This file is indexed.

/usr/include/InsightToolkit/Numerics/itkAmoebaOptimizer.h is in libinsighttoolkit3-dev 3.20.1-1.

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
148
149
150
151
/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    itkAmoebaOptimizer.h
  Language:  C++
  Date:      $Date$
  Version:   $Revision$

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#ifndef __itkAmoebaOptimizer_h
#define __itkAmoebaOptimizer_h

#include "itkSingleValuedNonLinearVnlOptimizer.h"
#include "vnl/algo/vnl_amoeba.h"

namespace itk
{
  
/** \class AmoebaOptimizer
 * \brief Wrap of the vnl_amoeba algorithm
 *
 * AmoebaOptimizer is a wrapper around the vnl_amoeba algorithm which
 * is an implementation of the Nelder-Meade downhill simplex
 * problem. For most problems, it is a few times slower than a
 * Levenberg-Marquardt algorithm but does not require derivatives of
 * its cost function. It works by creating a simplex (n+1 points in
 * ND space). The cost function is evaluated at each corner of the
 * simplex.  The simplex is then modified (by reflecting a corner
 * about the opposite edge, by shrinking the entire simplex, by
 * contracting one edge of the simplex, or by expanding the simplex)
 * in searching for the minimum of the cost function.
 *
 * The methods AutomaticInitialSimplex() and SetInitialSimplexDelta()
 * control whether the optimizer defines the initial simplex
 * automatically (by constructing a very small simplex around the
 * initial position) or uses a user supplied simplex size.
 *
 * AmoebaOptimizer can only minimize a function.
 *
 * \ingroup Numerics Optimizers
 */
class ITK_EXPORT AmoebaOptimizer : 
    public SingleValuedNonLinearVnlOptimizer
{
public:
  /** Standard "Self" typedef. */
  typedef AmoebaOptimizer                     Self;
  typedef SingleValuedNonLinearVnlOptimizer   Superclass;
  typedef SmartPointer<Self>                  Pointer;
  typedef SmartPointer<const Self>            ConstPointer;
  
  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Run-time type information (and related methods). */
  itkTypeMacro( AmoebaOptimizer, SingleValuedNonLinearVnlOptimizer );

  /**  Parameters type.
   *  It defines a position in the optimization search space. */
  typedef Superclass::ParametersType ParametersType;

  /** InternalParameters typedef. */
  typedef   vnl_vector<double>     InternalParametersType;

  /** Internal optimizer type. */
  typedef   vnl_amoeba             InternalOptimizerType;

  /** Method for getting access to the internal optimizer. */
  vnl_amoeba * GetOptimizer(void);

  /** Start optimization with an initial value. */
  void StartOptimization( void );

  /** Plug in a Cost Function into the optimizer  */
  virtual void SetCostFunction( SingleValuedCostFunction * costFunction );

  /** Set/Get the maximum number of iterations. The optimization algorithm will
   * terminate after the maximum number of iterations has been reached. 
   * The default value is 500. */
  virtual void SetMaximumNumberOfIterations( unsigned int n );
  itkGetConstMacro( MaximumNumberOfIterations, unsigned int );

  /** Set/Get the mode which determines how the amoeba algorithm
   * defines the initial simplex.  Default is
   * AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the
   * initial simplex is created with a default size. If
   * AutomaticInitialSimplex is off, then InitialSimplexDelta will be
   * used to define the initial simplex, setting the ith corner of the
   * simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ...,
   * x0[d-1]]. */
  itkSetMacro(AutomaticInitialSimplex, bool);
  itkBooleanMacro(AutomaticInitialSimplex);
  itkGetConstMacro(AutomaticInitialSimplex, bool);

  /** Set/Get the deltas that are used to define the initial simplex
   * when AutomaticInitialSimplex is off. */
  itkSetMacro(InitialSimplexDelta, ParametersType);
  itkGetConstMacro(InitialSimplexDelta, ParametersType);

  /** The optimization algorithm will terminate when the simplex
   * diameter and the difference in cost function at the corners of
   * the simplex falls below user specified thresholds.  The simplex
   * diameter threshold is set via method
   * SetParametersConvergenceTolerance() with the default value being
   * 1e-8.  The cost function convergence threshold is set via method
   * SetFunctionConvergenceTolerance() with the default value being
   * 1e-4. */
  virtual void SetParametersConvergenceTolerance( double tol );
  itkGetConstMacro( ParametersConvergenceTolerance, double );
  virtual void SetFunctionConvergenceTolerance( double tol );
  itkGetConstMacro( FunctionConvergenceTolerance, double );

  /** Report the reason for stopping. */
  const std::string GetStopConditionDescription() const;

  /** Return Current Value */
  MeasureType GetValue() const;

protected:
  AmoebaOptimizer();
  virtual ~AmoebaOptimizer();
  void PrintSelf(std::ostream& os, Indent indent) const;

  typedef Superclass::CostFunctionAdaptorType   CostFunctionAdaptorType;

private:
  AmoebaOptimizer(const Self&); //purposely not implemented
  void operator=(const Self&); //purposely not implemented
  
  bool                          m_OptimizerInitialized;
  InternalOptimizerType       * m_VnlOptimizer;
  unsigned int                  m_MaximumNumberOfIterations;
  double                        m_ParametersConvergenceTolerance;
  double                        m_FunctionConvergenceTolerance;

  bool                          m_AutomaticInitialSimplex;
  ParametersType                m_InitialSimplexDelta;

  OStringStream                 m_StopConditionDescription;
};

} // end namespace itk

#endif