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-rw-r--r--ml/dlib/tools/python/src/svm_c_trainer.cpp311
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diff --git a/ml/dlib/tools/python/src/svm_c_trainer.cpp b/ml/dlib/tools/python/src/svm_c_trainer.cpp
deleted file mode 100644
index 7b592abe7..000000000
--- a/ml/dlib/tools/python/src/svm_c_trainer.cpp
+++ /dev/null
@@ -1,311 +0,0 @@
-// Copyright (C) 2013 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-
-#include "opaque_types.h"
-#include <dlib/python.h>
-#include "testing_results.h"
-#include <dlib/matrix.h>
-#include <dlib/svm_threaded.h>
-
-using namespace dlib;
-using namespace std;
-
-typedef matrix<double,0,1> sample_type;
-typedef std::vector<std::pair<unsigned long,double> > sparse_vect;
-
-template <typename trainer_type>
-typename trainer_type::trained_function_type train (
- const trainer_type& trainer,
- const std::vector<typename trainer_type::sample_type>& samples,
- const std::vector<double>& labels
-)
-{
- pyassert(is_binary_classification_problem(samples,labels), "Invalid inputs");
- return trainer.train(samples, labels);
-}
-
-template <typename trainer_type>
-void set_epsilon ( trainer_type& trainer, double eps)
-{
- pyassert(eps > 0, "epsilon must be > 0");
- trainer.set_epsilon(eps);
-}
-
-template <typename trainer_type>
-double get_epsilon ( const trainer_type& trainer) { return trainer.get_epsilon(); }
-
-
-template <typename trainer_type>
-void set_cache_size ( trainer_type& trainer, long cache_size)
-{
- pyassert(cache_size > 0, "cache size must be > 0");
- trainer.set_cache_size(cache_size);
-}
-
-template <typename trainer_type>
-long get_cache_size ( const trainer_type& trainer) { return trainer.get_cache_size(); }
-
-
-template <typename trainer_type>
-void set_c ( trainer_type& trainer, double C)
-{
- pyassert(C > 0, "C must be > 0");
- trainer.set_c(C);
-}
-
-template <typename trainer_type>
-void set_c_class1 ( trainer_type& trainer, double C)
-{
- pyassert(C > 0, "C must be > 0");
- trainer.set_c_class1(C);
-}
-
-template <typename trainer_type>
-void set_c_class2 ( trainer_type& trainer, double C)
-{
- pyassert(C > 0, "C must be > 0");
- trainer.set_c_class2(C);
-}
-
-template <typename trainer_type>
-double get_c_class1 ( const trainer_type& trainer) { return trainer.get_c_class1(); }
-template <typename trainer_type>
-double get_c_class2 ( const trainer_type& trainer) { return trainer.get_c_class2(); }
-
-template <typename trainer_type>
-py::class_<trainer_type> setup_trainer_eps (
- py::module& m,
- const std::string& name
-)
-{
- return py::class_<trainer_type>(m, name.c_str())
- .def("train", train<trainer_type>)
- .def_property("epsilon", get_epsilon<trainer_type>, set_epsilon<trainer_type>);
-}
-
-template <typename trainer_type>
-py::class_<trainer_type> setup_trainer_eps_c (
- py::module& m,
- const std::string& name
-)
-{
- return setup_trainer_eps<trainer_type>(m, name)
- .def("set_c", set_c<trainer_type>)
- .def_property("c_class1", get_c_class1<trainer_type>, set_c_class1<trainer_type>)
- .def_property("c_class2", get_c_class2<trainer_type>, set_c_class2<trainer_type>);
-}
-
-template <typename trainer_type>
-py::class_<trainer_type> setup_trainer_eps_c_cache (
- py::module& m,
- const std::string& name
-)
-{
- return setup_trainer_eps_c<trainer_type>(m, name)
- .def_property("cache_size", get_cache_size<trainer_type>, set_cache_size<trainer_type>);
-}
-
-template <typename trainer_type>
-void set_gamma (
- trainer_type& trainer,
- double gamma
-)
-{
- pyassert(gamma > 0, "gamma must be > 0");
- trainer.set_kernel(typename trainer_type::kernel_type(gamma));
-}
-
-template <typename trainer_type>
-double get_gamma (
- const trainer_type& trainer
-)
-{
- return trainer.get_kernel().gamma;
-}
-
-// ----------------------------------------------------------------------------------------
-
-template <
- typename trainer_type
- >
-const binary_test _cross_validate_trainer (
- const trainer_type& trainer,
- const std::vector<typename trainer_type::sample_type>& x,
- const std::vector<double>& y,
- const unsigned long folds
-)
-{
- pyassert(is_binary_classification_problem(x,y), "Training data does not make a valid training set.");
- pyassert(1 < folds && folds <= x.size(), "Invalid number of folds given.");
- return cross_validate_trainer(trainer, x, y, folds);
-}
-
-template <
- typename trainer_type
- >
-const binary_test _cross_validate_trainer_t (
- const trainer_type& trainer,
- const std::vector<typename trainer_type::sample_type>& x,
- const std::vector<double>& y,
- const unsigned long folds,
- const unsigned long num_threads
-)
-{
- pyassert(is_binary_classification_problem(x,y), "Training data does not make a valid training set.");
- pyassert(1 < folds && folds <= x.size(), "Invalid number of folds given.");
- pyassert(1 < num_threads, "The number of threads specified must not be zero.");
- return cross_validate_trainer_threaded(trainer, x, y, folds, num_threads);
-}
-
-// ----------------------------------------------------------------------------------------
-
-void bind_svm_c_trainer(py::module& m)
-{
- namespace py = pybind11;
-
- // svm_c
- {
- typedef svm_c_trainer<radial_basis_kernel<sample_type> > T;
- setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_radial_basis")
- .def(py::init())
- .def_property("gamma", get_gamma<T>, set_gamma<T>);
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef svm_c_trainer<sparse_radial_basis_kernel<sparse_vect> > T;
- setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_sparse_radial_basis")
- .def(py::init())
- .def_property("gamma", get_gamma<T>, set_gamma<T>);
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef svm_c_trainer<histogram_intersection_kernel<sample_type> > T;
- setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_histogram_intersection")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef svm_c_trainer<sparse_histogram_intersection_kernel<sparse_vect> > T;
- setup_trainer_eps_c_cache<T>(m, "svm_c_trainer_sparse_histogram_intersection")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- // svm_c_linear
- {
- typedef svm_c_linear_trainer<linear_kernel<sample_type> > T;
- setup_trainer_eps_c<T>(m, "svm_c_trainer_linear")
- .def(py::init())
- .def_property("max_iterations", &T::get_max_iterations, &T::set_max_iterations)
- .def_property("force_last_weight_to_1", &T::forces_last_weight_to_1, &T::force_last_weight_to_1)
- .def_property("learns_nonnegative_weights", &T::learns_nonnegative_weights, &T::set_learns_nonnegative_weights)
- .def_property_readonly("has_prior", &T::has_prior)
- .def("set_prior", &T::set_prior)
- .def("be_verbose", &T::be_verbose)
- .def("be_quiet", &T::be_quiet);
-
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef svm_c_linear_trainer<sparse_linear_kernel<sparse_vect> > T;
- setup_trainer_eps_c<T>(m, "svm_c_trainer_sparse_linear")
- .def(py::init())
- .def_property("max_iterations", &T::get_max_iterations, &T::set_max_iterations)
- .def_property("force_last_weight_to_1", &T::forces_last_weight_to_1, &T::force_last_weight_to_1)
- .def_property("learns_nonnegative_weights", &T::learns_nonnegative_weights, &T::set_learns_nonnegative_weights)
- .def_property_readonly("has_prior", &T::has_prior)
- .def("set_prior", &T::set_prior)
- .def("be_verbose", &T::be_verbose)
- .def("be_quiet", &T::be_quiet);
-
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- // rvm
- {
- typedef rvm_trainer<radial_basis_kernel<sample_type> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_radial_basis")
- .def(py::init())
- .def_property("gamma", get_gamma<T>, set_gamma<T>);
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef rvm_trainer<sparse_radial_basis_kernel<sparse_vect> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_sparse_radial_basis")
- .def(py::init())
- .def_property("gamma", get_gamma<T>, set_gamma<T>);
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef rvm_trainer<histogram_intersection_kernel<sample_type> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_histogram_intersection")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef rvm_trainer<sparse_histogram_intersection_kernel<sparse_vect> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_sparse_histogram_intersection")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- // rvm linear
- {
- typedef rvm_trainer<linear_kernel<sample_type> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_linear")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-
- {
- typedef rvm_trainer<sparse_linear_kernel<sparse_vect> > T;
- setup_trainer_eps<T>(m, "rvm_trainer_sparse_linear")
- .def(py::init());
- m.def("cross_validate_trainer", _cross_validate_trainer<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"));
- m.def("cross_validate_trainer_threaded", _cross_validate_trainer_t<T>,
- py::arg("trainer"),py::arg("x"),py::arg("y"),py::arg("folds"),py::arg("num_threads"));
- }
-}
-
-
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-01 09:41:43 +0000