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Added the forces_last_weight_to_1() option to the svm_c_linear_trainer.

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This commit is contained in:
Davis King 2013-01-03 22:15:27 -05:00
parent 96264d3339
commit 9ab59297b2
2 changed files with 49 additions and 2 deletions
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30
dlib/svm/svm_c_linear_trainer.h
View File

@ -352,6 +352,7 @@ namespace dlib
eps = 0.001;
max_iterations = 10000;
learn_nonnegative_weights = false;
last_weight_1 = false;
}
explicit svm_c_linear_trainer (
@ -372,6 +373,7 @@ namespace dlib
eps = 0.001;
max_iterations = 10000;
learn_nonnegative_weights = false;
last_weight_1 = false;
}
void set_epsilon (
@ -443,6 +445,19 @@ namespace dlib
learn_nonnegative_weights = value;
}
bool forces_last_weight_to_1 (
) const
{
return last_weight_1;
}
void force_last_weight_to_1 (
bool should_last_weight_be_1
)
{
last_weight_1 = should_last_weight_be_1;
}
void set_c (
scalar_type C
)
@ -555,16 +570,26 @@ namespace dlib
typedef matrix<scalar_type,0,1> w_type;
w_type w;
const unsigned long num_dims = max_index_plus_one(x);
unsigned long num_nonnegative = 0;
if (learn_nonnegative_weights)
{
num_nonnegative = max_index_plus_one(x);
num_nonnegative = num_dims;
}
unsigned long force_weight_1_idx = std::numeric_limits<unsigned long>::max();
if (last_weight_1)
{
force_weight_1_idx = num_dims-1;
}
svm_objective = solver(
make_oca_problem_c_svm<w_type>(Cpos, Cneg, x, y, verbose, eps, max_iterations),
w,
num_nonnegative);
num_nonnegative,
force_weight_1_idx);
// put the solution into a decision function and then return it
decision_function<kernel_type> df;
@ -589,6 +614,7 @@ namespace dlib
bool verbose;
unsigned long max_iterations;
bool learn_nonnegative_weights;
bool last_weight_1;
};
// ----------------------------------------------------------------------------------------

21
dlib/svm/svm_c_linear_trainer_abstract.h
View File

@ -53,6 +53,7 @@ namespace dlib
- 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
!*/
explicit svm_c_linear_trainer (
@ -71,6 +72,7 @@ namespace dlib
- 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
!*/
void set_epsilon (
@ -170,6 +172,25 @@ namespace dlib
- #learns_nonnegative_weights() == value
!*/
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
!*/
void set_c (
scalar_type C
);