PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // SVMClassificationTorch.h 00004 // 00005 // Copyright (C) 2005 Olivier Delalleau 00006 // 00007 // Redistribution and use in source and binary forms, with or without 00008 // modification, are permitted provided that the following conditions are met: 00009 // 00010 // 1. Redistributions of source code must retain the above copyright 00011 // notice, this list of conditions and the following disclaimer. 00012 // 00013 // 2. Redistributions in binary form must reproduce the above copyright 00014 // notice, this list of conditions and the following disclaimer in the 00015 // documentation and/or other materials provided with the distribution. 00016 // 00017 // 3. The name of the authors may not be used to endorse or promote 00018 // products derived from this software without specific prior written 00019 // permission. 00020 // 00021 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR 00022 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00023 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN 00024 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00025 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 00026 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00027 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00028 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00029 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00030 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00031 // 00032 // This file is part of the PLearn library. For more information on the PLearn 00033 // library, go to the PLearn Web site at www.plearn.org 00034 00035 /* ******************************************************* 00036 * $Id: SVMClassificationTorch.h 4849 2006-01-27 15:20:09Z tihocan $ 00037 ******************************************************* */ 00038 00039 // Authors: Olivier Delalleau 00040 00044 #ifndef SVMClassificationTorch_INC 00045 #define SVMClassificationTorch_INC 00046 00047 #include <plearn_learners/generic/TorchLearner.h> 00048 #include <plearn/ker/Kernel.h> 00049 00050 namespace PLearn { 00051 00052 class SVMClassificationTorch: public TorchLearner { 00053 00054 private: 00055 00056 typedef TorchLearner inherited; 00057 00058 protected: 00059 00060 // ********************* 00061 // * protected options * 00062 // ********************* 00063 00064 public: 00065 00066 // ************************ 00067 // * public build options * 00068 // ************************ 00069 00070 real C; 00071 real cache_size; 00072 Ker kernel; 00073 int iter_msg; 00074 bool output_the_class; 00075 bool target_01; 00076 00077 // **************** 00078 // * Constructors * 00079 // **************** 00080 00082 SVMClassificationTorch(); 00083 00084 // ******************** 00085 // * PLearner methods * 00086 // ******************** 00087 00088 private: 00089 00091 void build_(); 00092 00093 protected: 00094 00096 static void declareOptions(OptionList& ol); 00097 00098 public: 00099 00100 // ************************ 00101 // **** Object methods **** 00102 // ************************ 00103 00105 virtual void build(); 00106 00108 virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies); 00109 00110 // Declares other standard object methods. 00111 PLEARN_DECLARE_OBJECT(SVMClassificationTorch); 00112 00113 // ************************** 00114 // **** PLearner methods **** 00115 // ************************** 00116 00119 // virtual int outputsize() const; 00120 00123 // virtual void forget(); 00124 00127 // virtual void train(); 00128 00130 virtual void computeOutput(const Vec& input, Vec& output) const; 00131 00133 virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 00134 const Vec& target, Vec& costs) const; 00135 00137 virtual TVec<std::string> getTestCostNames() const; 00138 00141 virtual TVec<std::string> getTrainCostNames() const; 00142 00144 virtual void setTrainingSet(VMat training_set, bool call_forget = true); 00145 00146 // *** SUBCLASS WRITING: *** 00147 // While in general not necessary, in case of particular needs 00148 // (efficiency concerns for ex) you may also want to overload 00149 // some of the following methods: 00150 // virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const; 00151 // virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const; 00152 // virtual void test(VMat testset, PP<VecStatsCollector> test_stats, VMat testoutputs=0, VMat testcosts=0) const; 00153 // virtual int nTestCosts() const; 00154 // virtual int nTrainCosts() const; 00155 // virtual void resetInternalState(); 00156 // virtual bool isStatefulLearner() const; 00157 00158 }; 00159 00160 // Declares a few other classes and functions related to this class. 00161 DECLARE_OBJECT_PTR(SVMClassificationTorch); 00162 00163 } // end of namespace PLearn 00164 00165 #endif 00166 00167 00168 /* 00169 Local Variables: 00170 mode:c++ 00171 c-basic-offset:4 00172 c-file-style:"stroustrup" 00173 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00174 indent-tabs-mode:nil 00175 fill-column:79 00176 End: 00177 */ 00178 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :