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Commit 4eb5bc6a authored by Davis King's avatar Davis King
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Made a proper spec 

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403505
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Showing with 237 additions and 129 deletions
dlib/svm/svm_c_linear_trainer.h
View file @ 4eb5bc6a
......@@ -372,6 +372,7 @@ namespace dlib
const scalar_type eps;
};
// ----------------------------------------------------------------------------------------
template <
typename matrix_type,
......@@ -390,6 +391,7 @@ namespace dlib
{
return oca_problem_c_svm<matrix_type, in_sample_vector_type, in_scalar_vector_type>(C_pos, C_neg, samples, labels, be_verbose, eps);
}
// ----------------------------------------------------------------------------------------
template <
......@@ -397,12 +399,6 @@ namespace dlib
>
class svm_c_linear_trainer
{
/*!
REQUIREMENTS ON K
is either linear_kernel or sparse_linear_kernel
WHAT THIS OBJECT REPRESENTS
!*/
public:
typedef K kernel_type;
......@@ -413,16 +409,6 @@ namespace dlib
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
- this object will not be verbose unless be_verbose() is called
!*/
{
Cpos = 1;
Cneg = 1;
......@@ -433,16 +419,6 @@ namespace dlib
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
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
......@@ -459,12 +435,6 @@ namespace dlib
void set_epsilon (
scalar_type eps_
)
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
......@@ -479,30 +449,15 @@ namespace dlib
const scalar_type get_epsilon (
) const { return eps; }
/*!
ensures
- returns the error epsilon that determines when training should stop.
Smaller values may result in a more accurate solution but take longer
to execute.
!*/
void be_verbose (
)
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
{
verbose = true;
}
void be_quiet (
)
/*!
ensures
- this object will not print anything to standard out
!*/
{
verbose = false;
}
......@@ -510,30 +465,18 @@ namespace dlib
void set_oca (
const oca& item
)
/*!
ensures
- #get_oca() == item
!*/
{
solver = item;
}
const oca get_oca (
) const
/*!
ensures
- returns a copy of the optimizer used to solve the SVM problem.
!*/
{
return solver;
}
const kernel_type get_kernel (
) const
/*!
ensures
- returns a copy of the kernel function in use by this object
!*/
{
return kernel_type();
}
......@@ -541,13 +484,6 @@ namespace dlib
void set_c (
scalar_type C
)
/*!
requires
- C > 0
ensures
- #get_c_class1() == C
- #get_c_class2() == C
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
......@@ -563,30 +499,12 @@ namespace dlib
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 ability of the
resulting classifier. Larger values encourage exact fitting
while smaller values of C may encourage better generalization.
!*/
{
return Cpos;
}
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 ability of the
resulting classifier. Larger values encourage exact fitting
while smaller values of C may encourage better generalization.
!*/
{
return Cneg;
}
......@@ -594,12 +512,6 @@ namespace dlib
void set_c_class1 (
scalar_type C
)
/*!
requires
- C > 0
ensures
- #get_c_class1() == C
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
......@@ -615,12 +527,6 @@ namespace dlib
void set_c_class2 (
scalar_type C
)
/*!
requires
- C > 0
ensures
- #get_c_class2() == C
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
......@@ -641,22 +547,6 @@ namespace dlib
const in_sample_vector_type& x,
const in_scalar_vector_type& y
) const
/*!
requires
- is_binary_classification_problem(x,y) == true
- x == a matrix or something convertible to a matrix via vector_to_matrix().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via vector_to_matrix().
Also, y should contain scalar_type objects.
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:
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
- else
- F(new_x) < 0
!*/
{
scalar_type obj;
return do_train(vector_to_matrix(x),vector_to_matrix(y),obj);
......@@ -672,23 +562,6 @@ namespace dlib
const in_scalar_vector_type& y,
scalar_type& svm_objective
) const
/*!
requires
- is_binary_classification_problem(x,y) == true
- x == a matrix or something convertible to a matrix via vector_to_matrix().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via vector_to_matrix().
Also, y should contain scalar_type objects.
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:
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
- else
- F(new_x) < 0
!*/
{
return do_train(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
}
......
dlib/svm/svm_c_linear_trainer_abstract.h
View file @ 4eb5bc6a
......@@ -3,8 +3,243 @@
#undef DLIB_SVM_C_LiNEAR_TRAINER_ABSTRACT_H__
#ifdef DLIB_SVM_C_LiNEAR_TRAINER_ABSTRACT_H__
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "function_abstract.h"
#include "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; 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
- this object will not be verbose unless be_verbose() is called
!*/
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
- this object will not be verbose unless be_verbose() is called
!*/
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 execute.
!*/
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()
!*/
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 ability 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 ability 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_binary_classification_problem(x,y) == true
- x == a matrix or something convertible to a matrix via vector_to_matrix().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via vector_to_matrix().
Also, y should contain scalar_type objects.
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_binary_classification_problem(x,y) == true
- x == a matrix or something convertible to a matrix via vector_to_matrix().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via vector_to_matrix().
Also, y should contain scalar_type objects.
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
!*/
};
}
......
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