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// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SIMPLIFY_LINEAR_DECiSION_FUNCTION_Hh_
#define DLIB_SIMPLIFY_LINEAR_DECiSION_FUNCTION_Hh_
#include "simplify_linear_decision_function_abstract.h"
#include "../algs.h"
#include "function.h"
#include "sparse_kernel.h"
#include "kernel.h"
#include <map>
#include <vector>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename T
>
decision_function<sparse_linear_kernel<T> > simplify_linear_decision_function (
const decision_function<sparse_linear_kernel<T> >& df
)
{
// don't do anything if we don't have to
if (df.basis_vectors.size() <= 1)
return df;
decision_function<sparse_linear_kernel<T> > new_df;
new_df.b = df.b;
new_df.basis_vectors.set_size(1);
new_df.alpha.set_size(1);
new_df.alpha(0) = 1;
// now compute the weighted sum of all the sparse basis_vectors in df
typedef typename T::value_type pair_type;
typedef typename pair_type::first_type key_type;
typedef typename pair_type::second_type value_type;
std::map<key_type, value_type> accum;
for (long i = 0; i < df.basis_vectors.size(); ++i)
{
typename T::const_iterator j = df.basis_vectors(i).begin();
const typename T::const_iterator end = df.basis_vectors(i).end();
for (; j != end; ++j)
{
accum[j->first] += df.alpha(i) * (j->second);
}
}
new_df.basis_vectors(0) = T(accum.begin(), accum.end());
return new_df;
}
// ----------------------------------------------------------------------------------------
template <
typename T
>
decision_function<linear_kernel<T> > simplify_linear_decision_function (
const decision_function<linear_kernel<T> >& df
)
{
// don't do anything if we don't have to
if (df.basis_vectors.size() <= 1)
return df;
decision_function<linear_kernel<T> > new_df;
new_df.b = df.b;
new_df.basis_vectors.set_size(1);
new_df.alpha.set_size(1);
new_df.alpha(0) = 1;
// now compute the weighted sum of all the basis_vectors in df
new_df.basis_vectors(0) = 0;
for (long i = 0; i < df.basis_vectors.size(); ++i)
{
new_df.basis_vectors(0) += df.alpha(i) * df.basis_vectors(i);
}
return new_df;
}
// ----------------------------------------------------------------------------------------
template <
typename T
>
decision_function<linear_kernel<T> > simplify_linear_decision_function (
const normalized_function<decision_function<linear_kernel<T> >, vector_normalizer<T> >& df
)
{
decision_function<linear_kernel<T> > new_df = simplify_linear_decision_function(df.function);
// now incorporate the normalization stuff into new_df
new_df.basis_vectors(0) = pointwise_multiply(new_df.basis_vectors(0), df.normalizer.std_devs());
new_df.b += dot(new_df.basis_vectors(0), df.normalizer.means());
return new_df;
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SIMPLIFY_LINEAR_DECiSION_FUNCTION_Hh_
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