// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_ABSTRACT_Hh_
#ifdef DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_ABSTRACT_Hh_
#include "../algs.h"
#include "structural_svm_assignment_problem.h"
#include "assignment_function_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename feature_extractor
>
class structural_assignment_trainer
{
/*!
REQUIREMENTS ON feature_extractor
It must be an object that implements an interface compatible with
the example_feature_extractor defined in dlib/svm/assignment_function_abstract.h.
WHAT THIS OBJECT REPRESENTS
This object is a tool for learning to solve an assignment problem based
on a training dataset of example assignments. The training procedure produces an
assignment_function object which can be used to predict the assignments of
new data.
Note that this is just a convenience wrapper around the
structural_svm_assignment_problem to make it look
similar to all the other trainers in dlib.
!*/
public:
typedef typename feature_extractor::lhs_element lhs_element;
typedef typename feature_extractor::rhs_element rhs_element;
typedef std::pair<std::vector<lhs_element>, std::vector<rhs_element> > sample_type;
typedef std::vector<long> label_type;
typedef assignment_function<feature_extractor> trained_function_type;
structural_assignment_trainer (
);
/*!
ensures
- #get_c() == 100
- this object isn't verbose
- #get_epsilon() == 0.01
- #get_num_threads() == 2
- #get_max_cache_size() == 5
- #get_feature_extractor() == a default initialized feature_extractor
- #forces_assignment() == false
- #get_loss_per_false_association() == 1
- #get_loss_per_missed_association() == 1
- #forces_last_weight_to_1() == false
!*/
explicit structural_assignment_trainer (
const feature_extractor& fe
);
/*!
ensures
- #get_c() == 100
- this object isn't verbose
- #get_epsilon() == 0.01
- #get_num_threads() == 2
- #get_max_cache_size() == 40
- #get_feature_extractor() == fe
- #forces_assignment() == false
- #get_loss_per_false_association() == 1
- #get_loss_per_missed_association() == 1
- #forces_last_weight_to_1() == false
!*/
const feature_extractor& get_feature_extractor (
) const;
/*!
ensures
- returns the feature extractor used by this object
!*/
void set_num_threads (
unsigned long num
);
/*!
ensures
- #get_num_threads() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- returns the number of threads used during training. You should
usually set this equal to the number of processing cores on your
machine.
!*/
void set_epsilon (
double eps
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
double get_epsilon (
) const;
/*!
ensures
- returns the error epsilon that determines when training should stop.
Smaller values may result in a more accurate solution but take longer
to train. You can think of this epsilon value as saying "solve the
optimization problem until the average number of assignment mistakes per
training sample is within epsilon of its optimal value".
!*/
void set_max_cache_size (
unsigned long max_size
);
/*!
ensures
- #get_max_cache_size() == max_size
!*/
unsigned long get_max_cache_size (
) const;
/*!
ensures
- During training, this object basically runs the assignment_function on
each training sample, over and over. To speed this up, it is possible to
cache the results of these invocations. This function returns the number
of cache elements per training sample kept in the cache. Note that a value
of 0 means caching is not used at all.
!*/
void set_loss_per_false_association (
double loss
);
/*!
requires
- loss > 0
ensures
- #get_loss_per_false_association() == loss
!*/
double get_loss_per_false_association (
) const;
/*!
ensures
- returns the amount of loss experienced for associating two objects
together that shouldn't be associated. If you care more about avoiding
accidental associations than ensuring all possible associations are
identified then then you can increase this value.
!*/
void set_loss_per_missed_association (
double loss
);
/*!
requires
- loss > 0
ensures
- #get_loss_per_missed_association() == loss
!*/
double get_loss_per_missed_association (
) const;
/*!
ensures
- returns the amount of loss experienced for failing to associate two
objects that are supposed to be associated. If you care more about
getting all the associations than avoiding accidentally associating
objects that shouldn't be associated then you can increase this value.
!*/
void be_verbose (
);
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
void be_quiet (
);
/*!
ensures
- this object will not print anything to standard out
!*/
void set_oca (
const oca& item
);
/*!
ensures
- #get_oca() == item
!*/
const oca get_oca (
) const;
/*!
ensures
- returns a copy of the optimizer used to solve the structural SVM problem.
!*/
void set_c (
double C
);
/*!
requires
- C > 0
ensures
- #get_c() = C
!*/
double get_c (
) const;
/*!
ensures
- returns the SVM regularization parameter. It is the parameter
that determines the trade-off between trying to fit the training
data (i.e. minimize the loss) or allowing more errors but hopefully
improving the generalization of the resulting assignment_function.
Larger values encourage exact fitting while smaller values of C may
encourage better generalization.
!*/
void set_forces_assignment (
bool new_value
);
/*!
ensures
- #forces_assignment() == new_value
!*/
bool forces_assignment(
) const;
/*!
ensures
- returns the value of the forces_assignment() parameter for the
assignment_functions generated by this object.
!*/
bool forces_last_weight_to_1 (
) const;
/*!
ensures
- returns true if this trainer has the constraint that the last weight in
the learned parameter vector must be 1. This is the weight corresponding
to the feature in the training vectors with the highest dimension.
- Forcing the last weight to 1 also disables the bias and therefore the
get_bias() field of the learned assignment_function will be 0 when
forces_last_weight_to_1() == true.
!*/
void force_last_weight_to_1 (
bool should_last_weight_be_1
);
/*!
ensures
- #forces_last_weight_to_1() == should_last_weight_be_1
!*/
const assignment_function<feature_extractor> train (
const std::vector<sample_type>& samples,
const std::vector<label_type>& labels
) const;
/*!
requires
- is_assignment_problem(samples,labels) == true
- if (forces_assignment()) then
- is_forced_assignment_problem(samples,labels) == true
ensures
- Uses the structural_svm_assignment_problem to train an
assignment_function on the given samples/labels training pairs.
The idea is to learn to predict a label given an input sample.
- returns a function F with the following properties:
- F(new_sample) == A set of assignments indicating how the elements of
new_sample.first match up with the elements of new_sample.second.
- F.forces_assignment() == forces_assignment()
- F.get_feature_extractor() == get_feature_extractor()
- if (forces_last_weight_to_1()) then
- F.get_bias() == 0
- F.get_weights()(F.get_weights().size()-1) == 1
!*/
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_ABSTRACT_Hh_