PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // BasisSelectionRegressor.h 00004 // 00005 // Copyright (C) 2006 Pascal Vincent 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 // Authors: Pascal Vincent 00036 00040 #ifndef BasisSelectionRegressor_INC 00041 #define BasisSelectionRegressor_INC 00042 00043 #include <plearn_learners/generic/PLearner.h> 00044 #include <plearn/math/RealFunction.h> 00045 #include <plearn/ker/Kernel.h> 00046 00047 namespace PLearn { 00048 00060 class BasisSelectionRegressor : public PLearner 00061 { 00062 typedef PLearner inherited; 00063 00064 public: 00065 //##### Public Build Options ############################################ 00066 bool consider_constant_function; 00067 TVec<RealFunc> explicit_functions; 00068 TVec<RealFunc> explicit_interaction_functions; 00069 TVec<string> explicit_interaction_variables; 00070 TVec<RealFunc> mandatory_functions; 00071 bool consider_raw_inputs; 00072 bool consider_normalized_inputs; 00073 bool consider_input_range_indicators; 00074 bool fixed_min_range; 00075 real indicator_desired_prob; 00076 real indicator_min_prob; 00077 TVec<Ker> kernels; 00078 mutable Mat kernel_centers; 00079 int n_kernel_centers_to_pick; 00080 bool consider_interaction_terms; 00081 int max_interaction_terms; 00082 int consider_n_best_for_interaction; 00083 int interaction_max_order; 00084 bool consider_sorted_encodings; 00085 int max_n_vals_for_sorted_encodings; 00086 bool normalize_features; 00087 PP<PLearner> learner; 00088 bool precompute_features; 00089 int n_threads; 00090 int thread_subtrain_length; 00091 bool use_all_basis; 00092 00093 //##### Public Learnt Options ############################################ 00094 TVec<RealFunc> selected_functions; 00095 Vec alphas; 00096 mutable Mat scores; 00097 00098 00099 struct thread_wawr; 00100 00101 public: 00102 //##### Public Member Functions ######################################### 00103 00105 // ### Make sure the implementation in the .cc 00106 // ### initializes all fields to reasonable default values. 00107 BasisSelectionRegressor(); 00108 00109 void printModelFunction(PStream& out) const; 00110 00111 //##### PLearner Member Functions ####################################### 00112 00115 virtual int outputsize() const; 00116 00120 virtual void forget(); 00121 00125 virtual void train(); 00126 00128 virtual void computeOutput(const Vec& input, Vec& output) const; 00129 00131 virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 00132 const Vec& target, Vec& costs) const; 00133 00136 virtual TVec<std::string> getTestCostNames() const; 00137 00138 00140 virtual void setTrainStatsCollector(PP<VecStatsCollector> statscol); 00141 00144 virtual TVec<std::string> getTrainCostNames() const; 00145 00147 virtual void setTrainingSet(VMat training_set, bool call_forget=true); 00148 00149 00150 //##### PLearn::Object Protocol ######################################### 00151 00152 // Declares other standard object methods. 00153 // ### If your class is not instantiatable (it has pure virtual methods) 00154 // ### you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS 00155 PLEARN_DECLARE_OBJECT(BasisSelectionRegressor); 00156 00157 // Simply calls inherited::build() then build_() 00158 virtual void build(); 00159 00161 virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies); 00162 00163 virtual void setExperimentDirectory(const PPath& the_expdir); 00164 00165 protected: 00166 //##### Protected Member Functions ###################################### 00167 00169 static void declareOptions(OptionList& ol); 00170 00171 private: 00172 //##### Private Member Functions ######################################## 00173 00175 void build_(); 00176 00177 void appendCandidateFunctionsOfSingleField(int fieldnum, TVec<RealFunc>& functions) const; 00178 void appendKernelFunctions(TVec<RealFunc>& functions) const; 00179 void appendConstantFunction(TVec<RealFunc>& functions) const; 00180 00182 void buildSimpleCandidateFunctions(); 00183 00188 void buildAllCandidateFunctions(); 00189 00191 TVec<RealFunc> buildTopCandidateFunctions(); 00192 00194 void findBestCandidateFunction(int& best_candidate_index, real& best_score) const; 00195 00197 void addInteractionFunction(RealFunc& f1, RealFunc& f2, TVec<RealFunc>& all_functions); 00198 00200 void computeOrder(RealFunc& func, int& order); 00201 00202 void computeWeightedAveragesWithResidue(const TVec<RealFunc>& functions, 00203 real& wsum, 00204 Vec& E_x, Vec& E_xx, 00205 real& E_y, real& E_yy, 00206 Vec& E_xy) const; 00207 00208 /* 00209 void computeWeightedCorrelationsWithY(const TVec<RealFunc>& functions, const Vec& Y, 00210 real& wsum, 00211 Vec& E_x, Vec& V_x, 00212 real& E_y, real& V_y, 00213 Vec& E_xy, Vec& V_xy, 00214 Vec& covar, Vec& correl, 00215 real min_variance = 1e-6) const; 00216 */ 00217 void appendFunctionToSelection(int candidate_index); 00218 void retrainLearner(); 00219 void initTargetsResidueWeight(); 00220 void recomputeFeatures(); 00221 void recomputeResidue(); 00222 void computeOutputFromFeaturevec(const Vec& featurevec, Vec& output) const; 00223 00224 protected: 00225 //##### Protected Data Members ########################################## 00226 00227 // Template learner. Each train step is done with a copy of this one. 00228 PP<PLearner> template_learner; 00229 00230 private: 00231 //##### Private Data Members ############################################ 00232 00233 TVec<RealFunc> simple_candidate_functions; 00234 TVec<RealFunc> candidate_functions; 00235 Mat features; 00236 Vec residue; 00237 Vec targets; 00238 Vec weights; 00239 double residue_sum; 00240 double residue_sum_sq; 00241 00242 mutable Vec input; 00243 mutable Vec targ; 00244 mutable Vec featurevec; 00245 }; 00246 00247 // Declares a few other classes and functions related to this class 00248 DECLARE_OBJECT_PTR(BasisSelectionRegressor); 00249 00250 } // end of namespace PLearn 00251 00252 #endif 00253 00254 00255 /* 00256 Local Variables: 00257 mode:c++ 00258 c-basic-offset:4 00259 c-file-style:"stroustrup" 00260 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00261 indent-tabs-mode:nil 00262 fill-column:79 00263 End: 00264 */ 00265 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :