This file is indexed.

/usr/include/ITK-4.5/itkConjugateGradientLineSearchOptimizerv4.hxx is in libinsighttoolkit4-dev 4.5.0-3.

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
/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef __itkConjugateGradientLineSearchOptimizerv4_hxx
#define __itkConjugateGradientLineSearchOptimizerv4_hxx

#include "itkConjugateGradientLineSearchOptimizerv4.h"

namespace itk
{

/**
 * Default constructor
 */
template<typename TInternalComputationValueType>
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>
::ConjugateGradientLineSearchOptimizerv4Template()
{
}

/**
 * Destructor
 */
template<typename TInternalComputationValueType>
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>
::~ConjugateGradientLineSearchOptimizerv4Template()
{}


/**
 *PrintSelf
 */
template<typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>
::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);
}

template<typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>
::StartOptimization( bool doOnlyInitialization)
{
  this->m_ConjugateGradient.SetSize( this->m_Metric->GetNumberOfParameters() );
  this->m_ConjugateGradient.Fill( itk::NumericTraits< TInternalComputationValueType >::Zero );
  this->m_LastGradient.SetSize( this->m_Metric->GetNumberOfParameters() );
  this->m_LastGradient.Fill( itk::NumericTraits< TInternalComputationValueType >::Zero );
  Superclass::StartOptimization( doOnlyInitialization );
}

/**
* Advance one Step following the gradient direction
*/
template<typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>
::AdvanceOneStep()
{
  itkDebugMacro("AdvanceOneStep");

  this->ModifyGradientByScales();
  if ( this->m_CurrentIteration == 0 )
    {
    this->EstimateLearningRate();
    }

  TInternalComputationValueType gamma = itk::NumericTraits< TInternalComputationValueType >::Zero;
  TInternalComputationValueType gammaDenom = inner_product( this->m_LastGradient , this->m_LastGradient );
  if ( gammaDenom > itk::NumericTraits< TInternalComputationValueType >::epsilon() )
    {
    gamma = inner_product( this->m_Gradient - this->m_LastGradient , this->m_Gradient ) / gammaDenom;
    }

  /** Modified Polak-Ribiere restart conditions */
  if ( gamma < 0 || gamma > 5 )
    {
    gamma = 0;
    }
  this->m_LastGradient = this->m_Gradient;
  this->m_ConjugateGradient = this->m_Gradient + this->m_ConjugateGradient * gamma;
  this->m_Gradient = this->m_ConjugateGradient;

  /* Estimate a learning rate for this step */
  this->m_LineSearchIterations = 0;
  this->m_LearningRate = this->GoldenSectionSearch( this->m_LearningRate * this->m_LowerLimit ,
                                                   this->m_LearningRate , this->m_LearningRate * this->m_UpperLimit  );

  /* Begin threaded gradient modification of m_Gradient variable. */
  this->ModifyGradientByLearningRate();

  try
    {
    /* Pass graident to transform and let it do its own updating. */
    this->m_Metric->UpdateTransformParameters( this->m_Gradient );
    }
  catch ( ExceptionObject & err )
    {
    this->m_StopCondition = Superclass::UPDATE_PARAMETERS_ERROR;
    this->m_StopConditionDescription << "UpdateTransformParameters error";
    this->StopOptimization();
      // Pass exception to caller
    throw err;
    }

  this->InvokeEvent( IterationEvent() );
}

}//namespace itk

#endif