[mlpack-svn] r10195 - in mlpack/trunk/src/mlpack: core/data core/io core/kernels core/math core/tree core/utilities methods/emst methods/fastica methods/infomax_ica methods/kernel_pca methods/linear_regression methods/mog methods/mvu methods/naive_bayes methods/nca methods/neighbor_search methods/neighbor_search/sort_policies methods/nnsvm methods/regression methods/svm tests tests/data
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Nov 8 18:57:27 EST 2011
Author: vlad321
Date: 2011-11-08 18:57:26 -0500 (Tue, 08 Nov 2011)
New Revision: 10195
Added:
mlpack/trunk/src/mlpack/tests/data/trainRes.csv
mlpack/trunk/src/mlpack/tests/sort_policy_test.cc
Removed:
mlpack/trunk/src/mlpack/core/data/load_save_test.cpp
mlpack/trunk/src/mlpack/core/io/cli_test.cpp
mlpack/trunk/src/mlpack/core/kernels/kernel_test.cc
mlpack/trunk/src/mlpack/core/math/math_test.cpp
mlpack/trunk/src/mlpack/core/tree/tree_test.cpp
mlpack/trunk/src/mlpack/core/utilities/save_restore_utility_test.cpp
mlpack/trunk/src/mlpack/methods/emst/emst_test.cc
mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv
mlpack/trunk/src/mlpack/methods/emst/union_find_test.cpp
mlpack/trunk/src/mlpack/methods/fastica/lin_alg_test.cpp
mlpack/trunk/src/mlpack/methods/infomax_ica/infomax_ica_test.cc
mlpack/trunk/src/mlpack/methods/kernel_pca/test_data_3_1000.csv
mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp
mlpack/trunk/src/mlpack/methods/mog/fake.csv
mlpack/trunk/src/mlpack/methods/mvu/roll.csv
mlpack/trunk/src/mlpack/methods/naive_bayes/nbc_test.cc
mlpack/trunk/src/mlpack/methods/naive_bayes/testRes.csv
mlpack/trunk/src/mlpack/methods/naive_bayes/testSet.csv
mlpack/trunk/src/mlpack/methods/naive_bayes/trainRes.csv
mlpack/trunk/src/mlpack/methods/naive_bayes/trainSet.csv
mlpack/trunk/src/mlpack/methods/nca/nca_test.cc
mlpack/trunk/src/mlpack/methods/neighbor_search/allkfn_test.cc
mlpack/trunk/src/mlpack/methods/neighbor_search/allknn_test.cc
mlpack/trunk/src/mlpack/methods/neighbor_search/sort_policies/sort_policy_test.cc
mlpack/trunk/src/mlpack/methods/neighbor_search/test_data_3_1000.csv
mlpack/trunk/src/mlpack/methods/nnsvm/nnsvm_test.cpp
mlpack/trunk/src/mlpack/methods/nnsvm/toy1.csv
mlpack/trunk/src/mlpack/methods/nnsvm/toy4.csv
mlpack/trunk/src/mlpack/methods/nnsvm/toy7.csv
mlpack/trunk/src/mlpack/methods/regression/ridge_regression_test.cc
mlpack/trunk/src/mlpack/methods/svm/c_testdata2.csv
mlpack/trunk/src/mlpack/methods/svm/c_testdata3.csv
mlpack/trunk/src/mlpack/methods/svm/c_traindata2.csv
mlpack/trunk/src/mlpack/methods/svm/c_traindata3.csv
mlpack/trunk/src/mlpack/methods/svm/r_testdata.csv
mlpack/trunk/src/mlpack/methods/svm/r_traindata.csv
mlpack/trunk/src/mlpack/methods/svm/svm_test.cc
Modified:
mlpack/trunk/src/mlpack/core/data/CMakeLists.txt
mlpack/trunk/src/mlpack/core/io/CMakeLists.txt
mlpack/trunk/src/mlpack/core/kernels/CMakeLists.txt
mlpack/trunk/src/mlpack/core/math/CMakeLists.txt
mlpack/trunk/src/mlpack/core/tree/CMakeLists.txt
mlpack/trunk/src/mlpack/core/utilities/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/fastica/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/infomax_ica/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/linear_regression/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/naive_bayes/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/nca/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/neighbor_search/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/nnsvm/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/regression/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/svm/CMakeLists.txt
mlpack/trunk/src/mlpack/tests/CMakeLists.txt
Log:
Cleaned up and moved all stray tests and test data that is included in the general mlpack_test.
Modified: mlpack/trunk/src/mlpack/core/data/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/data/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/data/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -18,13 +18,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-# test executable
-add_executable(load_save_test
- load_save_test.cpp
-)
-# link dependencies of test executable
-target_link_libraries(load_save_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/core/data/load_save_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/core/data/load_save_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/data/load_save_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,311 +0,0 @@
-/**
- * @file load_save_test.cpp
- * @author Ryan Curtin
- *
- * Tests for data::Load() and data::Save().
- */
-#include <sstream>
-
-#include "load.hpp"
-#include "save.hpp"
-
-#define BOOST_TEST_MODULE LoadSaveTest
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-
-/**
- * Make sure failure occurs when no extension given.
- */
-BOOST_AUTO_TEST_CASE(NoExtensionLoad) {
- arma::mat out;
- BOOST_REQUIRE(data::Load("noextension", out) == false);
-}
-
-/**
- * Make sure failure occurs when no extension given.
- */
-BOOST_AUTO_TEST_CASE(NoExtensionSave) {
- arma::mat out;
- BOOST_REQUIRE(data::Save("noextension", out) == false);
-}
-
-/**
- * Make sure load fails if the file does not exist.
- */
-BOOST_AUTO_TEST_CASE(NotExistLoad) {
- arma::mat out;
- BOOST_REQUIRE(data::Load("nonexistentfile_______________.csv", out) == false);
-}
-
-/**
- * Make sure a CSV is loaded correctly.
- */
-BOOST_AUTO_TEST_CASE(LoadCSVTest) {
- std::fstream f;
- f.open("test_file.csv", std::fstream::out);
-
- f << "1, 2, 3, 4" << std::endl;
- f << "5, 6, 7, 8" << std::endl;
-
- f.close();
-
- arma::mat test;
- BOOST_REQUIRE(data::Load("test_file.csv", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.csv");
-}
-
-/**
- * Make sure a CSV is saved correctly.
- */
-BOOST_AUTO_TEST_CASE(SaveCSVTest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- BOOST_REQUIRE(data::Save("test_file.csv", test) == true);
-
- // Load it in and make sure it is the same.
- BOOST_REQUIRE(data::Load("test_file.csv", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.csv");
-}
-
-/**
- * Make sure arma_ascii is loaded correctly.
- */
-BOOST_AUTO_TEST_CASE(LoadArmaASCIITest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- arma::mat testTrans = trans(test);
- BOOST_REQUIRE(testTrans.save("test_file.txt", arma::arma_ascii));
-
- BOOST_REQUIRE(data::Load("test_file.txt", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.txt");
-}
-
-/**
- * Make sure a CSV is saved correctly.
- */
-BOOST_AUTO_TEST_CASE(SaveArmaASCIITest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- BOOST_REQUIRE(data::Save("test_file.txt", test) == true);
-
- // Load it in and make sure it is the same.
- BOOST_REQUIRE(data::Load("test_file.txt", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.txt");
-}
-
-/**
- * Make sure raw_ascii is loaded correctly.
- */
-BOOST_AUTO_TEST_CASE(LoadRawASCIITest) {
- std::fstream f;
- f.open("test_file.txt", std::fstream::out);
-
- f << "1 2 3 4" << std::endl;
- f << "5 6 7 8" << std::endl;
-
- f.close();
-
- arma::mat test;
- BOOST_REQUIRE(data::Load("test_file.txt", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.txt");
-}
-
-/**
- * Make sure CSV is loaded correctly as .txt.
- */
-BOOST_AUTO_TEST_CASE(LoadCSVTxtTest) {
- std::fstream f;
- f.open("test_file.txt", std::fstream::out);
-
- f << "1, 2, 3, 4" << std::endl;
- f << "5, 6, 7, 8" << std::endl;
-
- f.close();
-
- arma::mat test;
- BOOST_REQUIRE(data::Load("test_file.txt", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.txt");
-}
-
-/**
- * Make sure arma_binary is loaded correctly.
- */
-BOOST_AUTO_TEST_CASE(LoadArmaBinaryTest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- arma::mat testTrans = trans(test);
- BOOST_REQUIRE(testTrans.quiet_save("test_file.bin", arma::arma_binary)
- == true);
-
- // Now reload through our interface.
- BOOST_REQUIRE(data::Load("test_file.bin", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.bin");
-}
-
-/**
- * Make sure arma_binary is saved correctly.
- */
-BOOST_AUTO_TEST_CASE(SaveArmaBinaryTest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- BOOST_REQUIRE(data::Save("test_file.bin", test) == true);
-
- BOOST_REQUIRE(data::Load("test_file.bin", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.bin");
-}
-
-/**
- * Make sure raw_binary is loaded correctly.
- */
-BOOST_AUTO_TEST_CASE(LoadRawBinaryTest) {
- arma::mat test = "1 2;"
- "3 4;"
- "5 6;"
- "7 8;";
-
- arma::mat testTrans = trans(test);
- BOOST_REQUIRE(testTrans.quiet_save("test_file.bin", arma::raw_binary)
- == true);
-
- // Now reload through our interface.
- BOOST_REQUIRE(data::Load("test_file.bin", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 1);
- BOOST_REQUIRE_EQUAL(test.n_cols, 8);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.bin");
-}
-
-/**
- * Make sure load as PGM is successful.
- */
-BOOST_AUTO_TEST_CASE(LoadPGMBinaryTest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- arma::mat testTrans = trans(test);
- BOOST_REQUIRE(testTrans.quiet_save("test_file.pgm", arma::pgm_binary)
- == true);
-
- // Now reload through our interface.
- BOOST_REQUIRE(data::Load("test_file.pgm", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.pgm");
-}
-
-/**
- * Make sure save as PGM is successful.
- */
-BOOST_AUTO_TEST_CASE(SavePGMBinaryTest) {
- arma::mat test = "1 5;"
- "2 6;"
- "3 7;"
- "4 8;";
-
- BOOST_REQUIRE(data::Save("test_file.pgm", test) == true);
-
- // Now reload through our interface.
- BOOST_REQUIRE(data::Load("test_file.pgm", test) == true);
-
- BOOST_REQUIRE_EQUAL(test.n_rows, 4);
- BOOST_REQUIRE_EQUAL(test.n_cols, 2);
-
- for (int i = 0; i < 8; i++)
- BOOST_REQUIRE_CLOSE(test[i], (double) (i + 1), 1e-5);
-
- // Remove the file.
- remove("test_file.pgm");
-}
Modified: mlpack/trunk/src/mlpack/core/io/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/io/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/io/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -31,16 +31,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-# test executable
-add_executable(cli_test
- cli_test.cpp
-)
-# link dependencies of test executable
-target_link_libraries(cli_test
- mlpack
- boost_program_options
- boost_unit_test_framework
-)
-
-
Deleted: mlpack/trunk/src/mlpack/core/io/cli_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/core/io/cli_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/io/cli_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,236 +0,0 @@
-/***
- * @file io_test.cc
- * @author Matthew Amidon, Ryan Curtin
- *
- * Test for the CLI input parameter system.
- */
-
-#include <iostream>
-#include <sstream>
-#include <sys/time.h>
-
-#include "optionshierarchy.hpp"
-#include "cli.hpp"
-#include "log.hpp"
-#include <mlpack/core.h>
-
-#define DEFAULT_INT 42
-
-//Include unit tests down here since we just have to
-#define BOOST_TEST_MODULE CLI_Test
-#include <boost/test/unit_test.hpp>
-
-#define BASH_RED "\033[0;31m"
-#define BASH_GREEN "\033[0;32m"
-#define BASH_YELLOW "\033[0;33m"
-#define BASH_CYAN "\033[0;36m"
-#define BASH_CLEAR "\033[0m"
-
-using namespace mlpack;
-using namespace mlpack::io;
-
-/***
- * @brief Tests that inserting elements into an OptionsHierarchy
- * properly updates the tree.
- *
- * @return True indicating all is well with OptionsHierarchy
- */
-BOOST_AUTO_TEST_CASE(TestHierarchy) {
- OptionsHierarchy tmp = OptionsHierarchy("UTest");
- std::string testName = std::string("UTest/test");
- std::string testDesc = std::string("Test description.");
- std::string testTID = TYPENAME(int);
-
- // Check that the hierarchy is properly named.
- std::string str = std::string("UTest");
- OptionsData node = tmp.GetNodeData();
-
- BOOST_REQUIRE_EQUAL(str.compare(node.node), 0);
- // Check that inserting a node actually inserts the node.
- // Note, that since all versions of append simply call the most qualified
- // overload, we will only test that one.
- tmp.AppendNode(testName, testTID, testDesc);
- BOOST_REQUIRE(tmp.FindNode(testName) != NULL);
-
- // Now check that the inserted node has the correct data.
- OptionsHierarchy* testHierarchy = tmp.FindNode(testName);
- OptionsData testData;
- if (testHierarchy != NULL) {
- node = testHierarchy->GetNodeData();
-
- BOOST_REQUIRE(testName.compare(node.node) == 0);
- BOOST_REQUIRE(testDesc.compare(node.desc) == 0);
- BOOST_REQUIRE(testTID.compare(node.tname) == 0);
- }
-}
-
-/***
- * @brief Tests that CLI works as intended, namely that CLI::Add
- * propagates successfully.
- *
- * @return True indicating all is well with CLI::Add, false otherwise.
- */
-BOOST_AUTO_TEST_CASE(TestCLIAdd) {
- // Check that the CLI::HasParam returns false if no value has been specified
- // on the commandline and ignores any programmatical assignments.
- CLI::Add<bool>("bool", "True or False", "global");
- BOOST_REQUIRE_EQUAL(CLI::HasParam("global/bool"), false);
- CLI::GetParam<bool>("global/bool") = true;
- // CLI::HasParam should return true.
- BOOST_REQUIRE_EQUAL(CLI::HasParam("global/bool"), true);
-
- //Check the description of our variable.
- BOOST_REQUIRE_EQUAL(CLI::GetDescription("global/bool").compare(
- std::string("True or False")) , 0);
- // Check that SanitizeString is sanitary.
- std::string tmp = CLI::SanitizeString("/foo/bar/fizz");
- BOOST_REQUIRE_EQUAL(tmp.compare(std::string("foo/bar/fizz/")),0);
-}
-
-/***
- * Test the output of CLI. We will pass bogus input to a stringstream so that
- * none of it gets to the screen.
- */
-BOOST_AUTO_TEST_CASE(TestPrefixedOutStreamBasic) {
- std::stringstream ss;
- PrefixedOutStream pss(ss, BASH_GREEN "[INFO ] " BASH_CLEAR);
-
- pss << "This shouldn't break anything" << std::endl;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR "This shouldn't break anything\n");
-
- ss.str("");
- pss << "Test the new lines...";
- pss << "shouldn't get 'Info' here." << std::endl;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR
- "Test the new lines...shouldn't get 'Info' here.\n");
-
- pss << "But now I should." << std::endl << std::endl;
- pss << "";
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR
- "Test the new lines...shouldn't get 'Info' here.\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR "But now I should.\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR "\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR "");
-}
-
-/**
- * @brief Tests that the various PARAM_* macros work properly
- * @return True indicating that all is well with CLI & Options.
- */
-BOOST_AUTO_TEST_CASE(TestOption) {
- // This test will involve creating an option, and making sure CLI reflects
- // this.
- PARAM(int, "test", "test desc", "test_parent", DEFAULT_INT, false);
-
- // Does CLI reflect this?
- BOOST_REQUIRE_EQUAL(CLI::HasParam("test_parent/test"), true);
-
- std::string desc = std::string("test desc");
-
- BOOST_REQUIRE_EQUAL(CLI::GetDescription("test_parent/test"), "test desc");
- BOOST_REQUIRE_EQUAL(CLI::GetParam<int>("test_parent/test"), DEFAULT_INT);
-}
-
-/***
- * Ensure that a Boolean option which we define is set correctly.
- */
-BOOST_AUTO_TEST_CASE(TestBooleanOption) {
- PARAM_FLAG("flag_test", "flag test description", "test_parent");
-
- BOOST_REQUIRE_EQUAL(CLI::HasParam("test_parent/flag_test"), false);
-
- BOOST_REQUIRE_EQUAL(CLI::GetDescription("test_parent/flag_test"),
- "flag test description");
-
- // Now check that CLI reflects that it is false by default.
- BOOST_REQUIRE_EQUAL(CLI::GetParam<bool>("test_parent/flag_test"), false);
-}
-
-
-/***
- * Test that we can correctly output Armadillo objects to PrefixedOutStream
- * objects.
- */
-BOOST_AUTO_TEST_CASE(TestArmadilloPrefixedOutStream) {
- // We will test this with both a vector and a matrix.
- arma::vec test("1.0 1.5 2.0 2.5 3.0 3.5 4.0");
-
- std::stringstream ss;
- PrefixedOutStream pss(ss, BASH_GREEN "[INFO ] " BASH_CLEAR);
-
- pss << test;
- // This should result in nothing being on the current line (since it clears
- // it).
- BOOST_REQUIRE_EQUAL(ss.str(), BASH_GREEN "[INFO ] " BASH_CLEAR " 1.0000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 1.5000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 2.0000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 2.5000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 3.0000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 3.5000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 4.0000\n");
-
- ss.str("");
- pss << trans(test);
- // This should result in there being stuff on the line.
- BOOST_REQUIRE_EQUAL(ss.str(), BASH_GREEN "[INFO ] " BASH_CLEAR
- " 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000 4.0000\n");
-
- arma::mat test2("1.0 1.5 2.0; 2.5 3.0 3.5; 4.0 4.5 4.99999");
- ss.str("");
- pss << test2;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR " 1.0000 1.5000 2.0000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 2.5000 3.0000 3.5000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 4.0000 4.5000 5.0000\n");
-
- // Try and throw a curveball by not clearing the line before outputting
- // something else. The PrefixedOutStream should not force Armadillo objects
- // onto their own lines.
- ss.str("");
- pss << "hello" << test2;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR "hello 1.0000 1.5000 2.0000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 2.5000 3.0000 3.5000\n"
- BASH_GREEN "[INFO ] " BASH_CLEAR " 4.0000 4.5000 5.0000\n");
-}
-
-/***
- * Test that we can correctly output things in general.
- */
-BOOST_AUTO_TEST_CASE(TestPrefixedOutStream) {
- std::stringstream ss;
- PrefixedOutStream pss(ss, BASH_GREEN "[INFO ] " BASH_CLEAR);
-
- pss << "hello world I am ";
- pss << 7;
-
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR "hello world I am 7");
-
- pss << std::endl;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR "hello world I am 7\n");
-
- ss.str("");
- pss << std::endl;
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR "\n");
-}
-
-/***
- * Test format modifiers.
- */
-BOOST_AUTO_TEST_CASE(TestPrefixedOutStreamModifiers) {
- std::stringstream ss;
- PrefixedOutStream pss(ss, BASH_GREEN "[INFO ] " BASH_CLEAR);
-
- pss << "I have a precise number which is ";
- pss << std::setw(6) << std::setfill('0') << 156;
-
- BOOST_REQUIRE_EQUAL(ss.str(),
- BASH_GREEN "[INFO ] " BASH_CLEAR
- "I have a precise number which is 000156");
-}
Modified: mlpack/trunk/src/mlpack/core/kernels/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/kernels/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/kernels/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -21,12 +21,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-# add test executable
-add_executable(kernel_test
- kernel_test.cc
-)
-target_link_libraries(kernel_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/core/kernels/kernel_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/core/kernels/kernel_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/kernels/kernel_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,225 +0,0 @@
-/***
- * @file kernel_test.cc
- * @author Ryan Curtin
- *
- * Tests for the various kernel classes.
- */
-
-#include "lmetric.hpp"
-#include "mahalanobis_distance.hpp"
-#include "cosine_distance.hpp"
-#include "gaussian_kernel.hpp"
-#include "linear_kernel.hpp"
-
-#define BOOST_TEST_MODULE Kernel Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::kernel;
-
-/***
- * Basic test of the Manhattan distance.
- */
-BOOST_AUTO_TEST_CASE(manhattan_distance) {
- // A couple quick tests.
- arma::vec a = "1.0 3.0 4.0";
- arma::vec b = "3.0 3.0 5.0";
-
- BOOST_REQUIRE_CLOSE(ManhattanDistance::Evaluate(a, b), 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(ManhattanDistance::Evaluate(b, a), 3.0, 1e-5);
-
- // Check also for when the root is taken (should be the same).
- BOOST_REQUIRE_CLOSE((LMetric<1, true>::Evaluate(a, b)), 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE((LMetric<1, true>::Evaluate(b, a)), 3.0, 1e-5);
-}
-
-/***
- * Basic test of squared Euclidean distance.
- */
-BOOST_AUTO_TEST_CASE(squared_euclidean_distance) {
- // Sample 2-dimensional vectors.
- arma::vec a = "1.0 2.0";
- arma::vec b = "0.0 -2.0";
-
- BOOST_REQUIRE_CLOSE(SquaredEuclideanDistance::Evaluate(a, b), 17.0, 1e-5);
- BOOST_REQUIRE_CLOSE(SquaredEuclideanDistance::Evaluate(b, a), 17.0, 1e-5);
-}
-
-/***
- * Basic test of Euclidean distance.
- */
-BOOST_AUTO_TEST_CASE(euclidean_distance) {
- arma::vec a = "1.0 3.0 5.0 7.0";
- arma::vec b = "4.0 0.0 2.0 0.0";
-
- BOOST_REQUIRE_CLOSE(EuclideanDistance::Evaluate(a, b), sqrt(76.0), 1e-5);
- BOOST_REQUIRE_CLOSE(EuclideanDistance::Evaluate(b, a), sqrt(76.0), 1e-5);
-}
-
-/***
- * Arbitrary test case for coverage.
- */
-BOOST_AUTO_TEST_CASE(arbitrary_case) {
- arma::vec a = "3.0 5.0 6.0 7.0";
- arma::vec b = "1.0 2.0 1.0 0.0";
-
- BOOST_REQUIRE_CLOSE(LMetric<3>::Evaluate(a, b), 503.0, 1e-5);
- BOOST_REQUIRE_CLOSE(LMetric<3>::Evaluate(b, a), 503.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE((LMetric<3, true>::Evaluate(a, b)), 7.95284762, 1e-5);
- BOOST_REQUIRE_CLOSE((LMetric<3, true>::Evaluate(b, a)), 7.95284762, 1e-5);
-}
-
-/***
- * Make sure two vectors of all zeros return zero distance, for a few different
- * powers.
- */
-BOOST_AUTO_TEST_CASE(lmetric_zeros) {
- arma::vec a(250);
- a.fill(0.0);
-
- // We cannot use a loop because compilers seem to be unable to unroll the loop
- // and realize the variable actually is knowable at compile-time.
- BOOST_REQUIRE((LMetric<1, false>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<1, true>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<2, false>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<2, true>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<3, false>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<3, true>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<4, false>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<4, true>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<5, false>::Evaluate(a, a)) == 0);
- BOOST_REQUIRE((LMetric<5, true>::Evaluate(a, a)) == 0);
-}
-
-/***
- * Simple test of Mahalanobis distance with unset covariance matrix.
- */
-BOOST_AUTO_TEST_CASE(md_unset_covariance) {
- MahalanobisDistance<false> md;
- arma::vec a = "1.0 2.0 2.0 3.0";
- arma::vec b = "0.0 0.0 1.0 3.0";
-
- BOOST_REQUIRE_CLOSE(md.Evaluate(a, b), 6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(md.Evaluate(b, a), 6.0, 1e-5);
-}
-
-/***
- * Simple test of Mahalanobis distance with unset covariance matrix and
- * t_take_root set to true.
- */
-BOOST_AUTO_TEST_CASE(md_root_unset_covariance) {
- MahalanobisDistance<true> md;
- arma::vec a = "1.0 2.0 2.5 5.0";
- arma::vec b = "0.0 2.0 0.5 8.0";
-
- BOOST_REQUIRE_CLOSE(md.Evaluate(a, b), sqrt(14.0), 1e-5);
- BOOST_REQUIRE_CLOSE(md.Evaluate(b, a), sqrt(14.0), 1e-5);
-}
-
-/***
- * Simple test with diagonal covariance matrix.
- */
-BOOST_AUTO_TEST_CASE(md_diagonal_covariance) {
- arma::mat cov = arma::eye<arma::mat>(5, 5);
- cov(0, 0) = 2.0;
- cov(1, 1) = 0.5;
- cov(2, 2) = 3.0;
- cov(3, 3) = 1.0;
- cov(4, 4) = 1.5;
- MahalanobisDistance<false> md(cov);
-
- arma::vec a = "1.0 2.0 2.0 4.0 5.0";
- arma::vec b = "2.0 3.0 1.0 1.0 0.0";
-
- BOOST_REQUIRE_CLOSE(md.Evaluate(a, b), 52.0, 1e-5);
- BOOST_REQUIRE_CLOSE(md.Evaluate(b, a), 52.0, 1e-5);
-}
-
-/***
- * More specific case with more difficult covariance matrix.
- */
-BOOST_AUTO_TEST_CASE(md_full_covariance) {
- arma::mat cov = "1.0 2.0 3.0 4.0;"
- "0.5 0.6 0.7 0.1;"
- "3.4 4.3 5.0 6.1;"
- "1.0 2.0 4.0 1.0;";
- MahalanobisDistance<false> md(cov);
-
- arma::vec a = "1.0 2.0 2.0 4.0";
- arma::vec b = "2.0 3.0 1.0 1.0";
-
- BOOST_REQUIRE_CLOSE(md.Evaluate(a, b), 15.7, 1e-5);
- BOOST_REQUIRE_CLOSE(md.Evaluate(b, a), 15.7, 1e-5);
-}
-
-/***
- * Simple test case for the cosine distance.
- */
-BOOST_AUTO_TEST_CASE(cosine_distance_same_angle) {
- arma::vec a = "1.0 2.0 3.0";
- arma::vec b = "2.0 4.0 6.0";
-
- BOOST_REQUIRE_SMALL(CosineDistance::Evaluate(a, b), 1e-5);
- BOOST_REQUIRE_SMALL(CosineDistance::Evaluate(b, a), 1e-5);
-}
-
-/***
- * Now let's have them be orthogonal.
- */
-BOOST_AUTO_TEST_CASE(cosine_distance_orthogonal) {
- arma::vec a = "0.0 1.0";
- arma::vec b = "1.0 0.0";
-
- BOOST_REQUIRE_CLOSE(CosineDistance::Evaluate(a, b), 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(CosineDistance::Evaluate(b, a), 1.0, 1e-5);
-}
-
-/***
- * Some random angle test.
- */
-BOOST_AUTO_TEST_CASE(cosine_distance_random_test) {
- arma::vec a = "0.1 0.2 0.3 0.4 0.5";
- arma::vec b = "1.2 1.0 0.8 -0.3 -0.5";
-
- BOOST_REQUIRE_CLOSE(CosineDistance::Evaluate(a, b), 1 - 0.1385349024, 1e-5);
- BOOST_REQUIRE_CLOSE(CosineDistance::Evaluate(b, a), 1 - 0.1385349024, 1e-5);
-}
-
-/***
- * Linear Kernel test.
- */
-BOOST_AUTO_TEST_CASE(linear_kernel) {
- arma::vec a = ".2 .3 .4 .1";
- arma::vec b = ".56 .21 .623 .82";
-
- LinearKernel lk;
- BOOST_REQUIRE_CLOSE(lk.Evaluate(a,b), .5062, 1e-5);
- BOOST_REQUIRE_CLOSE(lk.Evaluate(b,a), .5062, 1e-5);
-}
-
-/***
- * Linear Kernel test, orthogonal vectors.
- */
-BOOST_AUTO_TEST_CASE(linear_kernel_orthogonal) {
- arma::vec a = "1 0 0";
- arma::vec b = "0 0 1";
-
- LinearKernel lk;
- BOOST_REQUIRE_SMALL(lk.Evaluate(a,b), 1e-5);
- BOOST_REQUIRE_SMALL(lk.Evaluate(b,a), 1e-5);
-}
-
-BOOST_AUTO_TEST_CASE(gaussian_kernel) {
- arma::vec a = "1 0 0";
- arma::vec b = "0 1 0";
- arma::vec c = "0 0 1";
-
- GaussianKernel gk(.5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(a,b), .018315638888734, 1e-5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(b,a), .018315638888734, 1e-5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(a,c), .018315638888734, 1e-5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(c,a), .018315638888734, 1e-5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(b,c), .018315638888734, 1e-5);
- BOOST_REQUIRE_CLOSE(gk.Evaluate(c,b), .018315638888734, 1e-5);
-}
Modified: mlpack/trunk/src/mlpack/core/math/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/math/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/math/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -16,11 +16,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-add_executable(math_test
- math_test.cpp
-)
-target_link_libraries(math_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/core/math/math_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/core/math/math_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/math/math_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,521 +0,0 @@
-/***
- * @file math_test.cpp
- * @author Ryan Curtin
- *
- * Tests for everything in the math:: namespace.
- */
-#include "range.hpp"
-#include "math_misc.hpp"
-
-#define BOOST_TEST_MODULE Math_Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace math;
-
-/***
- * Verify that the empty constructor creates an empty range.
- */
-BOOST_AUTO_TEST_CASE(RangeEmptyConstructor)
-{
- Range x = Range();
-
- // Just verify that it is empty.
- BOOST_REQUIRE_GT(x.lo, x.hi);
-}
-
-/***
- * Verify that the point constructor correctly creates a range that is just a
- * point.
- */
-BOOST_AUTO_TEST_CASE(RangePointConstructor)
-{
- Range x(10.0);
-
- BOOST_REQUIRE_CLOSE(x.lo, x.hi, 1e-25);
- BOOST_REQUIRE_SMALL(x.width(), 1e-5);
- BOOST_REQUIRE_CLOSE(x.lo, 10.0, 1e-25);
- BOOST_REQUIRE_CLOSE(x.hi, 10.0, 1e-25);
-}
-
-/***
- * Verify that the range constructor correctly creates the range.
- */
-BOOST_AUTO_TEST_CASE(RangeConstructor)
-{
- Range x(0.5, 5.5);
-
- BOOST_REQUIRE_CLOSE(x.lo, 0.5, 1e-25);
- BOOST_REQUIRE_CLOSE(x.hi, 5.5, 1e-25);
-}
-
-/***
- * Test that we get the width correct.
- */
-BOOST_AUTO_TEST_CASE(RangeWidth)
-{
- Range x(0.0, 10.0);
-
- BOOST_REQUIRE_CLOSE(x.width(), 10.0, 1e-20);
-
- // Make it empty.
- x.hi = 0.0;
-
- BOOST_REQUIRE_SMALL(x.width(), 1e-5);
-
- // Make it negative.
- x.hi = -2.0;
-
- BOOST_REQUIRE_SMALL(x.width(), 1e-5);
-
- // Just one more test.
- x.lo = -5.2;
- x.hi = 5.2;
-
- BOOST_REQUIRE_CLOSE(x.width(), 10.4, 1e-5);
-}
-
-/**
- * Test that we get the midpoint correct.
- */
-BOOST_AUTO_TEST_CASE(RangeMidpoint)
-{
- Range x(0.0, 10.0);
-
- BOOST_REQUIRE_CLOSE(x.mid(), 5.0, 1e-5);
-
- x.lo = -5.0;
-
- BOOST_REQUIRE_CLOSE(x.mid(), 2.5, 1e-5);
-}
-
-/***
- * Test that we can expand to include other ranges correctly.
- */
-BOOST_AUTO_TEST_CASE(RangeIncludeOther)
-{
- // We need to test both |= and |.
- // We have three cases: non-overlapping; overlapping; equivalent, and then a
- // couple permutations (switch left with right and make sure it still works).
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- Range z(0.0, 2.0); // Used for operator|=().
- Range w;
- z |= y;
- w = x | y;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 5.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 5.0, 1e-5);
-
- // Switch operator precedence.
- z = y;
- z |= x;
- w = y | x;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 5.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 5.0, 1e-5);
-
- // Now make them overlapping.
- x = Range(0.0, 3.5);
- y = Range(3.0, 4.0);
-
- z = x;
- z |= y;
- w = x | y;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 4.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 4.0, 1e-5);
-
- // Switch operator precedence.
- z = y;
- z |= x;
- w = y | x;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 4.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 4.0, 1e-5);
-
- // Now the equivalent case.
- x = Range(0.0, 2.0);
- y = Range(0.0, 2.0);
-
- z = x;
- z |= y;
- w = x | y;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 2.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 2.0, 1e-5);
-
- z = y;
- z |= x;
- w = y | x;
-
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 2.0, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 2.0, 1e-5);
-}
-
-/***
- * Test that we can 'and' ranges correctly.
- */
-BOOST_AUTO_TEST_CASE(RangeIntersectOther)
-{
- // We need to test both &= and &.
- // We have three cases: non-overlapping, overlapping; equivalent, and then a
- // couple permutations (switch left with right and make sure it still works).
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- Range z(0.0, 2.0);
- Range w;
- z &= y;
- w = x & y;
-
- BOOST_REQUIRE_SMALL(z.width(), 1e-5);
- BOOST_REQUIRE_SMALL(w.width(), 1e-5);
-
- // Reverse operator precedence.
- z = y;
- z &= x;
- w = y & x;
-
- BOOST_REQUIRE_SMALL(z.width(), 1e-5);
- BOOST_REQUIRE_SMALL(w.width(), 1e-5);
-
- // Now make them overlapping.
- x = Range(0.0, 3.5);
- y = Range(3.0, 4.0);
-
- z = x;
- z &= y;
- w = x & y;
-
- BOOST_REQUIRE_CLOSE(z.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 3.5, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 3.5, 1e-5);
-
- // Reverse operator precedence.
- z = y;
- z &= x;
- w = y & x;
-
- BOOST_REQUIRE_CLOSE(z.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 3.5, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 3.5, 1e-5);
-
- // Now make them equivalent.
- x = Range(2.0, 4.0);
- y = Range(2.0, 4.0);
-
- z = x;
- z &= y;
- w = x & y;
-
- BOOST_REQUIRE_CLOSE(z.lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 4.0, 1e-5);
-}
-
-/**
- * Test multiplication of a range with a double.
- */
-BOOST_AUTO_TEST_CASE(RangeMultiply)
-{
- // We need to test both * and *=, as well as both cases of *.
- // We'll try with a couple of numbers: -1, 0, 2.
- // And we'll have a couple of cases for bounds: strictly less than zero;
- // including zero; and strictly greater than zero.
- //
- // So, nine total cases.
- Range x(-5.0, -3.0);
- Range y(-5.0, -3.0);
- Range z;
- Range w;
-
- y *= -1.0;
- z = x * -1.0;
- w = -1.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 5.0, 1e-5);
-
- y = x;
- y *= 0.0;
- z = x * 0.0;
- w = 0.0 * x;
-
- BOOST_REQUIRE_SMALL(y.lo, 1e-5);
- BOOST_REQUIRE_SMALL(y.hi, 1e-5);
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_SMALL(z.hi, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_SMALL(w.hi, 1e-5);
-
- y = x;
- y *= 2.0;
- z = x * 2.0;
- w = 2.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, -10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, -6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, -10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, -6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, -10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, -6.0, 1e-5);
-
- x = Range(-2.0, 2.0);
- y = x;
-
- y *= -1.0;
- z = x * -1.0;
- w = -1.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, -2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, -2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, -2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 2.0, 1e-5);
-
- y = x;
- y *= 0.0;
- z = x * 0.0;
- w = 0.0 * x;
-
- BOOST_REQUIRE_SMALL(y.lo, 1e-5);
- BOOST_REQUIRE_SMALL(y.hi, 1e-5);
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_SMALL(z.hi, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_SMALL(w.hi, 1e-5);
-
- y = x;
- y *= 2.0;
- z = x * 2.0;
- w = 2.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, -4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, -4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, -4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 4.0, 1e-5);
-
- x = Range(3.0, 5.0);
-
- y = x;
- y *= -1.0;
- z = x * -1.0;
- w = -1.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, -5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, -5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, -5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, -3.0, 1e-5);
-
- y = x;
- y *= 0.0;
- z = x * 0.0;
- w = 0.0 * x;
-
- BOOST_REQUIRE_SMALL(y.lo, 1e-5);
- BOOST_REQUIRE_SMALL(y.hi, 1e-5);
- BOOST_REQUIRE_SMALL(z.lo, 1e-5);
- BOOST_REQUIRE_SMALL(z.hi, 1e-5);
- BOOST_REQUIRE_SMALL(w.lo, 1e-5);
- BOOST_REQUIRE_SMALL(w.hi, 1e-5);
-
- y = x;
- y *= 2.0;
- z = x * 2.0;
- w = 2.0 * x;
-
- BOOST_REQUIRE_CLOSE(y.lo, 6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(y.hi, 10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.lo, 6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(z.hi, 10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.lo, 6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(w.hi, 10.0, 1e-5);
-}
-
-/**
- * Test equality operator.
- */
-BOOST_AUTO_TEST_CASE(RangeEquality)
-{
- // Three cases: non-overlapping, overlapping, equivalent. We should also
- // consider empty ranges, which are not necessarily equal...
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- // These are odd calls, but we don't want to use operator!= here.
- BOOST_REQUIRE_EQUAL((x == y), false);
- BOOST_REQUIRE_EQUAL((y == x), false);
-
- y = Range(1.0, 3.0);
-
- BOOST_REQUIRE_EQUAL((x == y), false);
- BOOST_REQUIRE_EQUAL((y == x), false);
-
- y = Range(0.0, 2.0);
-
- BOOST_REQUIRE_EQUAL((x == y), true);
- BOOST_REQUIRE_EQUAL((y == x), true);
-
- x = Range(1.0, -1.0); // Empty.
- y = Range(1.0, -1.0); // Also empty.
-
- BOOST_REQUIRE_EQUAL((x == y), true);
- BOOST_REQUIRE_EQUAL((y == x), true);
-
- // No need to test what it does if the empty ranges are different "ranges"
- // because we are not forcing behavior for that.
-}
-
-/**
- * Test inequality operator.
- */
-BOOST_AUTO_TEST_CASE(RangeInequality)
-{
- // We will use the same three cases as the RangeEquality test.
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- // Again, odd calls, but we want to force use of operator!=.
- BOOST_REQUIRE_EQUAL((x != y), true);
- BOOST_REQUIRE_EQUAL((y != x), true);
-
- y = Range(1.0, 3.0);
-
- BOOST_REQUIRE_EQUAL((x != y), true);
- BOOST_REQUIRE_EQUAL((y != x), true);
-
- y = Range(0.0, 2.0);
-
- BOOST_REQUIRE_EQUAL((x != y), false);
- BOOST_REQUIRE_EQUAL((y != x), false);
-
- x = Range(1.0, -1.0); // Empty.
- y = Range(1.0, -1.0); // Also empty.
-
- BOOST_REQUIRE_EQUAL((x != y), false);
- BOOST_REQUIRE_EQUAL((y != x), false);
-}
-
-/**
- * Test strict less-than operator.
- */
-BOOST_AUTO_TEST_CASE(RangeStrictLessThan)
-{
- // Three cases: non-overlapping, overlapping, and equivalent.
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- BOOST_REQUIRE_EQUAL((x < y), true);
- BOOST_REQUIRE_EQUAL((y < x), false);
-
- y = Range(1.0, 3.0);
-
- BOOST_REQUIRE_EQUAL((x < y), false);
- BOOST_REQUIRE_EQUAL((y < x), false);
-
- y = Range(0.0, 2.0);
-
- BOOST_REQUIRE_EQUAL((x < y), false);
- BOOST_REQUIRE_EQUAL((y < x), false);
-}
-
-/**
- * Test strict greater-than operator.
- */
-BOOST_AUTO_TEST_CASE(RangeStrictGreaterThan)
-{
- // Three cases: non-overlapping, overlapping, and equivalent.
- Range x(0.0, 2.0);
- Range y(3.0, 5.0);
-
- BOOST_REQUIRE_EQUAL((x > y), false);
- BOOST_REQUIRE_EQUAL((y > x), true);
-
- y = Range(1.0, 3.0);
-
- BOOST_REQUIRE_EQUAL((x > y), false);
- BOOST_REQUIRE_EQUAL((y > x), false);
-
- y = Range(0.0, 2.0);
-
- BOOST_REQUIRE_EQUAL((x > y), false);
- BOOST_REQUIRE_EQUAL((y > x), false);
-}
-
-/**
- * Test the Contains() operator.
- */
-BOOST_AUTO_TEST_CASE(RangeContains)
-{
- // We have three Range cases: strictly less than 0; overlapping 0; and
- // strictly greater than 0. Then the numbers we check can be the same three
- // cases, including one greater than and one less than the range. This should
- // be about 15 total cases.
- Range x(-2.0, -1.0);
-
- BOOST_REQUIRE(!x.Contains(-3.0));
- BOOST_REQUIRE(x.Contains(-2.0));
- BOOST_REQUIRE(x.Contains(-1.5));
- BOOST_REQUIRE(x.Contains(-1.0));
- BOOST_REQUIRE(!x.Contains(-0.5));
- BOOST_REQUIRE(!x.Contains(0.0));
- BOOST_REQUIRE(!x.Contains(1.0));
-
- x = Range(-1.0, 1.0);
-
- BOOST_REQUIRE(!x.Contains(-2.0));
- BOOST_REQUIRE(x.Contains(-1.0));
- BOOST_REQUIRE(x.Contains(0.0));
- BOOST_REQUIRE(x.Contains(1.0));
- BOOST_REQUIRE(!x.Contains(2.0));
-
- x = Range(1.0, 2.0);
-
- BOOST_REQUIRE(!x.Contains(-1.0));
- BOOST_REQUIRE(!x.Contains(0.0));
- BOOST_REQUIRE(!x.Contains(0.5));
- BOOST_REQUIRE(x.Contains(1.0));
- BOOST_REQUIRE(x.Contains(1.5));
- BOOST_REQUIRE(x.Contains(2.0));
- BOOST_REQUIRE(!x.Contains(2.5));
-
- // Now let's try it on an empty range.
- x = Range();
-
- BOOST_REQUIRE(!x.Contains(-10.0));
- BOOST_REQUIRE(!x.Contains(0.0));
- BOOST_REQUIRE(!x.Contains(10.0));
-
- // And an infinite range.
- x = Range(-DBL_MAX, DBL_MAX);
-
- BOOST_REQUIRE(x.Contains(-10.0));
- BOOST_REQUIRE(x.Contains(0.0));
- BOOST_REQUIRE(x.Contains(10.0));
-}
Modified: mlpack/trunk/src/mlpack/core/tree/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/tree/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/tree/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -23,13 +23,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-# test executable
-add_executable(tree_test
- tree_test.cpp
-)
-# link dependencies of test executable
-target_link_libraries(tree_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/core/tree/tree_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/core/tree/tree_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/tree/tree_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,1037 +0,0 @@
-/**
- * @file uselapack_test.cc
- *
- * Tests for LAPACK integration.
- */
-
-#include "bounds.hpp"
-#include "binary_space_tree.hpp"
-#include <mlpack/core/kernels/lmetric.hpp>
-#include <vector>
-
-#define BOOST_TEST_MODULE Tree_Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::math;
-using namespace mlpack::tree;
-using namespace mlpack::kernel;
-using namespace mlpack::bound;
-
-/***
- * Ensure that a bound, by default, is empty and has no dimensionality.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundEmptyConstructor) {
- HRectBound<2> b;
-
- BOOST_REQUIRE_EQUAL(b.dim(), 0);
-}
-
-/***
- * Ensure that when we specify the dimensionality in the constructor, it is
- * correct, and the bounds are all the empty set.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundDimConstructor) {
- HRectBound<2> b(2); // We'll do this with 2 and 5 dimensions.
-
- BOOST_REQUIRE_EQUAL(b.dim(), 2);
- BOOST_REQUIRE_SMALL(b[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[1].width(), 1e-5);
-
- b = HRectBound<2>(5);
-
- BOOST_REQUIRE_EQUAL(b.dim(), 5);
- BOOST_REQUIRE_SMALL(b[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[1].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[2].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[3].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[4].width(), 1e-5);
-}
-
-/***
- * Test the copy constructor.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundCopyConstructor) {
- HRectBound<2> b(2);
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 3.0);
-
- HRectBound<2> c(b);
-
- BOOST_REQUIRE_EQUAL(c.dim(), 2);
- BOOST_REQUIRE_SMALL(c[0].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(c[0].hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].hi, 3.0, 1e-5);
-}
-
-/***
- * Test the assignment operator.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundAssignmentOperator) {
- HRectBound<2> b(2);
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 3.0);
-
- HRectBound<2> c(4);
-
- c = b;
-
- BOOST_REQUIRE_EQUAL(c.dim(), 2);
- BOOST_REQUIRE_SMALL(c[0].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(c[0].hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].hi, 3.0, 1e-5);
-}
-
-/***
- * Test that clearing the dimensions resets the bound to empty.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundClear) {
- HRectBound<2> b(2); // We'll do this with two dimensions only.
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 4.0);
-
- // Now we just need to make sure that we clear the range.
- b.Clear();
-
- BOOST_REQUIRE_SMALL(b[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[1].width(), 1e-5);
-}
-
-/***
- * Ensure that we get the correct centroid for our bound.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundCentroid) {
- // Create a simple 3-dimensional bound.
- HRectBound<2> b(3);
-
- b[0] = Range(0.0, 5.0);
- b[1] = Range(-2.0, -1.0);
- b[2] = Range(-10.0, 50.0);
-
- arma::vec centroid;
-
- b.Centroid(centroid);
-
- BOOST_REQUIRE_EQUAL(centroid.n_elem, 3);
- BOOST_REQUIRE_CLOSE(centroid[0], 2.5, 1e-5);
- BOOST_REQUIRE_CLOSE(centroid[1], -1.5, 1e-5);
- BOOST_REQUIRE_CLOSE(centroid[2], 20.0, 1e-5);
-}
-
-/***
- * Ensure that we calculate the correct minimum distance between a point and a
- * bound.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundMinDistancePoint) {
- // We'll do the calculation in five dimensions, and we'll use three cases for
- // the point: point is outside the bound; point is on the edge of the bound;
- // point is inside the bound. In the latter two cases, the distance should be
- // zero.
- HRectBound<2> b(5);
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(1.0, 5.0);
- b[2] = Range(-2.0, 2.0);
- b[3] = Range(-5.0, -2.0);
- b[4] = Range(1.0, 2.0);
-
- arma::vec point = "-2.0 0.0 10.0 3.0 3.0";
-
- // This will be the Euclidean squared distance.
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 95.0, 1e-5);
-
- point = "2.0 5.0 2.0 -5.0 1.0";
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- point = "1.0 2.0 0.0 -2.0 1.5";
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-}
-
-/***
- * Ensure that we calculate the correct minimum distance between a bound and
- * another bound.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundMinDistanceBound) {
- // We'll do the calculation in five dimensions, and we can use six cases.
- // The other bound is completely outside the bound; the other bound is on the
- // edge of the bound; the other bound partially overlaps the bound; the other
- // bound fully overlaps the bound; the other bound is entirely inside the
- // bound; the other bound entirely envelops the bound.
- HRectBound<2> b(5);
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(1.0, 5.0);
- b[2] = Range(-2.0, 2.0);
- b[3] = Range(-5.0, -2.0);
- b[4] = Range(1.0, 2.0);
-
- HRectBound<2> c(5);
-
- // The other bound is completely outside the bound.
- c[0] = Range(-5.0, -2.0);
- c[1] = Range(6.0, 7.0);
- c[2] = Range(-2.0, 2.0);
- c[3] = Range(2.0, 5.0);
- c[4] = Range(3.0, 4.0);
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(c), 22.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MinDistance(b), 22.0, 1e-5);
-
- // The other bound is on the edge of the bound.
- c[0] = Range(-2.0, 0.0);
- c[1] = Range(0.0, 1.0);
- c[2] = Range(-3.0, -2.0);
- c[3] = Range(-10.0, -5.0);
- c[4] = Range(2.0, 3.0);
-
- BOOST_REQUIRE_SMALL(b.MinDistance(c), 1e-5);
- BOOST_REQUIRE_SMALL(c.MinDistance(b), 1e-5);
-
- // The other bound partially overlaps the bound.
- c[0] = Range(-2.0, 1.0);
- c[1] = Range(0.0, 2.0);
- c[2] = Range(-2.0, 2.0);
- c[3] = Range(-8.0, -4.0);
- c[4] = Range(0.0, 4.0);
-
- BOOST_REQUIRE_SMALL(b.MinDistance(c), 1e-5);
- BOOST_REQUIRE_SMALL(c.MinDistance(b), 1e-5);
-
- // The other bound fully overlaps the bound.
- BOOST_REQUIRE_SMALL(b.MinDistance(b), 1e-5);
- BOOST_REQUIRE_SMALL(c.MinDistance(c), 1e-5);
-
- // The other bound is entirely inside the bound / the other bound entirely
- // envelops the bound.
- c[0] = Range(-1.0, 3.0);
- c[1] = Range(0.0, 6.0);
- c[2] = Range(-3.0, 3.0);
- c[3] = Range(-7.0, 0.0);
- c[4] = Range(0.0, 5.0);
-
- BOOST_REQUIRE_SMALL(b.MinDistance(c), 1e-5);
- BOOST_REQUIRE_SMALL(c.MinDistance(b), 1e-5);
-}
-
-/***
- * Ensure that we calculate the correct maximum distance between a bound and a
- * point. This uses the same test cases as the MinDistance test.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundMaxDistancePoint) {
- // We'll do the calculation in five dimensions, and we'll use three cases for
- // the point: point is outside the bound; point is on the edge of the bound;
- // point is inside the bound. In the latter two cases, the distance should be
- // zero.
- HRectBound<2> b(5);
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(1.0, 5.0);
- b[2] = Range(-2.0, 2.0);
- b[3] = Range(-5.0, -2.0);
- b[4] = Range(1.0, 2.0);
-
- arma::vec point = "-2.0 0.0 10.0 3.0 3.0";
-
- // This will be the Euclidean squared distance.
- BOOST_REQUIRE_CLOSE(b.MaxDistance(point), 253.0, 1e-5);
-
- point = "2.0 5.0 2.0 -5.0 1.0";
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(point), 46.0, 1e-5);
-
- point = "1.0 2.0 0.0 -2.0 1.5";
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(point), 23.25, 1e-5);
-}
-
-/***
- * Ensure that we calculate the correct maximum distance between a bound and
- * another bound. This uses the same test cases as the MinDistance test.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundMaxDistanceBound) {
- // We'll do the calculation in five dimensions, and we can use six cases.
- // The other bound is completely outside the bound; the other bound is on the
- // edge of the bound; the other bound partially overlaps the bound; the other
- // bound fully overlaps the bound; the other bound is entirely inside the
- // bound; the other bound entirely envelops the bound.
- HRectBound<2> b(5);
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(1.0, 5.0);
- b[2] = Range(-2.0, 2.0);
- b[3] = Range(-5.0, -2.0);
- b[4] = Range(1.0, 2.0);
-
- HRectBound<2> c(5);
-
- // The other bound is completely outside the bound.
- c[0] = Range(-5.0, -2.0);
- c[1] = Range(6.0, 7.0);
- c[2] = Range(-2.0, 2.0);
- c[3] = Range(2.0, 5.0);
- c[4] = Range(3.0, 4.0);
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(c), 210.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MaxDistance(b), 210.0, 1e-5);
-
- // The other bound is on the edge of the bound.
- c[0] = Range(-2.0, 0.0);
- c[1] = Range(0.0, 1.0);
- c[2] = Range(-3.0, -2.0);
- c[3] = Range(-10.0, -5.0);
- c[4] = Range(2.0, 3.0);
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(c), 134.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MaxDistance(b), 134.0, 1e-5);
-
- // The other bound partially overlaps the bound.
- c[0] = Range(-2.0, 1.0);
- c[1] = Range(0.0, 2.0);
- c[2] = Range(-2.0, 2.0);
- c[3] = Range(-8.0, -4.0);
- c[4] = Range(0.0, 4.0);
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(c), 102.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MaxDistance(b), 102.0, 1e-5);
-
- // The other bound fully overlaps the bound.
- BOOST_REQUIRE_CLOSE(b.MaxDistance(b), 46.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MaxDistance(c), 61.0, 1e-5);
-
- // The other bound is entirely inside the bound / the other bound entirely
- // envelops the bound.
- c[0] = Range(-1.0, 3.0);
- c[1] = Range(0.0, 6.0);
- c[2] = Range(-3.0, 3.0);
- c[3] = Range(-7.0, 0.0);
- c[4] = Range(0.0, 5.0);
-
- BOOST_REQUIRE_CLOSE(b.MaxDistance(c), 100.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.MaxDistance(b), 100.0, 1e-5);
-
- // One last additional case. If the bound encloses only one point, the
- // maximum distance between it and itself is 0.
- HRectBound<2> d(2);
-
- d[0] = Range(2.0, 2.0);
- d[1] = Range(3.0, 3.0);
-
- BOOST_REQUIRE_SMALL(d.MaxDistance(d), 1e-5);
-}
-
-/***
- * Ensure that the ranges returned by RangeDistance() are equal to the minimum
- * and maximum distance. We will perform this test by creating random bounds
- * and comparing the behavior to MinDistance() and MaxDistance() -- so this test
- * is assuming that those passed and operate correctly.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundRangeDistanceBound) {
- srand(time(NULL));
-
- for (int i = 0; i < 50; i++) {
- size_t dim = rand() % 20;
-
- HRectBound<2> a(dim);
- HRectBound<2> b(dim);
-
- // We will set the low randomly and the width randomly for each dimension of
- // each bound.
- arma::vec lo_a(dim);
- arma::vec width_a(dim);
-
- lo_a.randu();
- width_a.randu();
-
- arma::vec lo_b(dim);
- arma::vec width_b(dim);
-
- lo_b.randu();
- width_b.randu();
-
- for (size_t j = 0; j < dim; j++) {
- a[j] = Range(lo_a[j], lo_a[j] + width_a[j]);
- b[j] = Range(lo_b[j], lo_b[j] + width_b[j]);
- }
-
- // Now ensure that MinDistance and MaxDistance report the same.
- Range r = a.RangeDistance(b);
- Range s = b.RangeDistance(a);
-
- BOOST_REQUIRE_CLOSE(r.lo, s.lo, 1e-5);
- BOOST_REQUIRE_CLOSE(r.hi, s.hi, 1e-5);
-
- BOOST_REQUIRE_CLOSE(r.lo, a.MinDistance(b), 1e-5);
- BOOST_REQUIRE_CLOSE(r.hi, a.MaxDistance(b), 1e-5);
-
- BOOST_REQUIRE_CLOSE(s.lo, b.MinDistance(a), 1e-5);
- BOOST_REQUIRE_CLOSE(s.hi, b.MaxDistance(a), 1e-5);
- }
-}
-
-/***
- * Ensure that the ranges returned by RangeDistance() are equal to the minimum
- * and maximum distance. We will perform this test by creating random bounds
- * and comparing the bheavior to MinDistance() and MaxDistance() -- so this test
- * is assuming that those passed and operate correctly. This is for the
- * bound-to-point case.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundRangeDistancePoint) {
- srand(time(NULL));
-
- for (int i = 0; i < 20; i++) {
- size_t dim = rand() % 20;
-
- HRectBound<2> a(dim);
-
- // We will set the low randomly and the width randomly for each dimension of
- // each bound.
- arma::vec lo_a(dim);
- arma::vec width_a(dim);
-
- lo_a.randu();
- width_a.randu();
-
- for (size_t j = 0; j < dim; j++)
- a[j] = Range(lo_a[j], lo_a[j] + width_a[j]);
-
- // Now run the test on a few points.
- for (int j = 0; j < 10; j++) {
- arma::vec point(dim);
-
- point.randu();
-
- Range r = a.RangeDistance(point);
-
- BOOST_REQUIRE_CLOSE(r.lo, a.MinDistance(point), 1e-5);
- BOOST_REQUIRE_CLOSE(r.hi, a.MaxDistance(point), 1e-5);
- }
- }
-}
-
-/***
- * Test that we can expand the bound to include a new point.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundOrOperatorPoint) {
- // Because this should be independent in each dimension, we can essentially
- // run five test cases at once.
- HRectBound<2> b(5);
-
- b[0] = Range(1.0, 3.0);
- b[1] = Range(2.0, 4.0);
- b[2] = Range(-2.0, -1.0);
- b[3] = Range(0.0, 0.0);
- b[4] = Range(); // Empty range.
-
- arma::vec point = "2.0 4.0 2.0 -1.0 6.0";
-
- b |= point;
-
- BOOST_REQUIRE_CLOSE(b[0].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[0].hi, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[1].lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[1].hi, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[2].lo, -2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[2].hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[3].lo, -1.0, 1e-5);
- BOOST_REQUIRE_SMALL(b[3].hi, 1e-5);
- BOOST_REQUIRE_CLOSE(b[4].lo, 6.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[4].hi, 6.0, 1e-5);
-}
-
-/***
- * Test that we can expand the bound to include another bound.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundOrOperatorBound) {
- // Because this should be independent in each dimension, we can run many tests
- // at once.
- HRectBound<2> b(8);
-
- b[0] = Range(1.0, 3.0);
- b[1] = Range(2.0, 4.0);
- b[2] = Range(-2.0, -1.0);
- b[3] = Range(4.0, 5.0);
- b[4] = Range(2.0, 4.0);
- b[5] = Range(0.0, 0.0);
- b[6] = Range();
- b[7] = Range(1.0, 3.0);
-
- HRectBound<2> c(8);
-
- c[0] = Range(-3.0, -1.0); // Entirely less than the other bound.
- c[1] = Range(0.0, 2.0); // Touching edges.
- c[2] = Range(-3.0, -1.5); // Partially overlapping.
- c[3] = Range(4.0, 5.0); // Identical.
- c[4] = Range(1.0, 5.0); // Entirely enclosing.
- c[5] = Range(2.0, 2.0); // A single point.
- c[6] = Range(1.0, 3.0);
- c[7] = Range(); // Empty set.
-
- HRectBound<2> d = c;
-
- b |= c;
- d |= b;
-
- BOOST_REQUIRE_CLOSE(b[0].lo, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[0].hi, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[0].lo, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[0].hi, 3.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[1].lo, 0.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[1].hi, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[1].lo, 0.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[1].hi, 4.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[2].lo, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[2].hi, -1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[2].lo, -3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[2].hi, -1.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[3].lo, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[3].hi, 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[3].lo, 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[3].hi, 5.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[4].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[4].hi, 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[4].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[4].hi, 5.0, 1e-5);
-
- BOOST_REQUIRE_SMALL(b[5].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(b[5].hi, 2.0, 1e-5);
- BOOST_REQUIRE_SMALL(d[5].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(d[5].hi, 2.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[6].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[6].hi, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[6].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[6].hi, 3.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(b[7].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b[7].hi, 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[7].lo, 1.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d[7].hi, 3.0, 1e-5);
-}
-
-/***
- * Test that the Contains() function correctly figures out whether or not a
- * point is in a bound.
- */
-BOOST_AUTO_TEST_CASE(HRectBoundContains) {
- // We can test a couple different points: completely outside the bound,
- // adjacent in one dimension to the bound, adjacent in all dimensions to the
- // bound, and inside the bound.
- HRectBound<2> b(3);
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(0.0, 2.0);
- b[2] = Range(0.0, 2.0);
-
- // Completely outside the range.
- arma::vec point = "-1.0 4.0 4.0";
- BOOST_REQUIRE(!b.Contains(point));
-
- // Completely outside, but one dimension is in the range.
- point = "-1.0 4.0 1.0";
- BOOST_REQUIRE(!b.Contains(point));
-
- // Outside, but one dimension is on the edge.
- point = "-1.0 0.0 3.0";
- BOOST_REQUIRE(!b.Contains(point));
-
- // Two dimensions are on the edge, but one is outside.
- point = "0.0 0.0 3.0";
- BOOST_REQUIRE(!b.Contains(point));
-
- // Completely on the edge (should be contained).
- point = "0.0 0.0 0.0";
- BOOST_REQUIRE(b.Contains(point));
-
- // Inside the range.
- point = "0.3 1.0 0.4";
- BOOST_REQUIRE(b.Contains(point));
-}
-
-BOOST_AUTO_TEST_CASE(TestBallBound) {
- DBallBound<> b1;
- DBallBound<> b2;
-
- // Create two balls with a center distance of 1 from each other.
- // Give the first one a radius of 0.3 and the second a radius of 0.4.
-
- b1.center().set_size(3);
- b1.center()[0] = 1;
- b1.center()[1] = 2;
- b1.center()[2] = 3;
- b1.set_radius(0.3);
-
- b2.center().set_size(3);
- b2.center()[0] = 1;
- b2.center()[1] = 2;
- b2.center()[2] = 4;
- b2.set_radius(0.4);
-
- BOOST_REQUIRE_CLOSE(sqrt(b1.MinDistanceSq(b2)), 1-0.3-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.RangeDistanceSq(b2).hi), 1+0.3+0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.RangeDistanceSq(b2).lo), 1-0.3-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(b1.RangeDistance(b2).hi, 1+0.3+0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(b1.RangeDistance(b2).lo, 1-0.3-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.MinToMidSq(b2)), 1-0.3, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.MinimaxDistanceSq(b2)), 1-0.3+0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.MidDistanceSq(b2)), 1.0, 1e-5);
-
- BOOST_REQUIRE_CLOSE(sqrt(b2.MinDistanceSq(b1)), 1-0.3-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MaxDistanceSq(b1)), 1+0.3+0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.RangeDistanceSq(b1).hi), 1+0.3+0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.RangeDistanceSq(b1).lo), 1-0.3-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MinToMidSq(b1)), 1-0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MinimaxDistanceSq(b1)), 1-0.4+0.3, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MidDistanceSq(b1)), 1.0, 1e-5);
-
- BOOST_REQUIRE(b1.Contains(b1.center()));
- BOOST_REQUIRE(!b1.Contains(b2.center()));
-
- BOOST_REQUIRE(!b2.Contains(b1.center()));
- BOOST_REQUIRE(b2.Contains(b2.center()));
- arma::vec b2point(3); // a point that's within the radius bot not the center
- b2point[0] = 1.1;
- b2point[1] = 2.1;
- b2point[2] = 4.1;
-
- BOOST_REQUIRE(b2.Contains(b2point));
-
- BOOST_REQUIRE_SMALL(sqrt(b1.MinDistanceSq(b1.center())), 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.MinDistanceSq(b2.center())), 1 - 0.3, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MinDistanceSq(b1.center())), 1 - 0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b2.MaxDistanceSq(b1.center())), 1 + 0.4, 1e-5);
- BOOST_REQUIRE_CLOSE(sqrt(b1.MaxDistanceSq(b2.center())), 1 + 0.3, 1e-5);
-}
-
-/***
- * Ensure that a bound, by default, is empty and has no dimensionality, and the
- * box size vector is empty.
- */
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundEmptyConstructor) {
- PeriodicHRectBound<2> b;
-
- BOOST_REQUIRE_EQUAL(b.dim(), 0);
- BOOST_REQUIRE_EQUAL(b.box().n_elem, 0);
-}
-
-/***
- * Ensure that when we specify the dimensionality in the constructor, it is
- * correct, and the bounds are all the empty set.
- */
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundBoxConstructor) {
- PeriodicHRectBound<2> b(arma::vec("5 6"));
-
- BOOST_REQUIRE_EQUAL(b.dim(), 2);
- BOOST_REQUIRE_SMALL(b[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[1].width(), 1e-5);
- BOOST_REQUIRE_EQUAL(b.box().n_elem, 2);
- BOOST_REQUIRE_CLOSE(b.box()[0], 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b.box()[1], 6.0, 1e-5);
-
- PeriodicHRectBound<2> d(arma::vec("2 3 4 5 6"));
-
- BOOST_REQUIRE_EQUAL(d.dim(), 5);
- BOOST_REQUIRE_SMALL(d[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(d[1].width(), 1e-5);
- BOOST_REQUIRE_SMALL(d[2].width(), 1e-5);
- BOOST_REQUIRE_SMALL(d[3].width(), 1e-5);
- BOOST_REQUIRE_SMALL(d[4].width(), 1e-5);
- BOOST_REQUIRE_EQUAL(d.box().n_elem, 5);
- BOOST_REQUIRE_CLOSE(d.box()[0], 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d.box()[1], 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d.box()[2], 4.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d.box()[3], 5.0, 1e-5);
- BOOST_REQUIRE_CLOSE(d.box()[4], 6.0, 1e-5);
-}
-
-/***
- * Test the copy constructor.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundCopyConstructor) {
- PeriodicHRectBound<2> b(arma::vec("3 4"));
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 3.0);
-
- PeriodicHRectBound<2> c(b);
-
- BOOST_REQUIRE_EQUAL(c.dim(), 2);
- BOOST_REQUIRE_SMALL(c[0].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(c[0].hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].hi, 3.0, 1e-5);
- BOOST_REQUIRE_EQUAL(c.box().n_elem, 2);
- BOOST_REQUIRE_CLOSE(c.box()[0], 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.box()[1], 4.0, 1e-5);
-}*/
-
-/***
- * Test the assignment operator.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundAssignmentOperator) {
- PeriodicHRectBound<2> b(arma::vec("3 4"));
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 3.0);
-
- PeriodicHRectBound<2> c(arma::vec("3 4 5 6"));
-
- c = b;
-
- BOOST_REQUIRE_EQUAL(c.dim(), 2);
- BOOST_REQUIRE_SMALL(c[0].lo, 1e-5);
- BOOST_REQUIRE_CLOSE(c[0].hi, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].lo, 2.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c[1].hi, 3.0, 1e-5);
- BOOST_REQUIRE_EQUAL(c.box().n_elem, 2);
- BOOST_REQUIRE_CLOSE(c.box()[0], 3.0, 1e-5);
- BOOST_REQUIRE_CLOSE(c.box()[1], 4.0, 1e-5);
-}*/
-
-/***
- * Ensure that we can set the box size correctly.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundSetBoxSize) {
- PeriodicHRectBound<2> b(arma::vec("1 2"));
-
- b.SetBoxSize(arma::vec("10 12"));
-
- BOOST_REQUIRE_EQUAL(b.box().n_elem, 2);
- BOOST_REQUIRE_CLOSE(b.box()[0], 10.0, 1e-5);
- BOOST_REQUIRE_CLOSE(b.box()[1], 12.0, 1e-5);
-}*/
-
-/***
- * Ensure that we can clear the dimensions correctly. This does not involve the
- * box size at all, so the test can be identical to the HRectBound test.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundClear) {
- // We'll do this with two dimensions only.
- PeriodicHRectBound<2> b(arma::vec("5 5"));
-
- b[0] = Range(0.0, 2.0);
- b[1] = Range(2.0, 4.0);
-
- // Now we just need to make sure that we clear the range.
- b.Clear();
-
- BOOST_REQUIRE_SMALL(b[0].width(), 1e-5);
- BOOST_REQUIRE_SMALL(b[1].width(), 1e-5);
-}*/
-
-/***
- * Ensure that we get the correct centroid for our bound.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundCentroid) {
- // Create a simple 3-dimensional bound. The centroid is not affected by the
- // periodic coordinates.
- PeriodicHRectBound<2> b(arma::vec("100 100 100"));
-
- b[0] = Range(0.0, 5.0);
- b[1] = Range(-2.0, -1.0);
- b[2] = Range(-10.0, 50.0);
-
- arma::vec centroid;
-
- b.Centroid(centroid);
-
- BOOST_REQUIRE_EQUAL(centroid.n_elem, 3);
- BOOST_REQUIRE_CLOSE(centroid[0], 2.5, 1e-5);
- BOOST_REQUIRE_CLOSE(centroid[1], -1.5, 1e-5);
- BOOST_REQUIRE_CLOSE(centroid[2], 20.0, 1e-5);
-}*/
-
-/***
- * Correctly calculate the minimum distance between the bound and a point in
- * periodic coordinates. We have to account for the shifts necessary in
- * periodic coordinates too, so that makes testing this a little more difficult.
- *
-BOOST_AUTO_TEST_CASE(PeriodicHRectBoundMinDistancePoint) {
- // First, we'll start with a simple 2-dimensional case where the point is
- // inside the bound, then on the edge of the bound, then barely outside the
- // bound. The box size will be large enough that this is basically the
- // HRectBound case.
- PeriodicHRectBound<2> b(arma::vec("100 100"));
-
- b[0] = Range(0.0, 5.0);
- b[1] = Range(2.0, 4.0);
-
- // Inside the bound.
- arma::vec point = "2.5 3.0";
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- // On the edge.
- point = "5.0 4.0";
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- // And just a little outside the bound.
- point = "6.0 5.0";
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 2.0, 1e-5);
-
- // Now we start to invoke the periodicity. This point will "alias" to (-1,
- // -1).
- point = "99.0 99.0";
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 10.0, 1e-5);
-
- // We will perform several tests on a one-dimensional bound.
- b = PeriodicHRectBound<2>(arma::vec("5.0"));
- point.set_size(1);
-
- b[0] = Range("2.0 4.0"); // Entirely inside box.
- point[0] = 7.5; // Inside first right image of the box.
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- b[0] = Range("0.0 5.0"); // Fills box fully.
- point[1] = 19.3; // Inside the box, which covers everything.
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- b[0] = Range("-10.0 10.0"); // Larger than the box.
- point[0] = -500.0; // Inside the box, which covers everything.
-
- BOOST_REQUIRE_SMALL(b.MinDistance(point), 1e-5);
-
- b[0] = Range("-2.0 1.0"); // Crosses over an edge.
- point[0] = 2.9; // The first right image of the bound starts at 3.0.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 0.01, 1e-5);
-
- b[0] = Range("2.0 4.0"); // Inside box.
- point[0] = 0.0; // Closest to the first left image of the bound.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 1.0, 1e-5);
-
- b[0] = Range("0.0 2.0"); // On edge of box.
- point[0] = 7.1; // 0.1 away from the first right image of the bound.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 0.01, 1e-5);
-
- b[0] = Range("-10.0 10.0"); // Box is of infinite size.
- point[0] = 810.0; // 800 away from the only image of the box.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 640000, 1e-5);
-
- b[0] = Range("2.0 4.0"); // Box size of -5 should function the same as 5.
- point[0] = -10.8; // Should alias to 4.2.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 0.04, 1e-5);
-
- // Switch our bound to a higher dimensionality. This should ensure that the
- // dimensions are independent like they should be.
- b = PeriodicHRectBound<2>(arma::vec("5.0 5.0 5.0 5.0 5.0 5.0 0.0 -5.0"));
-
- b[0] = Range("2.0 4.0"); // Entirely inside box.
- b[1] = Range("0.0 5.0"); // Fills box fully.
- b[2] = Range("-10.0 10.0"); // Larger than the box.
- b[3] = Range("-2.0 1.0"); // Crosses over an edge.
- b[4] = Range("2.0 4.0"); // Inside box.
- b[5] = Range("0.0 2.0"); // On edge of box.
- b[6] = Range("-10.0 10.0"); // Box is of infinite size.
- b[7] = Range("2.0 4.0"); // Box size of -5 should function the same as 5.
-
- point.set_size(8);
- point[0] = 7.5; // Inside first right image of the box.
- point[1] = 19.3; // Inside the box, which covers everything.
- point[2] = -500.0; // Inside the box, which covers everything.
- point[3] = 2.9; // The first right image of the bound starts at 3.0.
- point[4] = 0.0; // Closest to the first left image of the bound.
- point[5] = 7.1; // 0.1 away from the first right image of the bound.
- point[6] = 810.0; // 800 away from the only image of the box.
- point[7] = -10.8; // Should alias to 4.2.
-
- BOOST_REQUIRE_CLOSE(b.MinDistance(point), 640001.06, 1e-10);
-}*/
-
-/***
- * It seems as though Bill has stumbled across a bug where
- * BinarySpaceTree<>::count() returns something different than
- * BinarySpaceTree<>::count_. So, let's build a simple tree and make sure they
- * are the same.
- */
-BOOST_AUTO_TEST_CASE(tree_count_mismatch) {
- arma::mat dataset = "2.0 5.0 9.0 4.0 8.0 7.0;"
- "3.0 4.0 6.0 7.0 1.0 2.0 ";
-
- // Leaf size of 1.
- CLI::GetParam<int>("tree/leaf_size") = 1;
- BinarySpaceTree<HRectBound<2> > root_node(dataset);
-
- BOOST_REQUIRE(root_node.count() == 6);
- BOOST_REQUIRE(root_node.left()->count() == 3);
- BOOST_REQUIRE(root_node.left()->left()->count() == 2);
- BOOST_REQUIRE(root_node.left()->left()->left()->count() == 1);
- BOOST_REQUIRE(root_node.left()->left()->right()->count() == 1);
- BOOST_REQUIRE(root_node.left()->right()->count() == 1);
- BOOST_REQUIRE(root_node.right()->count() == 3);
- BOOST_REQUIRE(root_node.right()->left()->count() == 2);
- BOOST_REQUIRE(root_node.right()->left()->left()->count() == 1);
- BOOST_REQUIRE(root_node.right()->left()->right()->count() == 1);
- BOOST_REQUIRE(root_node.right()->right()->count() == 1);
-}
-
-// Forward declaration of methods we need for the next test.
-template<typename TreeType>
-bool CheckPointBounds(TreeType* node, const arma::mat& data);
-
-template<typename TreeType>
-void GenerateVectorOfTree(TreeType* node,
- size_t depth,
- std::vector<TreeType*>& v);
-
-template<int t_pow>
-bool DoBoundsIntersect(HRectBound<t_pow>& a,
- HRectBound<t_pow>& b,
- size_t ia,
- size_t ib);
-
-/***
- * Exhaustive kd-tree test based on #125.
- *
- * - Generate a random dataset of a random size.
- * - Build a tree on that dataset.
- * - Ensure all the permutation indices map back to the correct points.
- * - Verify that each point is contained inside all of the bounds of its parent
- * nodes.
- * - Verify that each bound at a particular level of the tree does not overlap
- * with any other bounds at that level.
- *
- * Then, we do that whole process a handful of times.
- */
-BOOST_AUTO_TEST_CASE(kd_tree_test) {
- typedef BinarySpaceTree<HRectBound<2> > TreeType;
-
- size_t max_runs = 10; // Ten total tests.
- size_t point_increments = 1000; // Range is from 2000 points to 11000.
-
- // Generate the dataset.
- srand(time(NULL));
-
- // Reset the leaf size as other tests have been naughty.
- // Also, a leaf size of 20 makes the test take too long.
- CLI::GetParam<int>("tree/leaf_size") = 20;
-
- for(size_t run = 0; run < max_runs; run++) {
- size_t dimensions = run + 2;
- size_t max_points = (run + 1) * point_increments;
-
- size_t size = max_points;
- arma::mat dataset = arma::mat(dimensions, size);
- arma::mat datacopy; // Used to test mappings.
-
- // Mappings for post-sort verification of data.
- std::vector<size_t> new_to_old;
- std::vector<size_t> old_to_new;
-
- // Generate data.
- dataset.randu();
- datacopy = dataset; // Save a copy.
-
- // Build the tree itself.
- TreeType root(dataset, new_to_old, old_to_new);
-
- // Ensure the size of the tree is correct.
- BOOST_REQUIRE_EQUAL(root.count(), size);
-
- // Check the forward and backward mappings for correctness.
- for(size_t i = 0; i < size; i++) {
- for(size_t j = 0; j < dimensions; j++) {
- BOOST_REQUIRE_EQUAL(dataset(j, i), datacopy(j, new_to_old[i]));
- BOOST_REQUIRE_EQUAL(dataset(j, old_to_new[i]), datacopy(j, i));
- }
- }
-
- // Now check that each point is contained inside of all bounds above it.
- CheckPointBounds(&root, dataset);
-
- // Now check that no peers overlap.
- std::vector<TreeType*> v;
- GenerateVectorOfTree(&root, 1, v);
-
- // Start with the first pair.
- size_t depth = 2;
- // Compare each peer against every other peer.
- while (depth < v.size()) {
- for (size_t i = depth; i < 2 * depth && i < v.size(); i++)
- for (size_t j = i + 1; j < 2 * depth && j < v.size(); j++)
- if (v[i] != NULL && v[j] != NULL)
- BOOST_REQUIRE(!DoBoundsIntersect(v[i]->bound(), v[j]->bound(),
- i, j));
-
- depth *= 2;
- }
- }
-
- // Reset it to the default value at the end of the test.
- CLI::GetParam<int>("tree/leaf_size") = 20;
-}
-
-// Recursively checks that each node contains all points that it claims to have.
-template<typename TreeType>
-bool CheckPointBounds(TreeType* node, const arma::mat& data) {
- if (node == NULL) // We have passed a leaf node.
- return true;
-
- TreeType* left = node->left();
- TreeType* right = node->right();
-
- size_t begin = node->begin();
- size_t count = node->count();
-
- // Check that each point which this tree claims is actually inside the tree.
- for (size_t index = begin; index < begin+count; index++) {
- if (!node->bound().Contains(data.col(index)))
- return false;
- }
-
- return CheckPointBounds(left, data) && CheckPointBounds(right, data);
-}
-
-template<int t_pow>
-bool DoBoundsIntersect(HRectBound<t_pow>& a,
- HRectBound<t_pow>& b,
- size_t ia,
- size_t ib) {
- size_t dimensionality = a.dim();
-
- Range r_a;
- Range r_b;
-
- for (size_t i = 0; i < dimensionality; i++) {
- r_a = a[i];
- r_b = b[i];
- if (r_a < r_b || r_a > r_b) // If a does not overlap b at all.
- return false;
- }
-
- return true;
-}
-
-template<typename TreeType>
-void GenerateVectorOfTree(TreeType* node,
- size_t depth,
- std::vector<TreeType*>& v) {
- if (node == NULL)
- return;
-
- if (depth >= v.size())
- v.resize(2 * depth + 1, NULL); // Resize to right size; fill with NULL.
-
- v[depth] = node;
-
- // Recurse to the left and right children.
- GenerateVectorOfTree(node->left(), depth * 2, v);
- GenerateVectorOfTree(node->right(), depth * 2 + 1, v);
-
- return;
-}
Modified: mlpack/trunk/src/mlpack/core/utilities/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/core/utilities/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/utilities/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -17,13 +17,3 @@
# Append sources (with directory name) to list of all MLPACK sources (used at
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-
-# add test executable
-add_executable(save_restore_utility_test
- save_restore_utility_test.cpp
-)
-target_link_libraries(save_restore_utility_test
- mlpack
- boost_unit_test_framework
- xml2
-)
Deleted: mlpack/trunk/src/mlpack/core/utilities/save_restore_utility_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/core/utilities/save_restore_utility_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/core/utilities/save_restore_utility_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,149 +0,0 @@
-/***
- * @file save_restore_model_test.cpp
- * @author Neil Slagle
- *
- * Here we have tests for the SaveRestoreModel class.
- */
-
-#include "save_restore_utility.hpp"
-
-#define BOOST_TEST_MODULE SaveRestoreModel Test
-#include <boost/test/unit_test.hpp>
-
-#define ARGSTR(a) a,#a
-
-/***
- * Exhibit proper save restore utility usage
- * of child class proper usage
- */
-class SaveRestoreTest
-{
- private:
- size_t anInt;
- SaveRestoreUtility saveRestore;
- public:
- SaveRestoreTest()
- {
- saveRestore = SaveRestoreUtility();
- }
- bool SaveModel (std::string filename)
- {
- saveRestore.SaveParameter (anInt, "anInt");
- return saveRestore.WriteFile (filename);
- }
- bool LoadModel (std::string filename)
- {
- bool success = saveRestore.ReadFile (filename);
- if (success)
- {
- anInt = saveRestore.LoadParameter (anInt, "anInt");
- }
- return success;
- }
- const size_t AnInt () { return anInt; }
- void AnInt (size_t s) { this->anInt = s; }
-};
-
-/***
- * Perform a save and restore on basic types.
- */
-BOOST_AUTO_TEST_CASE(save_basic_types)
-{
- bool b = false;
- char c = 67;
- unsigned u = 34;
- size_t s = 12;
- short sh = 100;
- int i = -23;
- float f = -2.34f;
- double d = 3.14159;
- std::string cc = "Hello world!";
-
- SaveRestoreUtility* sRM = new SaveRestoreUtility();
-
- sRM->SaveParameter (ARGSTR(b));
- sRM->SaveParameter (ARGSTR(c));
- sRM->SaveParameter (ARGSTR(u));
- sRM->SaveParameter (ARGSTR(s));
- sRM->SaveParameter (ARGSTR(sh));
- sRM->SaveParameter (ARGSTR(i));
- sRM->SaveParameter (ARGSTR(f));
- sRM->SaveParameter (ARGSTR(d));
- sRM->SaveParameter (ARGSTR(cc));
- sRM->WriteFile ("test_basic_types.xml");
-
- sRM->ReadFile ("test_basic_types.xml");
-
- bool b2 = sRM->LoadParameter (ARGSTR(b));
- char c2 = sRM->LoadParameter (ARGSTR(c));
- unsigned u2 = sRM->LoadParameter (ARGSTR(u));
- size_t s2 = sRM->LoadParameter (ARGSTR(s));
- short sh2 = sRM->LoadParameter (ARGSTR(sh));
- int i2 = sRM->LoadParameter (ARGSTR(i));
- float f2 = sRM->LoadParameter (ARGSTR(f));
- double d2 = sRM->LoadParameter (ARGSTR(d));
- std::string cc2 = sRM->LoadParameter (ARGSTR(cc));
-
- BOOST_REQUIRE (b == b2);
- BOOST_REQUIRE (c == c2);
- BOOST_REQUIRE (u == u2);
- BOOST_REQUIRE (s == s2);
- BOOST_REQUIRE (sh == sh2);
- BOOST_REQUIRE (i == i2);
- BOOST_REQUIRE (cc == cc2);
- BOOST_REQUIRE_CLOSE (f, f2, 1e-5);
- BOOST_REQUIRE_CLOSE (d, d2, 1e-5);
-
- delete sRM;
-}
-
-/***
- * Test the arma::mat functionality.
- */
-BOOST_AUTO_TEST_CASE(save_arma_mat)
-{
- arma::mat matrix;
- matrix << 1.2 << 2.3 << -0.1 << arma::endr
- << 3.5 << 2.4 << -1.2 << arma::endr
- << -0.1 << 3.4 << -7.8 << arma::endr;
- SaveRestoreUtility* sRM = new SaveRestoreUtility();
-
- sRM->SaveParameter (ARGSTR (matrix));
-
- sRM->WriteFile ("test_arma_mat_type.xml");
-
- sRM->ReadFile ("test_arma_mat_type.xml");
-
- arma::mat matrix2 = sRM->LoadParameter (ARGSTR (matrix));
-
- for (size_t row = 0; row < matrix.n_rows; ++row)
- {
- for (size_t column = 0; column < matrix.n_cols; ++column)
- {
- BOOST_REQUIRE_CLOSE(matrix(row,column), matrix2(row,column), 1e-5);
- }
- }
-
- delete sRM;
-}
-/***
- * Test SaveRestoreModel proper usage in child classes and loading from
- * separately defined objects
- */
-BOOST_AUTO_TEST_CASE(save_restore_model_child_class_usage)
-{
- SaveRestoreTest* saver = new SaveRestoreTest();
- SaveRestoreTest* loader = new SaveRestoreTest();
- size_t s = 1200;
- const char* filename = "anInt.xml";
-
- saver->AnInt (s);
- saver->SaveModel (filename);
- delete saver;
-
- loader->LoadModel (filename);
-
- BOOST_REQUIRE (loader->AnInt () == s);
-
- delete loader;
-}
Modified: mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -25,19 +25,3 @@
target_link_libraries(emst
mlpack
)
-
-add_executable(union_find_test
- union_find_test.cpp
-)
-target_link_libraries(union_find_test
- mlpack
- boost_unit_test_framework
-)
-
-add_executable(emst_test
- emst_test.cc
-)
-target_link_libraries(emst_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/emst/emst_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/emst_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/emst/emst_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,138 +0,0 @@
-/**
- * @file emst_test.cc
- *
- * Test file for EMST methods
- */
-#include <mlpack/core.h>
-#include "dtb.hpp"
-
-#define BOOST_TEST_MODULE EMST Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::emst;
-
-/***
- * Simple emst test with small, synthetic dataset. This is an
- * exhaustive test, which checks that each method for performing the calculation
- * (dual-tree, single-tree, naive) produces the correct results. The dataset is
- * in one dimension for simplicity -- the correct functionality of distance
- * functions is not tested here.
- */
-BOOST_AUTO_TEST_CASE(exhaustive_synthetic_test) {
- // Set up our data.
- arma::mat data(1, 11);
- data[0] = 0.05; // Row addressing is unnecessary (they are all 0).
- data[1] = 0.37;
- data[2] = 0.15;
- data[3] = 1.25;
- data[4] = 5.05;
- data[5] = -0.22;
- data[6] = -2.00;
- data[7] = -1.30;
- data[8] = 0.45;
- data[9] = 0.91;
- data[10] = 1.00;
-
-
- // Now perform the actual calculation.
- arma::mat results;
-
-
- DualTreeBoruvka dtb;
- dtb.Init(data);
- dtb.ComputeMST(results);
-
- // Now the exhaustive check for correctness.
-
- BOOST_REQUIRE(results(0, 0) == 1);
- BOOST_REQUIRE(results(0, 1) == 8);
- BOOST_REQUIRE_CLOSE(results(0, 2), 0.08, 1e-5);
-
- BOOST_REQUIRE(results(1, 0) == 9);
- BOOST_REQUIRE(results(1, 1) == 10);
- BOOST_REQUIRE_CLOSE(results(1, 2), 0.09, 1e-5);
-
- BOOST_REQUIRE(results(2, 0) == 0);
- BOOST_REQUIRE(results(2, 1) == 2);
- BOOST_REQUIRE_CLOSE(results(2, 2), 0.1, 1e-5);
-
- BOOST_REQUIRE(results(3, 0) == 1);
- BOOST_REQUIRE(results(3, 1) == 2);
- BOOST_REQUIRE_CLOSE(results(3, 2), 0.22, 1e-5);
-
- BOOST_REQUIRE(results(4, 0) == 3);
- BOOST_REQUIRE(results(4, 1) == 10);
- BOOST_REQUIRE_CLOSE(results(4, 2), 0.25, 1e-5);
-
- BOOST_REQUIRE(results(5, 0) == 0);
- BOOST_REQUIRE(results(5, 1) == 5);
- BOOST_REQUIRE_CLOSE(results(5, 2), 0.27, 1e-5);
-
- BOOST_REQUIRE(results(6, 0) == 8);
- BOOST_REQUIRE(results(6, 1) == 9);
- BOOST_REQUIRE_CLOSE(results(6, 2), 0.46, 1e-5);
-
- BOOST_REQUIRE(results(7, 0) == 6);
- BOOST_REQUIRE(results(7, 1) == 7);
- BOOST_REQUIRE_CLOSE(results(7, 2), 0.7, 1e-5);
-
- BOOST_REQUIRE(results(8, 0) == 5);
- BOOST_REQUIRE(results(8, 1) == 7);
- BOOST_REQUIRE_CLOSE(results(8, 2), 1.08, 1e-5);
-
- BOOST_REQUIRE(results(9, 0) == 3);
- BOOST_REQUIRE(results(9, 1) == 4);
- BOOST_REQUIRE_CLOSE(results(9, 2), 3.8, 1e-5);
-
-
-}
-
-/**
- * Test the dual tree method against the naive computation.
- *
- * Errors are produced if the results are not identical.
- */
-BOOST_AUTO_TEST_CASE(dual_tree_vs_naive) {
- arma::mat input_data;
-
- // Hard-coded filename: bad!
- // Code duplication: also bad!
- if (!input_data.load("test_data_3_1000.csv", arma::auto_detect, false,
- 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_data = arma::trans(input_data);
- arma::mat naive_data = arma::trans(input_data);
-
- // Reset parameters from last test.
- DualTreeBoruvka dtb;
- dtb.Init(dual_data);
-
- arma::mat dual_results;
- dtb.ComputeMST(dual_results);
-
- // Set naive mode.
- CLI::GetParam<bool>("naive/do_naive") = true;
-
- DualTreeBoruvka dtb_naive;
- dtb_naive.Init(naive_data);
-
- arma::mat naive_results;
- dtb_naive.ComputeMST(naive_results);
-
- BOOST_REQUIRE(dual_results.n_cols == naive_results.n_cols);
- BOOST_REQUIRE(dual_results.n_rows == naive_results.n_rows);
-
- for (size_t i = 0; i < dual_results.n_rows; i++) {
-
- BOOST_REQUIRE(dual_results(i,0) == naive_results(i,0));
- BOOST_REQUIRE(dual_results(i,1) == naive_results(i,1));
- BOOST_REQUIRE_CLOSE(dual_results(i,2), naive_results(i,2), 1e-5);
-
- }
-
-
-}
-
Deleted: mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,1000 +0,0 @@
-0.339406815,0.843176636,0.472701471
-0.212587646,0.351174901,0.81056695
-0.605649993,0.45338097,0.623370668
-0.269783539,0.482031883,0.36535861
-0.725254282,0.477113042,0.042100268
-0.529287901,0.776008587,0.303809928
-0.098778217,0.318454787,0.983422857
-0.685345453,0.837942768,0.540406673
-0.503220972,0.268813629,0.41488501
-0.160147626,0.255047893,0.04072469
-0.564535197,0.943435462,0.597070812
-0.663842864,0.276972185,0.02208704
-0.077321401,0.032366881,0.826784604
-0.794220519,0.319582218,0.266025433
-0.466815953,0.864683732,0.684380976
-0.680962499,0.009554527,0.484176898
-0.257862396,0.875068776,0.326253946
-0.695503778,0.695095604,0.955586038
-0.569205007,0.662786497,0.036489177
-0.604542917,0.250714055,0.232826165
-0.928175028,0.871769923,0.327107027
-0.362809806,0.270846833,0.917535106
-0.567471988,0.09223176,0.018232595
-0.30294,0.197331083,0.676067984
-0.136497436,0.991079028,0.640906359
-0.490191642,0.321877535,0.210121475
-0.886240693,0.124811844,0.109638108
-0.487537807,0.474289999,0.34183089
-0.038698268,0.515865087,0.984297254
-0.437309222,0.534489172,0.792665419
-0.898099395,0.461121197,0.785225662
-0.256850927,0.840446806,0.056158684
-0.335408063,0.806637161,0.913551388
-0.11329872,0.670392847,0.333361274
-0.954403847,0.024104509,0.325578493
-0.824152332,0.614355433,0.271931013
-0.729647547,0.666093053,0.579723184
-0.25675029,0.94037263,0.530553224
-0.799877963,0.555666351,0.056606945
-0.213120693,0.763046224,0.341926361
-0.975873714,0.554796483,0.049489218
-0.422782321,0.375502502,0.875514176
-0.732474122,0.920181004,0.273895723
-0.288070185,0.878065303,0.57017629
-0.269706239,0.854626516,0.607709975
-0.615118638,0.006748605,0.00278243
-0.655373608,0.348029869,0.909502319
-0.358287814,0.419322455,0.362741982
-0.152473842,0.659459939,0.497284571
-0.930791658,0.934132013,0.150924236
-0.792977546,0.953203388,0.465884431
-0.971953827,0.268751729,0.220474277
-0.244730747,0.056636753,0.088649766
-0.873554351,0.305649442,0.91790044
-0.26662478,0.221646762,0.310857157
-0.659541537,0.93018384,0.139339275
-0.833616742,0.833734413,0.551027856
-0.43405195,0.874582065,0.996443541
-0.442896336,0.118977275,0.03127628
-0.388886541,0.976070927,0.294801481
-0.14757794,0.195944854,0.129778502
-0.73209291,0.551685931,0.218866346
-0.85393572,0.675733762,0.501776114
-0.804291505,0.746786474,0.94053733
-0.199998362,0.403471102,0.614783956
-0.302029244,0.084831174,0.043490422
-0.458371115,0.076068613,0.940418665
-0.122287089,0.867270578,0.612001352
-0.423331474,0.370629389,0.407480574
-0.400056969,0.919523609,0.940521669
-0.74852813,0.872400563,0.915423635
-0.614934326,0.67621724,0.382278246
-0.0184522,0.545825352,0.74127138
-0.937453855,0.175662201,0.666301896
-0.504358818,0.251308945,0.849159677
-0.397410107,0.973000161,0.648601332
-0.398342217,0.110698975,0.916968596
-0.464980239,0.683124011,0.070633362
-0.787030874,0.393777381,0.731973049
-0.612845512,0.893440416,0.475331995
-0.241219407,0.792282417,0.389574277
-0.465756798,0.552685716,0.092092299
-0.908028882,0.837528446,0.794160948
-0.552741674,0.898614081,0.764312365
-0.607116253,0.958698621,0.334887326
-0.322583246,0.541387861,0.879874555
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Deleted: mlpack/trunk/src/mlpack/methods/emst/union_find_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/union_find_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/emst/union_find_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,50 +0,0 @@
-/**
- * @file union_find_test.cc
- *
- * @author Bill March (march at gatech.edu)
- *
- * Unit tests for the Union-Find data structure.
- */
-
-#include "union_find.hpp"
-
-#include <mlpack/core.h>
-
-#define BOOST_TEST_MODULE UnionFindTest
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::emst;
-
-BOOST_AUTO_TEST_CASE(TestFind) {
- static const size_t test_size_ = 10;
- UnionFind test_union_find_;
- test_union_find_.Init(test_size_);
-
- for (size_t i = 0; i < test_size_; i++) {
- BOOST_REQUIRE(test_union_find_.Find(i) == i);
- }
- test_union_find_.Union(0,1);
- test_union_find_.Union(1, 2);
-
- BOOST_REQUIRE(test_union_find_.Find(2) == test_union_find_.Find(0));
-
-}
-
-BOOST_AUTO_TEST_CASE(TestUnion) {
- static const size_t test_size_ = 10;
- UnionFind test_union_find_;
- test_union_find_.Init(test_size_);
-
- test_union_find_.Union(0, 1);
- test_union_find_.Union(2, 3);
- test_union_find_.Union(0, 2);
- test_union_find_.Union(5, 0);
- test_union_find_.Union(0, 6);
-
- BOOST_REQUIRE(test_union_find_.Find(0) == test_union_find_.Find(1));
- BOOST_REQUIRE(test_union_find_.Find(2) == test_union_find_.Find(3));
- BOOST_REQUIRE(test_union_find_.Find(1) == test_union_find_.Find(5));
- BOOST_REQUIRE(test_union_find_.Find(6) == test_union_find_.Find(3));
-}
-
Modified: mlpack/trunk/src/mlpack/methods/fastica/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/fastica/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/fastica/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -23,12 +23,3 @@
target_link_libraries(fastica
mlpack
)
-
-add_executable(lin_alg_test
- lin_alg.hpp
- lin_alg_test.cpp
-)
-target_link_libraries(lin_alg_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/fastica/lin_alg_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/fastica/lin_alg_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/fastica/lin_alg_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,126 +0,0 @@
-/***
- * lin_alg_test.cc
- *
- * Simple tests for things in the linalg__private namespace.
- * Partly so I can be sure that my changes are working.
- * Move to boost unit testing framework at some point.
- *
- * @author Ryan Curtin
- */
-#include <mlpack/core.h>
-
-#include "lin_alg.hpp"
-
-using namespace arma;
-using namespace mlpack;
-using namespace fastica;
-using namespace linalg__private;
-
-#define BOOST_TEST_MODULE linAlgTest
-#include <boost/test/unit_test.hpp>
-
-/***
- * Test for linalg__private::Center(). There are no edge cases here, so we'll
- * just try it once for now.
- */
-BOOST_AUTO_TEST_CASE(TestCenterA) {
- mat tmp(5, 5);
- // [[0 0 0 0 0]
- // [1 2 3 4 5]
- // [2 4 6 8 10]
- // [3 6 9 12 15]
- // [4 8 12 16 20]]
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 5; col++)
- tmp(row, col) = row * (col + 1);
- }
-
- mat tmp_out;
- Center(tmp, tmp_out);
-
- // average should be
- // [[0 3 6 9 12]]'
- // so result should be
- // [[ 0 0 0 0 0]
- // [-2 -1 0 1 2 ]
- // [-4 -2 0 2 4 ]
- // [-6 -3 0 3 6 ]
- // [-8 -4 0 4 8]]
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 5; col++) {
- BOOST_REQUIRE_CLOSE(tmp_out(row, col), (double) (col - 2) * row, 1e-5);
- }
- }
-}
-
-BOOST_AUTO_TEST_CASE(TestCenterB) {
- mat tmp(5, 6);
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 6; col++)
- tmp(row, col) = row * (col + 1);
- }
-
- mat tmp_out;
- Center(tmp, tmp_out);
-
- // average should be
- // [[0 3.5 7 10.5 14]]'
- // so result should be
- // [[ 0 0 0 0 0 0 ]
- // [-2.5 -1.5 -0.5 0.5 1.5 2.5]
- // [-5 -3 -1 1 3 5 ]
- // [-7.5 -4.5 -1.5 1.5 1.5 4.5]
- // [-10 -6 -2 2 6 10 ]]
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 6; col++) {
- BOOST_REQUIRE_CLOSE(tmp_out(row, col), (double) (col - 2.5) * row, 1e-5);
- }
- }
-}
-
-BOOST_AUTO_TEST_CASE(TestWhitenUsingEig) {
- // After whitening using eigendecomposition, the covariance of
- // our matrix will be I (or something very close to that).
- // We are loading a matrix from an external file... bad choice.
- mat tmp, tmp_centered, whitened, whitening_matrix;
-
- data::Load("trainSet.csv", tmp);
- Center(tmp, tmp_centered);
- WhitenUsingEig(tmp_centered, whitened, whitening_matrix);
-
- mat newcov = ccov(whitened);
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 5; col++) {
- if (row == col) {
- // diagonal will be 0 in the case of any zero-valued eigenvalues
- // (rank-deficient covariance case)
- if (std::abs(newcov(row, col)) > 1e-10)
- BOOST_REQUIRE_CLOSE(newcov(row, col), 1.0, 1e-10);
- } else {
- BOOST_REQUIRE_SMALL(newcov(row, col), 1e-10);
- }
- }
- }
-}
-
-BOOST_AUTO_TEST_CASE(TestOrthogonalize) {
- // Generate a random matrix; then, orthogonalize it and test if it's
- // orthogonal.
- mat tmp, orth;
- data::Load("fake.csv", tmp);
- Orthogonalize(tmp, orth);
-
- // test orthogonality
- mat test = ccov(orth);
- double ival = test(0, 0);
- for (size_t row = 0; row < test.n_rows; row++) {
- for (size_t col = 0; col < test.n_cols; col++) {
- if (row == col) {
- if (std::abs(test(row, col)) > 1e-10)
- BOOST_REQUIRE_CLOSE(test(row, col), ival, 1e-10);
- } else {
- BOOST_REQUIRE_SMALL(test(row, col), 1e-10);
- }
- }
- }
-}
Modified: mlpack/trunk/src/mlpack/methods/infomax_ica/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/infomax_ica/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/infomax_ica/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -16,14 +16,6 @@
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-add_executable(infomax_ica_test
- infomax_ica_test.cc
-)
-target_link_libraries(infomax_ica_test
- mlpack
- boost_unit_test_framework
-)
-
add_executable(infomax_ica
main.cc
)
Deleted: mlpack/trunk/src/mlpack/methods/infomax_ica/infomax_ica_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/infomax_ica/infomax_ica_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/infomax_ica/infomax_ica_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,62 +0,0 @@
-/**
- * @file main.cc
- *
- * Test driver for our infomax ICA method.
- */
-#include <mlpack/core.h>
-
-#include "infomax_ica.h"
-
-using namespace mlpack;
-using namespace infomax_ica;
-
-#define BOOST_TEST_MODULE TestInfomaxICA
-#include <boost/test/unit_test.hpp>
-
-void testSQRTM(InfomaxICA& icab_, arma::mat& m) {
- icab_.sqrtm(m);
-}
-BOOST_AUTO_TEST_CASE(SqrtM) {
- arma::mat testdatab_;
- double lambdab_ = 0.001;
- int bb_ = 5;
- double epsilonb_ = 0.001;
-
- data::Load("fake.csv", testdatab_);
-
- InfomaxICA icab_(lambdab_, bb_, epsilonb_);
-
- arma::mat intermediateb = icab_.sampleCovariance(testdatab_);
- testSQRTM(icab_, intermediateb);
-}
-
-BOOST_AUTO_TEST_CASE(TestCov) {
- arma::mat testdata_;
-
- double lambda_ = 0.001;
- int b_ = 5;
- double epsilon_ = 0.001;
-
- // load some test data that has been verified using the matlab
- // implementation of infomax
- data::Load("fake.csv", testdata_);
-
- InfomaxICA ica_(lambda_, b_, epsilon_);
- ica_.sampleCovariance(testdata_);
-}
-
-BOOST_AUTO_TEST_CASE(TestICA) {
- arma::mat testdata_;
- double lambda_ = 0.001;
- int b_ = 5;
- double epsilon_ = 0.001;
-
- // load some test data that has been verified using the matlab
- // implementation of infomax
- data::Load("fake.csv", testdata_);
-
- InfomaxICA ica_(lambda_, b_, epsilon_);
- arma::mat unmixing;
- ica_.applyICA(testdata_);
- ica_.getUnmixing(unmixing);
-}
Deleted: mlpack/trunk/src/mlpack/methods/kernel_pca/test_data_3_1000.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/kernel_pca/test_data_3_1000.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/kernel_pca/test_data_3_1000.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,1000 +0,0 @@
-0.339406815,0.843176636,0.472701471
-0.212587646,0.351174901,0.81056695
-0.605649993,0.45338097,0.623370668
-0.269783539,0.482031883,0.36535861
-0.725254282,0.477113042,0.042100268
-0.529287901,0.776008587,0.303809928
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Modified: mlpack/trunk/src/mlpack/methods/linear_regression/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -23,12 +23,3 @@
target_link_libraries(linear_regression
mlpack
)
-
-# test executable
-add_executable(linear_regression_test
- linear_regression_test.cpp
-)
-target_link_libraries(linear_regression_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,51 +0,0 @@
-#include <mlpack/core.h>
-#include "linear_regression.hpp"
-
-#define BOOST_TEST_MODULE Test_Linear_regression_Main
-#include <boost/test/unit_test.hpp>
-
-/**
- * Creates two 10x3 random matrices and one 10x1 "results" matrix.
- * Finds B in y=BX with one matrix, then predicts against the other.
- */
-BOOST_AUTO_TEST_CASE(LinearRegressionTest)
-{
- // predictors, points are 10x3 matrices
- arma::mat predictors, points;
-
- // responses is the "correct" value for each point in predictors, poitns
- arma::colvec responses;
-
- // the values we get back when we predict for points
- arma::rowvec predictions;
-
- // Initialize randomly
- predictors.randu(3,10);
- points.randu(3,10);
- // add 3 so that we have two clusters of points
- predictors.cols(0,4) += 3;
- points.cols(0,4) += 3;
-
- // Create y
- responses.zeros(10);
- // Create a second "class" for the first cluster of points
- for(size_t i = 0; i < 5; ++i)
- {
- responses(i) = 1;
- }
- responses += 1; // "classes" are 2,1
-
- predictions.zeros(responses.n_rows);
-
- // Initialize and predict
- mlpack::linear_regression::LinearRegression lr(predictors, responses);
- lr.predict(predictions, points);
-
- // Output result and verify we have less than .5 error from "correct" value
- // for each point
- std::cout << points << '\n' << predictions << '\n';
- for(size_t i = 0; i < predictions.n_cols; ++i)
- {
- assert( fabs(predictions(i) - responses(i)) < .5);
- }
-}
Deleted: mlpack/trunk/src/mlpack/methods/mog/fake.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/mog/fake.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/mog/fake.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,100 +0,0 @@
--0.28992,-0.13173,-0.61575
--1.2142,-1.1994,-0.4661
-0.031128,0.084721,-0.057328
--0.86099,-0.79692,-0.35194
--0.45353,-0.11696,-0.15629
-0.82319,0.31796,0.5654
-0.87603,0.62511,0.54203
--1.8458,-1.2202,-1.6845
-1.3571,0.86032,0.95889
--0.88157,-0.092799,-0.21391
--1.7254,-0.80534,-1.2231
--3.7444,-2.7681,-2.8587
--1.9593,-1.9499,-1.6215
-0.036513,-0.65684,-0.16414
--1.0091,-0.9538,-0.82084
--2.6299,-2.4503,-2.0805
-1.4404,0.91235,1.0368
-2.3232,1.7905,1.3835
--0.22953,0.29435,-0.55121
-0.52442,0.53273,0.29293
-1.9345,1.1211,1.5277
--0.71909,-0.91217,-0.13798
--1.0023,-0.31518,-0.83793
-0.88445,0.55393,0.63455
--0.14286,0.75819,0.10212
--0.57519,-0.41389,-0.16138
--0.54536,-0.54785,0.19468
-0.18259,-0.038652,-0.11602
-1.5583,1.5063,1.1687
-0.67228,0.25056,0.28417
-1.8047,1.3339,1.8819
-1.6772,1.8611,1.5816
-0.30442,0.39109,0.31353
--0.66552,-0.38255,-0.36366
--0.99614,-0.31771,0.10233
-0.65813,0.72853,0.66725
-1.3433,0.87076,0.9503
-0.5952,0.54358,0.15799
-0.80337,0.48182,0.92845
--1.0895,-0.77068,-0.43275
--1.2246,-1.0886,-0.81559
--3.2332,-2.2593,-1.8321
-2.36,1.5532,1.98
--1.417,-0.72475,-1.6775
-0.48999,0.27822,0.63959
-1.7494,1.1849,1.2174
-0.098846,0.48884,0.58291
--2.2389,-1.239,-1.5603
--2.5684,-2.0838,-2.044
-1.833,1.9878,1.488
--1.3408,-0.41722,-0.78372
-0.41603,-0.03137,-0.19393
-1.3308,0.97246,0.50187
-1.8763,1.4224,1.5111
-0.47414,0.35165,0.45279
-0.72983,0.62162,0.81429
-0.072535,-0.15613,0.19178
-0.24045,0.31484,0.73666
--0.63399,-0.44661,0.16174
--0.7575,-0.29941,-0.23175
--0.22151,0.13724,-0.053466
--0.61552,-0.30699,-0.18651
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Deleted: mlpack/trunk/src/mlpack/methods/mvu/roll.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/mvu/roll.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/mvu/roll.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,1000 +0,0 @@
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--4.4138,16.437,7.1548
--1.0799,11.711,-4.3358
-1.815,18.07,-4.7425
-4.1996,5.8304,-3.6545
-5.3347,15.43,-2.4188
-0.22034,1.1481,7.8227
-1.1371,15.216,-4.8119
-11.941,14.794,-3.0265
--3.1586,4.3312,-10.219
--5.6405,18.327,-8.7659
--2.6027,15.889,-10.431
-7.2617,8.841,-9.1321
--9.3185,0.027228,0.72747
-12.219,8.6586,-2.0928
-6.3597,14.032,0.86018
-3.7074,17.637,6.3153
--2.2332,5.4,7.8195
--0.45022,11.714,-4.5927
-11.399,19.977,-4.3524
--8.0585,4.6737,-6.0345
-0.24834,0.14751,7.8183
-6.0419,15.507,-0.9847
--4.1751,8.4059,-9.7326
-5.595,5.2855,4.0559
--2.5958,12.646,-3.0536
--0.091626,6.1632,-10.987
-2.4027,0.95606,7.1355
--2.7291,10.776,-2.8549
--8.8588,0.74415,-4.3842
-12.145,10.446,-2.3669
--9.2476,16.866,-3.0766
--1.2538,10.557,7.9124
--6.9203,5.0003,-7.5678
-10.34,1.8611,-6.1488
--3.257,18.972,7.59
-2.9357,13.261,-10.87
-4.4045,7.9594,-3.4815
-6.3163,6.6065,0.23766
-10.599,11.239,-5.7702
--9.2227,18.376,-3.1857
Modified: mlpack/trunk/src/mlpack/methods/naive_bayes/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -22,11 +22,3 @@
target_link_libraries(nbc
mlpack
)
-
-add_executable(nbc_test
- nbc_test.cc
-)
-target_link_libraries(nbc_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/naive_bayes/nbc_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/nbc_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/nbc_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,54 +0,0 @@
-#include <mlpack/core.h>
-#include "simple_nbc.h"
-
-#define BOOST_TEST_MODULE Test_Simple_NBC_Main
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace naive_bayes;
-
-BOOST_AUTO_TEST_CASE(SimpleNBCTest) {
- const char* filename_train_ = "trainSet.csv";
- const char* filename_test_ = "testSet.csv";
- const char* train_result_ = "trainRes.csv";
- const char* test_result_ = "testRes.csv";
- size_t number_of_classes_ = 2;
-
- arma::mat train_data, train_res, calc_mat;
- data::Load(filename_train_, train_data, true);
- data::Load(train_result_, train_res, true);
-
- CLI::GetParam<int>("nbc/classes") = number_of_classes_;
- SimpleNaiveBayesClassifier nbc_test_(train_data);
-
- size_t number_of_features = nbc_test_.means_.n_rows;
- calc_mat.zeros(2 * number_of_features + 1, number_of_classes_);
-
- for (size_t i = 0; i < number_of_features; i++) {
- for (size_t j = 0; j < number_of_classes_; j++) {
- calc_mat(i, j) = nbc_test_.means_(i, j);
- calc_mat(i + number_of_features, j) = nbc_test_.variances_(i, j);
- }
- }
- for (size_t i = 0; i < number_of_classes_; i++)
- calc_mat(2 * number_of_features, i) = nbc_test_.class_probabilities_(i);
-
- for(size_t i = 0; i < calc_mat.n_rows; i++) {
- for(size_t j = 0; j < number_of_classes_; j++) {
- BOOST_REQUIRE_CLOSE(train_res(i, j) + .00001, calc_mat(i, j), .01);
- }
- }
-
- arma::mat test_data, test_res;
- arma::vec test_res_vec, calc_vec;
- data::Load(filename_test_, test_data, true);
- data::Load(test_result_, test_res, true);
-
- nbc_test_.Classify(test_data, calc_vec);
-
- size_t number_of_datum = test_data.n_cols;
- test_res_vec = test_res.col(0);
-
- for(size_t i = 0; i < number_of_datum; i++)
- BOOST_REQUIRE_EQUAL(test_res_vec(i), calc_vec(i));
-}
Deleted: mlpack/trunk/src/mlpack/methods/naive_bayes/testRes.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/testRes.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/testRes.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1 +0,0 @@
-0,0,0,1,1,1,1
Deleted: mlpack/trunk/src/mlpack/methods/naive_bayes/testSet.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/testSet.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/testSet.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,7 +0,0 @@
-2,4,4,2,0
-2,5,4,2,0
-2,4,4,4,0
-8,4,4,2,1
-8,4,4,2,1
-8,5,4,2,1
-8,4,4,4,1
Deleted: mlpack/trunk/src/mlpack/methods/naive_bayes/trainRes.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/trainRes.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/trainRes.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,2 +0,0 @@
-2.75, 4.0000, 3.6875, 2.3750, 0.3333, 0.8000, 0.6292, 0.3833, 0.5161
-8.3333, 4.6667, 3.6667, 2.4, 0.8095, 3.3810, 0.6667, 0.4000, 0.4839
Deleted: mlpack/trunk/src/mlpack/methods/naive_bayes/trainSet.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/naive_bayes/trainSet.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/naive_bayes/trainSet.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,31 +0,0 @@
-3,3,3,3,0
-3,4,4,3,0
-3,4,4,3,0
-3,3,4,3,0
-3,6,4,3,0
-2,4,4,3,0
-2,4,4,1,0
-3,3,3,2,0
-3,4,4,2,0
-3,4,4,2,0
-3,3,4,2,0
-3,6,4,2,0
-2,4,4,2,0
-2,4,4,3,0
-2,4,1,2,0
-4,4,4,2,0
-9,3,3,3,1
-9,4,4,3,1
-9,4,4,3,1
-9,3,4,3,1
-9,6,4,3,1
-9,4,4,3,1
-9,4,4,1,1
-9,3,3,2,1
-8,4,4,2,1
-8,4,4,2,1
-8,3,4,2,1
-8,6,4,2,1
-7,8,4,3,1
-6,9,1,2,1
-8,5,4,2,1
Modified: mlpack/trunk/src/mlpack/methods/nca/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/nca/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nca/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -24,11 +24,3 @@
target_link_libraries(nca
mlpack
)
-
-add_executable(nca_test
- nca_test.cc
-)
-target_link_libraries(nca_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/nca/nca_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/nca/nca_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nca/nca_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,165 +0,0 @@
-/**
- * @file nca_test.cc
- * @author Ryan Curtin
- *
- * Unit tests for Neighborhood Components Analysis and related code (including
- * the softmax error function).
- */
-#include <mlpack/core.h>
-#include <mlpack/core/kernels/lmetric.hpp>
-#include "nca.h"
-
-#define BOOST_TEST_MODULE Neighborhood Components Analysis Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::kernel;
-using namespace mlpack::nca;
-
-//
-// Tests for the SoftmaxErrorFunction
-//
-
-/***
- * The Softmax error function should return the identity matrix as its initial
- * point.
- */
-BOOST_AUTO_TEST_CASE(softmax_initial_point) {
- // Cheap fake dataset.
- arma::mat data;
- data.randu(5, 5);
- arma::uvec labels;
- labels.zeros(5);
-
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- // Verify the initial point is the identity matrix.
- arma::mat initial_point = sef.GetInitialPoint();
- for (int row = 0; row < 5; row++) {
- for (int col = 0; col < 5; col++) {
- if (row == col)
- BOOST_REQUIRE_CLOSE(initial_point(row, col), 1.0, 1e-5);
- else
- BOOST_REQUIRE(initial_point(row, col) == 0.0);
- }
- }
-}
-
-/***
- * On a simple fake dataset, ensure that the initial function evaluation is
- * correct.
- */
-BOOST_AUTO_TEST_CASE(softmax_initial_evaluation) {
- // Useful but simple dataset with six points and two classes.
- arma::mat data = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
- " 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
- arma::uvec labels = " 0 0 0 1 1 1 ";
-
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- double objective = sef.Evaluate(arma::eye<arma::mat>(2, 2));
-
- // Result painstakingly calculated by hand by rcurtin (recorded forever in his
- // notebook). As a result of lack of precision of the by-hand result, the
- // tolerance is fairly high.
- BOOST_REQUIRE_CLOSE(objective, -1.5115, 0.01);
-}
-
-/***
- * On a simple fake dataset, ensure that the initial gradient evaluation is
- * correct.
- */
-BOOST_AUTO_TEST_CASE(softmax_initial_gradient) {
- // Useful but simple dataset with six points and two classes.
- arma::mat data = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
- " 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
- arma::uvec labels = " 0 0 0 1 1 1 ";
-
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- arma::mat gradient;
- sef.Gradient(arma::eye<arma::mat>(2, 2), gradient);
-
- // Results painstakingly calculated by hand by rcurtin (recorded forever in
- // his notebook). As a result of lack of precision of the by-hand result, the
- // tolerance is fairly high.
- BOOST_REQUIRE_CLOSE(gradient(0, 0), -0.089766, 0.05);
- BOOST_REQUIRE(gradient(1, 0) == 0.0);
- BOOST_REQUIRE(gradient(0, 1) == 0.0);
- BOOST_REQUIRE_CLOSE(gradient(1, 1), 1.63823, 0.01);
-}
-
-/***
- * On optimally separated datasets, ensure that the objective function is
- * optimal (equal to the negative number of points).
- */
-BOOST_AUTO_TEST_CASE(softmax_optimal_evaluation) {
- // Simple optimal dataset.
- arma::mat data = " 500 500 -500 -500;"
- " 1 0 1 0 ";
- arma::uvec labels = " 0 0 1 1 ";
-
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- double objective = sef.Evaluate(arma::eye<arma::mat>(2, 2));
-
- // Use a very close tolerance for optimality; we need to be sure this function
- // gives optimal results correctly.
- BOOST_REQUIRE_CLOSE(objective, -4.0, 1e-10);
-}
-
-/***
- * On optimally separated datasets, ensure that the gradient is zero.
- */
-BOOST_AUTO_TEST_CASE(softmax_optimal_gradient) {
- // Simple optimal dataset.
- arma::mat data = " 500 500 -500 -500;"
- " 1 0 1 0 ";
- arma::uvec labels = " 0 0 1 1 ";
-
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- arma::mat gradient;
- sef.Gradient(arma::eye<arma::mat>(2, 2), gradient);
-
- BOOST_REQUIRE(gradient(0, 0) == 0.0);
- BOOST_REQUIRE(gradient(0, 1) == 0.0);
- BOOST_REQUIRE(gradient(1, 0) == 0.0);
- BOOST_REQUIRE(gradient(1, 1) == 0.0);
-}
-
-//
-// Tests for the NCA algorithm.
-//
-
-/***
- * On our simple dataset, ensure that the NCA algorithm fully separates the
- * points.
- */
-BOOST_AUTO_TEST_CASE(nca_simple_dataset) {
- // Useful but simple dataset with six points and two classes.
- arma::mat data = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
- " 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
- arma::uvec labels = " 0 0 0 1 1 1 ";
-
- NCA<SquaredEuclideanDistance> nca(data, labels);
-
- arma::mat output_matrix;
- nca.LearnDistance(output_matrix);
-
- // Ensure that the objective function is better now.
- SoftmaxErrorFunction<SquaredEuclideanDistance> sef(data, labels);
-
- double init_obj = sef.Evaluate(arma::eye<arma::mat>(2, 2));
- double final_obj = sef.Evaluate(output_matrix);
- arma::mat final_gradient;
- sef.Gradient(output_matrix, final_gradient);
-
- // final_obj must be less than init_obj.
- BOOST_REQUIRE_LT(final_obj, init_obj);
- // Verify that final objective is optimal.
- BOOST_REQUIRE_CLOSE(final_obj, -6.0, 1e-8);
- // The solution is not unique, so the best we can do is ensure the gradient
- // norm is close to 0.
- BOOST_REQUIRE_LT(arma::norm(final_gradient, 2), 1e-10);
-}
Modified: mlpack/trunk/src/mlpack/methods/neighbor_search/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/neighbor_search/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/neighbor_search/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -23,14 +23,6 @@
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-add_executable(allknn_test
- allknn_test.cc
-)
-target_link_libraries(allknn_test
- mlpack
- boost_unit_test_framework
-)
-
add_executable(allknn
allknn_main.cc
)
@@ -38,25 +30,9 @@
mlpack
)
-add_executable(allkfn_test
- allkfn_test.cc
-)
-target_link_libraries(allkfn_test
- mlpack
- boost_unit_test_framework
-)
-
add_executable(allkfn
allkfn_main.cc
)
target_link_libraries(allkfn
mlpack
)
-
-add_executable(sort_policy_test
- sort_policies/sort_policy_test.cc
-)
-target_link_libraries(sort_policy_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/neighbor_search/allkfn_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/neighbor_search/allkfn_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/neighbor_search/allkfn_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,432 +0,0 @@
-/**
- * @file allkfn_test.cc
- *
- * Test file for AllkFN class
- */
-#include <mlpack/core.h>
-#include "neighbor_search.h"
-
-#define BOOST_TEST_MODULE AllkFN Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::neighbor;
-
-/***
- * Simple furthest-neighbors test with small, synthetic dataset. This is an
- * exhaustive test, which checks that each method for performing the calculation
- * (dual-tree, single-tree, naive) produces the correct results. An
- * eleven-point dataset and the ten furthest neighbors are taken. The dataset
- * is in one dimension for simplicity -- the correct functionality of distance
- * functions is not tested here.
- */
-BOOST_AUTO_TEST_CASE(exhaustive_synthetic_test) {
- // Set up our data.
- arma::mat data(1, 11);
- data[0] = 0.05; // Row addressing is unnecessary (they are all 0).
- data[1] = 0.35;
- data[2] = 0.15;
- data[3] = 1.25;
- data[4] = 5.05;
- data[5] = -0.22;
- data[6] = -2.00;
- data[7] = -1.30;
- data[8] = 0.45;
- data[9] = 0.90;
- data[10] = 1.00;
-
- // We will loop through three times, one for each method of performing the
- // calculation. We'll always use 10 neighbors, so set that parameter.
- CLI::GetParam<int>("neighbor_search/k") = 10;
- for (int i = 0; i < 3; i++) {
- AllkFN* allkfn;
- arma::mat data_mutable = data;
- switch(i) {
- case 0: // Use the dual-tree method.
- allkfn = new AllkFN(data_mutable);
- break;
- case 1: // Use the single-tree method.
- CLI::GetParam<bool>("neighbor_search/single_mode") = true;
- allkfn = new AllkFN(data_mutable);
- break;
- case 2: // Use the naive method.
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- allkfn = new AllkFN(data_mutable);
- break;
- }
-
- // Now perform the actual calculation.
- arma::Mat<size_t> neighbors;
- arma::mat distances;
- allkfn->ComputeNeighbors(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
- // result, distance comparisons are written out as (distance * distance) for
- // readability.
-
- // Neighbors of point 0.
- BOOST_REQUIRE(neighbors(9, 0) == 2);
- BOOST_REQUIRE_CLOSE(distances(9, 0), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 0) == 5);
- BOOST_REQUIRE_CLOSE(distances(8, 0), (0.27 * 0.27), 1e-5);
- BOOST_REQUIRE(neighbors(7, 0) == 1);
- BOOST_REQUIRE_CLOSE(distances(7, 0), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(6, 0) == 8);
- BOOST_REQUIRE_CLOSE(distances(6, 0), (0.40 * 0.40), 1e-5);
- BOOST_REQUIRE(neighbors(5, 0) == 9);
- BOOST_REQUIRE_CLOSE(distances(5, 0), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(4, 0) == 10);
- BOOST_REQUIRE_CLOSE(distances(4, 0), (0.95 * 0.95), 1e-5);
- BOOST_REQUIRE(neighbors(3, 0) == 3);
- BOOST_REQUIRE_CLOSE(distances(3, 0), (1.20 * 1.20), 1e-5);
- BOOST_REQUIRE(neighbors(2, 0) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 0), (1.35 * 1.35), 1e-5);
- BOOST_REQUIRE(neighbors(1, 0) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 0), (2.05 * 2.05), 1e-5);
- BOOST_REQUIRE(neighbors(0, 0) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 0), (5.00 * 5.00), 1e-5);
-
- // Neighbors of point 1.
- BOOST_REQUIRE(neighbors(9, 1) == 8);
- BOOST_REQUIRE_CLOSE(distances(9, 1), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 1) == 2);
- BOOST_REQUIRE_CLOSE(distances(8, 1), (0.20 * 0.20), 1e-5);
- BOOST_REQUIRE(neighbors(7, 1) == 0);
- BOOST_REQUIRE_CLOSE(distances(7, 1), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(6, 1) == 9);
- BOOST_REQUIRE_CLOSE(distances(6, 1), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(5, 1) == 5);
- BOOST_REQUIRE_CLOSE(distances(5, 1), (0.57 * 0.57), 1e-5);
- BOOST_REQUIRE(neighbors(4, 1) == 10);
- BOOST_REQUIRE_CLOSE(distances(4, 1), (0.65 * 0.65), 1e-5);
- BOOST_REQUIRE(neighbors(3, 1) == 3);
- BOOST_REQUIRE_CLOSE(distances(3, 1), (0.90 * 0.90), 1e-5);
- BOOST_REQUIRE(neighbors(2, 1) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 1), (1.65 * 1.65), 1e-5);
- BOOST_REQUIRE(neighbors(1, 1) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 1), (2.35 * 2.35), 1e-5);
- BOOST_REQUIRE(neighbors(0, 1) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 1), (4.70 * 4.70), 1e-5);
-
- // Neighbors of point 2.
- BOOST_REQUIRE(neighbors(9, 2) == 0);
- BOOST_REQUIRE_CLOSE(distances(9, 2), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 2) == 1);
- BOOST_REQUIRE_CLOSE(distances(8, 2), (0.20 * 0.20), 1e-5);
- BOOST_REQUIRE(neighbors(7, 2) == 8);
- BOOST_REQUIRE_CLOSE(distances(7, 2), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(6, 2) == 5);
- BOOST_REQUIRE_CLOSE(distances(6, 2), (0.37 * 0.37), 1e-5);
- BOOST_REQUIRE(neighbors(5, 2) == 9);
- BOOST_REQUIRE_CLOSE(distances(5, 2), (0.75 * 0.75), 1e-5);
- BOOST_REQUIRE(neighbors(4, 2) == 10);
- BOOST_REQUIRE_CLOSE(distances(4, 2), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(3, 2) == 3);
- BOOST_REQUIRE_CLOSE(distances(3, 2), (1.10 * 1.10), 1e-5);
- BOOST_REQUIRE(neighbors(2, 2) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 2), (1.45 * 1.45), 1e-5);
- BOOST_REQUIRE(neighbors(1, 2) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 2), (2.15 * 2.15), 1e-5);
- BOOST_REQUIRE(neighbors(0, 2) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 2), (4.90 * 4.90), 1e-5);
-
- // Neighbors of point 3.
- BOOST_REQUIRE(neighbors(9, 3) == 10);
- BOOST_REQUIRE_CLOSE(distances(9, 3), (0.25 * 0.25), 1e-5);
- BOOST_REQUIRE(neighbors(8, 3) == 9);
- BOOST_REQUIRE_CLOSE(distances(8, 3), (0.35 * 0.35), 1e-5);
- BOOST_REQUIRE(neighbors(7, 3) == 8);
- BOOST_REQUIRE_CLOSE(distances(7, 3), (0.80 * 0.80), 1e-5);
- BOOST_REQUIRE(neighbors(6, 3) == 1);
- BOOST_REQUIRE_CLOSE(distances(6, 3), (0.90 * 0.90), 1e-5);
- BOOST_REQUIRE(neighbors(5, 3) == 2);
- BOOST_REQUIRE_CLOSE(distances(5, 3), (1.10 * 1.10), 1e-5);
- BOOST_REQUIRE(neighbors(4, 3) == 0);
- BOOST_REQUIRE_CLOSE(distances(4, 3), (1.20 * 1.20), 1e-5);
- BOOST_REQUIRE(neighbors(3, 3) == 5);
- BOOST_REQUIRE_CLOSE(distances(3, 3), (1.47 * 1.47), 1e-5);
- BOOST_REQUIRE(neighbors(2, 3) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 3), (2.55 * 2.55), 1e-5);
- BOOST_REQUIRE(neighbors(1, 3) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 3), (3.25 * 3.25), 1e-5);
- BOOST_REQUIRE(neighbors(0, 3) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 3), (3.80 * 3.80), 1e-5);
-
- // Neighbors of point 4.
- BOOST_REQUIRE(neighbors(9, 4) == 3);
- BOOST_REQUIRE_CLOSE(distances(9, 4), (3.80 * 3.80), 1e-5);
- BOOST_REQUIRE(neighbors(8, 4) == 10);
- BOOST_REQUIRE_CLOSE(distances(8, 4), (4.05 * 4.05), 1e-5);
- BOOST_REQUIRE(neighbors(7, 4) == 9);
- BOOST_REQUIRE_CLOSE(distances(7, 4), (4.15 * 4.15), 1e-5);
- BOOST_REQUIRE(neighbors(6, 4) == 8);
- BOOST_REQUIRE_CLOSE(distances(6, 4), (4.60 * 4.60), 1e-5);
- BOOST_REQUIRE(neighbors(5, 4) == 1);
- BOOST_REQUIRE_CLOSE(distances(5, 4), (4.70 * 4.70), 1e-5);
- BOOST_REQUIRE(neighbors(4, 4) == 2);
- BOOST_REQUIRE_CLOSE(distances(4, 4), (4.90 * 4.90), 1e-5);
- BOOST_REQUIRE(neighbors(3, 4) == 0);
- BOOST_REQUIRE_CLOSE(distances(3, 4), (5.00 * 5.00), 1e-5);
- BOOST_REQUIRE(neighbors(2, 4) == 5);
- BOOST_REQUIRE_CLOSE(distances(2, 4), (5.27 * 5.27), 1e-5);
- BOOST_REQUIRE(neighbors(1, 4) == 7);
- BOOST_REQUIRE_CLOSE(distances(1, 4), (6.35 * 6.35), 1e-5);
- BOOST_REQUIRE(neighbors(0, 4) == 6);
- BOOST_REQUIRE_CLOSE(distances(0, 4), (7.05 * 7.05), 1e-5);
-
- // Neighbors of point 5.
- BOOST_REQUIRE(neighbors(9, 5) == 0);
- BOOST_REQUIRE_CLOSE(distances(9, 5), (0.27 * 0.27), 1e-5);
- BOOST_REQUIRE(neighbors(8, 5) == 2);
- BOOST_REQUIRE_CLOSE(distances(8, 5), (0.37 * 0.37), 1e-5);
- BOOST_REQUIRE(neighbors(7, 5) == 1);
- BOOST_REQUIRE_CLOSE(distances(7, 5), (0.57 * 0.57), 1e-5);
- BOOST_REQUIRE(neighbors(6, 5) == 8);
- BOOST_REQUIRE_CLOSE(distances(6, 5), (0.67 * 0.67), 1e-5);
- BOOST_REQUIRE(neighbors(5, 5) == 7);
- BOOST_REQUIRE_CLOSE(distances(5, 5), (1.08 * 1.08), 1e-5);
- BOOST_REQUIRE(neighbors(4, 5) == 9);
- BOOST_REQUIRE_CLOSE(distances(4, 5), (1.12 * 1.12), 1e-5);
- BOOST_REQUIRE(neighbors(3, 5) == 10);
- BOOST_REQUIRE_CLOSE(distances(3, 5), (1.22 * 1.22), 1e-5);
- BOOST_REQUIRE(neighbors(2, 5) == 3);
- BOOST_REQUIRE_CLOSE(distances(2, 5), (1.47 * 1.47), 1e-5);
- BOOST_REQUIRE(neighbors(1, 5) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 5), (1.78 * 1.78), 1e-5);
- BOOST_REQUIRE(neighbors(0, 5) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 5), (5.27 * 5.27), 1e-5);
-
- // Neighbors of point 6.
- BOOST_REQUIRE(neighbors(9, 6) == 7);
- BOOST_REQUIRE_CLOSE(distances(9, 6), (0.70 * 0.70), 1e-5);
- BOOST_REQUIRE(neighbors(8, 6) == 5);
- BOOST_REQUIRE_CLOSE(distances(8, 6), (1.78 * 1.78), 1e-5);
- BOOST_REQUIRE(neighbors(7, 6) == 0);
- BOOST_REQUIRE_CLOSE(distances(7, 6), (2.05 * 2.05), 1e-5);
- BOOST_REQUIRE(neighbors(6, 6) == 2);
- BOOST_REQUIRE_CLOSE(distances(6, 6), (2.15 * 2.15), 1e-5);
- BOOST_REQUIRE(neighbors(5, 6) == 1);
- BOOST_REQUIRE_CLOSE(distances(5, 6), (2.35 * 2.35), 1e-5);
- BOOST_REQUIRE(neighbors(4, 6) == 8);
- BOOST_REQUIRE_CLOSE(distances(4, 6), (2.45 * 2.45), 1e-5);
- BOOST_REQUIRE(neighbors(3, 6) == 9);
- BOOST_REQUIRE_CLOSE(distances(3, 6), (2.90 * 2.90), 1e-5);
- BOOST_REQUIRE(neighbors(2, 6) == 10);
- BOOST_REQUIRE_CLOSE(distances(2, 6), (3.00 * 3.00), 1e-5);
- BOOST_REQUIRE(neighbors(1, 6) == 3);
- BOOST_REQUIRE_CLOSE(distances(1, 6), (3.25 * 3.25), 1e-5);
- BOOST_REQUIRE(neighbors(0, 6) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 6), (7.05 * 7.05), 1e-5);
-
- // Neighbors of point 7.
- BOOST_REQUIRE(neighbors(9, 7) == 6);
- BOOST_REQUIRE_CLOSE(distances(9, 7), (0.70 * 0.70), 1e-5);
- BOOST_REQUIRE(neighbors(8, 7) == 5);
- BOOST_REQUIRE_CLOSE(distances(8, 7), (1.08 * 1.08), 1e-5);
- BOOST_REQUIRE(neighbors(7, 7) == 0);
- BOOST_REQUIRE_CLOSE(distances(7, 7), (1.35 * 1.35), 1e-5);
- BOOST_REQUIRE(neighbors(6, 7) == 2);
- BOOST_REQUIRE_CLOSE(distances(6, 7), (1.45 * 1.45), 1e-5);
- BOOST_REQUIRE(neighbors(5, 7) == 1);
- BOOST_REQUIRE_CLOSE(distances(5, 7), (1.65 * 1.65), 1e-5);
- BOOST_REQUIRE(neighbors(4, 7) == 8);
- BOOST_REQUIRE_CLOSE(distances(4, 7), (1.75 * 1.75), 1e-5);
- BOOST_REQUIRE(neighbors(3, 7) == 9);
- BOOST_REQUIRE_CLOSE(distances(3, 7), (2.20 * 2.20), 1e-5);
- BOOST_REQUIRE(neighbors(2, 7) == 10);
- BOOST_REQUIRE_CLOSE(distances(2, 7), (2.30 * 2.30), 1e-5);
- BOOST_REQUIRE(neighbors(1, 7) == 3);
- BOOST_REQUIRE_CLOSE(distances(1, 7), (2.55 * 2.55), 1e-5);
- BOOST_REQUIRE(neighbors(0, 7) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 7), (6.35 * 6.35), 1e-5);
-
- // Neighbors of point 8.
- BOOST_REQUIRE(neighbors(9, 8) == 1);
- BOOST_REQUIRE_CLOSE(distances(9, 8), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 8) == 2);
- BOOST_REQUIRE_CLOSE(distances(8, 8), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(7, 8) == 0);
- BOOST_REQUIRE_CLOSE(distances(7, 8), (0.40 * 0.40), 1e-5);
- BOOST_REQUIRE(neighbors(6, 8) == 9);
- BOOST_REQUIRE_CLOSE(distances(6, 8), (0.45 * 0.45), 1e-5);
- BOOST_REQUIRE(neighbors(5, 8) == 10);
- BOOST_REQUIRE_CLOSE(distances(5, 8), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(4, 8) == 5);
- BOOST_REQUIRE_CLOSE(distances(4, 8), (0.67 * 0.67), 1e-5);
- BOOST_REQUIRE(neighbors(3, 8) == 3);
- BOOST_REQUIRE_CLOSE(distances(3, 8), (0.80 * 0.80), 1e-5);
- BOOST_REQUIRE(neighbors(2, 8) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 8), (1.75 * 1.75), 1e-5);
- BOOST_REQUIRE(neighbors(1, 8) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 8), (2.45 * 2.45), 1e-5);
- BOOST_REQUIRE(neighbors(0, 8) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 8), (4.60 * 4.60), 1e-5);
-
- // Neighbors of point 9.
- BOOST_REQUIRE(neighbors(9, 9) == 10);
- BOOST_REQUIRE_CLOSE(distances(9, 9), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 9) == 3);
- BOOST_REQUIRE_CLOSE(distances(8, 9), (0.35 * 0.35), 1e-5);
- BOOST_REQUIRE(neighbors(7, 9) == 8);
- BOOST_REQUIRE_CLOSE(distances(7, 9), (0.45 * 0.45), 1e-5);
- BOOST_REQUIRE(neighbors(6, 9) == 1);
- BOOST_REQUIRE_CLOSE(distances(6, 9), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(5, 9) == 2);
- BOOST_REQUIRE_CLOSE(distances(5, 9), (0.75 * 0.75), 1e-5);
- BOOST_REQUIRE(neighbors(4, 9) == 0);
- BOOST_REQUIRE_CLOSE(distances(4, 9), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(3, 9) == 5);
- BOOST_REQUIRE_CLOSE(distances(3, 9), (1.12 * 1.12), 1e-5);
- BOOST_REQUIRE(neighbors(2, 9) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 9), (2.20 * 2.20), 1e-5);
- BOOST_REQUIRE(neighbors(1, 9) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 9), (2.90 * 2.90), 1e-5);
- BOOST_REQUIRE(neighbors(0, 9) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 9), (4.15 * 4.15), 1e-5);
-
- // Neighbors of point 10.
- BOOST_REQUIRE(neighbors(9, 10) == 9);
- BOOST_REQUIRE_CLOSE(distances(9, 10), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(8, 10) == 3);
- BOOST_REQUIRE_CLOSE(distances(8, 10), (0.25 * 0.25), 1e-5);
- BOOST_REQUIRE(neighbors(7, 10) == 8);
- BOOST_REQUIRE_CLOSE(distances(7, 10), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(6, 10) == 1);
- BOOST_REQUIRE_CLOSE(distances(6, 10), (0.65 * 0.65), 1e-5);
- BOOST_REQUIRE(neighbors(5, 10) == 2);
- BOOST_REQUIRE_CLOSE(distances(5, 10), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(4, 10) == 0);
- BOOST_REQUIRE_CLOSE(distances(4, 10), (0.95 * 0.95), 1e-5);
- BOOST_REQUIRE(neighbors(3, 10) == 5);
- BOOST_REQUIRE_CLOSE(distances(3, 10), (1.22 * 1.22), 1e-5);
- BOOST_REQUIRE(neighbors(2, 10) == 7);
- BOOST_REQUIRE_CLOSE(distances(2, 10), (2.30 * 2.30), 1e-5);
- BOOST_REQUIRE(neighbors(1, 10) == 6);
- BOOST_REQUIRE_CLOSE(distances(1, 10), (3.00 * 3.00), 1e-5);
- BOOST_REQUIRE(neighbors(0, 10) == 4);
- BOOST_REQUIRE_CLOSE(distances(0, 10), (4.05 * 4.05), 1e-5);
-
- // Clean the memory.
- delete allkfn;
- }
-}
-
-/***
- * Test the dual-tree furthest-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_))
- 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_);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- AllkFN allkfn_(dual_query, dual_references);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- AllkFN naive_(naive_query, naive_references);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allkfn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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 furthest-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_))
- 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_references(data_for_tree_);
- arma::mat naive_references(data_for_tree_);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- AllkFN allkfn_(dual_references);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- AllkFN naive_(naive_references);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allkfn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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 furthest-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_))
- BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
-
- arma::mat single_query(data_for_tree_);
- arma::mat naive_query(data_for_tree_);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = true;
- AllkFN allkfn_(single_query);
-
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- AllkFN naive_(naive_query);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allkfn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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);
- }
-}
Deleted: mlpack/trunk/src/mlpack/methods/neighbor_search/allknn_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/neighbor_search/allknn_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/neighbor_search/allknn_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,440 +0,0 @@
-/**
- * @file allknn_test.cc
- *
- * Test file for AllkNN class
- */
-#include <mlpack/core.h>
-#include "neighbor_search.h"
-
-#define BOOST_TEST_MODULE AllkNN Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::neighbor;
-
-/***
- * Simple nearest-neighbors test with small, synthetic dataset. This is an
- * exhaustive test, which checks that each method for performing the calculation
- * (dual-tree, single-tree, naive) produces the correct results. An
- * eleven-point dataset and the ten nearest neighbors are taken. The dataset is
- * in one dimension for simplicity -- the correct functionality of distance
- * functions is not tested here.
- */
-BOOST_AUTO_TEST_CASE(exhaustive_synthetic_test) {
- // Set up our data.
- arma::mat data(1, 11);
- data[0] = 0.05; // Row addressing is unnecessary (they are all 0).
- data[1] = 0.35;
- data[2] = 0.15;
- data[3] = 1.25;
- data[4] = 5.05;
- data[5] = -0.22;
- data[6] = -2.00;
- data[7] = -1.30;
- data[8] = 0.45;
- data[9] = 0.90;
- data[10] = 1.00;
-
- // We will loop through three times, one for each method of performing the
- // calculation. We'll always use 10 neighbors, so set that parameter.
- CLI::GetParam<int>("neighbor_search/k") = 10;
- for (int i = 0; i < 3; i++) {
- AllkNN* allknn;
- arma::mat data_mutable = data;
- switch(i) {
- case 0: // Use the dual-tree method.
- allknn = new AllkNN(data_mutable);
- break;
- case 1: // Use the single-tree method.
- CLI::GetParam<bool>("neighbor_search/single_mode") = true;
- allknn = new AllkNN(data_mutable);
- break;
- case 2: // Use the naive method.
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- allknn = new AllkNN(data_mutable);
- break;
- }
-
- // Now perform the actual calculation.
- arma::Mat<size_t> neighbors;
- arma::mat distances;
- allknn->ComputeNeighbors(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
- // result, distance comparisons are written out as (distance * distance) for
- // readability.
-
- // Neighbors of point 0.
- BOOST_REQUIRE(neighbors(0, 0) == 2);
- BOOST_REQUIRE_CLOSE(distances(0, 0), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 0) == 5);
- BOOST_REQUIRE_CLOSE(distances(1, 0), (0.27 * 0.27), 1e-5);
- BOOST_REQUIRE(neighbors(2, 0) == 1);
- BOOST_REQUIRE_CLOSE(distances(2, 0), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(3, 0) == 8);
- BOOST_REQUIRE_CLOSE(distances(3, 0), (0.40 * 0.40), 1e-5);
- BOOST_REQUIRE(neighbors(4, 0) == 9);
- BOOST_REQUIRE_CLOSE(distances(4, 0), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(5, 0) == 10);
- BOOST_REQUIRE_CLOSE(distances(5, 0), (0.95 * 0.95), 1e-5);
- BOOST_REQUIRE(neighbors(6, 0) == 3);
- BOOST_REQUIRE_CLOSE(distances(6, 0), (1.20 * 1.20), 1e-5);
- BOOST_REQUIRE(neighbors(7, 0) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 0), (1.35 * 1.35), 1e-5);
- BOOST_REQUIRE(neighbors(8, 0) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 0), (2.05 * 2.05), 1e-5);
- BOOST_REQUIRE(neighbors(9, 0) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 0), (5.00 * 5.00), 1e-5);
-
- // Neighbors of point 1.
- BOOST_REQUIRE(neighbors(0, 1) == 8);
- BOOST_REQUIRE_CLOSE(distances(0, 1), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 1) == 2);
- BOOST_REQUIRE_CLOSE(distances(1, 1), (0.20 * 0.20), 1e-5);
- BOOST_REQUIRE(neighbors(2, 1) == 0);
- BOOST_REQUIRE_CLOSE(distances(2, 1), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(3, 1) == 9);
- BOOST_REQUIRE_CLOSE(distances(3, 1), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(4, 1) == 5);
- BOOST_REQUIRE_CLOSE(distances(4, 1), (0.57 * 0.57), 1e-5);
- BOOST_REQUIRE(neighbors(5, 1) == 10);
- BOOST_REQUIRE_CLOSE(distances(5, 1), (0.65 * 0.65), 1e-5);
- BOOST_REQUIRE(neighbors(6, 1) == 3);
- BOOST_REQUIRE_CLOSE(distances(6, 1), (0.90 * 0.90), 1e-5);
- BOOST_REQUIRE(neighbors(7, 1) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 1), (1.65 * 1.65), 1e-5);
- BOOST_REQUIRE(neighbors(8, 1) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 1), (2.35 * 2.35), 1e-5);
- BOOST_REQUIRE(neighbors(9, 1) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 1), (4.70 * 4.70), 1e-5);
-
- // Neighbors of point 2.
- BOOST_REQUIRE(neighbors(0, 2) == 0);
- BOOST_REQUIRE_CLOSE(distances(0, 2), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 2) == 1);
- BOOST_REQUIRE_CLOSE(distances(1, 2), (0.20 * 0.20), 1e-5);
- BOOST_REQUIRE(neighbors(2, 2) == 8);
- BOOST_REQUIRE_CLOSE(distances(2, 2), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(3, 2) == 5);
- BOOST_REQUIRE_CLOSE(distances(3, 2), (0.37 * 0.37), 1e-5);
- BOOST_REQUIRE(neighbors(4, 2) == 9);
- BOOST_REQUIRE_CLOSE(distances(4, 2), (0.75 * 0.75), 1e-5);
- BOOST_REQUIRE(neighbors(5, 2) == 10);
- BOOST_REQUIRE_CLOSE(distances(5, 2), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(6, 2) == 3);
- BOOST_REQUIRE_CLOSE(distances(6, 2), (1.10 * 1.10), 1e-5);
- BOOST_REQUIRE(neighbors(7, 2) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 2), (1.45 * 1.45), 1e-5);
- BOOST_REQUIRE(neighbors(8, 2) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 2), (2.15 * 2.15), 1e-5);
- BOOST_REQUIRE(neighbors(9, 2) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 2), (4.90 * 4.90), 1e-5);
-
- // Neighbors of point 3.
- BOOST_REQUIRE(neighbors(0, 3) == 10);
- BOOST_REQUIRE_CLOSE(distances(0, 3), (0.25 * 0.25), 1e-5);
- BOOST_REQUIRE(neighbors(1, 3) == 9);
- BOOST_REQUIRE_CLOSE(distances(1, 3), (0.35 * 0.35), 1e-5);
- BOOST_REQUIRE(neighbors(2, 3) == 8);
- BOOST_REQUIRE_CLOSE(distances(2, 3), (0.80 * 0.80), 1e-5);
- BOOST_REQUIRE(neighbors(3, 3) == 1);
- BOOST_REQUIRE_CLOSE(distances(3, 3), (0.90 * 0.90), 1e-5);
- BOOST_REQUIRE(neighbors(4, 3) == 2);
- BOOST_REQUIRE_CLOSE(distances(4, 3), (1.10 * 1.10), 1e-5);
- BOOST_REQUIRE(neighbors(5, 3) == 0);
- BOOST_REQUIRE_CLOSE(distances(5, 3), (1.20 * 1.20), 1e-5);
- BOOST_REQUIRE(neighbors(6, 3) == 5);
- BOOST_REQUIRE_CLOSE(distances(6, 3), (1.47 * 1.47), 1e-5);
- BOOST_REQUIRE(neighbors(7, 3) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 3), (2.55 * 2.55), 1e-5);
- BOOST_REQUIRE(neighbors(8, 3) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 3), (3.25 * 3.25), 1e-5);
- BOOST_REQUIRE(neighbors(9, 3) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 3), (3.80 * 3.80), 1e-5);
-
- // Neighbors of point 4.
- BOOST_REQUIRE(neighbors(0, 4) == 3);
- BOOST_REQUIRE_CLOSE(distances(0, 4), (3.80 * 3.80), 1e-5);
- BOOST_REQUIRE(neighbors(1, 4) == 10);
- BOOST_REQUIRE_CLOSE(distances(1, 4), (4.05 * 4.05), 1e-5);
- BOOST_REQUIRE(neighbors(2, 4) == 9);
- BOOST_REQUIRE_CLOSE(distances(2, 4), (4.15 * 4.15), 1e-5);
- BOOST_REQUIRE(neighbors(3, 4) == 8);
- BOOST_REQUIRE_CLOSE(distances(3, 4), (4.60 * 4.60), 1e-5);
- BOOST_REQUIRE(neighbors(4, 4) == 1);
- BOOST_REQUIRE_CLOSE(distances(4, 4), (4.70 * 4.70), 1e-5);
- BOOST_REQUIRE(neighbors(5, 4) == 2);
- BOOST_REQUIRE_CLOSE(distances(5, 4), (4.90 * 4.90), 1e-5);
- BOOST_REQUIRE(neighbors(6, 4) == 0);
- BOOST_REQUIRE_CLOSE(distances(6, 4), (5.00 * 5.00), 1e-5);
- BOOST_REQUIRE(neighbors(7, 4) == 5);
- BOOST_REQUIRE_CLOSE(distances(7, 4), (5.27 * 5.27), 1e-5);
- BOOST_REQUIRE(neighbors(8, 4) == 7);
- BOOST_REQUIRE_CLOSE(distances(8, 4), (6.35 * 6.35), 1e-5);
- BOOST_REQUIRE(neighbors(9, 4) == 6);
- BOOST_REQUIRE_CLOSE(distances(9, 4), (7.05 * 7.05), 1e-5);
-
- // Neighbors of point 5.
- BOOST_REQUIRE(neighbors(0, 5) == 0);
- BOOST_REQUIRE_CLOSE(distances(0, 5), (0.27 * 0.27), 1e-5);
- BOOST_REQUIRE(neighbors(1, 5) == 2);
- BOOST_REQUIRE_CLOSE(distances(1, 5), (0.37 * 0.37), 1e-5);
- BOOST_REQUIRE(neighbors(2, 5) == 1);
- BOOST_REQUIRE_CLOSE(distances(2, 5), (0.57 * 0.57), 1e-5);
- BOOST_REQUIRE(neighbors(3, 5) == 8);
- BOOST_REQUIRE_CLOSE(distances(3, 5), (0.67 * 0.67), 1e-5);
- BOOST_REQUIRE(neighbors(4, 5) == 7);
- BOOST_REQUIRE_CLOSE(distances(4, 5), (1.08 * 1.08), 1e-5);
- BOOST_REQUIRE(neighbors(5, 5) == 9);
- BOOST_REQUIRE_CLOSE(distances(5, 5), (1.12 * 1.12), 1e-5);
- BOOST_REQUIRE(neighbors(6, 5) == 10);
- BOOST_REQUIRE_CLOSE(distances(6, 5), (1.22 * 1.22), 1e-5);
- BOOST_REQUIRE(neighbors(7, 5) == 3);
- BOOST_REQUIRE_CLOSE(distances(7, 5), (1.47 * 1.47), 1e-5);
- BOOST_REQUIRE(neighbors(8, 5) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 5), (1.78 * 1.78), 1e-5);
- BOOST_REQUIRE(neighbors(9, 5) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 5), (5.27 * 5.27), 1e-5);
-
- // Neighbors of point 6.
- BOOST_REQUIRE(neighbors(0, 6) == 7);
- BOOST_REQUIRE_CLOSE(distances(0, 6), (0.70 * 0.70), 1e-5);
- BOOST_REQUIRE(neighbors(1, 6) == 5);
- BOOST_REQUIRE_CLOSE(distances(1, 6), (1.78 * 1.78), 1e-5);
- BOOST_REQUIRE(neighbors(2, 6) == 0);
- BOOST_REQUIRE_CLOSE(distances(2, 6), (2.05 * 2.05), 1e-5);
- BOOST_REQUIRE(neighbors(3, 6) == 2);
- BOOST_REQUIRE_CLOSE(distances(3, 6), (2.15 * 2.15), 1e-5);
- BOOST_REQUIRE(neighbors(4, 6) == 1);
- BOOST_REQUIRE_CLOSE(distances(4, 6), (2.35 * 2.35), 1e-5);
- BOOST_REQUIRE(neighbors(5, 6) == 8);
- BOOST_REQUIRE_CLOSE(distances(5, 6), (2.45 * 2.45), 1e-5);
- BOOST_REQUIRE(neighbors(6, 6) == 9);
- BOOST_REQUIRE_CLOSE(distances(6, 6), (2.90 * 2.90), 1e-5);
- BOOST_REQUIRE(neighbors(7, 6) == 10);
- BOOST_REQUIRE_CLOSE(distances(7, 6), (3.00 * 3.00), 1e-5);
- BOOST_REQUIRE(neighbors(8, 6) == 3);
- BOOST_REQUIRE_CLOSE(distances(8, 6), (3.25 * 3.25), 1e-5);
- BOOST_REQUIRE(neighbors(9, 6) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 6), (7.05 * 7.05), 1e-5);
-
- // Neighbors of point 7.
- BOOST_REQUIRE(neighbors(0, 7) == 6);
- BOOST_REQUIRE_CLOSE(distances(0, 7), (0.70 * 0.70), 1e-5);
- BOOST_REQUIRE(neighbors(1, 7) == 5);
- BOOST_REQUIRE_CLOSE(distances(1, 7), (1.08 * 1.08), 1e-5);
- BOOST_REQUIRE(neighbors(2, 7) == 0);
- BOOST_REQUIRE_CLOSE(distances(2, 7), (1.35 * 1.35), 1e-5);
- BOOST_REQUIRE(neighbors(3, 7) == 2);
- BOOST_REQUIRE_CLOSE(distances(3, 7), (1.45 * 1.45), 1e-5);
- BOOST_REQUIRE(neighbors(4, 7) == 1);
- BOOST_REQUIRE_CLOSE(distances(4, 7), (1.65 * 1.65), 1e-5);
- BOOST_REQUIRE(neighbors(5, 7) == 8);
- BOOST_REQUIRE_CLOSE(distances(5, 7), (1.75 * 1.75), 1e-5);
- BOOST_REQUIRE(neighbors(6, 7) == 9);
- BOOST_REQUIRE_CLOSE(distances(6, 7), (2.20 * 2.20), 1e-5);
- BOOST_REQUIRE(neighbors(7, 7) == 10);
- BOOST_REQUIRE_CLOSE(distances(7, 7), (2.30 * 2.30), 1e-5);
- BOOST_REQUIRE(neighbors(8, 7) == 3);
- BOOST_REQUIRE_CLOSE(distances(8, 7), (2.55 * 2.55), 1e-5);
- BOOST_REQUIRE(neighbors(9, 7) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 7), (6.35 * 6.35), 1e-5);
-
- // Neighbors of point 8.
- BOOST_REQUIRE(neighbors(0, 8) == 1);
- BOOST_REQUIRE_CLOSE(distances(0, 8), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 8) == 2);
- BOOST_REQUIRE_CLOSE(distances(1, 8), (0.30 * 0.30), 1e-5);
- BOOST_REQUIRE(neighbors(2, 8) == 0);
- BOOST_REQUIRE_CLOSE(distances(2, 8), (0.40 * 0.40), 1e-5);
- BOOST_REQUIRE(neighbors(3, 8) == 9);
- BOOST_REQUIRE_CLOSE(distances(3, 8), (0.45 * 0.45), 1e-5);
- BOOST_REQUIRE(neighbors(4, 8) == 10);
- BOOST_REQUIRE_CLOSE(distances(4, 8), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(5, 8) == 5);
- BOOST_REQUIRE_CLOSE(distances(5, 8), (0.67 * 0.67), 1e-5);
- BOOST_REQUIRE(neighbors(6, 8) == 3);
- BOOST_REQUIRE_CLOSE(distances(6, 8), (0.80 * 0.80), 1e-5);
- BOOST_REQUIRE(neighbors(7, 8) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 8), (1.75 * 1.75), 1e-5);
- BOOST_REQUIRE(neighbors(8, 8) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 8), (2.45 * 2.45), 1e-5);
- BOOST_REQUIRE(neighbors(9, 8) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 8), (4.60 * 4.60), 1e-5);
-
- // Neighbors of point 9.
- BOOST_REQUIRE(neighbors(0, 9) == 10);
- BOOST_REQUIRE_CLOSE(distances(0, 9), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 9) == 3);
- BOOST_REQUIRE_CLOSE(distances(1, 9), (0.35 * 0.35), 1e-5);
- BOOST_REQUIRE(neighbors(2, 9) == 8);
- BOOST_REQUIRE_CLOSE(distances(2, 9), (0.45 * 0.45), 1e-5);
- BOOST_REQUIRE(neighbors(3, 9) == 1);
- BOOST_REQUIRE_CLOSE(distances(3, 9), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(4, 9) == 2);
- BOOST_REQUIRE_CLOSE(distances(4, 9), (0.75 * 0.75), 1e-5);
- BOOST_REQUIRE(neighbors(5, 9) == 0);
- BOOST_REQUIRE_CLOSE(distances(5, 9), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(6, 9) == 5);
- BOOST_REQUIRE_CLOSE(distances(6, 9), (1.12 * 1.12), 1e-5);
- BOOST_REQUIRE(neighbors(7, 9) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 9), (2.20 * 2.20), 1e-5);
- BOOST_REQUIRE(neighbors(8, 9) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 9), (2.90 * 2.90), 1e-5);
- BOOST_REQUIRE(neighbors(9, 9) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 9), (4.15 * 4.15), 1e-5);
-
- // Neighbors of point 10.
- BOOST_REQUIRE(neighbors(0, 10) == 9);
- BOOST_REQUIRE_CLOSE(distances(0, 10), (0.10 * 0.10), 1e-5);
- BOOST_REQUIRE(neighbors(1, 10) == 3);
- BOOST_REQUIRE_CLOSE(distances(1, 10), (0.25 * 0.25), 1e-5);
- BOOST_REQUIRE(neighbors(2, 10) == 8);
- BOOST_REQUIRE_CLOSE(distances(2, 10), (0.55 * 0.55), 1e-5);
- BOOST_REQUIRE(neighbors(3, 10) == 1);
- BOOST_REQUIRE_CLOSE(distances(3, 10), (0.65 * 0.65), 1e-5);
- BOOST_REQUIRE(neighbors(4, 10) == 2);
- BOOST_REQUIRE_CLOSE(distances(4, 10), (0.85 * 0.85), 1e-5);
- BOOST_REQUIRE(neighbors(5, 10) == 0);
- BOOST_REQUIRE_CLOSE(distances(5, 10), (0.95 * 0.95), 1e-5);
- BOOST_REQUIRE(neighbors(6, 10) == 5);
- BOOST_REQUIRE_CLOSE(distances(6, 10), (1.22 * 1.22), 1e-5);
- BOOST_REQUIRE(neighbors(7, 10) == 7);
- BOOST_REQUIRE_CLOSE(distances(7, 10), (2.30 * 2.30), 1e-5);
- BOOST_REQUIRE(neighbors(8, 10) == 6);
- BOOST_REQUIRE_CLOSE(distances(8, 10), (3.00 * 3.00), 1e-5);
- BOOST_REQUIRE(neighbors(9, 10) == 4);
- BOOST_REQUIRE_CLOSE(distances(9, 10), (4.05 * 4.05), 1e-5);
-
- // Clean the memory.
- delete allknn;
- }
-}
-
-/**
- * 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_))
- 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_);
-
- // Reset parameters from last test.
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
-
- AllkNN allknn_(dual_query, dual_references);
-
- // Set parameters right for naive run.
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- AllkNN naive_(naive_query, naive_references);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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_))
- 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_);
-
- // Reset parameters from last test.
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- AllkNN allknn_(dual_query);
-
- // Set naive mode.
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- AllkNN naive_(naive_query);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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_))
- 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_);
-
- // Reset parameters from last test.
- CLI::GetParam<bool>("neighbor_search/naive_mode") = false;
- CLI::GetParam<bool>("neighbor_search/single_mode") = true;
- AllkNN allknn_(single_query);
-
- // Set up computation for naive mode.
- CLI::GetParam<bool>("neighbor_search/single_mode") = false;
- CLI::GetParam<bool>("neighbor_search/naive_mode") = true;
- AllkNN naive_(naive_query);
-
- arma::Mat<size_t> resulting_neighbors_tree;
- arma::mat distances_tree;
- allknn_.ComputeNeighbors(resulting_neighbors_tree, distances_tree);
-
- arma::Mat<size_t> resulting_neighbors_naive;
- arma::mat 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);
- }
-}
Deleted: mlpack/trunk/src/mlpack/methods/neighbor_search/sort_policies/sort_policy_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/neighbor_search/sort_policies/sort_policy_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/neighbor_search/sort_policies/sort_policy_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,311 +0,0 @@
-/**
- * @file sort_policy_test.cc
- * @author Ryan Curtin
- *
- * Tests for each of the implementations of the SortPolicy class.
- */
-#include <mlpack/core.h>
-#include <mlpack/core/tree/bounds.hpp>
-#include <mlpack/core/tree/binary_space_tree.hpp>
-
-// Classes to test.
-#include "nearest_neighbor_sort.hpp"
-#include "furthest_neighbor_sort.hpp"
-
-#define BOOST_TEST_MODULE SortPolicy Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::neighbor;
-using namespace mlpack::bound;
-
-// Tests for NearestNeighborSort
-
-/***
- * Ensure the best distance for nearest neighbors is 0.
- */
-BOOST_AUTO_TEST_CASE(nns_best_distance) {
- BOOST_REQUIRE(NearestNeighborSort::BestDistance() == 0);
-}
-
-/***
- * Ensure the worst distance for nearest neighbors is DBL_MAX.
- */
-BOOST_AUTO_TEST_CASE(nns_worst_distance) {
- BOOST_REQUIRE(NearestNeighborSort::WorstDistance() == DBL_MAX);
-}
-
-/***
- * Make sure the comparison works for values strictly less than the reference.
- */
-BOOST_AUTO_TEST_CASE(nns_is_better_strict) {
- BOOST_REQUIRE(NearestNeighborSort::IsBetter(5.0, 6.0) == true);
-}
-
-/***
- * Warn in case the comparison is not strict.
- */
-BOOST_AUTO_TEST_CASE(nns_is_better_not_strict) {
- BOOST_WARN(NearestNeighborSort::IsBetter(6.0, 6.0) == true);
-}
-
-/***
- * A simple test case of where to insert when all the values in the list are
- * DBL_MAX.
- */
-BOOST_AUTO_TEST_CASE(nns_sort_distance_all_dbl_max) {
- arma::vec list(5);
- list.fill(DBL_MAX);
-
- // Should be inserted at the head of the list.
- BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 5.0) == 0);
-}
-
-/***
- * Another test case, where we are just putting the new value in the middle of
- * the list.
- */
-BOOST_AUTO_TEST_CASE(nns_sort_distance_2) {
- arma::vec list(3);
- list[0] = 0.66;
- list[1] = 0.89;
- list[2] = 1.14;
-
- // Run a couple possibilities through.
- BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.61) == 0);
- BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.76) == 1);
- BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.99) == 2);
- BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 1.22) ==
- (size_t() - 1));
-}
-
-/***
- * Very simple sanity check to ensure that bounds are working alright. We will
- * use a one-dimensional bound for simplicity.
- */
-BOOST_AUTO_TEST_CASE(nns_node_to_node_distance) {
- // Well, there's no easy way to make HRectBounds the way we want, so we have
- // to make them and then expand the region to include new points.
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_one;
- arma::vec utility(1);
- utility[0] = 0;
-
- node_one.bound() = HRectBound<2>(1);
- node_one.bound() |= utility;
- utility[0] = 1;
- node_one.bound() |= utility;
-
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_two;
- node_two.bound() = HRectBound<2>(1);
-
- utility[0] = 5;
- node_two.bound() |= utility;
- utility[0] = 6;
- node_two.bound() |= utility;
-
- // This should use the L2 squared distance.
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), 16.0, 1e-5);
-
- // And another just to be sure, from the other side.
- node_two.bound().Clear();
- utility[0] = -2;
- node_two.bound() |= utility;
- utility[0] = -1;
- node_two.bound() |= utility;
-
- // Again, the distance is the L2 squared distance.
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), 1.0, 1e-5);
-
- // Now, when the bounds overlap.
- node_two.bound().Clear();
- utility[0] = -0.5;
- node_two.bound() |= utility;
- utility[0] = 0.5;
- node_two.bound() |= utility;
-
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), 0.0, 1e-5);
-}
-
-/***
- * Another very simple sanity check for the point-to-node case, again in one
- * dimension.
- */
-BOOST_AUTO_TEST_CASE(nns_point_to_node_distance) {
- // Well, there's no easy way to make HRectBounds the way we want, so we have
- // to make them and then expand the region to include new points.
- arma::vec utility(1);
- utility[0] = 0;
-
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node;
- node.bound() = HRectBound<2>(1);
- node.bound() |= utility;
- utility[0] = 1;
- node.bound() |= utility;
-
- arma::vec point(1);
- point[0] = -0.5;
-
- // The distance is the L2 squared distance.
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
- &node), 0.25, 1e-5);
-
- // Now from the other side of the bound.
- point[0] = 1.5;
-
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
- &node), 0.25, 1e-5);
-
- // And now when the point is inside the bound.
- point[0] = 0.5;
-
- BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
- &node), 0.0, 1e-5);
-}
-
-// Tests for FurthestNeighborSort
-
-/***
- * Ensure the best distance for furthest neighbors is DBL_MAX.
- */
-BOOST_AUTO_TEST_CASE(fns_best_distance) {
- BOOST_REQUIRE(FurthestNeighborSort::BestDistance() == DBL_MAX);
-}
-
-/***
- * Ensure the worst distance for furthest neighbors is 0.
- */
-BOOST_AUTO_TEST_CASE(fns_worst_distance) {
- BOOST_REQUIRE(FurthestNeighborSort::WorstDistance() == 0);
-}
-
-/***
- * Make sure the comparison works for values strictly less than the reference.
- */
-BOOST_AUTO_TEST_CASE(fns_is_better_strict) {
- BOOST_REQUIRE(FurthestNeighborSort::IsBetter(5.0, 4.0) == true);
-}
-
-/***
- * Warn in case the comparison is not strict.
- */
-BOOST_AUTO_TEST_CASE(fns_is_better_not_strict) {
- BOOST_WARN(FurthestNeighborSort::IsBetter(6.0, 6.0) == true);
-}
-
-/***
- * A simple test case of where to insert when all the values in the list are
- * 0.
- */
-BOOST_AUTO_TEST_CASE(fns_sort_distance_all_zero) {
- arma::vec list(5);
- list.fill(0);
-
- // Should be inserted at the head of the list.
- BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 5.0) == 0);
-}
-
-/***
- * Another test case, where we are just putting the new value in the middle of
- * the list.
- */
-BOOST_AUTO_TEST_CASE(fns_sort_distance_2) {
- arma::vec list(3);
- list[0] = 1.14;
- list[1] = 0.89;
- list[2] = 0.66;
-
- // Run a couple possibilities through.
- BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 1.22) == 0);
- BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.93) == 1);
- BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.68) == 2);
- BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.62) ==
- (size_t() - 1));
-}
-
-/***
- * Very simple sanity check to ensure that bounds are working alright. We will
- * use a one-dimensional bound for simplicity.
- */
-BOOST_AUTO_TEST_CASE(fns_node_to_node_distance) {
- // Well, there's no easy way to make HRectBounds the way we want, so we have
- // to make them and then expand the region to include new points.
- arma::vec utility(1);
- utility[0] = 0;
-
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_one;
- node_one.bound() = HRectBound<2>(1);
- node_one.bound() |= utility;
- utility[0] = 1;
- node_one.bound() |= utility;
-
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_two;
- node_two.bound() = HRectBound<2>(1);
- utility[0] = 5;
- node_two.bound() |= utility;
- utility[0] = 6;
- node_two.bound() |= utility;
-
- // This should use the L2 squared distance.
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), 36.0, 1e-5);
-
- // And another just to be sure, from the other side.
- node_two.bound().Clear();
- utility[0] = -2;
- node_two.bound() |= utility;
- utility[0] = -1;
- node_two.bound() |= utility;
-
- // Again, the distance is the L2 squared distance.
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), 9.0, 1e-5);
-
- // Now, when the bounds overlap.
- node_two.bound().Clear();
- utility[0] = -0.5;
- node_two.bound() |= utility;
- utility[0] = 0.5;
- node_two.bound() |= utility;
-
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
- &node_two), (1.5 * 1.5), 1e-5);
-}
-
-/***
- * Another very simple sanity check for the point-to-node case, again in one
- * dimension.
- */
-BOOST_AUTO_TEST_CASE(fns_point_to_node_distance) {
- // Well, there's no easy way to make HRectBounds the way we want, so we have
- // to make them and then expand the region to include new points.
- arma::vec utility(1);
- utility[0] = 0;
-
- tree::BinarySpaceTree<HRectBound<2>, arma::mat> node;
- node.bound() = HRectBound<2>(1);
- node.bound() |= utility;
- utility[0] = 1;
- node.bound() |= utility;
-
- arma::vec point(1);
- point[0] = -0.5;
-
- // The distance is the L2 squared distance.
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
- &node), (1.5 * 1.5), 1e-5);
-
- // Now from the other side of the bound.
- point[0] = 1.5;
-
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
- &node), (1.5 * 1.5), 1e-5);
-
- // And now when the point is inside the bound.
- point[0] = 0.5;
-
- BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
- &node), 0.25, 1e-5);
-}
Deleted: mlpack/trunk/src/mlpack/methods/neighbor_search/test_data_3_1000.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/neighbor_search/test_data_3_1000.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/neighbor_search/test_data_3_1000.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,1000 +0,0 @@
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-0.770271664,0.743519141,0.117959307
-0.107862896,0.552555172,0.592259145
-0.445007388,0.046308389,0.69499137
-0.056486616,0.370154602,0.498507879
-0.347798483,0.541312622,0.44955603
-0.01637411,0.777726654,0.346640124
-0.918778501,0.247274577,0.931656904
-0.468325578,0.552066653,0.233304727
-0.558842714,0.30110019,0.237582706
-0.520406065,0.396600845,0.627623904
-0.42717615,0.55961213,0.312743984
-0.043819454,0.060632818,0.168267929
-0.151405047,0.276450913,0.385322692
-0.864539894,0.203199707,0.865006307
-0.866179018,0.649792248,0.369625823
-0.566181508,0.155001949,0.751738414
-0.022193506,0.262524266,0.378478591
-0.835870282,0.436869514,0.439857307
-0.54507765,0.825712044,0.425012638
-0.180124959,0.284189803,0.059324375
-0.91303517,0.659662103,0.021990781
-0.068890512,0.857174742,0.245915138
-0.146299591,0.2282098,0.992357695
-0.279495766,0.087424865,0.532747766
-0.095737503,0.107245868,0.190786801
-0.276947216,0.537071712,0.654100689
-0.010738646,0.40673838,0.479608479
-0.420307684,0.947352567,0.178277524
-0.108124774,0.127227634,0.278086371
-0.18958629,0.587262704,0.69187928
-0.814773727,0.220263054,0.007250506
-0.948149379,0.572617808,0.939774741
-0.150492895,0.970045889,0.979230909
-0.997567108,0.897085006,0.573132383
-0.039773611,0.517659257,0.317936584
-0.915778891,0.598912752,0.541405962
-0.081857212,0.994515385,0.261260636
Modified: mlpack/trunk/src/mlpack/methods/nnsvm/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/nnsvm/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nnsvm/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -18,14 +18,6 @@
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-add_executable(nnsvm_test
- nnsvm_test.cpp
-)
-target_link_libraries(nnsvm_test
- mlpack
- boost_unit_test_framework
-)
-
add_executable(nnsvm
nnsvm_main.cpp
)
Deleted: mlpack/trunk/src/mlpack/methods/nnsvm/nnsvm_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/nnsvm/nnsvm_test.cpp 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nnsvm/nnsvm_test.cpp 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,286 +0,0 @@
-/**
- * @file nnsvm_test.cc
- *
- * Test file for NNSVM class
- */
-#include <iostream>
-#include "nnsvm.hpp"
-#include <mlpack/core/kernels/linear_kernel.hpp>
-
-#define BOOST_TEST_MODULE NNSVM Test
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace mlpack::nnsvm;
-
-/***
- * 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);
-// }
-//}
Deleted: mlpack/trunk/src/mlpack/methods/nnsvm/toy1.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/nnsvm/toy1.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nnsvm/toy1.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,8 +0,0 @@
--1,1,1
--2,2,1
--3,3,1
--4,4,1
-1,-1,0
-2,-2,0
-3,-3,0
-4,-4,0
Deleted: mlpack/trunk/src/mlpack/methods/nnsvm/toy4.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/nnsvm/toy4.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nnsvm/toy4.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,20 +0,0 @@
-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
-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
-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
-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
-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
--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
-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
-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
-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
--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
-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
--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
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Deleted: mlpack/trunk/src/mlpack/methods/nnsvm/toy7.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/nnsvm/toy7.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/nnsvm/toy7.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,200 +0,0 @@
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-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
-4.0436,-0.1779,16,6.3,53.02,2063.1,0.4277,12.58,-4.546,47.126,40.201,1,31.759,61.439,9.7,47.555,52.51,-4.87,0.0659,-23,0
-0.1026,-0.0945,6,14.9,18.66,1382.4,-5.3173,6.7262,-7.1129,21.258,6.9558,1,28.86,18.518,10.52,29.58,27.365,-28.09,0.0057,0,0
--3.8549,0.0657,16,-2,-2.88,310.4,-6.6109,0,-13.361,45.486,-3.7291,0,14.135,-7.2295,-3.6,-7.63,12.495,-7.89,-3.1142,-11,0
-1.6878,0.3138,26,16.3,104,1848.4,-0.5485,9.1603,-3.3124,54.779,64.369,-1,29.399,92.02,25.16,58.875,53.995,-15.16,-0.5755,-7,0
--2.5046,-0.2948,12,4,4.6001,944.78,-3.4152,2.0648,-4.3948,-13.142,32.818,-1,31.621,2.925,18.18,5.52,22.705,-1.53,-1.7848,-2,0
-1.3379,-1.4972,31,-5.4,105.86,506.81,3.5891,13.123,-3.4572,26.884,50.669,3,22.967,56.845,16.74,54.435,45.72,2.93,-0.8295,-16,0
-2.9458,-0.4059,27,19.2,132.94,1466.8,-2.0312,11.315,-7.1218,63.938,47.036,1,30.939,47.839,12.48,68.61,46.135,2.62,-1.0866,-12,0
-0.7181,-0.6681,17,18.6,107.84,1505.5,3.0394,11.278,-1.5126,35.886,45.799,1,19.217,82.535,20.48,52.45,46.795,0.45,0.2042,-23,0
--0.0977,0.6092,10,-32,-55.34,-594.72,-7.2615,-0.4144,-12.755,-9.818,6.1117,0,6.7085,8.9224,1.1,-12.275,-0.585,-22.24,0.7999,-15,0
--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
--2.9211,0.0144,5,-26.6,-89.38,-68.479,-10.38,0,-11.584,6.1355,-13.547,-1,25.748,-27.403,2.12,-4.875,16.35,-19.04,-4.6781,-45,0
-0.8085,-0.1135,1,-7.8,-17.53,132.51,-8.9752,0,-8.362,5.3134,-27.054,1,24.445,1.3176,9.24,16.62,15.005,-14.95,-2.9307,-30,0
--1.5869,-0.7415,21,-5.9,18.99,761.61,-5.0233,0.6843,-12.187,19.223,1.8359,2,19.574,37.13,2.76,19.01,6.165,-2.66,-6.453,-5,0
--4.7617,-0.0465,7,7.2,-22.3,315.14,-13.297,-0.4815,-15.752,26.744,2.8514,2,6.5229,-49.112,-1.04,6.775,6.93,-37.69,-0.8049,-6,0
-0.8557,-0.6778,13,-15.2,-11.77,62.264,-8.8268,0.4082,-9.0521,10.889,8.6088,2,23.601,6.6705,13.12,-8.24,20.15,-22.61,-5.4804,-17,0
-0.2314,-0.8974,11,-14.5,-54.4,560.32,-9.4867,-1.0779,-10.009,-33.122,-21.062,1,18.475,17.681,-10.64,-12.39,47.12,-16.84,-4.5213,-42,0
--0.2776,-0.1825,25,-18.2,40.34,300.78,-8.1996,0.2114,-15.479,37.373,-3.5849,0,13.603,30.217,7.94,0.755,11.825,-7.79,-5.7419,2,0
--1.0254,-0.607,6,10.4,-21.57,845.36,-1.7928,4.488,-4.6268,37.708,29.412,1,21.242,61.839,8.22,5.245,33.545,-24.46,-4.4331,6,0
-1.4261,-1.0991,13,18.9,85.22,1821,5.3641,11.771,-1.4539,33.562,61.001,2,25.352,83.064,16.7,18.385,38.585,-0.7,-2.1668,-47,0
Modified: mlpack/trunk/src/mlpack/methods/regression/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/regression/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/regression/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -17,14 +17,6 @@
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
-add_executable(ridge_regression_test
- ridge_regression_test.cc
-)
-target_link_libraries(ridge_regression_test
- mlpack
- boost_unit_test_framework
-)
-
add_executable(ridge_regression
ridge_main.cc
)
Deleted: mlpack/trunk/src/mlpack/methods/regression/ridge_regression_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/regression/ridge_regression_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/regression/ridge_regression_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,89 +0,0 @@
-/*
- * =====================================================================================
- *
- * Filename: ridge_regression_test.cc
- *
- * Description:
- *
- * Version: 1.0
- * Created: 02/15/2009 01:34:25 PM EST
- * Revision: none
- * Compiler: gcc
- *
- * Author: Nikolaos Vasiloglou (NV), nvasil at ieee.org
- * Company: Georgia Tech Fastlab-ESP Lab
- *
- * =====================================================================================
- */
-#include <mlpack/core.h>
-#include "ridge_regression.h"
-#include "ridge_regression_util.h"
-
-#include <iostream>
-
-#define BOOST_TEST_MODULE RidgeRegressionTest
-#include <boost/test/unit_test.hpp>
-
-using namespace mlpack;
-using namespace regression;
-
-/*
-BOOST_AUTO_TEST_CASE(TestSVDNormalEquationRegressVersusSVDRegress) {
-
- arma::mat predictors_ = "1.2 4.2 2.1 0.3 4.2;"
- "3.1 1.1 4.7 1.8 0.4;"
- "2.5 3.3 9.1 7.4 0.1";
- arma::mat predictions_ = "0.4 0.33 0.8 1.4 3.3";
- arma::mat true_factors_ = "0; 0; 0";
-
- RidgeRegression engine_(predictors_, predictions_, true);
- engine_.SVDRegress(0);
- RidgeRegression svd_engine(predictors_, predictions_, false);
- svd_engine.SVDRegress(0);
- arma::mat factors, svd_factors;
-
- engine_.factors(&factors);
- svd_engine.factors(&svd_factors);
-
- for(size_t i=0; i<factors.n_rows; i++) {
- BOOST_REQUIRE_CLOSE(factors(i, 0), svd_factors(i, 0), 1e-5);
- }
-}
-*/
-
-BOOST_AUTO_TEST_CASE(TestVIFBasedFeatureSelection) {
- // Craft a synthetic dataset in which the third dimension is
- // completely dependent on the first and the second.
- arma::mat synthetic_data;
- arma::mat synthetic_data_target_training_values;
- synthetic_data.zeros(4, 5);
- synthetic_data_target_training_values.zeros(1, 5);
- for(size_t i = 0; i < 5; i++) {
- synthetic_data(0, i) = i;
- synthetic_data(1, i) = 3 * i + 1;
- synthetic_data(2, i) = 4;
- synthetic_data(3, i) = 5;
- synthetic_data_target_training_values(0, i) = i;
- }
- arma::Col<size_t> predictor_indices;
- arma::Col<size_t> prune_predictor_indices;
- arma::Col<size_t> output_predictor_indices;
- predictor_indices.zeros(4);
- predictor_indices[0] = 0;
- predictor_indices[1] = 1;
- predictor_indices[2] = 2;
- predictor_indices[3] = 3;
- prune_predictor_indices = predictor_indices;
- RidgeRegression engine_(synthetic_data, predictor_indices,
- synthetic_data_target_training_values);
- engine_.FeatureSelectedRegression(predictor_indices,
- prune_predictor_indices,
- synthetic_data_target_training_values,
- &output_predictor_indices);
- std::cout << "Output indices: ";
- for(size_t i = 0; i < output_predictor_indices.n_elem; i++) {
- std::cout << output_predictor_indices[i] << ' ';
- }
- std::cout << std::endl;
-}
-
Modified: mlpack/trunk/src/mlpack/methods/svm/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -24,11 +24,3 @@
target_link_libraries(svm
mlpack
)
-
-add_executable(svm_test
- svm_test.cc
-)
-target_link_libraries(svm_test
- mlpack
- boost_unit_test_framework
-)
Deleted: mlpack/trunk/src/mlpack/methods/svm/c_testdata2.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/c_testdata2.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/c_testdata2.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,20 +0,0 @@
-7.19906628001437787e-01, 1.83250823399634477e+00, 0
-1.37899419263889733e+01, 1.78198235122579263e+00, 1
-6.68859485848275703e-01, 2.14083320956715983e+00, 0
-1.84729928795588165e+01, 2.25024702760868101e+00, 1
-9.22802773268335819e-01, 1.61469358350834513e+00, 0
-2.06209849662245204e-01, 6.34699695340683490e-01, 1
-4.01062068250524817e-01, 1.65802752932441777e+00, 0
-5.02985607135568635e+00, 1.39976642741810831e+00, 1
-3.66471199955079319e-01, 1.62780588172739638e+00, 0
-1.56912570240400999e+01, 2.16941541650770953e+00, 1
-9.98909584711729304e-01, 2.00337906391517206e+00, 0
-1.31430438780891912e+01, 1.34410346059319719e+00, 1
-3.41572957272442523e-01, 1.16758463655951639e+00, 0
-9.53941410851637528e-01, 6.30271704462483373e-01, 1
-7.07135529120981432e-01, 2.17763537339756041e+00, 0
-9.68899714280338742e+00, 1.26922579378319256e+00, 1
-9.82393905512240706e-01, 2.36790583090293483e+00, 0
-1.31583349281727973e+01, 1.45115094722767868e+00, 1
-3.80991188521027202e-01, 9.05379134419085019e-01, 0
-1.86057436180327755e+01, 2.26941891469499968e+00, 1
Deleted: mlpack/trunk/src/mlpack/methods/svm/c_testdata3.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/c_testdata3.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/c_testdata3.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,30 +0,0 @@
-7.19906628001437787e-01, 1.83250823399634477e+00, 0
-7.50178118120030568e+00, 1.40286083282104723e+00, 1
-1.37899419263889733e+01, 1.78198235122579263e+00, 2
-6.68859485848275703e-01, 2.14083320956715983e+00, 0
-5.38965188683460106e+00, 1.76065058040462930e+00, 1
-1.84729928795588165e+01, 2.25024702760868101e+00, 2
-9.22802773268335819e-01, 1.61469358350834513e+00, 0
-1.50869173533687917e+00, 1.16481918686293984e+00, 1
-2.06209849662245204e-01, 6.34699695340683490e-01, 2
-4.01062068250524817e-01, 1.65802752932441777e+00, 0
-7.50323660089785172e+00, 2.02272848157339169e+00, 1
-5.02985607135568635e+00, 1.39976642741810831e+00, 2
-3.66471199955079319e-01, 1.62780588172739638e+00, 0
-6.49408345878780047e+00, 2.05530758041669959e+00, 1
-1.56912570240400999e+01, 2.16941541650770953e+00, 2
-9.98909584711729304e-01, 2.00337906391517206e+00, 0
-4.97454170834950204e+00, 1.74654187366671021e+00, 1
-1.31430438780891912e+01, 1.34410346059319719e+00, 2
-3.41572957272442523e-01, 1.16758463655951639e+00, 0
-1.58924989941960670e+00, 1.53946313589786321e+00, 1
-9.53941410851637528e-01, 6.30271704462483373e-01, 2
-7.07135529120981432e-01, 2.17763537339756041e+00, 0
-2.74465444159910765e+00, 1.63247014681458014e+00, 1
-9.68899714280338742e+00, 1.26922579378319256e+00, 2
-9.82393905512240706e-01, 2.36790583090293483e+00, 0
-4.17413972512545972e-01, 1.27445896238761902e+00, 1
-1.31583349281727973e+01, 1.45115094722767868e+00, 2
-3.80991188521027202e-01, 9.05379134419085019e-01, 0
-5.45688821256947176e-01, 5.84968416525502022e-01, 1
-1.86057436180327755e+01, 2.26941891469499968e+00, 2
Deleted: mlpack/trunk/src/mlpack/methods/svm/c_traindata2.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/c_traindata2.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/c_traindata2.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,20 +0,0 @@
-7.19906628001437787e-01, 1.83250823399634477e+00, 0
-1.37899419263889733e+01, 1.78198235122579263e+00, 1
-6.68859485848275703e-01, 2.14083320956715983e+00, 0
-1.84729928795588165e+01, 2.25024702760868101e+00, 1
-9.22802773268335819e-01, 1.61469358350834513e+00, 0
-2.06209849662245204e-01, 6.34699695340683490e-01, 1
-4.01062068250524817e-01, 1.65802752932441777e+00, 0
-5.02985607135568635e+00, 1.39976642741810831e+00, 1
-3.66471199955079319e-01, 1.62780588172739638e+00, 0
-1.56912570240400999e+01, 2.16941541650770953e+00, 1
-9.98909584711729304e-01, 2.00337906391517206e+00, 0
-1.31430438780891912e+01, 1.34410346059319719e+00, 1
-3.41572957272442523e-01, 1.16758463655951639e+00, 0
-9.53941410851637528e-01, 6.30271704462483373e-01, 1
-7.07135529120981432e-01, 2.17763537339756041e+00, 0
-9.68899714280338742e+00, 1.26922579378319256e+00, 1
-9.82393905512240706e-01, 2.36790583090293483e+00, 0
-1.31583349281727973e+01, 1.45115094722767868e+00, 1
-3.80991188521027202e-01, 9.05379134419085019e-01, 0
-1.86057436180327755e+01, 2.26941891469499968e+00, 1
Deleted: mlpack/trunk/src/mlpack/methods/svm/c_traindata3.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/c_traindata3.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/c_traindata3.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,30 +0,0 @@
-7.19906628001437787e-01, 1.83250823399634477e+00, 0
-7.50178118120030568e+00, 1.40286083282104723e+00, 1
-1.37899419263889733e+01, 1.78198235122579263e+00, 2
-6.68859485848275703e-01, 2.14083320956715983e+00, 0
-5.38965188683460106e+00, 1.76065058040462930e+00, 1
-1.84729928795588165e+01, 2.25024702760868101e+00, 2
-9.22802773268335819e-01, 1.61469358350834513e+00, 0
-1.50869173533687917e+00, 1.16481918686293984e+00, 1
-2.06209849662245204e-01, 6.34699695340683490e-01, 2
-4.01062068250524817e-01, 1.65802752932441777e+00, 0
-7.50323660089785172e+00, 2.02272848157339169e+00, 1
-5.02985607135568635e+00, 1.39976642741810831e+00, 2
-3.66471199955079319e-01, 1.62780588172739638e+00, 0
-6.49408345878780047e+00, 2.05530758041669959e+00, 1
-1.56912570240400999e+01, 2.16941541650770953e+00, 2
-9.98909584711729304e-01, 2.00337906391517206e+00, 0
-4.97454170834950204e+00, 1.74654187366671021e+00, 1
-1.31430438780891912e+01, 1.34410346059319719e+00, 2
-3.41572957272442523e-01, 1.16758463655951639e+00, 0
-1.58924989941960670e+00, 1.53946313589786321e+00, 1
-9.53941410851637528e-01, 6.30271704462483373e-01, 2
-7.07135529120981432e-01, 2.17763537339756041e+00, 0
-2.74465444159910765e+00, 1.63247014681458014e+00, 1
-9.68899714280338742e+00, 1.26922579378319256e+00, 2
-9.82393905512240706e-01, 2.36790583090293483e+00, 0
-4.17413972512545972e-01, 1.27445896238761902e+00, 1
-1.31583349281727973e+01, 1.45115094722767868e+00, 2
-3.80991188521027202e-01, 9.05379134419085019e-01, 0
-5.45688821256947176e-01, 5.84968416525502022e-01, 1
-1.86057436180327755e+01, 2.26941891469499968e+00, 2
Deleted: mlpack/trunk/src/mlpack/methods/svm/r_testdata.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/r_testdata.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/r_testdata.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,100 +0,0 @@
- -10.0000 -0.0544
- -9.7980 -0.0372
- -9.5960 -0.0178
- -9.3939 0.0033
- -9.1919 0.0251
- -8.9899 0.0469
- -8.7879 0.0677
- -8.5859 0.0866
- -8.3838 0.1029
- -8.1818 0.1157
- -7.9798 0.1243
- -7.7778 0.1282
- -7.5758 0.1269
- -7.3737 0.1203
- -7.1717 0.1082
- -6.9697 0.0909
- -6.7677 0.0688
- -6.5657 0.0425
- -6.3636 0.0126
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- -5.1515 -0.1757
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- -4.7475 -0.2105
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- -4.3434 -0.2147
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- -3.9394 -0.1817
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- -3.3333 -0.0572
- -3.1313 0.0033
- -2.9293 0.0719
- -2.7273 0.1476
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- -2.3232 0.3142
- -2.1212 0.4018
- -1.9192 0.4897
- -1.7172 0.5761
- -1.5152 0.6590
- -1.3131 0.7364
- -1.1111 0.8066
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- -0.5051 0.9580
- -0.3030 0.9848
- -0.1010 0.9983
- 0.1010 0.9983
- 0.3030 0.9848
- 0.5051 0.9580
- 0.7071 0.9187
- 0.9091 0.8678
- 1.1111 0.8066
- 1.3131 0.7364
- 1.5152 0.6590
- 1.7172 0.5761
- 1.9192 0.4897
- 2.1212 0.4018
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- 2.5253 0.2289
- 2.7273 0.1476
- 2.9293 0.0719
- 3.1313 0.0033
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- 6.5657 0.0425
- 6.7677 0.0688
- 6.9697 0.0909
- 7.1717 0.1082
- 7.3737 0.1203
- 7.5758 0.1269
- 7.7778 0.1282
- 7.9798 0.1243
- 8.1818 0.1157
- 8.3838 0.1029
- 8.5859 0.0866
- 8.7879 0.0677
- 8.9899 0.0469
- 9.1919 0.0251
- 9.3939 0.0033
- 9.5960 -0.0178
- 9.7980 -0.0372
- 10.0000 -0.0544
\ No newline at end of file
Deleted: mlpack/trunk/src/mlpack/methods/svm/r_traindata.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/r_traindata.csv 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/r_traindata.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,100 +0,0 @@
- -10.0000, 0.0320
- -9.7980, -0.0278
- -9.5960, -0.1029
- -9.3939, 0.0906
- -9.1919, -0.0187
- -8.9899, 0.0039
- -8.7879, -0.0426
- -8.5859, 0.1263
- -8.3838, 0.0064
- -8.1818, 0.1326
- -7.9798, -0.0722
- -7.7778, 0.0538
- -7.5758, 0.0717
- -7.3737, 0.0383
- -7.1717, 0.2191
- -6.9697, 0.0294
- -6.7677, 0.0434
- -6.5657, 0.0155
- -6.3636, -0.1546
- -6.1616, -0.2073
- -5.9596, 0.0041
- -5.7576, -0.1738
- -5.5556, -0.3314
- -5.3535, -0.2461
- -5.1515, -0.1544
- -4.9495, -0.1486
- -4.7475, -0.2004
- -4.5455, -0.1872
- -4.3434, -0.1577
- -4.1414, -0.3656
- -3.9394, -0.1174
- -3.7374, -0.0820
- -3.5354, -0.1071
- -3.3333, -0.1873
- -3.1313, -0.1252
- -2.9293, 0.1532
- -2.7273, 0.2315
- -2.5253, 0.3709
- -2.3232, 0.2153
- -2.1212, 0.2835
- -1.9192, 0.4431
- -1.7172, 0.5395
- -1.5152, 0.7708
- -1.3131, 0.6898
- -1.1111, 0.6505
- -0.9091, 0.8395
- -0.7071, 0.7864
- -0.5051, 0.9384
- -0.3030, 1.0267
- -0.1010, 1.0725
- 0.1010, 0.9840
- 0.3030, 0.7686
- 0.5051, 0.8936
- 0.7071, 1.0627
- 0.9091, 0.7831
- 1.1111, 0.8123
- 1.3131, 0.8007
- 1.5152, 0.5919
- 1.7172, 0.5758
- 1.9192, 0.5250
- 2.1212, 0.5198
- 2.3232, 0.2456
- 2.5253, 0.3966
- 2.7273, 0.1221
- 2.9293, 0.0072
- 3.1313, -0.0149
- 3.3333, 0.0280
- 3.5354, -0.1392
- 3.7374, -0.1942
- 3.9394, -0.2429
- 4.1414, -0.2517
- 4.3434, -0.0950
- 4.5455, -0.0775
- 4.7475, -0.1940
- 4.9495, -0.2474
- 5.1515, -0.0379
- 5.3535, -0.0198
- 5.5556, -0.1327
- 5.7576, -0.0131
- 5.9596, 0.0798
- 6.1616, -0.0475
- 6.3636, -0.0202
- 6.5657, 0.0412
- 6.7677, 0.1591
- 6.9697, -0.0203
- 7.1717, 0.0243
- 7.3737, 0.1238
- 7.5758, 0.0023
- 7.7778, 0.2166
- 7.9798, 0.3782
- 8.1818, 0.2474
- 8.3838, 0.2471
- 8.5859, 0.2333
- 8.7879, -0.0430
- 8.9899, 0.0008
- 9.1919, 0.0231
- 9.3939, -0.0013
- 9.5960, -0.0722
- 9.7980, 0.0545
- 10.0000, -0.0563
\ No newline at end of file
Deleted: mlpack/trunk/src/mlpack/methods/svm/svm_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/svm/svm_test.cc 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/methods/svm/svm_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -1,145 +0,0 @@
-#include <mlpack/core.h>
-
-#include "svm.h"
-
-#define BOOST_TEST_MODULE SVM test
-#include <boost/test/unit_test.hpp>
-
-using std::string;
-using namespace mlpack;
-using namespace mlpack::svm;
-
-// Create test data
-arma::mat matrix(20,3);
-bool first = true;
-
-/***
- *
- */
-/*
-BOOST_AUTO_TEST_CASE(test_test) {
- double x = 1.0, y = 1.0, z = 2.0;
-
- // We need to make sure someValue is 1.
- BOOST_REQUIRE_CLOSE(x, y, 1e-5);
- BOOST_REQUIRE_CLOSE(z, y, 1e-5);
-}
-*/
-
- //CLI::GetParam<>() = ;
-
-/**
- * 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);
-//}
Modified: mlpack/trunk/src/mlpack/tests/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2011-11-08 23:38:45 UTC (rev 10194)
+++ mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2011-11-08 23:57:26 UTC (rev 10195)
@@ -27,6 +27,7 @@
nnsvm_test.cpp
ridge_regression_test.cc
save_restore_utility_test.cpp
+ sort_policy_test.cc
svm_test.cc
tree_test.cpp
union_find_test.cpp
Added: mlpack/trunk/src/mlpack/tests/data/trainRes.csv
===================================================================
--- mlpack/trunk/src/mlpack/tests/data/trainRes.csv (rev 0)
+++ mlpack/trunk/src/mlpack/tests/data/trainRes.csv 2011-11-08 23:57:26 UTC (rev 10195)
@@ -0,0 +1,2 @@
+2.75, 4.0000, 3.6875, 2.3750, 0.3333, 0.8000, 0.6292, 0.3833, 0.5161
+8.3333, 4.6667, 3.6667, 2.4, 0.8095, 3.3810, 0.6667, 0.4000, 0.4839
Added: mlpack/trunk/src/mlpack/tests/sort_policy_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/tests/sort_policy_test.cc (rev 0)
+++ mlpack/trunk/src/mlpack/tests/sort_policy_test.cc 2011-11-08 23:57:26 UTC (rev 10195)
@@ -0,0 +1,310 @@
+/**
+ * @file sort_policy_test.cc
+ * @author Ryan Curtin
+ *
+ * Tests for each of the implementations of the SortPolicy class.
+ */
+#include <mlpack/core.h>
+#include <mlpack/core/tree/bounds.hpp>
+#include <mlpack/core/tree/binary_space_tree.hpp>
+
+// Classes to test.
+#include <mlpack/methods/neighbor_search/sort_policies/nearest_neighbor_sort.hpp>
+#include <mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+using namespace mlpack;
+using namespace mlpack::neighbor;
+using namespace mlpack::bound;
+
+// Tests for NearestNeighborSort
+
+/***
+ * Ensure the best distance for nearest neighbors is 0.
+ */
+BOOST_AUTO_TEST_CASE(nns_best_distance) {
+ BOOST_REQUIRE(NearestNeighborSort::BestDistance() == 0);
+}
+
+/***
+ * Ensure the worst distance for nearest neighbors is DBL_MAX.
+ */
+BOOST_AUTO_TEST_CASE(nns_worst_distance) {
+ BOOST_REQUIRE(NearestNeighborSort::WorstDistance() == DBL_MAX);
+}
+
+/***
+ * Make sure the comparison works for values strictly less than the reference.
+ */
+BOOST_AUTO_TEST_CASE(nns_is_better_strict) {
+ BOOST_REQUIRE(NearestNeighborSort::IsBetter(5.0, 6.0) == true);
+}
+
+/***
+ * Warn in case the comparison is not strict.
+ */
+BOOST_AUTO_TEST_CASE(nns_is_better_not_strict) {
+ BOOST_WARN(NearestNeighborSort::IsBetter(6.0, 6.0) == true);
+}
+
+/***
+ * A simple test case of where to insert when all the values in the list are
+ * DBL_MAX.
+ */
+BOOST_AUTO_TEST_CASE(nns_sort_distance_all_dbl_max) {
+ arma::vec list(5);
+ list.fill(DBL_MAX);
+
+ // Should be inserted at the head of the list.
+ BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 5.0) == 0);
+}
+
+/***
+ * Another test case, where we are just putting the new value in the middle of
+ * the list.
+ */
+BOOST_AUTO_TEST_CASE(nns_sort_distance_2) {
+ arma::vec list(3);
+ list[0] = 0.66;
+ list[1] = 0.89;
+ list[2] = 1.14;
+
+ // Run a couple possibilities through.
+ BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.61) == 0);
+ BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.76) == 1);
+ BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 0.99) == 2);
+ BOOST_REQUIRE(NearestNeighborSort::SortDistance(list, 1.22) ==
+ (size_t() - 1));
+}
+
+/***
+ * Very simple sanity check to ensure that bounds are working alright. We will
+ * use a one-dimensional bound for simplicity.
+ */
+BOOST_AUTO_TEST_CASE(nns_node_to_node_distance) {
+ // Well, there's no easy way to make HRectBounds the way we want, so we have
+ // to make them and then expand the region to include new points.
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_one;
+ arma::vec utility(1);
+ utility[0] = 0;
+
+ node_one.bound() = HRectBound<2>(1);
+ node_one.bound() |= utility;
+ utility[0] = 1;
+ node_one.bound() |= utility;
+
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_two;
+ node_two.bound() = HRectBound<2>(1);
+
+ utility[0] = 5;
+ node_two.bound() |= utility;
+ utility[0] = 6;
+ node_two.bound() |= utility;
+
+ // This should use the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), 16.0, 1e-5);
+
+ // And another just to be sure, from the other side.
+ node_two.bound().Clear();
+ utility[0] = -2;
+ node_two.bound() |= utility;
+ utility[0] = -1;
+ node_two.bound() |= utility;
+
+ // Again, the distance is the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), 1.0, 1e-5);
+
+ // Now, when the bounds overlap.
+ node_two.bound().Clear();
+ utility[0] = -0.5;
+ node_two.bound() |= utility;
+ utility[0] = 0.5;
+ node_two.bound() |= utility;
+
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), 0.0, 1e-5);
+}
+
+/***
+ * Another very simple sanity check for the point-to-node case, again in one
+ * dimension.
+ */
+BOOST_AUTO_TEST_CASE(nns_point_to_node_distance) {
+ // Well, there's no easy way to make HRectBounds the way we want, so we have
+ // to make them and then expand the region to include new points.
+ arma::vec utility(1);
+ utility[0] = 0;
+
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node;
+ node.bound() = HRectBound<2>(1);
+ node.bound() |= utility;
+ utility[0] = 1;
+ node.bound() |= utility;
+
+ arma::vec point(1);
+ point[0] = -0.5;
+
+ // The distance is the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
+ &node), 0.25, 1e-5);
+
+ // Now from the other side of the bound.
+ point[0] = 1.5;
+
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
+ &node), 0.25, 1e-5);
+
+ // And now when the point is inside the bound.
+ point[0] = 0.5;
+
+ BOOST_REQUIRE_CLOSE(NearestNeighborSort::BestPointToNodeDistance(point,
+ &node), 0.0, 1e-5);
+}
+
+// Tests for FurthestNeighborSort
+
+/***
+ * Ensure the best distance for furthest neighbors is DBL_MAX.
+ */
+BOOST_AUTO_TEST_CASE(fns_best_distance) {
+ BOOST_REQUIRE(FurthestNeighborSort::BestDistance() == DBL_MAX);
+}
+
+/***
+ * Ensure the worst distance for furthest neighbors is 0.
+ */
+BOOST_AUTO_TEST_CASE(fns_worst_distance) {
+ BOOST_REQUIRE(FurthestNeighborSort::WorstDistance() == 0);
+}
+
+/***
+ * Make sure the comparison works for values strictly less than the reference.
+ */
+BOOST_AUTO_TEST_CASE(fns_is_better_strict) {
+ BOOST_REQUIRE(FurthestNeighborSort::IsBetter(5.0, 4.0) == true);
+}
+
+/***
+ * Warn in case the comparison is not strict.
+ */
+BOOST_AUTO_TEST_CASE(fns_is_better_not_strict) {
+ BOOST_WARN(FurthestNeighborSort::IsBetter(6.0, 6.0) == true);
+}
+
+/***
+ * A simple test case of where to insert when all the values in the list are
+ * 0.
+ */
+BOOST_AUTO_TEST_CASE(fns_sort_distance_all_zero) {
+ arma::vec list(5);
+ list.fill(0);
+
+ // Should be inserted at the head of the list.
+ BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 5.0) == 0);
+}
+
+/***
+ * Another test case, where we are just putting the new value in the middle of
+ * the list.
+ */
+BOOST_AUTO_TEST_CASE(fns_sort_distance_2) {
+ arma::vec list(3);
+ list[0] = 1.14;
+ list[1] = 0.89;
+ list[2] = 0.66;
+
+ // Run a couple possibilities through.
+ BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 1.22) == 0);
+ BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.93) == 1);
+ BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.68) == 2);
+ BOOST_REQUIRE(FurthestNeighborSort::SortDistance(list, 0.62) ==
+ (size_t() - 1));
+}
+
+/***
+ * Very simple sanity check to ensure that bounds are working alright. We will
+ * use a one-dimensional bound for simplicity.
+ */
+BOOST_AUTO_TEST_CASE(fns_node_to_node_distance) {
+ // Well, there's no easy way to make HRectBounds the way we want, so we have
+ // to make them and then expand the region to include new points.
+ arma::vec utility(1);
+ utility[0] = 0;
+
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_one;
+ node_one.bound() = HRectBound<2>(1);
+ node_one.bound() |= utility;
+ utility[0] = 1;
+ node_one.bound() |= utility;
+
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node_two;
+ node_two.bound() = HRectBound<2>(1);
+ utility[0] = 5;
+ node_two.bound() |= utility;
+ utility[0] = 6;
+ node_two.bound() |= utility;
+
+ // This should use the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), 36.0, 1e-5);
+
+ // And another just to be sure, from the other side.
+ node_two.bound().Clear();
+ utility[0] = -2;
+ node_two.bound() |= utility;
+ utility[0] = -1;
+ node_two.bound() |= utility;
+
+ // Again, the distance is the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), 9.0, 1e-5);
+
+ // Now, when the bounds overlap.
+ node_two.bound().Clear();
+ utility[0] = -0.5;
+ node_two.bound() |= utility;
+ utility[0] = 0.5;
+ node_two.bound() |= utility;
+
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestNodeToNodeDistance(&node_one,
+ &node_two), (1.5 * 1.5), 1e-5);
+}
+
+/***
+ * Another very simple sanity check for the point-to-node case, again in one
+ * dimension.
+ */
+BOOST_AUTO_TEST_CASE(fns_point_to_node_distance) {
+ // Well, there's no easy way to make HRectBounds the way we want, so we have
+ // to make them and then expand the region to include new points.
+ arma::vec utility(1);
+ utility[0] = 0;
+
+ tree::BinarySpaceTree<HRectBound<2>, arma::mat> node;
+ node.bound() = HRectBound<2>(1);
+ node.bound() |= utility;
+ utility[0] = 1;
+ node.bound() |= utility;
+
+ arma::vec point(1);
+ point[0] = -0.5;
+
+ // The distance is the L2 squared distance.
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
+ &node), (1.5 * 1.5), 1e-5);
+
+ // Now from the other side of the bound.
+ point[0] = 1.5;
+
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
+ &node), (1.5 * 1.5), 1e-5);
+
+ // And now when the point is inside the bound.
+ point[0] = 0.5;
+
+ BOOST_REQUIRE_CLOSE(FurthestNeighborSort::BestPointToNodeDistance(point,
+ &node), 0.25, 1e-5);
+}
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