/usr/include/dlib/svm/roc_trainer.h is in libdlib-dev 18.18-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | // Copyright (C) 2009 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_ROC_TRAINEr_H_
#define DLIB_ROC_TRAINEr_H_
#include "roc_trainer_abstract.h"
#include "../algs.h"
#include <limits>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename trainer_type
>
class roc_trainer_type
{
public:
typedef typename trainer_type::kernel_type kernel_type;
typedef typename trainer_type::scalar_type scalar_type;
typedef typename trainer_type::sample_type sample_type;
typedef typename trainer_type::mem_manager_type mem_manager_type;
typedef typename trainer_type::trained_function_type trained_function_type;
roc_trainer_type (
) : desired_accuracy(0), class_selection(0){}
roc_trainer_type (
const trainer_type& trainer_,
const scalar_type& desired_accuracy_,
const scalar_type& class_selection_
) : trainer(trainer_), desired_accuracy(desired_accuracy_), class_selection(class_selection_)
{
// make sure requires clause is not broken
DLIB_ASSERT(0 <= desired_accuracy && desired_accuracy <= 1 &&
(class_selection == -1 || class_selection == +1),
"\t roc_trainer_type::roc_trainer_type()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t desired_accuracy: " << desired_accuracy
<< "\n\t class_selection: " << class_selection
);
}
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const trained_function_type train (
const in_sample_vector_type& samples,
const in_scalar_vector_type& labels
) const
/*!
requires
- is_binary_classification_problem(samples, labels) == true
!*/
{
// make sure requires clause is not broken
DLIB_ASSERT(is_binary_classification_problem(samples, labels),
"\t roc_trainer_type::train()"
<< "\n\t invalid inputs were given to this function"
);
return do_train(mat(samples), mat(labels));
}
private:
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const trained_function_type do_train (
const in_sample_vector_type& samples,
const in_scalar_vector_type& labels
) const
{
trained_function_type df = trainer.train(samples, labels);
// clear out the old bias
df.b = 0;
// obtain all the scores from the df using all the class_selection labeled samples
std::vector<double> scores;
for (long i = 0; i < samples.size(); ++i)
{
if (labels(i) == class_selection)
scores.push_back(df(samples(i)));
}
if (class_selection == +1)
std::sort(scores.rbegin(), scores.rend());
else
std::sort(scores.begin(), scores.end());
// now pick out the index that gives us the desired accuracy with regards to selected class
unsigned long idx = static_cast<unsigned long>(desired_accuracy*scores.size() + 0.5);
if (idx >= scores.size())
idx = scores.size()-1;
df.b = scores[idx];
// In this case add a very small extra amount to the bias so that all the samples
// with the class_selection label are classified correctly.
if (desired_accuracy == 1)
{
if (class_selection == +1)
df.b -= std::numeric_limits<scalar_type>::epsilon()*df.b;
else
df.b += std::numeric_limits<scalar_type>::epsilon()*df.b;
}
return df;
}
trainer_type trainer;
scalar_type desired_accuracy;
scalar_type class_selection;
};
// ----------------------------------------------------------------------------------------
template <
typename trainer_type
>
const roc_trainer_type<trainer_type> roc_c1_trainer (
const trainer_type& trainer,
const typename trainer_type::scalar_type& desired_accuracy
) { return roc_trainer_type<trainer_type>(trainer, desired_accuracy, +1); }
// ----------------------------------------------------------------------------------------
template <
typename trainer_type
>
const roc_trainer_type<trainer_type> roc_c2_trainer (
const trainer_type& trainer,
const typename trainer_type::scalar_type& desired_accuracy
) { return roc_trainer_type<trainer_type>(trainer, desired_accuracy, -1); }
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_ROC_TRAINEr_H_
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