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+/**
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+* @file classifyDatasetGPHIK.cpp
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+* @brief Perform classification on an arbitrary dataset with the GPHIK-Classifier
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+* @author Alexander Freytag
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+* @date 16-09-2013
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+*/
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+
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+// STL-includes
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+#include <iostream>
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+#include <vector>
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+
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+// NICE-core includes
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+#include <core/basics/Config.h>
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+#include <core/basics/Exception.h>
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+#include <core/vector/MatrixT.h>
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+#include <core/vector/SparseVectorT.h>
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+
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+// gp-hik-core includes
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+#include "gp-hik-core/GPHIKClassifier.h"
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+
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+
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+void readSparseExamples ( const std::string & fn, std::vector< NICE::SparseVector * > & examples, NICE::Vector & labels )
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+{
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+ // initially cleaning of variables
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+ examples.clear();
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+ labels.clear();
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+
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+ std::vector<double> labels_std;
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+ labels_std.clear();
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+
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+ std::cerr << "Reading " << fn << std::endl;
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+ std::ifstream ifs ( fn.c_str(), std::ios::in );
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+ if ( ! ifs.good() )
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+ {
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+ std::cerr << "Unable to read " << fn << std::endl;
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+ return;
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+ }
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+
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+ // read until no new line is in the file anymore
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+ while ( !ifs.eof() )
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+ {
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+ int classno;
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+ if ( !(ifs >> classno) )
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+ break;
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+
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+ labels_std.push_back( classno );
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+
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+ NICE::SparseVector *v = new NICE::SparseVector;
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+ /* needed format in every line:
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+ * SVECTOR dimension size index value index value ... END
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+ * with
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+ * SVECTOR -- starting flag
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+ * dimension -- overall feature dimension and
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+ * size -- number of non-zero entries for the current feature vector
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+ * index -- integer value specifying a non-zero dimension
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+ * value -- double value specifying the value for the corresp. non-zero dimension
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+ * END -- ending flag
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+ */
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+ try
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+ {
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+ v->restore ( ifs, NICE::SparseVector::FORMAT_INDEX );
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+ }
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+ catch ( NICE::Exception excep)
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+ {
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+ std::cerr << "Error while reading features. Error message: " << excep.what() << std::endl;
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+ break;
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+ }
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+
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+
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+ examples.push_back ( v );
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+ }
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+ ifs.close();
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+
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+ labels = NICE::Vector( labels_std );
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+}
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+
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+void mapClassNumbersToIndices( const NICE::Vector & labels, std::map<int,int> & mapClassNoToIdx )
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+{
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+ mapClassNoToIdx.clear();
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+ int classCnt ( 0 );
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+
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+ for ( NICE::Vector::const_iterator it_labels = labels.begin(); it_labels != labels.end(); it_labels++ )
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+ {
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+ if ( mapClassNoToIdx.find( *it_labels ) == mapClassNoToIdx.end() )
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+ {
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+ mapClassNoToIdx.insert( std::pair<int,int>( (int) round(*it_labels), classCnt ) );
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+ classCnt++;
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+ }
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+ }
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+}
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+
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+
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+int main (int argc, char* argv[])
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+{
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+ std::set_terminate(__gnu_cxx::__verbose_terminate_handler);
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+
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+ NICE::Config conf ( argc, argv );
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+
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+ NICE::GPHIKClassifier classifier ( &conf, "GPHIKClassifier" );
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+
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+ // ========================================================================
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+ // TRAINING STEP
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+ // ========================================================================
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+
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+ // read training data
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+ std::vector< NICE::SparseVector * > examplesTrain;
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+ NICE::Vector labelsTrain;
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+
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+ std::string s_fn_trainingSet = conf.gS("main", "trainset");
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+ readSparseExamples ( s_fn_trainingSet, examplesTrain, labelsTrain );
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+
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+ //map the occuring classes to a minimal set of indices
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+ std::map<int,int> map_classNoToClassIdx_train; // < classNo, Idx>
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+
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+ mapClassNumbersToIndices( labelsTrain, map_classNoToClassIdx_train );
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+
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+ //how many different classes do we have in the training set?
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+ int i_noClassesTrain ( map_classNoToClassIdx_train.size() );
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+
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+ // train GPHIK classifier
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+ classifier.train ( examplesTrain, labelsTrain );
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+
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+ // ========================================================================
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+ // TEST STEP
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+ // ========================================================================
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+
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+ // read test data
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+ std::vector< NICE::SparseVector * > examplesTest;
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+ NICE::Vector labelsTest;
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+
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+ std::string s_fn_testSet = conf.gS("main", "testset");
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+ readSparseExamples ( s_fn_testSet, examplesTest, labelsTest );
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+
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+ //map the occuring classes to a minimal set of indices
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+ std::map<int,int> map_classNoToClassIdx_test; // < classNo, Idx>
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+
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+ mapClassNumbersToIndices( labelsTest, map_classNoToClassIdx_test );
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+
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+ //how many different classes do we have in the test set?
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+ int i_noClassesTest ( map_classNoToClassIdx_test.size() );
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+
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+ // evaluate GPHIK classifier on unseen test data
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+ int idx ( 0 );
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+ NICE::SparseVector scores; /* not needed in this evaluation, so we just declare it ones */
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+
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+ NICE::Matrix confusion ( i_noClassesTest, i_noClassesTrain, 0.0 );
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+
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+ for (std::vector< NICE::SparseVector *>::const_iterator itTestExamples = examplesTest.begin(); itTestExamples != examplesTest.end(); itTestExamples++, idx++)
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+ {
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+ int classno_groundtruth = labelsTest( idx );
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+ int classno_predicted;
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+
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+ classifier.classify ( *itTestExamples, classno_predicted, scores /* not needed anyway in that evaluation*/ );
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+
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+
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+ int idx_classno_groundtruth ( map_classNoToClassIdx_test[ classno_groundtruth ] );
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+ int idx_classno_predicted ( map_classNoToClassIdx_train[ classno_predicted ] );
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+
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+ confusion( idx_classno_groundtruth, idx_classno_predicted ) += 1;
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+ }
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+
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+
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+ confusion.normalizeRowsL1();
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+ std::cerr << confusion << std::endl;
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+
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+ std::cerr << "average recognition rate: " << confusion.trace()/confusion.rows() << std::endl;
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+
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+
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+ // ========================================================================
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+ // clean up memory
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+ // ========================================================================
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+
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+ // release memore of feature vectors from training set
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+ for (std::vector< NICE::SparseVector *>::const_iterator itTrainExamples = examplesTrain.begin(); itTrainExamples != examplesTrain.end(); itTrainExamples++ )
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+ {
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+ delete *itTrainExamples;
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+ }
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+
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+ // release memore of feature vectors from test set
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+ for (std::vector< NICE::SparseVector *>::const_iterator itTestExamples = examplesTest.begin(); itTestExamples != examplesTest.end(); itTestExamples++ )
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+ {
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+ delete *itTestExamples;
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+ }
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+ return 0;
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+}
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Johannes Ruehle