This file is indexed.

/usr/include/shogun/structure/DualLibQPBMSOSVM.h is in libshogun-dev 3.2.0-7.5.

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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2012 Michal Uricar
 * Copyright (C) 2012 Michal Uricar
 */

#ifndef _DUALLIBQPBMSOSVM__H__
#define _DUALLIBQPBMSOSVM__H__

#include <shogun/machine/LinearStructuredOutputMachine.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/structure/BmrmStatistics.h>

namespace shogun
{

/**
 * Enum
 * Training method selection
 */
enum ESolver
{
	BMRM=1,		/**< Standard BMRM algorithm. */
	PPBMRM=2,	/**< Proximal Point BMRM (BMRM with prox-term) */
	P3BMRM=3,	/**< Proximal Point P-BMRM (multiple cutting plane models) */
	NCBM=4
};

/**
 * @brief Class DualLibQPBMSOSVM that uses Bundle Methods for Regularized Risk
 * Minimization algorithms for structured output (SO) problems [1] presented
 * in [2].
 *
 * [1] Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.
 *	   Support Vector Machine Learning for Interdependent and Structured Ouput
 *	   Spaces.
 *	   http://www.cs.cornell.edu/People/tj/publications/tsochantaridis_etal_04a.pdf
 *
 * [2] Teo, C.H., Vishwanathan, S.V.N, Smola, A. and Quoc, V.Le.
 *     Bundle Methods for Regularized Risk Minimization
 *     http://users.cecs.anu.edu.au/~chteo/pub/TeoVisSmoLe10.pdf
 */
class CDualLibQPBMSOSVM : public CLinearStructuredOutputMachine
{
	public:
		/** default constructor */
		CDualLibQPBMSOSVM();

		/** constructor
		 *
		 * @param model		Structured Model
		 * @param labs			Structured labels
		 * @param _lambda		Regularization constant
		 * @param W				initial solution of weight vector
		 */
		CDualLibQPBMSOSVM(
				CStructuredModel*		model,
				CStructuredLabels*		labs,
				float64_t				_lambda,
				SGVector< float64_t >	W=0);

		/** destructor */
		virtual ~CDualLibQPBMSOSVM();

		/** @return name of SGSerializable */
		virtual const char* get_name() const { return "DualLibQPBMSOSVM"; }

		/** set lambda
		 *
		 * @param _lambda	Regularization constant
		 */
		inline void set_lambda(float64_t _lambda) { m_lambda=_lambda; }

		/** get lambda
		 *
		 * @return Regularization constant
		 */
		inline float64_t get_lambda() { return m_lambda; }

		/** set relative tolerance
		 *
		 * @param TolRel	Relative tolerance
		 */
		inline void set_TolRel(float64_t TolRel) { m_TolRel=TolRel; }

		/** get relative tolerance
		 *
		 * @return Relative tolerance
		 */
		inline float64_t get_TolRel() { return m_TolRel; }

		/** set absolute tolerance
		 *
		 * @param TolAbs	Absolute tolerance
		 */
		inline void set_TolAbs(float64_t TolAbs) { m_TolAbs=TolAbs; }

		/** get absolute tolerance
		 *
		 * @return Absolute tolerance
		 */
		inline float64_t get_TolAbs() { return m_TolAbs; }

		/** set size of cutting plane buffer
		 *
		 * @param BufSize	Size of the cutting plane buffer (i.e. maximal number of
		 *					iterations)
		 */
		inline void set_BufSize(uint32_t BufSize) { m_BufSize=BufSize; }

		/** get size of cutting plane buffer
		 *
		 * @return Size of the cutting plane buffer
		 */
		inline uint32_t get_BufSize() { return m_BufSize; }

		/** set ICP removal flag
		 *
		 * @param cleanICP	Flag that enables/disables inactive cutting plane removal
		 *					feature
		 */
		inline void set_cleanICP(bool cleanICP) { m_cleanICP=cleanICP; }

		/** get ICP removal flag
		 *
		 * @return Status of inactive cutting plane removal feature (enabled/disabled)
		 */
		inline bool get_cleanICP() { return m_cleanICP; }

		/** set number of iterations for cleaning ICP
		 *
		 * @param cleanAfter	Specifies number of iterations that inactive cutting
		 *						planes has to be inactive for to be removed
		 */
		inline void set_cleanAfter(uint32_t cleanAfter) { m_cleanAfter=cleanAfter; }

		/** get number of iterations for cleaning ICP
		 *
		 * @return Number of iterations that inactive cutting planes has to be
		 *			inactive for to be removed
		 */
		inline uint32_t get_cleanAfter() { return m_cleanAfter; }

		/** set K
		 *
		 * @param K	Parameter K
		 */
		inline void set_K(float64_t K) { m_K=K; }

		/** get K
		 *
		 * @return K
		 */
		inline float64_t get_K() { return m_K; }

		/** set Tmax
		 *
		 * @param Tmax Parameter Tmax
		 */
		inline void set_Tmax(uint32_t Tmax) { m_Tmax=Tmax; }

		/** get Tmax
		 *
		 * @return Tmax
		 */
		inline uint32_t get_Tmax() { return m_Tmax; }

		/** set number of cutting plane models
		 *
		 * @param cp_models	Number of cutting plane models
		 */
		inline void set_cp_models(uint32_t cp_models) { m_cp_models=cp_models; }

		/** get number of cutting plane models
		 *
		 * @return Number of cutting plane models
		 */
		inline uint32_t get_cp_models() { return m_cp_models; }

		/** get bmrm result
		 *
		 * @return Result returned from Bundle Method algorithm
		 */
		inline BmrmStatistics get_result() { return m_result; }

		/** get training algorithm
		 *
		 * @return Type of Bundle Method solver used for training
		 */
		inline ESolver get_solver() { return m_solver; }

		/** set training algorithm
		 *
		 * @param solver	Type of Bundle Method solver used for training
		 */
		inline void set_solver(ESolver solver) { m_solver=solver; }

		/** set initial value of weight vector w
		 *
		 * @param W     initial weight vector
		 */
		inline void set_w(SGVector< float64_t > W)
		{
			REQUIRE(W.vlen == m_model->get_dim(), "Dimension of the initial "
					"solution must match the model's dimension!\n");
			m_w=W;
		}

		/** get classifier type
		 *
		 * @return classifier type CT_LIBQPSOSVM
		 */
		virtual EMachineType get_classifier_type();

	protected:
		/** train dual SO-SVM
		 *
		 */
		bool train_machine(CFeatures* data=NULL);

	private:
		/** init parameters
		 *
		 */
		void init();

	private:

		/** lambda */
		float64_t m_lambda;

		/** TolRel */
		float64_t m_TolRel;

		/** TolAbs */
		float64_t m_TolAbs;

		/** BufSize */
		uint32_t m_BufSize;

		/** Clean ICP */
		bool m_cleanICP;

		/** Clean ICP after n-th iteration */
		uint32_t m_cleanAfter;

		/** K */
		float64_t m_K;

		/** Tmax */
		uint32_t m_Tmax;

		/** number of cutting plane models */
		uint32_t m_cp_models;

		/** BMRM result */
		BmrmStatistics m_result;

		/** training algorithm */
		ESolver m_solver;

}; /* class CDualLibQPBMSOSVM */

} /* namespace shogun */

#endif /* _DUALLIBQPBMSOSVM__H__ */