diff options
Diffstat (limited to 'ml/dlib/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h | 147 |
1 files changed, 0 insertions, 147 deletions
diff --git a/ml/dlib/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h b/ml/dlib/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h deleted file mode 100644 index cda4af91e..000000000 --- a/ml/dlib/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h +++ /dev/null @@ -1,147 +0,0 @@ -// Copyright (C) 2012 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_Hh_ -#ifdef DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_Hh_ - -#include "../array/array_kernel_abstract.h" -#include <vector> -#include "../matrix/matrix_abstract.h" - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename graph_labeler, - typename graph_type - > - matrix<double,1,2> test_graph_labeling_function ( - const graph_labeler& labeler, - const dlib::array<graph_type>& samples, - const std::vector<std::vector<bool> >& labels - ); - /*! - requires - - is_graph_labeling_problem(samples,labels) == true - - graph_labeler == an object with an interface compatible with the - dlib::graph_labeler object. - - the following must be a valid expression: labeler(samples[0]); - ensures - - This function tests the accuracy of the given graph labeler against - the sample graphs and their associated labels. In particular, this - function returns a matrix R such that: - - R(0) == The fraction of nodes which are supposed to have a label of - true that are labeled as such by the labeler. - - R(1) == The fraction of nodes which are supposed to have a label of - false that are labeled as such by the labeler. - Therefore, if R is [1,1] then the labeler makes perfect predictions while - an R of [0,0] indicates that it gets everything wrong. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename graph_labeler, - typename graph_type - > - matrix<double,1,2> test_graph_labeling_function ( - const graph_labeler& labeler, - const dlib::array<graph_type>& samples, - const std::vector<std::vector<bool> >& labels, - const std::vector<std::vector<double> >& losses - ); - /*! - requires - - is_graph_labeling_problem(samples,labels) == true - - graph_labeler == an object with an interface compatible with the - dlib::graph_labeler object. - - the following must be a valid expression: labeler(samples[0]); - - if (losses.size() != 0) then - - sizes_match(labels, losses) == true - - all_values_are_nonnegative(losses) == true - ensures - - This overload of test_graph_labeling_function() does the same thing as the - one defined above, except that instead of counting 1 for each labeling - mistake, it weights each mistake according to the corresponding value in - losses. That is, instead of counting a value of 1 for making a mistake on - samples[i].node(j), this routine counts a value of losses[i][j]. Under this - interpretation, the loss values represent how useful it is to correctly label - each node. Therefore, the values returned represent fractions of overall - labeling utility rather than raw labeling accuracy. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename trainer_type, - typename graph_type - > - matrix<double,1,2> cross_validate_graph_labeling_trainer ( - const trainer_type& trainer, - const dlib::array<graph_type>& samples, - const std::vector<std::vector<bool> >& labels, - const long folds - ); - /*! - requires - - is_graph_labeling_problem(samples,labels) == true - - 1 < folds <= samples.size() - - trainer_type == an object which trains some kind of graph labeler object - (e.g. structural_graph_labeling_trainer) - ensures - - performs k-fold cross validation by using the given trainer to solve the - given graph labeling problem for the given number of folds. Each fold - is tested using the output of the trainer and the average classification - accuracy from all folds is returned. In particular, this function returns - a matrix R such that: - - R(0) == The fraction of nodes which are supposed to have a label of - true that are labeled as such by the learned labeler. - - R(1) == The fraction of nodes which are supposed to have a label of - false that are labeled as such by the learned labeler. - Therefore, if R is [1,1] then the labeler makes perfect predictions while - an R of [0,0] indicates that it gets everything wrong. - - The number of folds used is given by the folds argument. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename trainer_type, - typename graph_type - > - matrix<double,1,2> cross_validate_graph_labeling_trainer ( - const trainer_type& trainer, - const dlib::array<graph_type>& samples, - const std::vector<std::vector<bool> >& labels, - const std::vector<std::vector<double> >& losses, - const long folds - ); - /*! - requires - - is_graph_labeling_problem(samples,labels) == true - - 1 < folds <= samples.size() - - trainer_type == an object which trains some kind of graph labeler object - (e.g. structural_graph_labeling_trainer) - - if (losses.size() != 0) then - - sizes_match(labels, losses) == true - - all_values_are_nonnegative(losses) == true - ensures - - This overload of cross_validate_graph_labeling_trainer() does the same thing - as the one defined above, except that instead of counting 1 for each labeling - mistake, it weights each mistake according to the corresponding value in - losses. That is, instead of counting a value of 1 for making a mistake on - samples[i].node(j), this routine counts a value of losses[i][j]. Under this - interpretation, the loss values represent how useful it is to correctly label - each node. Therefore, the values returned represent fractions of overall - labeling utility rather than raw labeling accuracy. - !*/ - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_Hh_ - - - |