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#include "opaque_types.h" |
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#include <dlib/python.h> |
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#include "testing_results.h" |
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#include <dlib/matrix.h> |
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#include <dlib/svm_threaded.h> |
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using namespace dlib; |
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using namespace std; |
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typedef matrix<double,0,1> sample_type; |
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typedef std::vector<std::pair<unsigned long,double> > sparse_vect; |
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template <typename trainer_type> |
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typename trainer_type::trained_function_type train ( |
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const trainer_type& trainer, |
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const std::vector<typename trainer_type::sample_type>& samples, |
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const std::vector<double>& labels |
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) |
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{ |
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pyassert(is_binary_classification_problem(samples,labels), "Invalid inputs"); |
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return trainer.train(samples, labels); |
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} |
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template <typename trainer_type> |
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void set_epsilon ( trainer_type& trainer, double eps) |
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{ |
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pyassert(eps > 0, "epsilon must be > 0"); |
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trainer.set_epsilon(eps); |
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} |
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template <typename trainer_type> |
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double get_epsilon ( const trainer_type& trainer) { return trainer.get_epsilon(); } |
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template <typename trainer_type> |
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void set_cache_size ( trainer_type& trainer, long cache_size) |
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{ |
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pyassert(cache_size > 0, "cache size must be > 0"); |
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trainer.set_cache_size(cache_size); |
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} |
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template <typename trainer_type> |
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long get_cache_size ( const trainer_type& trainer) { return trainer.get_cache_size(); } |
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template <typename trainer_type> |
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void set_c ( trainer_type& trainer, double C) |
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{ |
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pyassert(C > 0, "C must be > 0"); |
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trainer.set_c(C); |
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} |
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template <typename trainer_type> |
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void set_c_class1 ( trainer_type& trainer, double C) |
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{ |
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pyassert(C > 0, "C must be > 0"); |
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trainer.set_c_class1(C); |
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} |
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template <typename trainer_type> |
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void set_c_class2 ( trainer_type& trainer, double C) |
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{ |
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pyassert(C > 0, "C must be > 0"); |
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trainer.set_c_class2(C); |
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} |
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template <typename trainer_type> |
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double get_c_class1 ( const trainer_type& trainer) { return trainer.get_c_class1(); } |
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template <typename trainer_type> |
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double get_c_class2 ( const trainer_type& trainer) { return trainer.get_c_class2(); } |
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template <typename trainer_type> |
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py::class_<trainer_type> setup_trainer_eps ( |
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py::module& m, |
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const std::string& name |
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) |
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{ |
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return py::class_<trainer_type>(m, name.c_str()) |
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.def("train", train<trainer_type>) |
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.def_property("epsilon", get_epsilon<trainer_type>, set_epsilon<trainer_type>); |
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} |
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template <typename trainer_type> |
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py::class_<trainer_type> setup_trainer_eps_c ( |
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py::module& m, |
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const std::string& name |
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) |
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{ |
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return setup_trainer_eps<trainer_type>(m, name) |
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.def("set_c", set_c<trainer_type>) |
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.def_property("c_class1", get_c_class1<trainer_type>, set_c_class1<trainer_type>) |
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.def_property("c_class2", get_c_class2<trainer_type>, set_c_class2<trainer_type>); |
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} |
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template <typename trainer_type> |
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py::class_<trainer_type> setup_trainer_eps_c_cache ( |
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py::module& m, |
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const std::string& name |
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) |
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{ |
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return setup_trainer_eps_c<trainer_type>(m, name) |
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.def_property("cache_size", get_cache_size<trainer_type>, set_cache_size<trainer_type>); |
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} |
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template <typename trainer_type> |
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void set_gamma ( |
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trainer_type& trainer, |
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double gamma |
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) |
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{ |
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pyassert(gamma > 0, "gamma must be > 0"); |
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trainer.set_kernel(typename trainer_type::kernel_type(gamma)); |
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} |
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template <typename trainer_type> |
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double get_gamma ( |
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const trainer_type& trainer |
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) |
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{ |
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return trainer.get_kernel().gamma; |
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} |
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template < |
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typename trainer_type |
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> |
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const binary_test _cross_validate_trainer ( |
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const trainer_type& trainer, |
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const std::vector<typename trainer_type::sample_type>& x, |
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const std::vector<double>& y, |
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const unsigned long folds |
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) |
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{ |
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pyassert(is_binary_classification_problem(x,y), "Training data does not make a valid training set."); |
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pyassert(1 < folds && folds <= x.size(), "Invalid number of folds given."); |
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return cross_validate_trainer(trainer, x, y, folds); |
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} |
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template < |
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typename trainer_type |
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> |
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const binary_test _cross_validate_trainer_t ( |
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const trainer_type& trainer, |
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const std::vector<typename trainer_type::sample_type>& x, |
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const std::vector<double>& y, |
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const unsigned long folds, |
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const unsigned long num_threads |
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) |
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{ |
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pyassert(is_binary_classification_problem(x,y), "Training data does not make a valid training set."); |
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pyassert(1 < folds && folds <= x.size(), "Invalid number of folds given."); |
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pyassert(1 < num_threads, "The number of threads specified must not be zero."); |
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return cross_validate_trainer_threaded(trainer, x, y, folds, num_threads); |
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} |
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void bind_svm_c_trainer(py::module& m) |
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{ |
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namespace py = pybind11; |
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{ |
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typedef svm_c_trainer<radial_basis_kernel<sample_type> > T; |
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setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_radial_basis") |
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.def(py::init()) |
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.def_property("gamma", get_gamma<T>, set_gamma<T>); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef svm_c_trainer<sparse_radial_basis_kernel<sparse_vect> > T; |
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setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_sparse_radial_basis") |
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.def(py::init()) |
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.def_property("gamma", get_gamma<T>, set_gamma<T>); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef svm_c_trainer<histogram_intersection_kernel<sample_type> > T; |
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setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_histogram_intersection") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef svm_c_trainer<sparse_histogram_intersection_kernel<sparse_vect> > T; |
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setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_sparse_histogram_intersection") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef svm_c_linear_trainer<linear_kernel<sample_type> > T; |
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setup_trainer_eps_c<T>(m, "svm_c_trainer_linear") |
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.def(py::init()) |
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.def_property("max_iterations", &T::get_max_iterations, &T::set_max_iterations) |
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.def_property("force_last_weight_to_1", &T::forces_last_weight_to_1, &T::force_last_weight_to_1) |
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.def_property("learns_nonnegative_weights", &T::learns_nonnegative_weights, &T::set_learns_nonnegative_weights) |
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.def_property_readonly("has_prior", &T::has_prior) |
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.def("set_prior", &T::set_prior) |
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.def("be_verbose", &T::be_verbose) |
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.def("be_quiet", &T::be_quiet); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef svm_c_linear_trainer<sparse_linear_kernel<sparse_vect> > T; |
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setup_trainer_eps_c<T>(m, "svm_c_trainer_sparse_linear") |
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.def(py::init()) |
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.def_property("max_iterations", &T::get_max_iterations, &T::set_max_iterations) |
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.def_property("force_last_weight_to_1", &T::forces_last_weight_to_1, &T::force_last_weight_to_1) |
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.def_property("learns_nonnegative_weights", &T::learns_nonnegative_weights, &T::set_learns_nonnegative_weights) |
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.def_property_readonly("has_prior", &T::has_prior) |
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.def("set_prior", &T::set_prior) |
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.def("be_verbose", &T::be_verbose) |
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.def("be_quiet", &T::be_quiet); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<radial_basis_kernel<sample_type> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_radial_basis") |
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.def(py::init()) |
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.def_property("gamma", get_gamma<T>, set_gamma<T>); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<sparse_radial_basis_kernel<sparse_vect> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_sparse_radial_basis") |
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.def(py::init()) |
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.def_property("gamma", get_gamma<T>, set_gamma<T>); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<histogram_intersection_kernel<sample_type> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_histogram_intersection") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<sparse_histogram_intersection_kernel<sparse_vect> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_sparse_histogram_intersection") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<linear_kernel<sample_type> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_linear") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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{ |
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typedef rvm_trainer<sparse_linear_kernel<sparse_vect> > T; |
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setup_trainer_eps<T>(m, "rvm_trainer_sparse_linear") |
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.def(py::init()); |
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m.def("cross_validate_trainer", _cross_validate_trainer<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds")); |
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m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>, |
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py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads")); |
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} |
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} |
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