This file is indexed.

/usr/include/shark/Models/LinearClassifier.h is in libshark-dev 3.0.1+ds1-2ubuntu1.

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
/*!
 * \brief       Implements the Linear Classifier class of the shark library
 * 
 * \author      O. Krause
 * \date        2013
 *
 *
 * \par Copyright 1995-2015 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://image.diku.dk/shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
#ifndef SHARK_ML_MODEL_LINEARCLASSIFIER_H
#define SHARK_ML_MODEL_LINEARCLASSIFIER_H

#include <shark/Models/LinearModel.h>
#include <shark/Models/Converter.h>
namespace shark {

/*! \brief Basic linear classifier.
 *
 *  The LinearClassifier class is a multi class classifier model
 *  suited for linear discriminant analysis. For c classes
 *  \f$ 0, \dots, c-1 \f$  the model computes
 *   
 *  \f$ \arg \max_i w_i^T x + b_i \f$
 *  
 *  Thus is it a linear model with arg max computation.
 *  The internal linear model can be queried using decisionFunction().
 */ 
template<class VectorType = RealVector>
class LinearClassifier : public ArgMaxConverter<LinearModel<VectorType> >
{
public:
	LinearClassifier(){}

	std::string name() const
	{ return "LinearClassifier"; }
};
}
#endif