[mlpack-git] master: Rename AllkFN to KFN, and AllkNN to KNN. (64c049e)
gitdub at mlpack.org
gitdub at mlpack.org
Tue Apr 19 19:00:38 EDT 2016
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/56b53a09e2d46b65f4d80560964487f1e193d345...64c049efa2df2661ce5b321a60c4178c3439d025
>---------------------------------------------------------------
commit 64c049efa2df2661ce5b321a60c4178c3439d025
Author: Ryan Curtin <ryan at ratml.org>
Date: Tue Apr 19 16:00:38 2016 -0700
Rename AllkFN to KFN, and AllkNN to KNN.
>---------------------------------------------------------------
64c049efa2df2661ce5b321a60c4178c3439d025
src/mlpack/core.hpp | 7 +-
src/mlpack/core/util/cli.hpp | 4 +-
src/mlpack/methods/cf/cf.cpp | 6 +-
.../methods/kmeans/dual_tree_kmeans_impl.hpp | 2 +-
src/mlpack/methods/mean_shift/mean_shift_impl.hpp | 4 +-
src/mlpack/methods/mvu/mvu.cpp | 6 +-
src/mlpack/methods/neighbor_search/kfn_main.cpp | 8 +-
src/mlpack/methods/neighbor_search/knn_main.cpp | 6 +-
src/mlpack/tests/CMakeLists.txt | 4 +-
src/mlpack/tests/allkrann_search_test.cpp | 4 +-
src/mlpack/tests/{allkfn_test.cpp => kfn_test.cpp} | 44 +++++-----
src/mlpack/tests/{allknn_test.cpp => knn_test.cpp} | 96 +++++++++++-----------
src/mlpack/tests/lsh_test.cpp | 8 +-
src/mlpack/tests/rectangle_tree_test.cpp | 32 ++++----
src/mlpack/tests/sdp_primal_dual_test.cpp | 4 +-
src/mlpack/tests/serialization_test.cpp | 18 ++--
16 files changed, 126 insertions(+), 127 deletions(-)
diff --git a/src/mlpack/core.hpp b/src/mlpack/core.hpp
index 7cc8c78..9df2947 100644
--- a/src/mlpack/core.hpp
+++ b/src/mlpack/core.hpp
@@ -51,8 +51,6 @@
* A full list of executables is given below:
*
* - mlpack_adaboost
- * - mlpack_kfn
- * - mlpack_knn
* - mlpack_allkrann
* - mlpack_cf
* - mlpack_decision_stump
@@ -68,7 +66,9 @@
* - mlpack_hmm_generate
* - mlpack_hoeffding_tree
* - mlpack_kernel_pca
+ * - mlpack_kfn
* - mlpack_kmeans
+ * - mlpack_knn
* - mlpack_lars
* - mlpack_linear_regression
* - mlpack_local_coordinate_coding
@@ -126,8 +126,7 @@
* - Simple Least-Squares Linear Regression -
* mlpack::regression::LinearRegression
* - Sparse Coding - mlpack::sparse_coding::SparseCoding
- * - Tree-based neighbor search (AllkNN, AllkFN) -
- * mlpack::neighbor::NeighborSearch
+ * - Tree-based neighbor search (KNN, KFN) - mlpack::neighbor::NeighborSearch
* - Tree-based range search - mlpack::range::RangeSearch
*
* @section remarks Final Remarks
diff --git a/src/mlpack/core/util/cli.hpp b/src/mlpack/core/util/cli.hpp
index 362e1ea..f6ec7dc 100644
--- a/src/mlpack/core/util/cli.hpp
+++ b/src/mlpack/core/util/cli.hpp
@@ -423,8 +423,8 @@ struct ParamData
* The flag (boolean) type automatically defaults to false; it is specified
* merely as a flag on the command line (no '=true' is required).
*
- * Here is an example of a few parameters being defined; this is for the AllkNN
- * executable (methods/neighbor_search/allknn_main.cpp):
+ * Here is an example of a few parameters being defined; this is for the KNN
+ * executable (methods/neighbor_search/knn_main.cpp):
*
* @code
* PARAM_STRING_REQ("reference_file", "File containing the reference dataset.",
diff --git a/src/mlpack/methods/cf/cf.cpp b/src/mlpack/methods/cf/cf.cpp
index f229f06..daf121c 100644
--- a/src/mlpack/methods/cf/cf.cpp
+++ b/src/mlpack/methods/cf/cf.cpp
@@ -72,7 +72,7 @@ void CF::GetRecommendations(const size_t numRecs,
// Calculate the neighborhood of the queried users.
// This should be a templatized option.
- neighbor::AllkNN a(stretchedH);
+ neighbor::KNN a(stretchedH);
arma::mat resultingDistances; // Temporary storage.
a.Search(query, numUsersForSimilarity, neighborhood, resultingDistances);
@@ -154,7 +154,7 @@ double CF::Predict(const size_t user, const size_t item) const
// Calculate the neighborhood of the queried users.
// This should be a templatized option.
- neighbor::AllkNN a(stretchedH, false, true /* single-tree mode */);
+ neighbor::KNN a(stretchedH, false, true /* single-tree mode */);
arma::mat resultingDistances; // Temporary storage.
a.Search(query, numUsersForSimilarity, neighborhood, resultingDistances);
@@ -193,7 +193,7 @@ void CF::Predict(const arma::Mat<size_t>& combinations,
queries.col(i) = stretchedH.col(users[i]);
// Now calculate the neighborhood of these users.
- neighbor::AllkNN a(stretchedH);
+ neighbor::KNN a(stretchedH);
arma::mat distances;
arma::Mat<size_t> neighborhood;
diff --git a/src/mlpack/methods/kmeans/dual_tree_kmeans_impl.hpp b/src/mlpack/methods/kmeans/dual_tree_kmeans_impl.hpp
index ef9d51a..9bcf464 100644
--- a/src/mlpack/methods/kmeans/dual_tree_kmeans_impl.hpp
+++ b/src/mlpack/methods/kmeans/dual_tree_kmeans_impl.hpp
@@ -145,7 +145,7 @@ double DualTreeKMeans<MetricType, MatType, TreeType>::Iterate(
interclusterDistances.set_size(1, centroids.n_cols);
}
- // We won't use the AllkNN class here because we have our own set of rules.
+ // We won't use the KNN class here because we have our own set of rules.
lastIterationCentroids = centroids;
typedef DualTreeKMeansRules<MetricType, Tree> RuleType;
RuleType rules(centroidTree->Dataset(), dataset, assignments, upperBounds,
diff --git a/src/mlpack/methods/mean_shift/mean_shift_impl.hpp b/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
index 152dadc..57dcfee 100644
--- a/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
+++ b/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
@@ -48,7 +48,7 @@ template<bool UseKernel, typename KernelType, typename MatType>
double MeanShift<UseKernel, KernelType, MatType>::
EstimateRadius(const MatType& data, double ratio)
{
- neighbor::AllkNN neighborSearch(data);
+ neighbor::KNN neighborSearch(data);
/**
* For each point in dataset, select nNeighbors nearest points and get
@@ -257,7 +257,7 @@ inline void MeanShift<UseKernel, KernelType, MatType>::Cluster(
}
// Assign centroids to each point.
- neighbor::AllkNN neighborSearcher(centroids);
+ neighbor::KNN neighborSearcher(centroids);
arma::mat neighborDistances;
arma::Mat<size_t> resultingNeighbors;
neighborSearcher.Search(data, 1, resultingNeighbors, neighborDistances);
diff --git a/src/mlpack/methods/mvu/mvu.cpp b/src/mlpack/methods/mvu/mvu.cpp
index 4026c24..4796e07 100644
--- a/src/mlpack/methods/mvu/mvu.cpp
+++ b/src/mlpack/methods/mvu/mvu.cpp
@@ -50,13 +50,13 @@ void MVU::Unfold(const size_t newDim,
mvuSolver.AModes().ones();
mvuSolver.AModes()[0] = 0;
- // Now all of the other constraints. We first have to run AllkNN to get the
+ // Now all of the other constraints. We first have to run KNN to get the
// list of nearest neighbors.
arma::Mat<size_t> neighbors;
arma::mat distances;
- AllkNN allknn(data);
- allknn.Search(numNeighbors, neighbors, distances);
+ KNN knn(data);
+ knn.Search(numNeighbors, neighbors, distances);
// Add each of the other constraints. They are sparse constraints:
// Tr(A_ij K) = d_ij;
diff --git a/src/mlpack/methods/neighbor_search/kfn_main.cpp b/src/mlpack/methods/neighbor_search/kfn_main.cpp
index c8ebbfe..f159796 100644
--- a/src/mlpack/methods/neighbor_search/kfn_main.cpp
+++ b/src/mlpack/methods/neighbor_search/kfn_main.cpp
@@ -1,9 +1,9 @@
/**
w
- * @file allkfn_main.cpp
+ * @file kfn_main.cpp
* @author Ryan Curtin
*
- * Implementation of the AllkFN executable. Allows some number of standard
+ * Implementation of the KFN executable. Allows some number of standard
* options.
*/
#include <mlpack/core.hpp>
@@ -33,8 +33,8 @@ PROGRAM_INFO("All K-Furthest-Neighbors",
"point in 'input.csv' and store the distances in 'distances.csv' and the "
"neighbors in the file 'neighbors.csv':"
"\n\n"
- "$ allkfn --k=5 --reference_file=input.csv --distances_file=distances.csv\n"
- " --neighbors_file=neighbors.csv"
+ "$ mlpack_kfn --k=5 --reference_file=input.csv "
+ "--distances_file=distances.csv\n --neighbors_file=neighbors.csv"
"\n\n"
"The output files are organized such that row i and column j in the "
"neighbors output file corresponds to the index of the point in the "
diff --git a/src/mlpack/methods/neighbor_search/knn_main.cpp b/src/mlpack/methods/neighbor_search/knn_main.cpp
index 172f897..188e92e 100644
--- a/src/mlpack/methods/neighbor_search/knn_main.cpp
+++ b/src/mlpack/methods/neighbor_search/knn_main.cpp
@@ -1,5 +1,5 @@
/**
- * @file allknn_main.cpp
+ * @file knn_main.cpp
* @author Ryan Curtin
*
* Implementation of the AllkNN executable. Allows some number of standard
@@ -34,8 +34,8 @@ PROGRAM_INFO("k-Nearest-Neighbors",
"point in 'input.csv' and store the distances in 'distances.csv' and the "
"neighbors in the file 'neighbors.csv':"
"\n\n"
- "$ allknn --k=5 --reference_file=input.csv --distances_file=distances.csv\n"
- " --neighbors_file=neighbors.csv"
+ "$ mlpack_nn --k=5 --reference_file=input.csv "
+ "--distances_file=distances.csv\n --neighbors_file=neighbors.csv"
"\n\n"
"The output files are organized such that row i and column j in the "
"neighbors output file corresponds to the index of the point in the "
diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt
index e5b08b4..7d1b407 100644
--- a/src/mlpack/tests/CMakeLists.txt
+++ b/src/mlpack/tests/CMakeLists.txt
@@ -5,8 +5,6 @@ add_executable(mlpack_test
adaboost_test.cpp
adam_test.cpp
ada_delta_test.cpp
- allkfn_test.cpp
- allknn_test.cpp
allkrann_search_test.cpp
arma_extend_test.cpp
aug_lagrangian_test.cpp
@@ -28,7 +26,9 @@ add_executable(mlpack_test
kernel_test.cpp
kernel_pca_test.cpp
kernel_traits_test.cpp
+ kfn_test.cpp
kmeans_test.cpp
+ knn_test.cpp
lars_test.cpp
lbfgs_test.cpp
lin_alg_test.cpp
diff --git a/src/mlpack/tests/allkrann_search_test.cpp b/src/mlpack/tests/allkrann_search_test.cpp
index 0ea62b6..c42c10b 100644
--- a/src/mlpack/tests/allkrann_search_test.cpp
+++ b/src/mlpack/tests/allkrann_search_test.cpp
@@ -563,7 +563,7 @@ BOOST_AUTO_TEST_CASE(MoveConstructorTest)
arma::mat copy(dataset);
AllkRANN moveknn(std::move(copy));
- AllkRANN allknn(dataset);
+ AllkRANN knn(dataset);
BOOST_REQUIRE_EQUAL(copy.n_elem, 0);
BOOST_REQUIRE_EQUAL(moveknn.ReferenceSet().n_rows, 3);
@@ -573,7 +573,7 @@ BOOST_AUTO_TEST_CASE(MoveConstructorTest)
arma::Mat<size_t> moveNeighbors, neighbors;
moveknn.Search(1, moveNeighbors, moveDistances);
- allknn.Search(1, neighbors, distances);
+ knn.Search(1, neighbors, distances);
BOOST_REQUIRE_EQUAL(moveNeighbors.n_rows, neighbors.n_rows);
BOOST_REQUIRE_EQUAL(moveNeighbors.n_rows, neighbors.n_rows);
diff --git a/src/mlpack/tests/allkfn_test.cpp b/src/mlpack/tests/kfn_test.cpp
similarity index 96%
rename from src/mlpack/tests/allkfn_test.cpp
rename to src/mlpack/tests/kfn_test.cpp
index 2a36acb..2701a6a 100644
--- a/src/mlpack/tests/allkfn_test.cpp
+++ b/src/mlpack/tests/kfn_test.cpp
@@ -1,7 +1,7 @@
/**
- * @file allkfntest.cpp
+ * @file kfn_test.cpp
*
- * Tests for AllkFN (all-k-furthest-neighbors).
+ * Tests for KFN (k-furthest-neighbors).
*/
#include <mlpack/core.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -15,7 +15,7 @@ using namespace mlpack::tree;
using namespace mlpack::metric;
using namespace mlpack::bound;
-BOOST_AUTO_TEST_SUITE(AllkFNTest);
+BOOST_AUTO_TEST_SUITE(KFNTest);
/**
* Simple furthest-neighbors test with small, synthetic dataset. This is an
@@ -51,25 +51,25 @@ BOOST_AUTO_TEST_CASE(ExhaustiveSyntheticTest)
TreeType* tree = new TreeType(data, oldFromNew, newFromOld, 1);
for (int i = 0; i < 3; i++)
{
- AllkFN* allkfn;
+ KFN* kfn;
switch (i)
{
case 0: // Use the dual-tree method.
- allkfn = new AllkFN(tree, false);
+ kfn = new KFN(tree, false);
break;
case 1: // Use the single-tree method.
- allkfn = new AllkFN(tree, true);
+ kfn = new KFN(tree, true);
break;
case 2: // Use the naive method.
- allkfn = new AllkFN(tree->Dataset(), true);
+ kfn = new KFN(tree->Dataset(), true);
break;
}
// Now perform the actual calculation.
arma::Mat<size_t> neighbors;
arma::mat distances;
- allkfn->Search(10, neighbors, distances);
+ kfn->Search(10, neighbors, distances);
// Now the exhaustive check for correctness. This will be long. We must
// also remember that the distances returned are squared distances. As a
@@ -319,7 +319,7 @@ BOOST_AUTO_TEST_CASE(ExhaustiveSyntheticTest)
BOOST_REQUIRE_CLOSE(distances(0, newFromOld[10]), 4.05, 1e-5);
// Clean the memory.
- delete allkfn;
+ delete kfn;
}
// We are responsible for the tree, too.
@@ -340,13 +340,13 @@ BOOST_AUTO_TEST_CASE(DualTreeVsNaive1)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkFN allkfn(dataset);
+ KFN kfn(dataset);
- AllkFN naive(dataset, true);
+ KFN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allkfn.Search(dataset, 15, neighborsTree, distancesTree);
+ kfn.Search(dataset, 15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -374,13 +374,13 @@ BOOST_AUTO_TEST_CASE(DualTreeVsNaive2)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkFN allkfn(dataset);
+ KFN kfn(dataset);
- AllkFN naive(dataset, true);
+ KFN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allkfn.Search(15, neighborsTree, distancesTree);
+ kfn.Search(15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -408,13 +408,13 @@ BOOST_AUTO_TEST_CASE(SingleTreeVsNaive)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkFN allkfn(dataset, false, true);
+ KFN kfn(dataset, false, true);
- AllkFN naive(dataset, true);
+ KFN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allkfn.Search(15, neighborsTree, distancesTree);
+ kfn.Search(15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -445,7 +445,7 @@ BOOST_AUTO_TEST_CASE(SingleCoverTreeTest)
NeighborSearch<FurthestNeighborSort, LMetric<2>, arma::mat, StandardCoverTree>
coverTreeSearch(&tree, true);
- AllkFN naive(data, true);
+ KFN naive(data, true);
arma::Mat<size_t> coverTreeNeighbors;
arma::mat coverTreeDistances;
@@ -471,7 +471,7 @@ BOOST_AUTO_TEST_CASE(DualCoverTreeTest)
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
- AllkFN tree(dataset);
+ KFN tree(dataset);
arma::Mat<size_t> kdNeighbors;
arma::mat kdDistances;
@@ -517,7 +517,7 @@ BOOST_AUTO_TEST_CASE(SingleBallTreeTest)
NeighborSearch<FurthestNeighborSort, LMetric<2>, arma::mat, BallTree>
ballTreeSearch(&tree, true);
- AllkFN naive(tree.Dataset(), true);
+ KFN naive(tree.Dataset(), true);
arma::Mat<size_t> ballTreeNeighbors;
arma::mat ballTreeDistances;
@@ -543,7 +543,7 @@ BOOST_AUTO_TEST_CASE(DualBallTreeTest)
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
- AllkFN tree(dataset);
+ KFN tree(dataset);
arma::Mat<size_t> kdNeighbors;
arma::mat kdDistances;
diff --git a/src/mlpack/tests/allknn_test.cpp b/src/mlpack/tests/knn_test.cpp
similarity index 96%
rename from src/mlpack/tests/allknn_test.cpp
rename to src/mlpack/tests/knn_test.cpp
index 54ff6e8..32dcec3 100644
--- a/src/mlpack/tests/allknn_test.cpp
+++ b/src/mlpack/tests/knn_test.cpp
@@ -1,7 +1,7 @@
/**
- * @file allknn_test.cpp
+ * @file knn_test.cpp
*
- * Test file for AllkNN class.
+ * Test file for KNN class.
*/
#include <mlpack/core.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -18,7 +18,7 @@ using namespace mlpack::tree;
using namespace mlpack::metric;
using namespace mlpack::bound;
-BOOST_AUTO_TEST_SUITE(AllkNNTest);
+BOOST_AUTO_TEST_SUITE(KNNTest);
/**
* Test that Unmap() works in the dual-tree case (see unmap.hpp).
@@ -162,15 +162,15 @@ BOOST_AUTO_TEST_CASE(SingleTreeUnmapTest)
}
/**
- * Test that an empty AllkNN object will throw exceptions when Search() is
+ * Test that an empty KNN object will throw exceptions when Search() is
* called.
*/
BOOST_AUTO_TEST_CASE(EmptySearchTest)
{
- AllkNN empty;
+ KNN empty;
arma::mat dataset = arma::randu<arma::mat>(5, 100);
- AllkNN::Tree queryTree(dataset);
+ KNN::Tree queryTree(dataset);
arma::Mat<size_t> neighbors;
arma::mat distances;
@@ -187,10 +187,10 @@ BOOST_AUTO_TEST_CASE(EmptySearchTest)
*/
BOOST_AUTO_TEST_CASE(TrainTest)
{
- AllkNN empty;
+ KNN empty;
arma::mat dataset = arma::randu<arma::mat>(5, 100);
- AllkNN baseline(dataset);
+ KNN baseline(dataset);
arma::Mat<size_t> neighbors, baselineNeighbors;
arma::mat distances, baselineDistances;
@@ -221,16 +221,16 @@ BOOST_AUTO_TEST_CASE(TrainTest)
*/
BOOST_AUTO_TEST_CASE(TrainTreeTest)
{
- AllkNN empty;
+ KNN empty;
arma::mat dataset = arma::randu<arma::mat>(5, 100);
- AllkNN baseline(dataset);
+ KNN baseline(dataset);
arma::Mat<size_t> neighbors, baselineNeighbors;
arma::mat distances, baselineDistances;
std::vector<size_t> oldFromNewReferences;
- AllkNN::Tree tree(dataset, oldFromNewReferences);
+ KNN::Tree tree(dataset, oldFromNewReferences);
empty.Train(&tree);
empty.Search(5, neighbors, distances);
@@ -270,10 +270,10 @@ BOOST_AUTO_TEST_CASE(TrainTreeTest)
*/
BOOST_AUTO_TEST_CASE(NaiveTrainTreeTest)
{
- AllkNN empty(true);
+ KNN empty(true);
arma::mat dataset = arma::randu<arma::mat>(5, 100);
- AllkNN::Tree tree(dataset);
+ KNN::Tree tree(dataset);
BOOST_REQUIRE_THROW(empty.Train(&tree), std::invalid_argument);
}
@@ -286,8 +286,8 @@ BOOST_AUTO_TEST_CASE(MoveConstructorTest)
arma::mat dataset = arma::randu<arma::mat>(3, 200);
arma::mat copy(dataset);
- AllkNN moveknn(std::move(copy));
- AllkNN allknn(dataset);
+ KNN moveknn(std::move(copy));
+ KNN knn(dataset);
BOOST_REQUIRE_EQUAL(copy.n_elem, 0);
BOOST_REQUIRE_EQUAL(moveknn.ReferenceSet().n_rows, 3);
@@ -297,7 +297,7 @@ BOOST_AUTO_TEST_CASE(MoveConstructorTest)
arma::Mat<size_t> moveNeighbors, neighbors;
moveknn.Search(1, moveNeighbors, moveDistances);
- allknn.Search(1, neighbors, distances);
+ knn.Search(1, neighbors, distances);
BOOST_REQUIRE_EQUAL(moveNeighbors.n_rows, neighbors.n_rows);
BOOST_REQUIRE_EQUAL(moveNeighbors.n_cols, neighbors.n_cols);
@@ -321,7 +321,7 @@ BOOST_AUTO_TEST_CASE(MoveTrainTest)
arma::mat dataset = arma::randu<arma::mat>(3, 200);
// Do it in tree mode, and in naive mode.
- AllkNN knn;
+ KNN knn;
knn.Train(std::move(dataset));
arma::mat distances;
@@ -376,25 +376,25 @@ BOOST_AUTO_TEST_CASE(ExhaustiveSyntheticTest)
TreeType* tree = new TreeType(data, oldFromNew, newFromOld, 1);
for (int i = 0; i < 3; i++)
{
- AllkNN* allknn;
+ KNN* knn;
switch (i)
{
case 0: // Use the dual-tree method.
- allknn = new AllkNN(tree, false);
+ knn = new KNN(tree, false);
break;
case 1: // Use the single-tree method.
- allknn = new AllkNN(tree, true);
+ knn = new KNN(tree, true);
break;
case 2: // Use the naive method.
- allknn = new AllkNN(tree->Dataset(), true);
+ knn = new KNN(tree->Dataset(), true);
break;
}
// Now perform the actual calculation.
arma::Mat<size_t> neighbors;
arma::mat distances;
- allknn->Search(10, neighbors, distances);
+ knn->Search(10, neighbors, distances);
// Now the exhaustive check for correctness. This will be long. We must
// also remember that the distances returned are squared distances. As a
@@ -644,7 +644,7 @@ BOOST_AUTO_TEST_CASE(ExhaustiveSyntheticTest)
BOOST_REQUIRE_CLOSE(distances(9, newFromOld[10]), 4.05, 1e-5);
// Clean the memory.
- delete allknn;
+ delete knn;
}
// Delete the tree.
@@ -665,13 +665,13 @@ BOOST_AUTO_TEST_CASE(DualTreeVsNaive1)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkNN allknn(dataset);
+ KNN knn(dataset);
- AllkNN naive(dataset, true);
+ KNN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allknn.Search(dataset, 15, neighborsTree, distancesTree);
+ knn.Search(dataset, 15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -699,14 +699,14 @@ BOOST_AUTO_TEST_CASE(DualTreeVsNaive2)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkNN allknn(dataset);
+ KNN knn(dataset);
// Set naive mode.
- AllkNN naive(dataset, true);
+ KNN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allknn.Search(15, neighborsTree, distancesTree);
+ knn.Search(15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -734,14 +734,14 @@ BOOST_AUTO_TEST_CASE(SingleTreeVsNaive)
if (!data::Load("test_data_3_1000.csv", dataset))
BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
- AllkNN allknn(dataset, false, true);
+ KNN knn(dataset, false, true);
// Set up computation for naive mode.
- AllkNN naive(dataset, true);
+ KNN naive(dataset, true);
arma::Mat<size_t> neighborsTree;
arma::mat distancesTree;
- allknn.Search(15, neighborsTree, distancesTree);
+ knn.Search(15, neighborsTree, distancesTree);
arma::Mat<size_t> neighborsNaive;
arma::mat distancesNaive;
@@ -771,7 +771,7 @@ BOOST_AUTO_TEST_CASE(SingleCoverTreeTest)
NeighborSearch<NearestNeighborSort, LMetric<2>, arma::mat, StandardCoverTree>
coverTreeSearch(&tree, true);
- AllkNN naive(data, true);
+ KNN naive(data, true);
arma::Mat<size_t> coverTreeNeighbors;
arma::mat coverTreeDistances;
@@ -797,7 +797,7 @@ BOOST_AUTO_TEST_CASE(DualCoverTreeTest)
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
- AllkNN tree(dataset);
+ KNN tree(dataset);
arma::Mat<size_t> kdNeighbors;
arma::mat kdDistances;
@@ -842,7 +842,7 @@ BOOST_AUTO_TEST_CASE(SingleBallTreeTest)
NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat, BallTree>
ballTreeSearch(&tree, true);
- AllkNN naive(tree.Dataset(), true);
+ KNN naive(tree.Dataset(), true);
arma::Mat<size_t> ballTreeNeighbors;
arma::mat ballTreeDistances;
@@ -868,7 +868,7 @@ BOOST_AUTO_TEST_CASE(DualBallTreeTest)
arma::mat dataset;
data::Load("test_data_3_1000.csv", dataset);
- AllkNN tree(dataset);
+ KNN tree(dataset);
arma::Mat<size_t> kdNeighbors;
arma::mat kdDistances;
@@ -889,7 +889,7 @@ BOOST_AUTO_TEST_CASE(DualBallTreeTest)
}
// Make sure sparse nearest neighbors works with kd trees.
-BOOST_AUTO_TEST_CASE(SparseAllkNNKDTreeTest)
+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
@@ -903,10 +903,10 @@ BOOST_AUTO_TEST_CASE(SparseAllkNNKDTreeTest)
arma::mat denseReference(referenceDataset);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::sp_mat,
- KDTree> SparseAllkNN;
+ KDTree> SparseKNN;
- SparseAllkNN a(referenceDataset);
- AllkNN naive(denseReference, true);
+ SparseKNN a(referenceDataset);
+ KNN naive(denseReference, true);
arma::mat sparseDistances;
arma::Mat<size_t> sparseNeighbors;
@@ -927,7 +927,7 @@ BOOST_AUTO_TEST_CASE(SparseAllkNNKDTreeTest)
}
/*
-BOOST_AUTO_TEST_CASE(SparseAllkNNCoverTreeTest)
+BOOST_AUTO_TEST_CASE(SparseKNNCoverTreeTest)
{
typedef CoverTree<LMetric<2, true>, FirstPointIsRoot,
NeighborSearchStat<NearestNeighborSort>, arma::sp_mat> SparseCoverTree;
@@ -944,7 +944,7 @@ BOOST_AUTO_TEST_CASE(SparseAllkNNCoverTreeTest)
arma::mat denseReference(referenceDataset);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance,
- SparseCoverTree> SparseAllkNN;
+ SparseCoverTree> SparseKNN;
arma::mat sparseDistances;
arma::Mat<size_t> sparseNeighbors;
@@ -990,7 +990,7 @@ BOOST_AUTO_TEST_CASE(KNNModelTest)
for (size_t j = 0; j < 2; ++j)
{
// Get a baseline.
- AllkNN knn(referenceData);
+ KNN knn(referenceData);
arma::Mat<size_t> baselineNeighbors;
arma::mat baselineDistances;
knn.Search(queryData, 3, baselineNeighbors, baselineDistances);
@@ -1054,7 +1054,7 @@ BOOST_AUTO_TEST_CASE(KNNModelMonochromaticTest)
for (size_t j = 0; j < 2; ++j)
{
// Get a baseline.
- AllkNN knn(referenceData);
+ KNN knn(referenceData);
arma::Mat<size_t> baselineNeighbors;
arma::mat baselineDistances;
knn.Search(3, baselineNeighbors, baselineDistances);
@@ -1101,7 +1101,7 @@ BOOST_AUTO_TEST_CASE(KNNModelMonochromaticTest)
BOOST_AUTO_TEST_CASE(DoubleReferenceSearchTest)
{
arma::mat dataset = arma::randu<arma::mat>(5, 500);
- AllkNN knn(std::move(dataset));
+ KNN knn(std::move(dataset));
arma::mat distances, secondDistances;
arma::Mat<size_t> neighbors, secondNeighbors;
@@ -1125,14 +1125,14 @@ BOOST_AUTO_TEST_CASE(NeighborPtrDeleteTest)
// Build the tree ourselves.
std::vector<size_t> oldFromNewReferences;
- AllkNN::Tree tree(dataset);
- AllkNN allknn(&tree);
+ KNN::Tree tree(dataset);
+ KNN knn(&tree);
// Now make a query set.
arma::mat queryset = arma::randu<arma::mat>(5, 50);
arma::mat distances;
arma::Mat<size_t> neighbors;
- allknn.Search(queryset, 3, neighbors, distances);
+ knn.Search(queryset, 3, neighbors, distances);
// These will (hopefully) fail is either the neighbors or the distances matrix
// has been accidentally deleted.
diff --git a/src/mlpack/tests/lsh_test.cpp b/src/mlpack/tests/lsh_test.cpp
index fae78e2..d425666 100644
--- a/src/mlpack/tests/lsh_test.cpp
+++ b/src/mlpack/tests/lsh_test.cpp
@@ -60,7 +60,7 @@ BOOST_AUTO_TEST_CASE(NumTablesTest)
data::Load(testSet, qdata, true);
// Run classic knn on reference data.
- AllkNN knn(rdata);
+ KNN knn(rdata);
arma::Mat<size_t> groundTruth;
arma::mat groundDistances;
knn.Search(qdata, k, groundTruth, groundDistances);
@@ -132,7 +132,7 @@ BOOST_AUTO_TEST_CASE(HashWidthTest)
data::Load(testSet, qdata, true);
// Run classic knn on reference data.
- AllkNN knn(rdata);
+ KNN knn(rdata);
arma::Mat<size_t> groundTruth;
arma::mat groundDistances;
knn.Search(qdata, k, groundTruth, groundDistances);
@@ -192,7 +192,7 @@ BOOST_AUTO_TEST_CASE(NumProjTest)
data::Load(testSet, qdata, true);
// Run classic knn on reference data.
- AllkNN knn(rdata);
+ KNN knn(rdata);
arma::Mat<size_t> groundTruth;
arma::mat groundDistances;
knn.Search(qdata, k, groundTruth, groundDistances);
@@ -252,7 +252,7 @@ BOOST_AUTO_TEST_CASE(RecallTest)
data::Load(testSet, qdata, true);
// Run classic knn on reference data.
- AllkNN knn(rdata);
+ KNN knn(rdata);
arma::Mat<size_t> groundTruth;
arma::mat groundDistances;
knn.Search(qdata, k, groundTruth, groundDistances);
diff --git a/src/mlpack/tests/rectangle_tree_test.cpp b/src/mlpack/tests/rectangle_tree_test.cpp
index 42092b6..56a3915 100644
--- a/src/mlpack/tests/rectangle_tree_test.cpp
+++ b/src/mlpack/tests/rectangle_tree_test.cpp
@@ -422,11 +422,11 @@ BOOST_AUTO_TEST_CASE(PointDeletion)
// Single-tree search.
NeighborSearch<NearestNeighborSort, metric::LMetric<2, true>, arma::mat,
- RTree> allknn1(&tree, true);
+ RTree> knn1(&tree, true);
arma::Mat<size_t> neighbors1;
arma::mat distances1;
- allknn1.Search(querySet, 5, neighbors1, distances1);
+ knn1.Search(querySet, 5, neighbors1, distances1);
arma::mat newDataset;
newDataset = dataset;
@@ -436,9 +436,9 @@ BOOST_AUTO_TEST_CASE(PointDeletion)
arma::mat distances2;
// Nearest neighbor search the naive way.
- AllkNN allknn2(newDataset, true, true);
+ KNN knn2(newDataset, true, true);
- allknn2.Search(querySet, 5, neighbors2, distances2);
+ knn2.Search(querySet, 5, neighbors2, distances2);
for (size_t i = 0; i < neighbors1.size(); i++)
{
@@ -512,14 +512,14 @@ BOOST_AUTO_TEST_CASE(PointDynamicAdd)
// Nearest neighbor search with the R tree.
NeighborSearch<NearestNeighborSort, metric::LMetric<2, true>, arma::mat,
- RTree> allknn1(&tree, true);
+ RTree> knn1(&tree, true);
- allknn1.Search(5, neighbors1, distances1);
+ knn1.Search(5, neighbors1, distances1);
// Nearest neighbor search the naive way.
- AllkNN allknn2(dataset, true, true);
+ KNN knn2(dataset, true, true);
- allknn2.Search(5, neighbors2, distances2);
+ knn2.Search(5, neighbors2, distances2);
for (size_t i = 0; i < neighbors1.size(); i++)
{
@@ -545,7 +545,7 @@ BOOST_AUTO_TEST_CASE(SingleTreeTraverserTest)
// Nearest neighbor search with the R tree.
NeighborSearch<NearestNeighborSort, metric::LMetric<2, true>, arma::mat,
- RStarTree> allknn1(&rTree, true);
+ RStarTree> knn1(&rTree, true);
BOOST_REQUIRE_EQUAL(rTree.NumDescendants(), 1000);
@@ -554,12 +554,12 @@ BOOST_AUTO_TEST_CASE(SingleTreeTraverserTest)
CheckExactContainment(rTree);
CheckHierarchy(rTree);
- allknn1.Search(5, neighbors1, distances1);
+ knn1.Search(5, neighbors1, distances1);
// Nearest neighbor search the naive way.
- AllkNN allknn2(dataset, true, true);
+ KNN knn2(dataset, true, true);
- allknn2.Search(5, neighbors2, distances2);
+ knn2.Search(5, neighbors2, distances2);
for (size_t i = 0; i < neighbors1.size(); i++)
{
@@ -595,7 +595,7 @@ BOOST_AUTO_TEST_CASE(XTreeTraverserTest)
// Nearest neighbor search with the X tree.
NeighborSearch<NearestNeighborSort, metric::LMetric<2, true>, TreeType>
- allknn1(&xTree, dataset, true);
+ knn1(&xTree, dataset, true);
BOOST_REQUIRE_EQUAL(xTree.NumDescendants(), numP);
@@ -604,12 +604,12 @@ BOOST_AUTO_TEST_CASE(XTreeTraverserTest)
CheckExactContainment(xTree);
CheckHierarchy(xTree);
- allknn1.Search(5, neighbors1, distances1);
+ knn1.Search(5, neighbors1, distances1);
// Nearest neighbor search the naive way.
- AllkNN allknn2(dataset, true, true);
+ KNN knn2(dataset, true, true);
- allknn2.Search(5, neighbors2, distances2);
+ knn2.Search(5, neighbors2, distances2);
for (size_t i = 0; i < neighbors1.size(); i++)
{
diff --git a/src/mlpack/tests/sdp_primal_dual_test.cpp b/src/mlpack/tests/sdp_primal_dual_test.cpp
index e442535..f3ed242 100644
--- a/src/mlpack/tests/sdp_primal_dual_test.cpp
+++ b/src/mlpack/tests/sdp_primal_dual_test.cpp
@@ -552,8 +552,8 @@ BOOST_AUTO_TEST_CASE(CorrelationCoeffToySdp)
//
// arma::Mat<size_t> neighbors;
// arma::mat distances;
-// AllkNN allknn(origData);
-// allknn.Search(numNeighbors, neighbors, distances);
+// KNN knn(origData);
+// knn.Search(numNeighbors, neighbors, distances);
//
// SDP<arma::sp_mat> sdp(numPoints, numNeighbors * numPoints, 1);
// sdp.C().eye(numPoints, numPoints);
diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp
index dc17bac..9bddbc2 100644
--- a/src/mlpack/tests/serialization_test.cpp
+++ b/src/mlpack/tests/serialization_test.cpp
@@ -801,16 +801,16 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionTest)
BOOST_REQUIRE_CLOSE(lr.Lambda(), lrBinary.Lambda(), 1e-5);
}
-BOOST_AUTO_TEST_CASE(AllkNNTest)
+BOOST_AUTO_TEST_CASE(KNNTest)
{
- using neighbor::AllkNN;
+ using neighbor::KNN;
arma::mat dataset = arma::randu<arma::mat>(5, 2000);
- AllkNN allknn(dataset, false, false);
+ KNN knn(dataset, false, false);
- AllkNN knnXml, knnText, knnBinary;
+ KNN knnXml, knnText, knnBinary;
- SerializeObjectAll(allknn, knnXml, knnText, knnBinary);
+ SerializeObjectAll(knn, knnXml, knnText, knnBinary);
// Now run nearest neighbor and make sure the results are the same.
arma::mat querySet = arma::randu<arma::mat>(5, 1000);
@@ -818,7 +818,7 @@ BOOST_AUTO_TEST_CASE(AllkNNTest)
arma::mat distances, xmlDistances, textDistances, binaryDistances;
arma::Mat<size_t> neighbors, xmlNeighbors, textNeighbors, binaryNeighbors;
- allknn.Search(querySet, 5, neighbors, distances);
+ knn.Search(querySet, 5, neighbors, distances);
knnXml.Search(querySet, 5, xmlNeighbors, xmlDistances);
knnText.Search(querySet, 5, textNeighbors, textDistances);
knnBinary.Search(querySet, 5, binaryNeighbors, binaryDistances);
@@ -1124,7 +1124,7 @@ BOOST_AUTO_TEST_CASE(NaiveBayesSerializationTest)
BOOST_AUTO_TEST_CASE(RASearchTest)
{
using neighbor::AllkRANN;
- using neighbor::AllkNN;
+ using neighbor::KNN;
arma::mat dataset = arma::randu<arma::mat>(5, 200);
arma::mat otherDataset = arma::randu<arma::mat>(5, 100);
@@ -1145,8 +1145,8 @@ BOOST_AUTO_TEST_CASE(RASearchTest)
arma::mat distances, xmlDistances, textDistances, binaryDistances;
arma::Mat<size_t> neighbors, xmlNeighbors, textNeighbors, binaryNeighbors;
- AllkNN allknn(dataset); // Exact search.
- allknn.Search(querySet, 10, neighbors, distances);
+ KNN knn(dataset); // Exact search.
+ knn.Search(querySet, 10, neighbors, distances);
krannXml.Search(querySet, 5, xmlNeighbors, xmlDistances);
krannText.Search(querySet, 5, textNeighbors, textDistances);
krannBinary.Search(querySet, 5, binaryNeighbors, binaryDistances);
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