source: src/FunctionApproximation/TrainingData.hpp@ 0f3042

Last change on this file since 0f3042 was af2c7ec, checked in by Frederik Heber <heber@…>, 12 years ago

TrainingData now generates internal list of all arguments.

  • Property mode set to 100644
File size: 4.1 KB
Line 
1/*
2 * TrainingData.hpp
3 *
4 * Created on: 15.10.2012
5 * Author: heber
6 */
7
8#ifndef TRAININGDATA_HPP_
9#define TRAININGDATA_HPP_
10
11// include config.h
12#ifdef HAVE_CONFIG_H
13#include <config.h>
14#endif
15
16#include <iosfwd>
17#include <boost/function.hpp>
18
19#include "Fragmentation/Homology/HomologyContainer.hpp"
20#include "FunctionApproximation/FunctionApproximation.hpp"
21#include "FunctionApproximation/FunctionModel.hpp"
22
23/** This class encapsulates the training data for a given potential function
24 * to learn.
25 *
26 * The data is added piece-wise by calling the operator() with a specific
27 * Fragment.
28 *
29 * In TrainingData::operator() we construct first all pair-wise distances as
30 * list of all arguments. Then, these are filtered depending on the specific
31 * FunctionModel's Filter and only these are handed to down to evaluate it.
32 *
33 */
34class TrainingData
35{
36public:
37 //!> typedef for a range within the HomologyContainer at which fragments to look at
38 typedef std::pair<
39 HomologyContainer::const_iterator,
40 HomologyContainer::const_iterator> range_t;
41 //!> Training tuple input vector pair
42 typedef FunctionApproximation::inputs_t InputVector_t;
43 //!> Training tuple output vector pair
44 typedef FunctionApproximation::outputs_t OutputVector_t;
45 //!> Typedef for a table with columns of all distances and the energy
46 typedef std::vector< std::vector<double> > DistanceEnergyTable_t;
47
48public:
49 /** Constructor for class TrainingData.
50 *
51 */
52 explicit TrainingData(const FunctionModel::filter_t &_filter) :
53 filter(_filter)
54 {}
55
56 /** Destructor for class TrainingData.
57 *
58 */
59 ~TrainingData()
60 {}
61
62 /** We go through the given \a range of homologous fragments and call
63 * TrainingData::filter on them in order to gather the distance and
64 * the energy value, stored internally.
65 *
66 * \param range given range within a HomologyContainer of homologous fragments
67 */
68 void operator()(const range_t &range);
69
70 /** Getter for const access to internal training data inputs.
71 *
72 * \return const ref to training tuple of input vector
73 */
74 const InputVector_t& getTrainingInputs() const {
75 return ArgumentVector;
76 }
77
78 /** Getter for const access to internal list of all pair-wise distances.
79 *
80 * \return const ref to all arguments
81 */
82 const InputVector_t& getAllArguments() const {
83 return DistanceVector;
84 }
85
86 /** Getter for const access to internal training data outputs.
87 *
88 * \return const ref to training tuple of output vector
89 */
90 const OutputVector_t& getTrainingOutputs() const {
91 return EnergyVector;
92 }
93
94 /** Returns the average of each component over all OutputVectors.
95 *
96 * This is useful for initializing the offset of the potential.
97 *
98 * @return average output vector
99 */
100 const FunctionModel::results_t getTrainingOutputAverage() const;
101
102 /** Calculate the L2 error of a given \a model against the stored training data.
103 *
104 * \param model model whose L2 error to calculate
105 * \return sum of squared differences at training tuples
106 */
107 const double getL2Error(const FunctionModel &model) const;
108
109 /** Calculate the Lmax error of a given \a model against the stored training data.
110 *
111 * \param model model whose Lmax error to calculate
112 * \return maximum difference over all training tuples
113 */
114 const double getLMaxError(const FunctionModel &model) const;
115
116 /** Creates a table of columns with all distances and the energy.
117 *
118 * \return array with first columns containing distances, last column energy
119 */
120 const DistanceEnergyTable_t getDistanceEnergyTable() const;
121
122private:
123 // prohibit use of default constructor, as we always require extraction functor.
124 TrainingData();
125
126private:
127 //!> private training data vector
128 InputVector_t DistanceVector;
129 OutputVector_t EnergyVector;
130 //!> list of all filtered arguments over all tuples
131 InputVector_t ArgumentVector;
132 //!> function to be used for training input data extraction from a fragment
133 const FunctionModel::filter_t filter;
134};
135
136// print training data for debugging
137std::ostream &operator<<(std::ostream &out, const TrainingData &data);
138
139#endif /* TRAININGDATA_HPP_ */
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