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// Copyright (C) 2013 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <boost/python.hpp>
#include <dlib/matrix.h>
#include <boost/python/args.hpp>
#include "pyassert.h"
#include "boost_python_utils.h"
#include <dlib/svm.h>
using namespace dlib;
using namespace std;
using namespace boost::python;
class svm_struct_dense : public structural_svm_problem<matrix<double,0,1> >
{
public:
svm_struct_dense (
object& problem_,
long num_dimensions_,
long num_samples_
) :
num_dimensions(num_dimensions_),
num_samples(num_samples_),
problem(problem_)
{}
virtual long get_num_dimensions (
) const { return num_dimensions; }
virtual long get_num_samples (
) const { return num_samples; }
virtual void get_truth_joint_feature_vector (
long idx,
feature_vector_type& psi
) const
{
problem.attr("get_truth_joint_feature_vector")(idx,boost::ref(psi));
}
virtual void separation_oracle (
const long idx,
const matrix_type& current_solution,
scalar_type& loss,
feature_vector_type& psi
) const
{
loss = extract<double>(problem.attr("separation_oracle")(idx,boost::ref(current_solution),boost::ref(psi)));
}
private:
const long num_dimensions;
const long num_samples;
object& problem;
};
// ----------------------------------------------------------------------------------------
/*
class svm_struct_sparse : public structural_svm_problem<matrix<double,0,1>,
std::vector<std::pair<unsigned long,double> >
{
};
*/
// ----------------------------------------------------------------------------------------
matrix<double,0,1> solve_structural_svm_problem(
object problem
)
{
const double C = extract<double>(problem.attr("C"));
const bool be_verbose = hasattr(problem,"be_verbose") && extract<bool>(problem.attr("be_verbose"));
const bool use_sparse_feature_vectors = hasattr(problem,"use_sparse_feature_vectors") &&
extract<bool>(problem.attr("use_sparse_feature_vectors"));
double eps = 0.001;
unsigned long max_cache_size = 10;
if (hasattr(problem, "epsilon"))
eps = extract<double>(problem.attr("epsilon"));
if (hasattr(problem, "max_cache_size"))
eps = extract<double>(problem.attr("max_cache_size"));
const long num_samples = extract<long>(problem.attr("num_samples"));
const long num_dimensions = extract<long>(problem.attr("num_dimensions"));
if (be_verbose)
{
cout << "C: " << C << endl;
cout << "epsilon: " << eps << endl;
cout << "max_cache_size: " << max_cache_size << endl;
cout << "num_samples: " << num_samples << endl;
cout << "num_dimensions: " << num_dimensions << endl;
cout << "use_sparse_feature_vectors: " << std::boolalpha << use_sparse_feature_vectors << endl;
cout << endl;
}
svm_struct_dense prob(problem, num_dimensions, num_samples);
prob.set_c(C);
prob.set_epsilon(eps);
prob.set_max_cache_size(max_cache_size);
if (be_verbose)
prob.be_verbose();
oca solver;
matrix<double,0,1> w;
solver(prob, w);
return w;
}
// ----------------------------------------------------------------------------------------
void bind_svm_struct()
{
def("solve_structural_svm_problem",solve_structural_svm_problem);
}
// ----------------------------------------------------------------------------------------