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dlib
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Switched this code to use the oca object's ability to force a weight to 1 

instead of rolling its own implementation.
This commit is contained in:
Davis King 2013-01-03 22:00:02 -05:00
parent 277b47ae58
commit b445ddbd8d
1 changed files with 14 additions and 21 deletions
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35
dlib/svm/svm_rank_trainer.h
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@ -37,15 +37,13 @@ namespace dlib
const std::vector<ranking_pair<sample_type> >& samples_,
const bool be_verbose_,
const scalar_type eps_,
const unsigned long max_iter,
const bool last_weight_1_
const unsigned long max_iter
) :
samples(samples_),
C(C_),
be_verbose(be_verbose_),
eps(eps_),
max_iterations(max_iter),
last_weight_1(last_weight_1_)
max_iterations(max_iter)
{
}
@ -113,8 +111,6 @@ namespace dlib
// rank flips. So a risk of 0.1 would mean that rank flips happen < 10% of the
// time.
if(last_weight_1)
w(w.size()-1) = 1;
std::vector<double> rel_scores;
std::vector<double> nonrel_scores;
@ -163,12 +159,6 @@ namespace dlib
risk *= scale;
subgradient = scale*subgradient;
if(last_weight_1)
{
w(w.size()-1) = 0;
subgradient(w.size()-1) = 0;
}
}
private:
@ -183,7 +173,6 @@ namespace dlib
const bool be_verbose;
const scalar_type eps;
const unsigned long max_iterations;
const bool last_weight_1;
};
// ----------------------------------------------------------------------------------------
@ -198,12 +187,11 @@ namespace dlib
const std::vector<ranking_pair<sample_type> >& samples,
const bool be_verbose,
const scalar_type eps,
const unsigned long max_iterations,
const bool last_weight_1
const unsigned long max_iterations
)
{
return oca_problem_ranking_svm<matrix_type, sample_type>(
C, samples, be_verbose, eps, max_iterations, last_weight_1);
C, samples, be_verbose, eps, max_iterations);
}
// ----------------------------------------------------------------------------------------
@ -385,12 +373,17 @@ namespace dlib
num_nonnegative = num_dims;
}
solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations, last_weight_1),
w,
num_nonnegative);
unsigned long force_weight_1_idx = std::numeric_limits<unsigned long>::max();
if (last_weight_1)
{
force_weight_1_idx = num_dims-1;
}
solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations),
w,
num_nonnegative,
force_weight_1_idx);
if(last_weight_1)
w(w.size()-1) = 1;
// put the solution into a decision function and then return it
decision_function<kernel_type> df;