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<div class="title">model_search.h</div> </div>
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<a href="model__search_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">/* +---------------------------------------------------------------------------+</span>
<a name="l00002"></a>00002 <span class="comment"> | The Mobile Robot Programming Toolkit (MRPT) C++ library |</span>
<a name="l00003"></a>00003 <span class="comment"> | |</span>
<a name="l00004"></a>00004 <span class="comment"> | http://www.mrpt.org/ |</span>
<a name="l00005"></a>00005 <span class="comment"> | |</span>
<a name="l00006"></a>00006 <span class="comment"> | Copyright (C) 2005-2011 University of Malaga |</span>
<a name="l00007"></a>00007 <span class="comment"> | |</span>
<a name="l00008"></a>00008 <span class="comment"> | This software was written by the Machine Perception and Intelligent |</span>
<a name="l00009"></a>00009 <span class="comment"> | Robotics Lab, University of Malaga (Spain). |</span>
<a name="l00010"></a>00010 <span class="comment"> | Contact: Jose-Luis Blanco <jlblanco@ctima.uma.es> |</span>
<a name="l00011"></a>00011 <span class="comment"> | |</span>
<a name="l00012"></a>00012 <span class="comment"> | This file is part of the MRPT project. |</span>
<a name="l00013"></a>00013 <span class="comment"> | |</span>
<a name="l00014"></a>00014 <span class="comment"> | MRPT is free software: you can redistribute it and/or modify |</span>
<a name="l00015"></a>00015 <span class="comment"> | it under the terms of the GNU General Public License as published by |</span>
<a name="l00016"></a>00016 <span class="comment"> | the Free Software Foundation, either version 3 of the License, or |</span>
<a name="l00017"></a>00017 <span class="comment"> | (at your option) any later version. |</span>
<a name="l00018"></a>00018 <span class="comment"> | |</span>
<a name="l00019"></a>00019 <span class="comment"> | MRPT is distributed in the hope that it will be useful, |</span>
<a name="l00020"></a>00020 <span class="comment"> | but WITHOUT ANY WARRANTY; without even the implied warranty of |</span>
<a name="l00021"></a>00021 <span class="comment"> | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |</span>
<a name="l00022"></a>00022 <span class="comment"> | GNU General Public License for more details. |</span>
<a name="l00023"></a>00023 <span class="comment"> | |</span>
<a name="l00024"></a>00024 <span class="comment"> | You should have received a copy of the GNU General Public License |</span>
<a name="l00025"></a>00025 <span class="comment"> | along with MRPT. If not, see <http://www.gnu.org/licenses/>. |</span>
<a name="l00026"></a>00026 <span class="comment"> | |</span>
<a name="l00027"></a>00027 <span class="comment"> +---------------------------------------------------------------------------+ */</span>
<a name="l00028"></a>00028
<a name="l00029"></a>00029 <span class="preprocessor">#ifndef math_modelsearch_h</span>
<a name="l00030"></a>00030 <span class="preprocessor"></span><span class="preprocessor">#define math_modelsearch_h</span>
<a name="l00031"></a>00031 <span class="preprocessor"></span>
<a name="l00032"></a>00032 <span class="preprocessor">#include <<a class="code" href="utils__defs_8h.html">mrpt/utils/utils_defs.h</a>></span>
<a name="l00033"></a>00033
<a name="l00034"></a>00034 <span class="keyword">namespace </span>mrpt {
<a name="l00035"></a>00035 <span class="keyword">namespace </span>math {
<a name="l00036"></a>00036
<a name="l00037"></a>00037 <span class="comment"></span>
<a name="l00038"></a>00038 <span class="comment"> /** Model search implementations: RANSAC and genetic algorithm</span>
<a name="l00039"></a>00039 <span class="comment"> *</span>
<a name="l00040"></a>00040 <span class="comment"> * The type \a TModelFit is a user-supplied struct/class that implements this interface:</span>
<a name="l00041"></a>00041 <span class="comment"> * - Types:</span>
<a name="l00042"></a>00042 <span class="comment"> * - \a Real : The numeric type to use (typ: double, float)</span>
<a name="l00043"></a>00043 <span class="comment"> * - \a Model : The type of the model to be fitted (for example: A matrix, a TLine2D, a TPlane3D, ...)</span>
<a name="l00044"></a>00044 <span class="comment"> * - Methods:</span>
<a name="l00045"></a>00045 <span class="comment"> * - size_t getSampleCount() const : return the number of samples. This should not change during a model search.</span>
<a name="l00046"></a>00046 <span class="comment"> * - bool fitModel( const vector_size_t& useIndices, Model& model ) const : This function fits a model to the data selected by the indices. The return value indicates the success, hence false means a degenerate case, where no model was found.</span>
<a name="l00047"></a>00047 <span class="comment"> * - Real testSample( size_t index, const Model& model ) const : return some value that indicates how well a sample fits to the model. This way the thresholding is moved to the searching procedure and the model just tells how good a sample is.</span>
<a name="l00048"></a>00048 <span class="comment"> *</span>
<a name="l00049"></a>00049 <span class="comment"> * There are two methods provided in this class to fit a model:</span>
<a name="l00050"></a>00050 <span class="comment"> * - \a ransacSingleModel (RANSAC): Just like mrpt::math::RANSAC_Template</span>
<a name="l00051"></a>00051 <span class="comment"> *</span>
<a name="l00052"></a>00052 <span class="comment"> * - \a geneticSingleModel (Genetic): Provides a mixture of a genetic and the ransac algorithm.</span>
<a name="l00053"></a>00053 <span class="comment"> * Instead of selecting a set of data in each iteration, it takes more (ex. 10) and order these model</span>
<a name="l00054"></a>00054 <span class="comment"> * using some fitness function: the average error of the inliers scaled by the number of outliers (This</span>
<a name="l00055"></a>00055 <span class="comment"> * fitness might require some fine tuning). Than the (ex 10) new kernel for the next iteration is created as follows:</span>
<a name="l00056"></a>00056 <span class="comment"> * - Take the best kernels (as for the original ransac)</span>
<a name="l00057"></a>00057 <span class="comment"> * - Select two kernels ( with a higher probability for the better models) and let the new kernel be a subset of the two original kernels ( additionally to leave the local minimums an additional random seed might appear - mutation)</span>
<a name="l00058"></a>00058 <span class="comment"> * - Generate some new random samples.</span>
<a name="l00059"></a>00059 <span class="comment"> *</span>
<a name="l00060"></a>00060 <span class="comment"> * For an example of usage, see "samples/model_search_test/"</span>
<a name="l00061"></a>00061 <span class="comment"> * \sa mrpt::math::RANSAC_Template, another RANSAC implementation where models can be matrices only.</span>
<a name="l00062"></a>00062 <span class="comment"> *</span>
<a name="l00063"></a>00063 <span class="comment"> * \author Zoltar Gaal</span>
<a name="l00064"></a>00064 <span class="comment"> * \ingroup ransac_grp</span>
<a name="l00065"></a>00065 <span class="comment"> */</span>
<a name="l00066"></a>00066 <span class="keyword">class </span><a class="code" href="base_2include_2mrpt_2base_2link__pragmas_8h.html#a6045fa0129b1a3d6c8bf895470e66574">BASE_IMPEXP</a> ModelSearch {
<a name="l00067"></a>00067 <span class="keyword">private</span>:<span class="comment"></span>
<a name="l00068"></a>00068 <span class="comment"> //! Select random (unique) indices from the 0..p_size sequence</span>
<a name="l00069"></a>00069 <span class="comment"></span> <span class="keywordtype">void</span> pickRandomIndex( <span class="keywordtype">size_t</span> p_size, <span class="keywordtype">size_t</span> p_pick, <a class="code" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a>& p_ind );
<a name="l00070"></a>00070 <span class="comment"></span>
<a name="l00071"></a>00071 <span class="comment"> /** Select random (unique) indices from the set.</span>
<a name="l00072"></a>00072 <span class="comment"> * The set is destroyed during pick */</span>
<a name="l00073"></a>00073 <span class="keywordtype">void</span> pickRandomIndex( <a class="code" href="classstd_1_1set.html" title="STL class.">std::set<size_t></a> p_set, <span class="keywordtype">size_t</span> p_pick, <a class="code" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a>& p_ind );
<a name="l00074"></a>00074
<a name="l00075"></a>00075 <span class="keyword">public</span>:
<a name="l00076"></a>00076 <span class="keyword">template</span><<span class="keyword">typename</span> TModelFit>
<a name="l00077"></a>00077 <span class="keywordtype">bool</span> ransacSingleModel( <span class="keyword">const</span> TModelFit& p_state,
<a name="l00078"></a>00078 <span class="keywordtype">size_t</span> p_kernelSize,
<a name="l00079"></a>00079 <span class="keyword">const</span> <span class="keyword">typename</span> TModelFit::Real& p_fitnessThreshold,
<a name="l00080"></a>00080 <span class="keyword">typename</span> TModelFit::Model& p_bestModel,
<a name="l00081"></a>00081 <a class="code" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a>& p_inliers );
<a name="l00082"></a>00082
<a name="l00083"></a>00083 <span class="keyword">private</span>:
<a name="l00084"></a>00084 <span class="keyword">template</span><<span class="keyword">typename</span> TModelFit>
<a name="l00085"></a>00085 <span class="keyword">struct </span>TSpecies {
<a name="l00086"></a><a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a20fac198de3e915e045d5d06ac68d165">00086</a> <span class="keyword">typename</span> TModelFit::Model <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a20fac198de3e915e045d5d06ac68d165">model</a>;
<a name="l00087"></a><a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a2ef47944185882c18de73a83e59f6abb">00087</a> <a class="code" href="classstd_1_1vector.html">vector_size_t</a> <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a2ef47944185882c18de73a83e59f6abb">sample</a>;
<a name="l00088"></a><a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a3aa811d483dbc952e6448d48a41beb96">00088</a> <a class="code" href="classstd_1_1vector.html">vector_size_t</a> <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a3aa811d483dbc952e6448d48a41beb96">inliers</a>;
<a name="l00089"></a><a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a28195905a9e11889cbcec6a876d87525">00089</a> <span class="keyword">typename</span> TModelFit::Real <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a28195905a9e11889cbcec6a876d87525">fitness</a>;
<a name="l00090"></a>00090
<a name="l00091"></a><a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html#a59e15d3ac8e5f287d3f60976cb48c3c6">00091</a> <span class="keyword">static</span> <span class="keywordtype">bool</span> compare( <span class="keyword">const</span> <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html">TSpecies</a>* p_a, <span class="keyword">const</span> <a class="code" href="structmrpt_1_1math_1_1_model_search_1_1_t_species.html">TSpecies</a>* p_b )
<a name="l00092"></a>00092 {
<a name="l00093"></a>00093 <span class="keywordflow">return</span> p_a->fitness < p_b->fitness;
<a name="l00094"></a>00094 }
<a name="l00095"></a>00095 };
<a name="l00096"></a>00096
<a name="l00097"></a>00097 <span class="keyword">public</span>:
<a name="l00098"></a>00098 <span class="keyword">template</span><<span class="keyword">typename</span> TModelFit>
<a name="l00099"></a>00099 <span class="keywordtype">bool</span> geneticSingleModel( <span class="keyword">const</span> TModelFit& p_state,
<a name="l00100"></a>00100 <span class="keywordtype">size_t</span> p_kernelSize,
<a name="l00101"></a>00101 <span class="keyword">const</span> <span class="keyword">typename</span> TModelFit::Real& p_fitnessThreshold,
<a name="l00102"></a>00102 <span class="keywordtype">size_t</span> p_populationSize,
<a name="l00103"></a>00103 <span class="keywordtype">size_t</span> p_maxIteration,
<a name="l00104"></a>00104 <span class="keyword">typename</span> TModelFit::Model& p_bestModel,
<a name="l00105"></a>00105 <a class="code" href="classstd_1_1vector.html">vector_size_t</a>& p_inliers );
<a name="l00106"></a>00106 }; <span class="comment">// end of class</span>
<a name="l00107"></a>00107
<a name="l00108"></a>00108 } <span class="comment">// namespace math</span>
<a name="l00109"></a>00109 } <span class="comment">// namespace mrpt</span>
<a name="l00110"></a>00110
<a name="l00111"></a>00111 <span class="comment">// Template implementations:</span>
<a name="l00112"></a>00112 <span class="preprocessor">#include "<a class="code" href="model__search__impl_8h.html">model_search_impl.h</a>"</span>
<a name="l00113"></a>00113
<a name="l00114"></a>00114 <span class="preprocessor">#endif // math_modelsearch_h</span>
</pre></div></div>
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