Point Cloud Library (PCL) 1.13.0
Loading...
Searching...
No Matches
fern_trainer.h
1/*
2 * Software License Agreement (BSD License)
3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2010-2011, Willow Garage, Inc.
6 *
7 * All rights reserved.
8 *
9 * Redistribution and use in source and binary forms, with or without
10 * modification, are permitted provided that the following conditions
11 * are met:
12 *
13 * * Redistributions of source code must retain the above copyright
14 * notice, this list of conditions and the following disclaimer.
15 * * Redistributions in binary form must reproduce the above
16 * copyright notice, this list of conditions and the following
17 * disclaimer in the documentation and/or other materials provided
18 * with the distribution.
19 * * Neither the name of Willow Garage, Inc. nor the names of its
20 * contributors may be used to endorse or promote products derived
21 * from this software without specific prior written permission.
22 *
23 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34 * POSSIBILITY OF SUCH DAMAGE.
35 *
36 */
37
38#pragma once
39
40#include <pcl/common/common.h>
41#include <pcl/ml/feature_handler.h>
42#include <pcl/ml/ferns/fern.h>
43#include <pcl/ml/stats_estimator.h>
44
45#include <vector>
46
47namespace pcl {
48
49/** Trainer for a Fern. */
50template <class FeatureType,
51 class DataSet,
52 class LabelType,
53 class ExampleIndex,
54 class NodeType>
55class PCL_EXPORTS FernTrainer {
56
57public:
58 /** Constructor. */
60
61 /** Sets the feature handler used to create and evaluate features.
62 *
63 * \param[in] feature_handler the feature handler
64 */
65 inline void
68 {
69 feature_handler_ = &feature_handler;
70 }
71
72 /** Sets the object for estimating the statistics for tree nodes.
73 *
74 * \param[in] stats_estimator the statistics estimator
75 */
76 inline void
79 {
80 stats_estimator_ = &stats_estimator;
81 }
82
83 /** Sets the maximum depth of the learned tree.
84 *
85 * \param[in] fern_depth maximum depth of the learned tree
86 */
87 inline void
88 setFernDepth(const std::size_t fern_depth)
89 {
90 fern_depth_ = fern_depth;
91 }
92
93 /** Sets the number of features used to find optimal decision features.
94 *
95 * \param[in] num_of_features the number of features
96 */
97 inline void
98 setNumOfFeatures(const std::size_t num_of_features)
99 {
100 num_of_features_ = num_of_features;
101 }
102
103 /** Sets the number of thresholds tested for finding the optimal decision
104 * threshold on the feature responses.
105 *
106 * \param[in] num_of_threshold the number of thresholds
107 */
108 inline void
109 setNumOfThresholds(const std::size_t num_of_threshold)
110 {
111 num_of_thresholds_ = num_of_threshold;
112 }
113
114 /** Sets the input data set used for training.
115 *
116 * \param[in] data_set the data set used for training
117 */
118 inline void
119 setTrainingDataSet(DataSet& data_set)
120 {
121 data_set_ = data_set;
122 }
123
124 /** Example indices that specify the data used for training.
125 *
126 * \param[in] examples the examples
127 */
128 inline void
129 setExamples(std::vector<ExampleIndex>& examples)
130 {
131 examples_ = examples;
132 }
133
134 /** Sets the label data corresponding to the example data.
135 *
136 * \param[in] label_data the label data
137 */
138 inline void
139 setLabelData(std::vector<LabelType>& label_data)
140 {
141 label_data_ = label_data;
142 }
143
144 /** Trains a decision tree using the set training data and settings.
145 *
146 * \param[out] fern destination for the trained tree
147 */
148 void
149 train(Fern<FeatureType, NodeType>& fern);
150
151protected:
152 /** Creates uniformely distrebuted thresholds over the range of the supplied
153 * values.
154 *
155 * \param[in] num_of_thresholds the number of thresholds to create
156 * \param[in] values the values for estimating the expected value range
157 * \param[out] thresholds the resulting thresholds
158 */
159 static void
160 createThresholdsUniform(const std::size_t num_of_thresholds,
161 std::vector<float>& values,
162 std::vector<float>& thresholds);
163
164private:
165 /** Desired depth of the learned fern. */
166 std::size_t fern_depth_;
167 /** Number of features used to find optimal decision features. */
168 std::size_t num_of_features_;
169 /** Number of thresholds. */
170 std::size_t num_of_thresholds_;
171
172 /** FeatureHandler instance, responsible for creating and evaluating features. */
174 /** StatsEstimator instance, responsible for gathering stats about a node. */
176
177 /** The training data set. */
178 DataSet data_set_;
179 /** The label data. */
180 std::vector<LabelType> label_data_;
181 /** The example data. */
182 std::vector<ExampleIndex> examples_;
183};
184
185} // namespace pcl
186
187#include <pcl/ml/impl/ferns/fern_trainer.hpp>
Utility class interface which is used for creating and evaluating features.
Class representing a Fern.
Definition fern.h:49
Trainer for a Fern.
void setTrainingDataSet(DataSet &data_set)
Sets the input data set used for training.
void setFernDepth(const std::size_t fern_depth)
Sets the maximum depth of the learned tree.
void setNumOfFeatures(const std::size_t num_of_features)
Sets the number of features used to find optimal decision features.
void setStatsEstimator(pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator)
Sets the object for estimating the statistics for tree nodes.
void setNumOfThresholds(const std::size_t num_of_threshold)
Sets the number of thresholds tested for finding the optimal decision threshold on the feature respon...
void setFeatureHandler(pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler)
Sets the feature handler used to create and evaluate features.
void setLabelData(std::vector< LabelType > &label_data)
Sets the label data corresponding to the example data.
void setExamples(std::vector< ExampleIndex > &examples)
Example indices that specify the data used for training.
Class interface for gathering statistics for decision tree learning.
Define standard C methods and C++ classes that are common to all methods.