PLearn 0.1
LinearInductiveTransferClassifier.h
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00001 // -*- C++ -*-
00002 
00003 // LinearInductiveTransferClassifier.h
00004 //
00005 // Copyright (C) 2006 Hugo Larochelle 
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: .pyskeleton_header 544 2003-09-01 00:05:31Z plearner $ 
00037    ******************************************************* */
00038 
00039 // Authors: Hugo Larochelle
00040 
00044 #ifndef LinearInductiveTransferClassifier_INC
00045 #define LinearInductiveTransferClassifier_INC
00046 
00047 #include <plearn_learners/generic/PLearner.h>
00048 #include <plearn/opt/Optimizer.h>
00049 #include <plearn/var/VarArray.h>
00050 #include <plearn_learners/online/RBMLayer.h>
00051 
00052 namespace PLearn {
00053 
00058 class LinearInductiveTransferClassifier : public PLearner
00059 {
00060     typedef PLearner inherited;
00061 
00062 public:
00063     //#####  Public Build Options  ############################################
00064 
00066     Mat class_reps;
00068     PP<Optimizer> optimizer;
00070     int batch_size;
00072     real weight_decay; 
00074     string penalty_type;
00076     string initialization_method;
00081     string model_type;
00084     bool dont_consider_train_targets;
00086     bool use_bias_in_weights_prediction;
00089     bool multi_target_classifier;
00092     real sigma_min;
00094     int nhidden;
00096     int rbm_nstages;
00098     real rbm_learning_rate;
00100     PP<RBMLayer> visible_layer;
00102     PP<RBMLayer> hidden_layer;
00103 
00104 public:
00105     //#####  Public Member Functions  #########################################
00106 
00108     LinearInductiveTransferClassifier();
00109 
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 
00140     virtual TVec<std::string> getTrainCostNames() const;
00141 
00142 
00143     // *** SUBCLASS WRITING: ***
00144     // While in general not necessary, in case of particular needs 
00145     // (efficiency concerns for ex) you may also want to overload
00146     // some of the following methods:
00147     virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const;
00148     // virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const;
00149     // virtual void test(VMat testset, PP<VecStatsCollector> test_stats, VMat testoutputs=0, VMat testcosts=0) const;
00150     // virtual int nTestCosts() const;
00151     // virtual int nTrainCosts() const;
00152     // virtual void resetInternalState();
00153     // virtual bool isStatefulLearner() const;
00154 
00155     
00156     //#####  PLearn::Object Protocol  #########################################
00157 
00158     // Declares other standard object methods.
00159     PLEARN_DECLARE_OBJECT(LinearInductiveTransferClassifier);
00160 
00161     // Simply calls inherited::build() then build_() 
00162     virtual void build();
00163 
00165     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00166 
00167 protected:
00168     //#####  Protected Options  ###############################################
00169 
00171     int noutputs;
00173     Var input;
00175     Var output;
00177     Var sup_output;
00179     Var new_output;
00181     Var target;
00183     Var sup_target;
00185     Var new_target;
00187     Var sampleweight;
00189     Var A;
00191     Var s;
00193     Var class_reps_var;
00195     VarArray costs; 
00197     VarArray new_costs; 
00199     VarArray params;
00201     Vec paramsvalues;
00203     VarArray penalties;
00205     Var training_cost; 
00207     Var test_costs;
00209     VarArray invars;
00211     Vec seen_targets;
00213     Vec unseen_targets;
00214 
00216     mutable Func f; 
00218     mutable Func test_costf; 
00220     mutable Func output_and_target_to_cost; 
00222     mutable Func sup_test_costf; 
00224     mutable Func sup_output_and_target_to_cost; 
00225 
00226     // Neural networks variables
00228     Var W;
00229 //    //! Parameters for hidden to output layer weights prediction
00230 //    VarArray As;
00231 //    //! Parameters for input to hidden layer weights prediction
00232 //    VarArray Ws;
00233 //    //! Scale parameter for input to hidden layer weights prediction
00234 //    VarArray s_hids;
00235 //    //! Hidden layer neurons
00236 //    VarArray hidden_neurons;
00237     
00238 protected:
00239     //#####  Protected Member Functions  ######################################
00240     
00242     static void declareOptions(OptionList& ol);
00243 
00247     Var hiddenLayer(const Var& input, const Var& weights, string transfer_func, Var& before_transfer_function, bool use_cubed_value=false);
00248 
00253     void buildOutputFromInput(const Var& the_input, Var& hidden_layer, Var& before_transfer_func);
00254 
00256     void buildTargetAndWeight();
00257 
00259     void buildFuncs(const Var& the_input, const Var& the_output, const Var& the_target, const Var& the_sampleweight);
00260 
00264     void fillWeights(const Var& weights, bool zero_first_row, 
00265                      real scale_with_this=-1);
00266 
00268     virtual void buildPenalties();
00269 
00270 private: 
00271     //#####  Private Member Functions  ########################################
00272 
00274     void build_();
00275 
00276 private:
00277     //#####  Private Data Members  ############################################
00278 
00279     // The rest of the private stuff goes here
00280 };
00281 
00282 // Declares a few other classes and functions related to this class
00283 DECLARE_OBJECT_PTR(LinearInductiveTransferClassifier);
00284   
00285 } // end of namespace PLearn
00286 
00287 #endif
00288 
00289 
00290 /*
00291   Local Variables:
00292   mode:c++
00293   c-basic-offset:4
00294   c-file-style:"stroustrup"
00295   c-file-offsets:((innamespace . 0)(inline-open . 0))
00296   indent-tabs-mode:nil
00297   fill-column:79
00298   End:
00299 */
00300 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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