Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 0 insertions, 110 deletions

diff --git a/ml/dlib/dlib/svm/simplify_linear_decision_function.h b/ml/dlib/dlib/svm/simplify_linear_decision_function.h
deleted file mode 100644
index 4f5bef6f3..000000000
--- a/ml/dlib/dlib/svm/simplify_linear_decision_function.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// 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_
-