AbstractBudgetMaintenanceStrategy.h
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief Abstract Budget maintenance strategy
6  *
7  * \par
8  * This holds the interface for any budget maintenance strategy.
9  *
10  *
11  *
12  *
13  * \author Aydin Demircioglu
14  * \date 2014
15  *
16  *
17  * \par Copyright 1995-2017 Shark Development Team
18  *
19  * <BR><HR>
20  * This file is part of Shark.
21  * <http://shark-ml.org/>
22  *
23  * Shark is free software: you can redistribute it and/or modify
24  * it under the terms of the GNU Lesser General Public License as published
25  * by the Free Software Foundation, either version 3 of the License, or
26  * (at your option) any later version.
27  *
28  * Shark is distributed in the hope that it will be useful,
29  * but WITHOUT ANY WARRANTY; without even the implied warranty of
30  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
31  * GNU Lesser General Public License for more details.
32  *
33  * You should have received a copy of the GNU Lesser General Public License
34  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
35  *
36  */
37 //===========================================================================
38 
39 
40 #ifndef SHARK_MODELS_ABSTRACTBUDGETMAINTENANCESTRATEGY_H
41 #define SHARK_MODELS_ABSTRACTBUDGETMAINTENANCESTRATEGY_H
42 
43 #include <shark/Data/Dataset.h>
44 #include <shark/Data/DataView.h>
45 #include <shark/Models/Kernels/AbstractKernelFunction.h>
46 #include <shark/Models/Kernels/KernelExpansion.h>
47 
48 
49 namespace shark
50 {
51 
52 ///
53 /// \brief This is the abstract interface for any budget maintenance strategy.
54 ///
55 /// To allow for easy exchange of budget maintenance strategies, each of
56 /// them should derive from this class. The only function it defines is addToModel,
57 /// which, when implemented, will add a given supportvector and given alphas
58 /// to the provided model by applying the respective budget maintenance strategy.
59 /// (Note that not all merging strategies need the alphas, but some do)
60 ///
61 template<class InputType>
62 class AbstractBudgetMaintenanceStrategy
63 {
64 
65 public:
66  typedef KernelExpansion<InputType> ModelType;
67  typedef LabeledData<InputType, unsigned int> DataType;
68  typedef typename DataType::element_type ElementType;
69 
70  AbstractBudgetMaintenanceStrategy()
71  { }
72 
73 
74  /// this is the main interface, which adds a given supportvector with
75  /// given alpha coefficients to the model.
76  ///
77  /// @param[in,out] model the model the strategy will work with
78  /// @param[in] alpha alphas for the new budget vector
79  /// @param[in] supportVector the vector to add to the model by applying the maintenance strategy
80  ///
81  virtual void addToModel(ModelType& model, InputType const& alpha, ElementType const& supportVector) = 0;
82 
83 
84 
85  /// this will find the vector with the smallest alpha, measured in 2-norm
86  /// in the given model. now there is a special case: if there is somewhere a zero
87  /// coefficient, then obviously this is the smallest element. in this case we
88  /// just proceed as usual. the caller must decide what to do with such a vector.
89  /// \par note: The model is not allowed to be empty and an exception is thrown in this case.
90  ///
91  /// @param[in] model the model we want to search
92  /// @param[out] minIndex the index of the vector with smallest coefficient
93  /// @param[out] minAlpha the 2-norm of the alpha coefficient of the found vector
94  ///
95  static void findSmallestVector(ModelType const& model, size_t &minIndex, double &minAlpha){
96  SHARK_RUNTIME_CHECK(model.alpha().size1(), "Model is empty!");
97  // we do not have it, so we remove the vector with the
98  // smallest 'influcence', measured by the smallest alpha
99 
100  minAlpha = norm_2(row(model.alpha(), 0));
101  minIndex = 0;
102 
103  for(size_t j = 1; j < model.alpha().size1(); j++)
104  {
105  double currentNorm = blas::norm_2(row(model.alpha(), j));
106 
107  if(currentNorm < minAlpha)
108  {
109  minAlpha = norm_2(row(model.alpha(), j));
110  minIndex = j;
111  }
112  }
113  }
114 
115 
116  /// return the class name
117  std::string name() const
118  { return "AbstractBudgetMaintenanceStrategy"; }
119 };
120 
121 
122 }
123 #endif