// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SVM_C_LiNEAR_TRAINER_ABSTRACT_Hh_
#ifdef DLIB_SVM_C_LiNEAR_TRAINER_ABSTRACT_Hh_
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "function_abstract.h"
#include "kernel_abstract.h"
#include "sparse_kernel_abstract.h"
namespace dlib
{
template <
typename K
>
class svm_c_linear_trainer
{
/*!
REQUIREMENTS ON K
Is either linear_kernel or sparse_linear_kernel.
WHAT THIS OBJECT REPRESENTS
This object represents a tool for training the C formulation of
a support vector machine. It is optimized for the case where
linear kernels are used.
In particular, it is implemented using the OCAS algorithm
described in the following paper:
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
Vojtech Franc, Soren Sonnenburg; Journal of Machine Learning
Research, 10(Oct):2157--2192, 2009.
!*/
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_c_linear_trainer (
);
/*!
ensures
- This object is properly initialized and ready to be used
to train a support vector machine.
- #get_oca() == oca() (i.e. an instance of oca with default parameters)
- #get_c_class1() == 1
- #get_c_class2() == 1
- #get_epsilon() == 0.001
- #get_relative_epsilon() == 0.0001
- this object will not be verbose unless be_verbose() is called
- #get_max_iterations() == 10000
- #learns_nonnegative_weights() == false
- #force_last_weight_to_1() == false
- #has_prior() == false
!*/
explicit svm_c_linear_trainer (
const scalar_type& C
);
/*!
requires
- C > 0
ensures
- This object is properly initialized and ready to be used
to train a support vector machine.
- #get_oca() == oca() (i.e. an instance of oca with default parameters)
- #get_c_class1() == C
- #get_c_class2() == C
- #get_epsilon() == 0.001
- #get_relative_epsilon() == 0.0001
- this object will not be verbose unless be_verbose() is called
- #get_max_iterations() == 10000
- #learns_nonnegative_weights() == false
- #force_last_weight_to_1() == false
- #has_prior() == false
!*/
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.
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 probability of misclassification is within
epsilon of its optimal value".
In particular, the solver will terminate when the risk is within eps of optimal.
I.e. it stops if the "risk gap" is less than eps.
!*/
void set_relative_epsilon (
scalar_type eps
);
/*!
requires
- eps > 0
ensures
- #get_relative_epsilon() == eps
!*/
const scalar_type get_relative_epsilon (
) const;
/*!
ensures
- returns the relative error epsilon that determines when training should stop.
Smaller values may result in a more accurate solution but take longer to
train. In particular, when the ratio of the risk gap to current risk is less than
get_relative_epsilon() the solver will terminate.
This means that, unlike get_epsilon(), get_relative_epsilon() is a relative
measure of accuracy. To say this another way, the solver terminates when the
possible improvement in risk is less than get_relative_epsilon() fraction of the
current risk.
!*/
void set_max_iterations (
unsigned long max_iter
);
/*!
ensures
- #get_max_iterations() == max_iter
!*/
unsigned long get_max_iterations (
);
/*!
ensures
- returns the maximum number of iterations the SVM optimizer is allowed to
run before it is required to stop and return a result.
!*/
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 SVM problem.
!*/
const kernel_type get_kernel (
) const;
/*!
ensures
- returns a copy of the kernel function in use by this object. Since
the linear kernels don't have any parameters this function just
returns kernel_type()
!*/
bool learns_nonnegative_weights (
) const;
/*!
ensures
- The output of training is a weight vector and a bias value. These
two things define the resulting decision function. That is, the
decision function simply takes the dot product between the learned
weight vector and a test sample, then subtracts the bias value.
Therefore, if learns_nonnegative_weights() == true then the resulting
learned weight vector will always have non-negative entries. The
bias value may still be negative though.
!*/
void set_learns_nonnegative_weights (
bool value
);
/*!
ensures
- #learns_nonnegative_weights() == value
- if (value == true) then
- #has_prior() == false
!*/
void set_prior (
const trained_function_type& prior
);
/*!
requires
- prior == a function produced by a call to this class's train() function.
Therefore, it must be the case that:
- prior.basis_vectors.size() == 1
- prior.alpha(0) == 1
ensures
- Subsequent calls to train() will try to learn a function similar to the
given prior.
- #has_prior() == true
- #learns_nonnegative_weights() == false
- #forces_last_weight_to_1() == false
!*/
bool has_prior (
) const
/*!
ensures
- returns true if a prior has been set and false otherwise. Having a prior
set means that you have called set_prior() and supplied a previously
trained function as a reference. In this case, any call to train() will
try to learn a function that matches the behavior of the prior as close
as possible but also fits the supplied training data. In more technical
detail, having a prior means we replace the ||w||^2 regularizer with one
of the form ||w-prior||^2 where w is the set of parameters for a learned
function.
!*/
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 b
field of the learned decision_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
- if (should_last_weight_be_1 == true) then
- #has_prior() == false
!*/
void set_c (
scalar_type C
);
/*!
requires
- C > 0
ensures
- #get_c_class1() == C
- #get_c_class2() == C
!*/
const scalar_type get_c_class1 (
) const;
/*!
ensures
- returns the SVM regularization parameter for the +1 class.
It is the parameter that determines the trade off between
trying to fit the +1 training data exactly or allowing more errors
but hopefully improving the generalization of the resulting
classifier. Larger values encourage exact fitting while
smaller values of C may encourage better generalization.
!*/
const scalar_type get_c_class2 (
) const;
/*!
ensures
- returns the SVM regularization parameter for the -1 class.
It is the parameter that determines the trade off between
trying to fit the -1 training data exactly or allowing more errors
but hopefully improving the generalization of the resulting
classifier. Larger values encourage exact fitting while
smaller values of C may encourage better generalization.
!*/
void set_c_class1 (
scalar_type C
);
/*!
requires
- C > 0
ensures
- #get_c_class1() == C
!*/
void set_c_class2 (
scalar_type C
);
/*!
requires
- C > 0
ensures
- #get_c_class2() == C
!*/
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y
) const;
/*!
requires
- is_learning_problem(x,y) == true
(Note that it is ok for x.size() == 1)
- All elements of y must be equal to +1 or -1
- x == a matrix or something convertible to a matrix via mat().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via mat().
Also, y should contain scalar_type objects.
- if (has_prior()) then
- The vectors in x must have the same dimensionality as the vectors
used to train the prior given to set_prior().
ensures
- trains a C support vector classifier given the training samples in x and
labels in y.
- returns a decision function F with the following properties:
- F.alpha.size() == 1
- F.basis_vectors.size() == 1
- F.alpha(0) == 1
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
- else
- F(new_x) < 0
!*/
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& svm_objective
) const;
/*!
requires
- is_learning_problem(x,y) == true
(Note that it is ok for x.size() == 1)
- All elements of y must be equal to +1 or -1
- x == a matrix or something convertible to a matrix via mat().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via mat().
Also, y should contain scalar_type objects.
- if (has_prior()) then
- The vectors in x must have the same dimensionality as the vectors
used to train the prior given to set_prior().
ensures
- trains a C support vector classifier given the training samples in x and
labels in y.
- #svm_objective == the final value of the SVM objective function
- returns a decision function F with the following properties:
- F.alpha.size() == 1
- F.basis_vectors.size() == 1
- F.alpha(0) == 1
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
- else
- F(new_x) < 0
!*/
};
}
#endif // DLIB_SVM_C_LiNEAR_TRAINER_ABSTRACT_Hh_