// -------------------------------------------------------------------------- // OpenMS -- Open-Source Mass Spectrometry // -------------------------------------------------------------------------- // Copyright The OpenMS Team -- Eberhard Karls University Tuebingen, // ETH Zurich, and Freie Universitaet Berlin 2002-2013. // // This software is released under a three-clause BSD license: // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // * Neither the name of any author or any participating institution // may be used to endorse or promote products derived from this software // without specific prior written permission. // For a full list of authors, refer to the file AUTHORS. // -------------------------------------------------------------------------- // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING // INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, // EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, // PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; // OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, // WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR // OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF // ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. // // -------------------------------------------------------------------------- // $Maintainer: George Rosenberger $ // $Authors: George Rosenberger, Hannes Roest $ // -------------------------------------------------------------------------- #include /////////////////////////// #include /////////////////////////// using namespace std; using namespace OpenMS; /////////////////////////// START_TEST(MRMRTNormalizer, "$Id: MRMRTNormalizer_test.C 10915 2013-04-04 20:14:57Z aiche $") ///////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// // no constructor / destructor of static class // MRMRTNormalizer() // ~MRMRTNormalizer() // START_SECTION((static int outlier_candidate(std::vector & x, std::vector & y))) { static const double arrx1[] = { 1.1, 2.0,3.3,3.9,4.9,6.2 }; std::vector x1 (arrx1, arrx1 + sizeof(arrx1) / sizeof(arrx1[0]) ); static const double arry1[] = { 0.9, 1.9,3.0,3.7,5.2,6.1 }; std::vector y1 (arry1, arry1 + sizeof(arry1) / sizeof(arry1[0]) ); int c1 = MRMRTNormalizer::outlier_candidate(x1,y1); TEST_EQUAL(c1,4); static const double arrx2[] = { 1,2,3,4,5,6 }; std::vector x2 (arrx2, arrx2 + sizeof(arrx2) / sizeof(arrx2[0]) ); static const double arry2[] = { 1,2,3,4,5,6}; std::vector y2 (arry2, arry2 + sizeof(arry2) / sizeof(arry2[0]) ); int c2 = MRMRTNormalizer::outlier_candidate(x2,y2); TEST_EQUAL(c2,0); } END_SECTION START_SECTION((static std::vector > rm_outliers(std::vector > & pairs, double rsq_limit, double coverage_limit))) { static const double arrx1[] = { 1.1,2.0,3.3,3.9,4.9,6.2 }; std::vector x1 (arrx1, arrx1 + sizeof(arrx1) / sizeof(arrx1[0]) ); static const double arry1[] = { 0.9,1.9,3.0,3.7,5.2,6.1 }; std::vector y1 (arry1, arry1 + sizeof(arry1) / sizeof(arry1[0]) ); std::vector > input1; for (Size i = 0; i < x1.size(); i++) { input1.push_back(std::make_pair(x1[i], y1[i])); } std::vector > output1 = MRMRTNormalizer::rm_outliers(input1, 0.9, 0.5); TEST_EQUAL( output1.size() , input1.size() ); static const double arrx2[] = { 1.1,2.0,3.3,3.9,4.9,6.2 }; std::vector x2 (arrx2, arrx2 + sizeof(arrx2) / sizeof(arrx2[0]) ); static const double arry2[] = { 0.9,1.9,7.0,3.7,5.2,6.1 }; std::vector y2 (arry2, arry2 + sizeof(arry2) / sizeof(arry2[0]) ); std::vector > input2; for (Size i = 0; i < x2.size(); i++) { input2.push_back(std::make_pair(x2[i], y2[i])); } std::vector > output2 = MRMRTNormalizer::rm_outliers(input2, 0.9, 0.5); TEST_EQUAL( output2.size() , input2.size() - 1 ); TEST_EQUAL( output2[0].first, input2[0].first ); TEST_EQUAL( output2[1].second, input2[1].second ); TEST_EQUAL( output2[2].first, input2[3].first ); TEST_EQUAL( output2[3].second, input2[4].second ); static const double arrx3[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,1,21,22,23,24,25,26,27,28,29,30 }; std::vector x3 (arrx3, arrx3 + sizeof(arrx3) / sizeof(arrx3[0]) ); static const double arry3[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,1,22,23,24,25,26,27,28,29,30 }; std::vector y3 (arry3, arry3 + sizeof(arry3) / sizeof(arry3[0]) ); std::vector > input3; for (Size i = 0; i < x3.size(); i++) { input3.push_back(std::make_pair(x3[i], y3[i])); } std::vector > output3 = MRMRTNormalizer::rm_outliers(input3, 0.9, 0.2); TEST_EQUAL( output3.size() , input3.size() - 2 ); TEST_EQUAL( output3[18].first, input3[18].first ); TEST_EQUAL( output3[19].second, input3[21].second ); } END_SECTION START_SECTION(( static double chauvenet_probability(std::vector< double > &residuals, int pos) )) { static const double arr1[] = { 1,2,3,4,2,10,11,75,5,8,3,5,6,9,130 }; std::vector data1 (arr1, arr1 + sizeof(arr1) / sizeof(arr1[0]) ); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 0), 0.61831553); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 1), 0.6387955); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 2), 0.65955473); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 3), 0.68057951); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 4), 0.6387955); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 5), 0.81146293); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 6), 0.8339146); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 7), 0.10161557); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 8), 0.70185552); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 9), 0.76703896); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 10), 0.65955473); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 11), 0.70185552); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 12), 0.72336784); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 13), 0.78916526); TEST_REAL_SIMILAR( MRMRTNormalizer::chauvenet_probability(data1, 14), 0.00126358); } END_SECTION START_SECTION((static bool chauvenet(std::vector & residuals, int pos))) { static const double arr1[] = { 1,2,3,4,2,10,11,75,5,8,3,5,6,9,130 }; std::vector data1 (arr1, arr1 + sizeof(arr1) / sizeof(arr1[0]) ); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 0), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 1), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 2), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 3), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 4), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 5), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 6), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 7), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 8), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 9), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 10), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 11), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 12), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 13), false); TEST_EQUAL( MRMRTNormalizer::chauvenet(data1, 14), true); } END_SECTION ///////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// END_TEST