mirror of https://github.com/CGAL/cgal
Fix clang warnings
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aac29a8949
commit
0880106e1f
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@ -52,13 +52,14 @@ include_directories( BEFORE ../../include )
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include( CGAL_CreateSingleSourceCGALProgram )
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create_single_source_cgal_program( "example_classification.cpp" )
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create_single_source_cgal_program( "example_generation_and_training.cpp" )
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create_single_source_cgal_program( "example_feature.cpp" )
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set(needed_cxx_features cxx_rvalue_references cxx_variadic_templates)
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create_single_source_cgal_program( "example_classification.cpp" CXX_FEATURES ${needed_cxx_features} )
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create_single_source_cgal_program( "example_generation_and_training.cpp" CXX_FEATURES ${needed_cxx_features} )
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create_single_source_cgal_program( "example_feature.cpp" CXX_FEATURES ${needed_cxx_features} )
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if( OpenCV_FOUND )
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include_directories( ${OpenCV_INCLUDE_DIRS} )
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create_single_source_cgal_program( "example_random_forest.cpp" )
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create_single_source_cgal_program( "example_random_forest.cpp" CXX_FEATURES ${needed_cxx_features} )
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target_link_libraries( example_random_forest ${OpenCV_LIBS} )
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else()
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message(STATUS "OpenCV not found, random forest example won't be compiled.")
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@ -20,6 +20,15 @@
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#ifndef CGAL_CLASSIFICATION_H
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#define CGAL_CLASSIFICATION_H
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#if defined(CGAL_CLASSIFICATION_VERBOSE)
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#define CGAL_CLASSIFICATION_SILENT false
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#else
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#define CGAL_CLASSIFICATION_SILENT true
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#endif
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#define CGAL_CLASSIFICATION_CERR \
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if(CGAL_CLASSIFICATION_SILENT) {} else std::cerr
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#include <CGAL/Classification/classify.h>
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#include <CGAL/Classification/Sum_of_weighted_features_classifier.h>
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@ -74,7 +74,6 @@ public:
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\return a handle to the newly added feature.
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*/
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#if (!defined(CGAL_CFG_NO_CPP0X_VARIADIC_TEMPLATES) && !defined(CGAL_CFG_NO_CPP0X_RVALUE_REFERENCE)) || DOXYGEN_RUNNING
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template <typename Feature, typename ... T>
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Feature_handle add (T&& ... t)
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{
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@ -84,53 +83,6 @@ public:
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mutex_unlock();
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return fh;
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}
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#else
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template <typename Feature, typename T1>
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Feature_handle add (T1& t1)
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{
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Feature_handle fh (new Feature(t1));
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mutex_lock();
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m_features.push_back (fh);
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mutex_unlock();
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return fh;
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}
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template <typename Feature, typename T1, typename T2>
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Feature_handle add (T1& t1, T2& t2)
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{
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Feature_handle fh (new Feature(t1, t2));
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mutex_lock();
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m_features.push_back (fh);
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mutex_unlock();
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return fh;
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}
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template <typename Feature, typename T1, typename T2, typename T3>
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Feature_handle add (T1& t1, T2& t2, T3& t3)
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{
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Feature_handle fh (new Feature(t1, t2, t3));
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mutex_lock();
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m_features.push_back (fh);
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mutex_unlock();
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return fh;
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}
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template <typename Feature, typename T1, typename T2, typename T3, typename T4>
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Feature_handle add (T1& t1, T2& t2, T3& t3, T4& t4)
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{
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Feature_handle fh (new Feature(t1, t2, t3, t4));
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mutex_lock();
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m_features.push_back (fh);
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mutex_unlock();
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return fh;
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}
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template <typename Feature, typename T1, typename T2, typename T3, typename T4, typename T5>
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Feature_handle add (T1& t1, T2& t2, T3& t3, T4& t4, T5& t5)
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{
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Feature_handle fh (new Feature(t1, t2, t3, t4, t5));
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mutex_lock();
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m_features.push_back (fh);
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mutex_unlock();
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return fh;
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}
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#endif
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/*!
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\brief Removes a feature.
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@ -38,19 +38,15 @@
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#include <tbb/mutex.h>
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#endif // CGAL_LINKED_WITH_TBB
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//#define CGAL_CLASSIFICATION_VERBOSE
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#if defined(CGAL_CLASSIFICATION_VERBOSE)
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#define CGAL_CLASSIFICATION_CERR std::cerr
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#if defined(CGAL_CLASSTRAINING_VERBOSE)
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#define CGAL_CLASSTRAINING_SILENT false
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#else
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#define CGAL_CLASSIFICATION_CERR std::ostream(0)
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#define CGAL_CLASSTRAINING_SILENT true
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#endif
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//#define CGAL_CLASSTRAINING_VERBOSE
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#if defined(CGAL_CLASSTRAINING_VERBOSE)
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#define CGAL_CLASSTRAINING_CERR std::cerr
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#else
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#define CGAL_CLASSTRAINING_CERR std::ostream(0)
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#endif
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#define CGAL_CLASSTRAINING_CERR \
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if(CGAL_CLASSIFICATION_SILENT) {} else std::cerr
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namespace CGAL {
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@ -150,6 +146,18 @@ private:
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};
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#endif // CGAL_LINKED_WITH_TBB
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struct Feature_training
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{
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std::size_t i;
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float wmin;
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float wmax;
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float factor;
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bool operator<(const Feature_training& other) const
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{
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return (wmin / wmax) < (other.wmin / other.wmax);
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}
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};
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const Label_set& m_labels;
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const Feature_set& m_features;
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@ -316,18 +324,6 @@ public:
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std::vector<float> best_weights (m_features.size(), 1.);
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struct Feature_training
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{
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std::size_t i;
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float wmin;
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float wmax;
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float factor;
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bool operator<(const Feature_training& other) const
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{
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return (wmin / wmax) < (other.wmin / other.wmax);
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}
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};
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std::vector<Feature_training> feature_train;
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std::size_t nb_trials = 100;
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float wmin = 1e-5f, wmax = 1e5f;
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@ -507,18 +503,6 @@ public:
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std::vector<float> best_weights (m_features.size(), 1.);
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struct Feature_training
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{
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std::size_t i;
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float wmin;
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float wmax;
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float factor;
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bool operator<(const Feature_training& other) const
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{
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return (wmin / wmax) < (other.wmin / other.wmax);
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}
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};
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std::vector<Feature_training> feature_train;
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std::size_t nb_trials = 100;
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float wmin = 1e-5, wmax = 1e5;
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