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