[mlpack-svn] r10391 - mlpack/trunk/src/mlpack/tests
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Thu Nov 24 02:26:26 EST 2011
Author: rcurtin
Date: 2011-11-24 02:26:26 -0500 (Thu, 24 Nov 2011)
New Revision: 10391
Removed:
mlpack/trunk/src/mlpack/tests/nnsvm_test.cpp
mlpack/trunk/src/mlpack/tests/svm_test.cpp
Modified:
mlpack/trunk/src/mlpack/tests/CMakeLists.txt
Log:
Remove SVM test and NNSVM test.
Modified: mlpack/trunk/src/mlpack/tests/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2011-11-24 07:25:55 UTC (rev 10390)
+++ mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2011-11-24 07:26:26 UTC (rev 10391)
@@ -27,10 +27,8 @@
math_test.cpp
nbc_test.cpp
nca_test.cpp
- nnsvm_test.cpp
save_restore_utility_test.cpp
sort_policy_test.cpp
- svm_test.cpp
tree_test.cpp
union_find_test.cpp
)
Deleted: mlpack/trunk/src/mlpack/tests/nnsvm_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/tests/nnsvm_test.cpp 2011-11-24 07:25:55 UTC (rev 10390)
+++ mlpack/trunk/src/mlpack/tests/nnsvm_test.cpp 2011-11-24 07:26:26 UTC (rev 10391)
@@ -1,290 +0,0 @@
-/**
- * @file nnsvm_test.cc
- *
- * Test file for NNSVM class.
- */
-#include <iostream>
-#include <mlpack/methods/nnsvm/nnsvm.hpp>
-#include <mlpack/core/kernels/linear_kernel.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::nnsvm;
-
-BOOST_AUTO_TEST_SUITE(NNSVMTest);
-
-/**
- * Simple nonnegative SVM test with small, synthetic dataset.
- */
-BOOST_AUTO_TEST_CASE(linear_kernel_test_1)
-{
- // Initialize the matrix.
- arma::mat data;
-
- data << -1.0 << 1.0 << 1.0 << arma::endr
- << -2.0 << 2.0 << 1.0 << arma::endr
- << -3.0 << 3.0 << 1.0 << arma::endr
- << -4.0 << 4.0 << 1.0 << arma::endr
- << 1.0 << -1.0 << 0.0 << arma::endr
- << 2.0 << -2.0 << 0.0 << arma::endr
- << 3.0 << -3.0 << 0.0 << arma::endr
- << 4.0 << -4.0 << 0.0 << arma::endr;
-
- // Test the linear kernel.
- NNSVM<kernel::LinearKernel> nnsvm;
-
- nnsvm.InitTrain(data, 2);
- double calculatedThreshold = nnsvm.getThreshold();
- size_t calculatedSupportVectorCount = nnsvm.getSupportVectorCount();
- const arma::vec calculatedSupportVectorCoefficients =
- nnsvm.getSupportVectorCoefficients();
- const arma::vec calculatedWeightVector = nnsvm.getWeightVector();
-
- // Check for correctness on the linear kernel.
- BOOST_REQUIRE(calculatedSupportVectorCount == 3);
- BOOST_REQUIRE_CLOSE(calculatedThreshold, -1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[0],
- 3.7499785159728178, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[1],
- 6.2500214840271884, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[2], -10.000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[0], 0.00000000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[1], 0.00000000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[2], 0.00000000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[3], 0.00017187221748210524, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[4], 0.00000000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[5], 0.00000000, 1e-5);
- BOOST_REQUIRE_CLOSE(calculatedWeightVector[6], 0.00000000, 1e-5);
-
-}
-
-//BOOST_AUTO_TEST_CASE(linear_kernel_test_2)
-//{
-// // initialize the matrix
-// arma::mat data;
-//
-// data << 2.8532 << 0.6808 << -18 << 6.5 << -26.23 << -273.67 << 3.1747
-// << 1.4824 << 2.0161 << -11.142 << -31.166 << 0 << -1.0324
-// << -8.2685 << 3.48 << 8.545 << 6.575 << -7.89 << 1.9919 << -7
-// << 1 << arma::endr
-// << 0.0578 << 1.3971 << -24 << -12.1 << -100.13 << -553.18 << 2.3804
-// << -0.5607 << 4.5496 << -24.574 << -24.968 << -1 << -13.534 << -31.45
-// << 1.54 << -13.05 << -11.725 << -2.55 << 1.8087 << 10 << 1
-// << arma::endr
-// << 0.1804 << 1.6302 << -26 << -6.4 << -81.28 << -529.73 << 2.0123
-// << 1.0011 << 2.1322 << -38.049 << -22.548 << -1 << -17.921
-// << -29.786 << -4.2 << -5.885 << -12.065 << -0.18 << 3.7498
-// << 8 << 1 << arma::endr
-// << 2.2685 << 0.77 << -26 << -5 << -63.74 << -619.41 << 3.79 << 1.4824
-// << 2.6626 << -11.553 << -10.084 << -1 << -23.293 << -24.595 << 5.06
-// << 1.81 << -6.8 << -2.74 << 1.4367 << 2 << 1 << arma::endr
-// << 1.1094 << 1.3512 << -27 << -5.9 << -97.98 << -888.82 << 4.7719
-// << -0.8833 << 7.0048 << -41.996 << -25.473 << 0 << -17.197 << -31.864
-// << 2.96 << -12.92 << -21.625 << -4.66 << 4.1161 << 14 << 1
-// << arma::endr
-// << -0.7448 << 0.639 << -33 << -4.3 << -121.87 << -1240.2 << 2.574
-// << -11.96 << -1.4605 << -23.163 << -41.738 << 0 << -26.078
-// << -65.459 << -3.46 << -43.665 << -47.125 << 0.36 << 1.5793
-// << 11 << 1 << arma::endr
-// << 2.1242 << 1.0001 << -27 << -1.5 << -85.01 << -753.81 << 5.8026
-// << 0.4733 << 7.3244 << -28.602 << -22.096 << -1 << -25.72 << -5.058
-// << 1.96 << -2.715 << -36.435 << 6.23 << 4.0664 << 4 << 1 << arma::endr
-// << 1.5147 << 1.5999 << -30 << 9.5 << -48.64 << -552.72 << 6.8646
-// << -2.7514 << 9.765 << -32.567 << -26.009 << -1 << -21.244 << -42.178
-// << -4.38 << -14.655 << -35.8 << -8.5 << 3.5578 << -3 << 1
-// << arma::endr
-// << 0.2762 << 0.4498 << -33 << -14.5 << -108.06 << -1848.7 << 3.3279
-// << -4.7404 << 8.3787 << -69.738 << -45.989 << -2 << -20.988 << -87.26
-// << 5.26 << -41.045 << -46.53 << 2.92 << 0.2006 << 17 << 1
-// << arma::endr
-// << -0.8361 << 0.5906 << -35 << -9.9 << -123.2 << -1535.6 << 1.7498
-// << -9.412 << 4.8324 << -61.991 << -45.081 << -1 << -28.325 << -85.874
-// << -10.38 << -36.53 << -51.315 << -4.12 << 1.5208 << 28 << 1
-// << arma::endr
-// << 3.4862 << 0.1746 << -8 << -12.6 << -48.05 << -74.518 << -5.3072 << 0
-// << -0.6322 << 14.706 << 4.6319 << -1 << -5.1945 << -10.268 << -13.8
-// << -18.11 << -2.625 << -9.7 << 0.0399 << -11 << 0 << arma::endr
-// << -1.332 << -0.5399 << 6 << -11.5 << -7.1 << -41.538 << -0.4827
-// << 0.1494 << 2.6331 << 13.711 << -20.669 << 1 << -2.307 << -16.457
-// << -5.3 << -21.65 << 3.57 << 6.82 << -1.4101 << -6 << 0
-// << arma::endr
-// << 0.6689 << 0.1837 << 2 << -1.9 << 10.22 << 176.41 << -1.2294 << 0
-// << -1.7885 << -16.553 << -2.9748 << 0 << 9.304 << 10.097 << -4.32
-// << -7.325 << -6.375 << 13.96 << -0.1116 << 25 << 0 << arma::endr
-// << -0.9639 << -0.6035 << 15 << 17.7 << 82.64 << 849.54 << 1.7272
-// << 2.3738 << -2.3887 << -4.4992 << 31.715 << -1 << 18.632 << -4.4991
-// << 13 << 8.955 << 12.725 << -19.04 << 0.218 << -24 << 0 << arma::endr
-// << -3.5669 << -0.4114 << 19 << 11.8 << 84.35 << 749.12 << -3.8668
-// << 1.9631 << 3.2414 << 25.695 << 28.835 << 1 << 30.208 << 34.002
-// << 14.78 << 46.875 << 24.84 << 8.1 << -1.7294 << -7 << 0 << arma::endr
-// << 1.5881 << -0.023 << 24 << 6.8 << 108.42 << 1255.7 << 2.0539 << 12.079
-// << 1.7954 << 21.008 << 33.312 << 0 << 27.819 << 22.083 << 15.94
-// << 71.565 << 49.805 << 3.94 << 0.3416 << -12 << 0 << arma::endr
-// << -1.5349 << -0.5047 << 10 << -12.2 << 12.26 << 476.02 << -4.4551 << 0
-// << -7.213 << 7.56 << 2.4955 << 0 << 18.81 << -13.185 << -11 << 9.86
-// << -12.555 << -3.63 << -2.4305 << -7 << 0 << arma::endr
-// << 1.9474 << -1.9588 << 26 << 29.8 << 156.44 << 1885.6 << 1.7485
-// << 12.592 << -4.1963 << 17.719 << 45.104 << 0 << 29.739 << 82.289
-// << 17 << 62.15 << 41.805 << -13.57 << 1.4464 << -48 << 0
-// << arma::endr
-// << 1.9065 << -0.1575 << 24 << -8.5 << 59.85 << 577 << -6.8442 << -0.4728
-// << -1.5632 << 19.76 << 13.641 << -2 << 32.432 << 34.894 << -0.9
-// << 52.775 << 23.125 << -24 << -8.2289 << -18 << 0 << arma::endr
-// << -0.6037 << -0.4235 << 21 << 18.6 << 137.71 << 1506.4 << -0.9627
-// << 11.03 << -2.7523 << 47.119 << 58.999 << 1 << 21.233 << 74.764
-// << 21.96 << 72.49 << 54.675 << -10.04 << 1.916 << -12 << 0
-// << arma::endr;
-//
-// // test the linear kernel
-// NNSVM<SVMLinearKernel>* nnsvm = new NNSVM<SVMLinearKernel>();
-//
-// nnsvm.InitTrain(data, 2);
-// double calculatedThreshold = nnsvm.getThreshold();
-// size_t calculatedSupportVectorCount = nnsvm.getSupportVectorCount();
-// const arma::vec calculatedSupportVectorCoefficients =
-// nnsvm.getSupportVectorCoefficients();
-// const arma::vec calculatedWeightVector = nnsvm.getWeightVector();
-//
-// // check for correctness on the linear kernel
-// BOOST_REQUIRE(calculatedSupportVectorCount == 6);
-// BOOST_REQUIRE_CLOSE(calculatedThreshold, -39.793784137999701, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[0],
-// 0.06875628588658407, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[1],
-// 3.1607079296638054, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[2],
-// 0.0039166013511236601, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[3],
-// 3.7130510722124139, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[4],
-// 0.43343566949350276, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedSupportVectorCoefficients[5],
-// -10.000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[0], 0.00000000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[1], 0.00000000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[2], 0.00000000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[3], 0.00000000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[4], 0.053222444790909262, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[5], 0.00000000, 1e-5);
-// BOOST_REQUIRE_CLOSE(calculatedWeightVector[6], 0.00000000, 1e-5);
-//
-//}
-///***
-// * Test the dual-tree nearest-neighbors method with the naive method. This
-// * uses both a query and reference dataset.
-// *
-// * Errors are produced if the results are not identical.
-// */
-//BOOST_AUTO_TEST_CASE(dual_tree_vs_naive_1)
-//{
-// arma::mat data_for_tree_;
-//
-// // Hard-coded filename: bad!
-// if (data::Load("test_data_3_1000.csv", data_for_tree_) != true)
-// {
-// BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
-// }
-//
-// // Set up matrices to work with.
-// arma::mat dual_query(data_for_tree_);
-// arma::mat dual_references(data_for_tree_);
-// arma::mat naive_query(data_for_tree_);
-// arma::mat naive_references(data_for_tree_);
-//
-// AllkNN allknn_(dual_query, dual_references, 20, 5);
-// AllkNN naive_(naive_query, naive_references, 1 /* leaf_size ignored */, 5,
-// AllkNN::NAIVE);
-//
-// arma::Col<size_t> resulting_neighbors_tree;
-// arma::vec distances_tree;
-// allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-//
-// arma::Col<size_t> resulting_neighbors_naive;
-// arma::vec distances_naive;
-// naive_.ComputeNeighbors(resulting_neighbors_naive, distances_naive);
-//
-// for (size_t i = 0; i < resulting_neighbors_tree.n_elem; i++)
-// {
-// BOOST_REQUIRE(resulting_neighbors_tree[i] == resulting_neighbors_naive[i]);
-// BOOST_REQUIRE_CLOSE(distances_tree[i], distances_naive[i], 1e-5);
-// }
-//}
-//
-///***
-// * Test the dual-tree nearest-neighbors method with the naive method. This uses
-// * only a reference dataset.
-// *
-// * Errors are produced if the results are not identical.
-// */
-//BOOST_AUTO_TEST_CASE(dual_tree_vs_naive_2)
-//{
-// arma::mat data_for_tree_;
-//
-// // Hard-coded filename: bad!
-// // Code duplication: also bad!
-// if (data::Load("test_data_3_1000.csv", data_for_tree_) != true)
-// {
-// BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
-// }
-//
-// // Set up matrices to work with (may not be necessary with no ALIAS_MATRIX?).
-// arma::mat dual_query(data_for_tree_);
-// arma::mat naive_query(data_for_tree_);
-//
-// AllkNN allknn_(dual_query, 20, 1);
-// AllkNN naive_(naive_query, 1 /* leaf_size ignored with naive */, 1,
-// AllkNN::NAIVE);
-//
-// arma::Col<size_t> resulting_neighbors_tree;
-// arma::vec distances_tree;
-// allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-//
-// arma::Col<size_t> resulting_neighbors_naive;
-// arma::vec distances_naive;
-// naive_.ComputeNeighbors(resulting_neighbors_naive, distances_naive);
-//
-// for (size_t i = 0; i < resulting_neighbors_tree.n_elem; i++) {
-// BOOST_REQUIRE(resulting_neighbors_tree[i] == resulting_neighbors_naive[i]);
-// BOOST_REQUIRE_CLOSE(distances_tree[i], distances_naive[i], 1e-5);
-// }
-//}
-//
-///***
-// * Test the single-tree nearest-neighbors method with the naive method. This
-// * uses only a reference dataset.
-// *
-// * Errors are produced if the results are not identical.
-// */
-//BOOST_AUTO_TEST_CASE(single_tree_vs_naive)
-//{
-// arma::mat data_for_tree_;
-//
-// // Hard-coded filename: bad!
-// // Code duplication: also bad!
-// if (data::Load("test_data_3_1000.csv", data_for_tree_) != true)
-// BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
-//
-// // Set up matrices to work with (may not be necessary with no ALIAS_MATRIX?).
-// arma::mat single_query(data_for_tree_);
-// arma::mat naive_query(data_for_tree_);
-//
-// AllkNN allknn_(single_query, 20, 5, AllkNN::MODE_SINGLE);
-// AllkNN naive_(naive_query, 1 /* leaf_size ignored with naive */, 5,
-// AllkNN::NAIVE);
-//
-// arma::Col<size_t> resulting_neighbors_tree;
-// arma::vec distances_tree;
-// allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-//
-// arma::Col<size_t> resulting_neighbors_naive;
-// arma::vec distances_naive;
-// naive_.ComputeNeighbors(resulting_neighbors_naive, distances_naive);
-//
-// for (size_t i = 0; i < resulting_neighbors_tree.n_elem; i++) {
-// BOOST_REQUIRE(resulting_neighbors_tree[i] == resulting_neighbors_naive[i]);
-// BOOST_REQUIRE_CLOSE(distances_tree[i], distances_naive[i], 1e-5);
-// }
-//}
-
-BOOST_AUTO_TEST_SUITE_END();
Deleted: mlpack/trunk/src/mlpack/tests/svm_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/tests/svm_test.cpp 2011-11-24 07:25:55 UTC (rev 10390)
+++ mlpack/trunk/src/mlpack/tests/svm_test.cpp 2011-11-24 07:26:26 UTC (rev 10391)
@@ -1,141 +0,0 @@
-/**
- * @file svm_test.cpp
- *
- * Test for SVM.
- */
-#include <mlpack/core.h>
-#include <mlpack/methods/svm/svm.h>
-
-#include <boost/test/unit_test.hpp>
-
-using std::string;
-using namespace mlpack;
-using namespace mlpack::svm;
-
-BOOST_AUTO_TEST_SUITE(SVMTest);
-
-// Create test data
-arma::mat matrix(20, 3);
-bool first = true;
-
-/**
- * Creates the data to train and test with and prints it to stdout. Should only
- * have any effect once.
- */
-void setup()
-{
- if (!first)
- return;
- first = false;
-
- CLI::GetParam<bool>("svm/shrink") = true;
- CLI::GetParam<double>("svm/epsilon") = .1;
- CLI::GetParam<double>("svm/sigma") = 1;
- // Protect the test from taking forever
- CLI::GetParam<size_t>("svm/n_iter") = 10000;
-
- matrix <<
- 7.19906628001437787e-01 << 1.83250823399634477e+00 << 0 << arma::endr <<
- 1.37899419263889733e+01 << 1.78198235122579263e+00 << 1 << arma::endr <<
- 6.68859485848275703e-01 << 2.14083320956715983e+00 << 0 << arma::endr <<
- 1.84729928795588165e+01 << 2.25024702760868101e+00 << 1 << arma::endr <<
- 9.22802773268335819e-01 << 1.61469358350834513e+00 << 0 << arma::endr <<
- 2.06209849662245204e-01 << 6.34699695340683490e-01 << 1 << arma::endr <<
- 4.01062068250524817e-01 << 1.65802752932441777e+00 << 0 << arma::endr <<
- 5.02985607135568635e+00 << 1.39976642741810831e+00 << 1 << arma::endr <<
- 3.66471199955079319e-01 << 1.62780588172739638e+00 << 0 << arma::endr <<
- 1.56912570240400999e+01 << 2.16941541650770953e+00 << 1 << arma::endr <<
- 9.98909584711729304e-01 << 2.00337906391517206e+00 << 0 << arma::endr <<
- 1.31430438780891912e+01 << 1.34410346059319719e+00 << 1 << arma::endr <<
- 3.41572957272442523e-01 << 1.16758463655951639e+00 << 0 << arma::endr <<
- 9.53941410851637528e-01 << 6.30271704462483373e-01 << 1 << arma::endr <<
- 7.07135529120981432e-01 << 2.17763537339756041e+00 << 0 << arma::endr <<
- 9.68899714280338742e+00 << 1.26922579378319256e+00 << 1 << arma::endr <<
- 9.82393905512240706e-01 << 2.36790583090293483e+00 << 0 << arma::endr <<
- 1.31583349281727973e+01 << 1.45115094722767868e+00 << 1 << arma::endr <<
- 3.80991188521027202e-01 << 9.05379134419085019e-01 << 0 << arma::endr <<
- 1.86057436180327755e+01 << 2.26941891469499968e+00 << 1 << arma::endr;
- matrix = trans(matrix);
-
- std::cout << matrix << std::endl;
-}
-
-/**
- * Compares predicted values with known values to see if the prediction/training
- * works.
- *
- * @param learner_typeid Magic number for selecting between classification and
- * regression.
- * @param data The dataset with the data to predict with.
- * @param svm The SVM class instance that has been trained for this data, et al.
- */
-template<typename T>
-void verify(size_t learner_typeid, arma::mat& data, SVM<T>& svm)
-{
- for (size_t i = 0; i < data.n_cols; i++)
- {
- arma::vec testvec = data.col(i);
-
- double predictedvalue = svm.Predict(learner_typeid, testvec);
- BOOST_REQUIRE_CLOSE(predictedvalue, data(data.n_rows - 1, i), 1e-6);
- }
-}
-
-/**
- * Trains a classifier with a linear kernel and checks predictions against
- * classes.
- */
-//BOOST_AUTO_TEST_CASE(svm_classification_linear_kernel_test) {
-// setup();
-//
-// arma::mat trainingdata;
-// trainingdata = matrix;
-// SVM<SVMLinearKernel> svm;
-// svm.InitTrain(0,trainingdata); // 0 for classification
-// verify(0,trainingdata,svm);
-//}
-
-/**
- * Trains a classifier with a gaussian kernel and checks predictions against
- * classes.
- */
-BOOST_AUTO_TEST_CASE(svm_classification_gaussian_kernel_test)
-{
- setup();
-
- arma::mat trainingdata;
- trainingdata = matrix;
- SVM<SVMRBFKernel> svm;
- svm.InitTrain(0,trainingdata); // 0 for classification
- verify(0,trainingdata,svm);
-}
-
-/**
- * Trains a classifier with a linear kernel and checks predictions against
- * classes, using regression. TODO: BROKEN
- */
-//BOOST_AUTO_TEST_CASE(svm_regression_linear_kernel_test) {
-// setup();
-//
-// arma::mat trainingdata;
-// trainingdata = matrix;
-// SVM<SVMLinearKernel> svm;
-// svm.InitTrain(1,trainingdata); // 0 for classification
-// //verify(1,trainingdata,svm);
-//}
-
-/**
- * Trains a classifier with a gaussian kernel and checks predictions against
- * classes, using regression. TODO: BROKEN
- */
-//BOOST_AUTO_TEST_CASE(svm_regression_gaussian_kernel_test) {
-// setup();
-//
-// arma::mat trainingdata;
-// trainingdata = matrix;
-// SVM<SVMRBFKernel> svm;
-// svm.InitTrain(1,trainingdata); // 0 for classification
-// //verify(1,trainingdata,svm);
-//}
-
-BOOST_AUTO_TEST_SUITE_END();
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