coco
210 lines
6.7 KiB
C++
210 lines
6.7 KiB
C++
// Copyright (C) 2010 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
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#ifdef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
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#include <cmath>
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#include <limits>
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#include "../matrix/matrix_abstract.h"
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#include "../algs.h"
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#include "function_abstract.h"
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#include "kernel_abstract.h"
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#include "../optimization/optimization_solve_qp3_using_smo_abstract.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename K
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>
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class svr_trainer
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{
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/*!
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REQUIREMENTS ON K
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is a kernel function object as defined in dlib/svm/kernel_abstract.h
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WHAT THIS OBJECT REPRESENTS
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This object implements a trainer for performing epsilon-insensitive support
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vector regression. It is implemented using the SMO algorithm.
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The implementation of the eps-SVR training algorithm used by this object is based
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on the following paper:
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- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
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machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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!*/
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public:
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef decision_function<kernel_type> trained_function_type;
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svr_trainer (
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);
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/*!
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ensures
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- This object is properly initialized and ready to be used
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to train a support vector machine.
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- #get_c() == 1
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- #get_epsilon_insensitivity() == 0.1
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- #get_cache_size() == 200
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- #get_epsilon() == 0.001
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!*/
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void set_cache_size (
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long cache_size
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);
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/*!
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requires
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- cache_size > 0
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ensures
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- #get_cache_size() == cache_size
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!*/
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const long get_cache_size (
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) const;
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/*!
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ensures
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- returns the number of megabytes of cache this object will use
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when it performs training via the this->train() function.
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(bigger values of this may make training go faster but won't affect
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the result. However, too big a value will cause you to run out of
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memory, obviously.)
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!*/
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void set_epsilon (
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scalar_type eps
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);
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/*!
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requires
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- eps > 0
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ensures
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- #get_epsilon() == eps
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!*/
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const scalar_type get_epsilon (
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) const;
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/*!
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ensures
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- returns the error epsilon that determines when training should stop.
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Generally a good value for this is 0.001. Smaller values may result
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in a more accurate solution but take longer to execute.
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!*/
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void set_epsilon_insensitivity (
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scalar_type eps
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);
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/*!
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requires
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- eps > 0
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ensures
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- #get_epsilon_insensitivity() == eps
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!*/
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const scalar_type get_epsilon_insensitivity (
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) const;
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/*!
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ensures
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- This object tries to find a function which minimizes the
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regression error on a training set. This error is measured
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in the following way:
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- if (abs(predicted_value - true_labeled_value) < eps) then
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- The error is 0. That is, any function which gets within
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eps of the correct output is good enough.
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- else
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- The error grows linearly once it gets bigger than eps
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So epsilon-insensitive regression means we do regression but
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stop trying to fit a data point once it is "close enough".
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This function returns that eps value which controls what we
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mean by "close enough".
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!*/
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void set_kernel (
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const kernel_type& k
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);
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/*!
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ensures
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- #get_kernel() == k
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!*/
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const kernel_type& get_kernel (
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) const;
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/*!
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ensures
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- returns a copy of the kernel function in use by this object
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!*/
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void set_c (
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scalar_type C
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);
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/*!
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requires
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- C > 0
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ensures
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- #get_c() == C
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!*/
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const scalar_type get_c (
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) const;
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/*!
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ensures
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- returns the SVR regularization parameter. It is the parameter that
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determines the trade-off between trying to reduce the training error
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or allowing more errors but hopefully improving the generalization
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of the resulting decision_function. Larger values encourage exact
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fitting while smaller values of C may encourage better generalization.
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!*/
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template <
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typename in_sample_vector_type,
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typename in_scalar_vector_type
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>
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const decision_function<kernel_type> train (
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const in_sample_vector_type& x,
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const in_scalar_vector_type& y
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) const;
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/*!
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requires
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- is_learning_problem(x,y) == true
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- x == a matrix or something convertible to a matrix via mat().
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Also, x should contain sample_type objects.
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- y == a matrix or something convertible to a matrix via mat().
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Also, y should contain scalar_type objects.
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ensures
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- performs support vector regression given the training samples in x and
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target values in y.
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- returns a decision_function F with the following properties:
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- F(new_x) == predicted y value
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!*/
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void swap (
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svr_trainer& item
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);
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/*!
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ensures
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- swaps *this and item
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!*/
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};
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template <typename K>
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void swap (
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svr_trainer<K>& a,
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svr_trainer<K>& b
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) { a.swap(b); }
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/*!
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provides a global swap
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
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