diff options
Diffstat (limited to 'ml/dlib/tools/python/src/decision_functions.cpp')
| -rw-r--r-- | ml/dlib/tools/python/src/decision_functions.cpp | 263 |
1 files changed, 0 insertions, 263 deletions
diff --git a/ml/dlib/tools/python/src/decision_functions.cpp b/ml/dlib/tools/python/src/decision_functions.cpp deleted file mode 100644 index a93fe49b9..000000000 --- a/ml/dlib/tools/python/src/decision_functions.cpp +++ /dev/null @@ -1,263 +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/svm.h> - -using namespace dlib; -using namespace std; - -namespace py = pybind11; - -typedef matrix<double,0,1> sample_type; -typedef std::vector<std::pair<unsigned long,double> > sparse_vect; - -template <typename decision_function> -double predict ( - const decision_function& df, - const typename decision_function::kernel_type::sample_type& samp -) -{ - typedef typename decision_function::kernel_type::sample_type T; - if (df.basis_vectors.size() == 0) - { - return 0; - } - else if (is_matrix<T>::value && df.basis_vectors(0).size() != samp.size()) - { - std::ostringstream sout; - sout << "Input vector should have " << df.basis_vectors(0).size() - << " dimensions, not " << samp.size() << "."; - PyErr_SetString( PyExc_ValueError, sout.str().c_str() ); - throw py::error_already_set(); - } - return df(samp); -} - -template <typename kernel_type> -void add_df ( - py::module& m, - const std::string name -) -{ - typedef decision_function<kernel_type> df_type; - py::class_<df_type>(m, name.c_str()) - .def("__call__", &predict<df_type>) - .def(py::pickle(&getstate<df_type>, &setstate<df_type>)); -} - -template <typename df_type> -typename df_type::sample_type get_weights( - const df_type& df -) -{ - if (df.basis_vectors.size() == 0) - { - PyErr_SetString( PyExc_ValueError, "Decision function is empty." ); - throw py::error_already_set(); - } - df_type temp = simplify_linear_decision_function(df); - return temp.basis_vectors(0); -} - -template <typename df_type> -typename df_type::scalar_type get_bias( - const df_type& df -) -{ - if (df.basis_vectors.size() == 0) - { - PyErr_SetString( PyExc_ValueError, "Decision function is empty." ); - throw py::error_already_set(); - } - return df.b; -} - -template <typename df_type> -void set_bias( - df_type& df, - double b -) -{ - if (df.basis_vectors.size() == 0) - { - PyErr_SetString( PyExc_ValueError, "Decision function is empty." ); - throw py::error_already_set(); - } - df.b = b; -} - -template <typename kernel_type> -void add_linear_df ( - py::module &m, - const std::string name -) -{ - typedef decision_function<kernel_type> df_type; - py::class_<df_type>(m, name.c_str()) - .def("__call__", predict<df_type>) - .def_property_readonly("weights", &get_weights<df_type>) - .def_property("bias", get_bias<df_type>, set_bias<df_type>) - .def(py::pickle(&getstate<df_type>, &setstate<df_type>)); -} - -// ---------------------------------------------------------------------------------------- - -std::string binary_test__str__(const binary_test& item) -{ - std::ostringstream sout; - sout << "class1_accuracy: "<< item.class1_accuracy << " class2_accuracy: "<< item.class2_accuracy; - return sout.str(); -} -std::string binary_test__repr__(const binary_test& item) { return "< " + binary_test__str__(item) + " >";} - -std::string regression_test__str__(const regression_test& item) -{ - std::ostringstream sout; - sout << "mean_squared_error: "<< item.mean_squared_error << " R_squared: "<< item.R_squared; - sout << " mean_average_error: "<< item.mean_average_error << " mean_error_stddev: "<< item.mean_error_stddev; - return sout.str(); -} -std::string regression_test__repr__(const regression_test& item) { return "< " + regression_test__str__(item) + " >";} - -std::string ranking_test__str__(const ranking_test& item) -{ - std::ostringstream sout; - sout << "ranking_accuracy: "<< item.ranking_accuracy << " mean_ap: "<< item.mean_ap; - return sout.str(); -} -std::string ranking_test__repr__(const ranking_test& item) { return "< " + ranking_test__str__(item) + " >";} - -// ---------------------------------------------------------------------------------------- - -template <typename K> -binary_test _test_binary_decision_function ( - const decision_function<K>& dec_funct, - const std::vector<typename K::sample_type>& x_test, - const std::vector<double>& y_test -) { return binary_test(test_binary_decision_function(dec_funct, x_test, y_test)); } - -template <typename K> -regression_test _test_regression_function ( - const decision_function<K>& reg_funct, - const std::vector<typename K::sample_type>& x_test, - const std::vector<double>& y_test -) { return regression_test(test_regression_function(reg_funct, x_test, y_test)); } - -template < typename K > -ranking_test _test_ranking_function1 ( - const decision_function<K>& funct, - const std::vector<ranking_pair<typename K::sample_type> >& samples -) { return ranking_test(test_ranking_function(funct, samples)); } - -template < typename K > -ranking_test _test_ranking_function2 ( - const decision_function<K>& funct, - const ranking_pair<typename K::sample_type>& sample -) { return ranking_test(test_ranking_function(funct, sample)); } - - -void bind_decision_functions(py::module &m) -{ - add_linear_df<linear_kernel<sample_type> >(m, "_decision_function_linear"); - add_linear_df<sparse_linear_kernel<sparse_vect> >(m, "_decision_function_sparse_linear"); - - add_df<histogram_intersection_kernel<sample_type> >(m, "_decision_function_histogram_intersection"); - add_df<sparse_histogram_intersection_kernel<sparse_vect> >(m, "_decision_function_sparse_histogram_intersection"); - - add_df<polynomial_kernel<sample_type> >(m, "_decision_function_polynomial"); - add_df<sparse_polynomial_kernel<sparse_vect> >(m, "_decision_function_sparse_polynomial"); - - add_df<radial_basis_kernel<sample_type> >(m, "_decision_function_radial_basis"); - add_df<sparse_radial_basis_kernel<sparse_vect> >(m, "_decision_function_sparse_radial_basis"); - - add_df<sigmoid_kernel<sample_type> >(m, "_decision_function_sigmoid"); - add_df<sparse_sigmoid_kernel<sparse_vect> >(m, "_decision_function_sparse_sigmoid"); - - - m.def("test_binary_decision_function", _test_binary_decision_function<linear_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sparse_linear_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<radial_basis_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sparse_radial_basis_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<polynomial_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sparse_polynomial_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<histogram_intersection_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sparse_histogram_intersection_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sigmoid_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - m.def("test_binary_decision_function", _test_binary_decision_function<sparse_sigmoid_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("labels")); - - m.def("test_regression_function", _test_regression_function<linear_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sparse_linear_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<radial_basis_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sparse_radial_basis_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<histogram_intersection_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sparse_histogram_intersection_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sigmoid_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sparse_sigmoid_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<polynomial_kernel<sample_type> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - m.def("test_regression_function", _test_regression_function<sparse_polynomial_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples"), py::arg("targets")); - - m.def("test_ranking_function", _test_ranking_function1<linear_kernel<sample_type> >, - py::arg("function"), py::arg("samples")); - m.def("test_ranking_function", _test_ranking_function1<sparse_linear_kernel<sparse_vect> >, - py::arg("function"), py::arg("samples")); - m.def("test_ranking_function", _test_ranking_function2<linear_kernel<sample_type> >, - py::arg("function"), py::arg("sample")); - m.def("test_ranking_function", _test_ranking_function2<sparse_linear_kernel<sparse_vect> >, - py::arg("function"), py::arg("sample")); - - - py::class_<binary_test>(m, "_binary_test") - .def("__str__", binary_test__str__) - .def("__repr__", binary_test__repr__) - .def_readwrite("class1_accuracy", &binary_test::class1_accuracy, - "A value between 0 and 1, measures accuracy on the +1 class.") - .def_readwrite("class2_accuracy", &binary_test::class2_accuracy, - "A value between 0 and 1, measures accuracy on the -1 class."); - - py::class_<ranking_test>(m, "_ranking_test") - .def("__str__", ranking_test__str__) - .def("__repr__", ranking_test__repr__) - .def_readwrite("ranking_accuracy", &ranking_test::ranking_accuracy, - "A value between 0 and 1, measures the fraction of times a relevant sample was ordered before a non-relevant sample.") - .def_readwrite("mean_ap", &ranking_test::mean_ap, - "A value between 0 and 1, measures the mean average precision of the ranking."); - - py::class_<regression_test>(m, "_regression_test") - .def("__str__", regression_test__str__) - .def("__repr__", regression_test__repr__) - .def_readwrite("mean_average_error", ®ression_test::mean_average_error, - "The mean average error of a regression function on a dataset.") - .def_readwrite("mean_error_stddev", ®ression_test::mean_error_stddev, - "The standard deviation of the absolute value of the error of a regression function on a dataset.") - .def_readwrite("mean_squared_error", ®ression_test::mean_squared_error, - "The mean squared error of a regression function on a dataset.") - .def_readwrite("R_squared", ®ression_test::R_squared, - "A value between 0 and 1, measures the squared correlation between the output of a \n" - "regression function and the target values."); -} - - - |