(KNNModel::TreeTypes::X_TREE, true);
models[9] = KNNModel(KNNModel::TreeTypes::X_TREE, false);
models[10] = KNNModel(KNNModel::TreeTypes::BALL_TREE, true);
models[11] = KNNModel(KNNModel::TreeTypes::BALL_TREE, false);
for (size_t j = 0; j < 2; ++j)
{
// Get a baseline.
KNN exact(referenceData);
arma::Mat<size_t> neighborsExact;
arma::mat distancesExact;
exact.Search(3, neighborsExact, distancesExact);
for (size_t i = 0; i < 12; ++i)
{
// We only have a std::move() constructor... so copy the data.
arma::mat referenceCopy(referenceData);
if (j == 0)
models[i].BuildModel(std::move(referenceCopy), 20, false, false, 0.05);
if (j == 1)
models[i].BuildModel(std::move(referenceCopy), 20, false, true, 0.05);
arma::Mat<size_t> neighborsAprox;
arma::mat distancesAprox;
models[i].Search(3, neighborsAprox, distancesAprox);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_rows, neighborsExact.n_rows);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_cols, neighborsExact.n_cols);
BOOST_REQUIRE_EQUAL(neighborsAprox.n_elem, neighborsExact.n_elem);
BOOST_REQUIRE_EQUAL(distancesAprox.n_rows, distancesExact.n_rows);
BOOST_REQUIRE_EQUAL(distancesAprox.n_cols, distancesExact.n_cols);
BOOST_REQUIRE_EQUAL(distancesAprox.n_elem, distancesExact.n_elem);
for (size_t k = 0; k < distancesAprox.n_elem; ++k)
REQUIRE_RELATIVE_ERR(distancesAprox[k], distancesExact[k], 0.05);
}
}
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/arma_extend_test.cpp
+1 -1
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@@ -7,7 +7,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace arma;
src/mlpack/tests/armadillo_svd_test.cpp
+1 -1
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@@ -2,7 +2,7 @@
#include <mlpack/methods/cf/svd_wrapper.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(ArmadilloSVDTest);
src/mlpack/tests/aug_lagrangian_test.cpp
+1 -1
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@@ -10,7 +10,7 @@
#include <mlpack/core/optimizers/aug_lagrangian/aug_lagrangian.hpp>
#include <mlpack/core/optimizers/aug_lagrangian/aug_lagrangian_test_functions.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::optimization;
src/mlpack/tests/binarize_test.cpp
+1 -1
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@@ -9,7 +9,7 @@
#include <mlpack/core/math/random.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace arma;
src/mlpack/tests/cf_test.cpp
+1 -1
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@@ -10,7 +10,7 @@
#include <iostream>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
BOOST_AUTO_TEST_SUITE(CFTest);
src/mlpack/tests/cli_test.cpp
+1 -1
View File
@@ -22,7 +22,7 @@
#define DEFAULT_INT 42
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#define BASH_RED "\033[0;31m"
#define BASH_GREEN "\033[0;32m"
src/mlpack/tests/convolution_test.cpp
+1 -1
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@@ -13,7 +13,7 @@
#include <mlpack/methods/ann/convolution_rules/svd_convolution.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/convolutional_network_test.cpp
+1 -1
View File
@@ -23,7 +23,7 @@
#include <mlpack/methods/ann/cnn.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/cosine_tree_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/core/tree/cosine_tree/cosine_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(CosineTreeTest);
src/mlpack/tests/decision_stump_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/decision_stump/decision_stump.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::decision_stump;
src/mlpack/tests/det_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
*/
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
// This trick does not work on Windows. We will have to comment out the tests
// that depend on it.
src/mlpack/tests/distribution_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::distribution;
src/mlpack/tests/emst_test.cpp
+1 -1
View File
@@ -6,7 +6,7 @@
#include <mlpack/core.hpp>
#include <mlpack/methods/emst/dtb.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include <mlpack/core/tree/cover_tree.hpp>
src/mlpack/tests/fastmks_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/fastmks/fastmks_model.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
using namespace mlpack;
src/mlpack/tests/feedforward_network_test.cpp
+1 -1
View File
@@ -24,7 +24,7 @@
#include <mlpack/core/optimizers/rmsprop/rmsprop.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/gmm_test.cpp
+1 -1
View File
@@ -15,7 +15,7 @@
#include <mlpack/methods/gmm/eigenvalue_ratio_constraint.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::gmm;
src/mlpack/tests/hmm_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/gmm/gmm.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::hmm;
src/mlpack/tests/hoeffding_tree_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/hoeffding_trees/binary_numeric_split.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
#include <stack>
src/mlpack/tests/ind2sub_test.cpp
+1 -1
View File
@@ -6,7 +6,7 @@
*/
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(ind2subTest);
src/mlpack/tests/init_rules_test.cpp
+1 -1
View File
@@ -14,7 +14,7 @@
#include <mlpack/methods/ann/init_rules/zero_init.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/kernel_pca_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/methods/kernel_pca/kernel_pca.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(KernelPCATest);
src/mlpack/tests/kernel_test.cpp
+1 -1
View File
@@ -19,7 +19,7 @@
#include <mlpack/core/metrics/mahalanobis_distance.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::kernel;
src/mlpack/tests/kernel_traits_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::kernel;
src/mlpack/tests/kfn_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <mlpack/core/tree/cover_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
src/mlpack/tests/kmeans_test.cpp
+1 -1
View File
@@ -18,7 +18,7 @@
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::kmeans;
src/mlpack/tests/knn_test.cpp
+4 -2
View File
@@ -10,7 +10,7 @@
#include <mlpack/core/tree/cover_tree.hpp>
#include <mlpack/core/tree/example_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
@@ -888,7 +888,9 @@ BOOST_AUTO_TEST_CASE(DualBallTreeTest)
}
}
// Make sure sparse nearest neighbors works with kd trees.
/**
* Make sure sparse nearest neighbors works with kd trees.
*/
BOOST_AUTO_TEST_CASE(SparseKNNKDTreeTest)
{
// The dimensionality of these datasets must be high so that the probability
src/mlpack/tests/krann_search_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/core/tree/cover_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include <mlpack/methods/rann/ra_search.hpp>
#include <mlpack/methods/rann/ra_model.hpp>
src/mlpack/tests/lars_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/methods/lars/lars.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::regression;
src/mlpack/tests/layer_traits_test.cpp
+1 -1
View File
@@ -13,7 +13,7 @@
#include <mlpack/methods/ann/layer/multiclass_classification_layer.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/lbfgs_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/core/optimizers/lbfgs/test_functions.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack::optimization;
using namespace mlpack::optimization::test;
src/mlpack/tests/lin_alg_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/core/math/lin_alg.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace arma;
using namespace mlpack;
src/mlpack/tests/linear_regression_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/methods/linear_regression/linear_regression.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::regression;
src/mlpack/tests/load_save_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::data;
src/mlpack/tests/local_coordinate_coding_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/methods/local_coordinate_coding/lcc.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
using namespace arma;
src/mlpack/tests/log_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
src/mlpack/tests/logistic_regression_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/core/optimizers/sgd/sgd.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::regression;
src/mlpack/tests/lrsdp_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/core/optimizers/sdp/lrsdp.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::optimization;
src/mlpack/tests/lsh_test.cpp
+1 -1
View File
@@ -6,7 +6,7 @@
#include <mlpack/core.hpp>
#include <mlpack/core/metrics/lmetric.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include <mlpack/methods/lsh/lsh_search.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
src/mlpack/tests/lstm_peephole_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/ann/layer/lstm_layer.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/math_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/core/math/random.hpp>
#include <mlpack/core/math/range.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace math;
src/mlpack/tests/matrix_completion_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/matrix_completion/matrix_completion.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::matrix_completion;
src/mlpack/tests/maximal_inputs_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/sparse_autoencoder/maximal_inputs.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
src/mlpack/tests/mean_shift_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/mean_shift/mean_shift.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::meanshift;
src/mlpack/tests/metric_test.cpp
+1 -1
View File
@@ -6,7 +6,7 @@
#include <mlpack/core.hpp>
#include <mlpack/core/metrics/lmetric.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace std;
using namespace mlpack::metric;
src/mlpack/tests/minibatch_sgd_test.cpp
+1 -1
View File
@@ -13,7 +13,7 @@
#include <mlpack/methods/logistic_regression/logistic_regression.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace std;
using namespace arma;
src/mlpack/tests/mlpack_test.cpp
+1 -1
View File
@@ -17,7 +17,7 @@
#endif
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
/**
* Provide a global fixture for each test.
src/mlpack/tests/nbc_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/methods/naive_bayes/naive_bayes_classifier.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace naive_bayes;
src/mlpack/tests/nca_test.cpp
+1 -1
View File
@@ -11,7 +11,7 @@
#include <mlpack/core/optimizers/lbfgs/lbfgs.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::metric;
src/mlpack/tests/network_util_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/ann/init_rules/random_init.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/nmf_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/amf/update_rules/nmf_mult_dist.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(NMFTest);
src/mlpack/tests/nystroem_method_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include <mlpack/methods/nystroem_method/ordered_selection.hpp>
#include <mlpack/methods/nystroem_method/random_selection.hpp>
src/mlpack/tests/pca_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/pca/pca.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(PCATest);
src/mlpack/tests/perceptron_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/perceptron/perceptron.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace arma;
src/mlpack/tests/performance_functions_test.cpp
+1 -1
View File
@@ -11,7 +11,7 @@
#include <mlpack/methods/ann/performance_functions/sse_function.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/pooling_rules_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/methods/ann/pooling_rules/mean_pooling.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/quic_svd_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/quic_svd/quic_svd.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(QUICSVDTest);
src/mlpack/tests/radical_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/core.hpp>
#include <mlpack/methods/radical/radical.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(RadicalTest);
src/mlpack/tests/range_search_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/core/tree/cover_tree.hpp>
#include <mlpack/methods/range_search/rs_model.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::range;
src/mlpack/tests/rectangle_tree_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
src/mlpack/tests/recurrent_network_test.cpp
+1 -1
View File
@@ -20,7 +20,7 @@
#include <mlpack/methods/ann/init_rules/nguyen_widrow_init.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::ann;
src/mlpack/tests/regularized_svd_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/regularized_svd/regularized_svd.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::svd;
src/mlpack/tests/rmsprop_test.cpp
+1 -1
View File
@@ -20,7 +20,7 @@
#include <mlpack/methods/ann/layer/base_layer.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace arma;
using namespace mlpack;
src/mlpack/tests/sa_test.cpp
+1 -1
View File
@@ -14,7 +14,7 @@
#include <mlpack/core/metrics/mahalanobis_distance.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace std;
using namespace arma;
src/mlpack/tests/sdp_primal_dual_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::optimization;
src/mlpack/tests/serialization.hpp
+1 -1
View File
@@ -17,7 +17,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
namespace mlpack {
src/mlpack/tests/serialization_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
#include <mlpack/core/dists/regression_distribution.hpp>
src/mlpack/tests/sgd_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/core/optimizers/sgd/test_function.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace std;
using namespace arma;
src/mlpack/tests/softmax_regression_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/methods/softmax_regression/softmax_regression.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::regression;
src/mlpack/tests/sort_policy_test.cpp
+1 -1
View File
@@ -12,7 +12,7 @@
#include <mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::neighbor;
src/mlpack/tests/sparse_autoencoder_test.cpp
+1 -1
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@@ -8,7 +8,7 @@
#include <mlpack/methods/sparse_autoencoder/sparse_autoencoder.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::nn;
src/mlpack/tests/sparse_coding_test.cpp
+1 -1
View File
@@ -11,7 +11,7 @@
#include <mlpack/methods/sparse_coding/sparse_coding.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
#include "serialization.hpp"
using namespace arma;
src/mlpack/tests/split_data_test.cpp
+1 -1
View File
@@ -8,7 +8,7 @@
#include <mlpack/core/data/split_data.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace arma;
src/mlpack/tests/svd_batch_test.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
#include <mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(SVDBatchTest);
src/mlpack/tests/svd_incremental_test.cpp
+1 -1
View File
@@ -9,7 +9,7 @@
#include <mlpack/methods/amf/termination_policies/validation_RMSE_termination.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(SVDIncrementalTest);
src/mlpack/tests/termination_policy_test.cpp
+1 -1
View File
@@ -10,7 +10,7 @@
#include <mlpack/methods/amf/update_rules/nmf_mult_div.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
BOOST_AUTO_TEST_SUITE(TerminationPolicyTest);
src/mlpack/tests/old_boost_test_definitions.hpp → src/mlpack/tests/test_tools.hpp
+9 -5
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@@ -1,12 +1,11 @@
/**
* @file old_boost_test_definitions.hpp
* @file test_tools.hpp
* @author Ryan Curtin
*
* Ancient Boost.Test versions don't act how we expect. This file includes the
* things we need to fix that.
* This file includes some useful macros for tests.
*/
#ifndef MLPACK_TESTS_OLD_BOOST_TEST_DEFINITIONS_HPP
#define MLPACK_TESTS_OLD_BOOST_TEST_DEFINITIONS_HPP
#ifndef MLPACK_TESTS_TEST_TOOLS_HPP
#define MLPACK_TESTS_TEST_TOOLS_HPP
#include <boost/version.hpp>
@@ -35,4 +34,9 @@
#endif
// Require the approximation L to be within a relative error of E respect to the
// actual value R.
#define REQUIRE_RELATIVE_ERR( L, R, E ) \
BOOST_REQUIRE_LE( abs((R) - (L)), (E) * abs(R))
#endif
src/mlpack/tests/tree_test.cpp
+1 -1
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@@ -14,7 +14,7 @@
#include <stack>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::math;
src/mlpack/tests/tree_traits_test.cpp
+1 -1
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@@ -15,7 +15,7 @@
#include <mlpack/core/tree/rectangle_tree.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::tree;
src/mlpack/tests/union_find_test.cpp
+1 -1
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@@ -8,7 +8,7 @@
#include <mlpack/core.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
#include "test_tools.hpp"
using namespace mlpack;
using namespace mlpack::emst;