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

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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 std::vector<ranking_pair<sample_type> >& samples_,
const bool be_verbose_, const bool be_verbose_,
const scalar_type eps_, const scalar_type eps_,
const unsigned long max_iter, const unsigned long max_iter
const bool last_weight_1_
) : ) :
samples(samples_), samples(samples_),
C(C_), C(C_),
be_verbose(be_verbose_), be_verbose(be_verbose_),
eps(eps_), eps(eps_),
max_iterations(max_iter), max_iterations(max_iter)
last_weight_1(last_weight_1_)
{ {
} }
@ -113,8 +111,6 @@ namespace dlib
// rank flips. So a risk of 0.1 would mean that rank flips happen < 10% of the // rank flips. So a risk of 0.1 would mean that rank flips happen < 10% of the
// time. // time.
if(last_weight_1)
w(w.size()-1) = 1;
std::vector<double> rel_scores; std::vector<double> rel_scores;
std::vector<double> nonrel_scores; std::vector<double> nonrel_scores;
@ -163,12 +159,6 @@ namespace dlib
risk *= scale; risk *= scale;
subgradient = scale*subgradient; subgradient = scale*subgradient;
if(last_weight_1)
{
w(w.size()-1) = 0;
subgradient(w.size()-1) = 0;
}
} }
private: private:
@ -183,7 +173,6 @@ namespace dlib
const bool be_verbose; const bool be_verbose;
const scalar_type eps; const scalar_type eps;
const unsigned long max_iterations; 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 std::vector<ranking_pair<sample_type> >& samples,
const bool be_verbose, const bool be_verbose,
const scalar_type eps, const scalar_type eps,
const unsigned long max_iterations, const unsigned long max_iterations
const bool last_weight_1
) )
{ {
return oca_problem_ranking_svm<matrix_type, sample_type>( 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; num_nonnegative = num_dims;
} }
solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations, last_weight_1), unsigned long force_weight_1_idx = std::numeric_limits<unsigned long>::max();
w, if (last_weight_1)
num_nonnegative); {
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 // put the solution into a decision function and then return it
decision_function<kernel_type> df; decision_function<kernel_type> df;