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Diffstat (limited to 'ml/dlib/dlib/svm/svm_one_class_trainer_abstract.h')
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1 files changed, 0 insertions, 201 deletions
diff --git a/ml/dlib/dlib/svm/svm_one_class_trainer_abstract.h b/ml/dlib/dlib/svm/svm_one_class_trainer_abstract.h deleted file mode 100644 index 6b55919ad..000000000 --- a/ml/dlib/dlib/svm/svm_one_class_trainer_abstract.h +++ /dev/null @@ -1,201 +0,0 @@ -// Copyright (C) 2010 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_SVm_ONE_CLASS_TRAINER_ABSTRACT_ -#ifdef DLIB_SVm_ONE_CLASS_TRAINER_ABSTRACT_ - -#include <cmath> -#include <limits> -#include "../matrix/matrix_abstract.h" -#include "../algs.h" -#include "function_abstract.h" -#include "kernel_abstract.h" -#include "../optimization/optimization_solve_qp3_using_smo_abstract.h" - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename K - > - class svm_one_class_trainer - { - /*! - REQUIREMENTS ON K - is a kernel function object as defined in dlib/svm/kernel_abstract.h - - WHAT THIS OBJECT REPRESENTS - This object implements a trainer for a support vector machine for - solving one-class classification problems. It is implemented using the SMO - algorithm. - - The implementation of the training algorithm used by this object is based - on the following excellent paper: - - Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector - machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm - - !*/ - - public: - typedef K kernel_type; - typedef typename kernel_type::scalar_type scalar_type; - typedef typename kernel_type::sample_type sample_type; - typedef typename kernel_type::mem_manager_type mem_manager_type; - typedef decision_function<kernel_type> trained_function_type; - - svm_one_class_trainer ( - ); - /*! - ensures - - This object is properly initialized and ready to be used - to train a support vector machine. - - #get_nu() == 0.1 - - #get_cache_size() == 200 - - #get_epsilon() == 0.001 - !*/ - - svm_one_class_trainer ( - const kernel_type& kernel, - const scalar_type& nu - ); - /*! - requires - - 0 < nu <= 1 - ensures - - This object is properly initialized and ready to be used - to train a support vector machine. - - #get_kernel() == kernel - - #get_nu() == nu - - #get_cache_size() == 200 - - #get_epsilon() == 0.001 - !*/ - - void set_cache_size ( - long cache_size - ); - /*! - requires - - cache_size > 0 - ensures - - #get_cache_size() == cache_size - !*/ - - const long get_cache_size ( - ) const; - /*! - ensures - - returns the number of megabytes of cache this object will use - when it performs training via the this->train() function. - (bigger values of this may make training go faster but won't affect - the result. However, too big a value will cause you to run out of - memory, obviously.) - !*/ - - void set_epsilon ( - scalar_type eps - ); - /*! - requires - - eps > 0 - ensures - - #get_epsilon() == eps - !*/ - - const scalar_type get_epsilon ( - ) const; - /*! - ensures - - returns the error epsilon that determines when training should stop. - Generally a good value for this is 0.001. Smaller values may result - in a more accurate solution but take longer to execute. - !*/ - - void set_kernel ( - const kernel_type& k - ); - /*! - ensures - - #get_kernel() == k - !*/ - - const kernel_type& get_kernel ( - ) const; - /*! - ensures - - returns a copy of the kernel function in use by this object - !*/ - - void set_nu ( - scalar_type nu - ); - /*! - requires - - 0 < nu <= 1 - ensures - - #get_nu() == nu - !*/ - - const scalar_type get_nu ( - ) const; - /*! - ensures - - returns the nu svm parameter. This is a value between 0 and - 1. It is the parameter that determines the trade off between - trying to fit the training data exactly or allowing more errors - but hopefully improving the generalization ability of the - resulting classifier. Smaller values encourage exact fitting - while larger values of nu may encourage better generalization. - For more information you should consult the papers referenced - above. - !*/ - - template < - typename in_sample_vector_type - > - const decision_function<kernel_type> train ( - const in_sample_vector_type& x - ) const; - /*! - requires - - x.size() > 0 - - is_col_vector(x) == true - - x == a matrix or something convertible to a matrix via mat(). - Also, x should contain sample_type objects. - ensures - - trains a one-class support vector classifier given the training samples in x. - Training is done when the error is less than get_epsilon(). - - returns a decision function F with the following properties: - - if (new_x is a sample predicted to arise from the distribution - which generated the training samples) then - - F(new_x) >= 0 - - else - - F(new_x) < 0 - !*/ - - void swap ( - svm_one_class_trainer& item - ); - /*! - ensures - - swaps *this and item - !*/ - }; - - template <typename K> - void swap ( - svm_one_class_trainer<K>& a, - svm_one_class_trainer<K>& b - ) { a.swap(b); } - /*! - provides a global swap - !*/ - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_SVm_ONE_CLASS_TRAINER_ABSTRACT_ - - - |