PLearn 0.1
GaussianDBNClassification.h
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00001 // -*- C++ -*-
00002 
00003 // GaussianDBNClassification.h
00004 //
00005 // Copyright (C) 2006 Pascal Lamblin
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: Dan Popovici
00036 
00040 #ifndef GaussianDBNClassification_INC
00041 #define GaussianDBNClassification_INC
00042 
00043 #include <plearn_learners/distributions/PDistribution.h>
00044 #include "RBMMixedLayer.h"
00045 #include "RBMMultinomialLayer.h"
00046 #include "RBMQLParameters.h"
00047 
00048 namespace PLearn {
00049 
00050 class RBMLayer;
00051 class RBMParameters;
00052 class RBMLLParameters;
00053 class RBMJointLLParameters;
00054 // Note: cannot use forward declaration with PP<>. Thus the following lines are
00055 // commented out. It *can* sometimes work though depending on the includes
00056 // around, this is why there are still some forward declarations above.
00057 // class RBMMixedLayer;
00058 // class RBMMultinomialLayer; 
00059 // class RBMQLParameters;
00060 
00067 class GaussianDBNClassification : public PDistribution
00068 {
00069     typedef PDistribution inherited;
00070 
00071 public:
00072     //#####  Public Build Options  ############################################
00073 
00076 
00078     real learning_rate;
00079 
00081     real weight_decay;
00082 
00088     string initialization_method;
00089 
00092     int n_layers;
00093 
00096     TVec< PP<RBMLayer> > layers;
00097 
00099     PP<RBMLayer> last_layer;
00100 
00102     PP<RBMMultinomialLayer> target_layer;
00103 
00105     PP<RBMMixedLayer> joint_layer;
00106 
00109     TVec< PP<RBMLLParameters> > params;
00110 
00111     PP<RBMQLParameters> input_params;
00112 
00114     PP<RBMLLParameters> target_params;
00115 
00118     PP<RBMJointLLParameters> joint_params;
00119 
00122     TVec<int> training_schedule;
00123 
00130     string fine_tuning_method;
00131 
00132     bool use_sample_rather_than_expectation_in_positive_phase_statistics;
00133 
00134 public:
00135     //#####  Public Member Functions  #########################################
00136 
00138     // ### Make sure the implementation in the .cc
00139     // ### initializes all fields to reasonable default values.
00140     GaussianDBNClassification();
00141 
00142 
00143     //#####  PDistribution Member Functions  ##################################
00144 
00146     virtual real density(const Vec& y) const;
00147 
00149     virtual real log_density(const Vec& y) const;
00150 
00152     virtual real survival_fn(const Vec& y) const;
00153 
00155     virtual real cdf(const Vec& y) const;
00156 
00158     virtual void expectation(Vec& mu) const;
00159 
00161     virtual void variance(Mat& cov) const;
00162 
00165     virtual void generate(Vec& y) const;
00166 
00167     //### Override this method if you need it (and if your distribution can
00168     //### handle it. Default version calls PLERROR.
00173     // virtual void generatePredictorGivenPredicted(Vec& x, const Vec& y);
00174 
00176     //### See help in PDistribution.h.
00177     virtual bool setPredictorPredictedSizes(int the_predictor_size,
00178                                             int the_predicted_size,
00179                                             bool call_parent = true);
00180 
00182     //### See help in PDistribution.h.
00183     virtual void setPredictor(const Vec& predictor, bool call_parent = true)
00184                               const;
00185 
00186     // ### These methods may be overridden for efficiency purpose:
00187     /*
00188     //### Default version calls setPredictorPredictedSises(0,-1) and generate
00193     virtual void generateJoint(Vec& xy);
00194 
00195     //### Default version calls generateJoint and discards y
00200     virtual void generatePredictor(Vec& x);
00201 
00202     //### Default version calls generateJoint and discards x
00207     virtual void generatePredicted(Vec& y);
00208     */
00209 
00210 
00211     //#####  PLearner Member Functions  #######################################
00212 
00213     // ### Default version of inputsize returns learner->inputsize()
00214     // ### If this is not appropriate, you should uncomment this and define
00215     // ### it properly in the .cc
00216     // virtual int inputsize() const;
00217 
00225     virtual void forget();
00226 
00230     virtual void train();
00231 
00235     virtual void computeCostsFromOutputs(const Vec& input, const Vec& output,
00236                                          const Vec& target, Vec& costs) const;
00237 
00238     virtual TVec<string> getTestCostNames() const;
00239 
00240     //#####  PLearn::Object Protocol  #########################################
00241 
00242     // Declares other standard object methods.
00243     // ### If your class is not instantiatable (it has pure virtual methods)
00244     // ### you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS
00245     PLEARN_DECLARE_OBJECT(GaussianDBNClassification);
00246 
00247     // Simply calls inherited::build() then build_()
00248     virtual void build();
00249 
00251     // (PLEASE IMPLEMENT IN .cc)
00252     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00253 
00254 protected:
00255     //#####  Protected Options  ###############################################
00256 
00257     // ### Declare protected option fields (such as learned parameters) here
00258     // ...
00259 
00261     mutable TVec< Vec > activation_gradients;
00262 
00264     mutable TVec< Vec > expectation_gradients;
00265 
00267     mutable Vec output_gradient;
00268 
00269 
00270 protected:
00271     //#####  Protected Member Functions  ######################################
00272 
00273     virtual void greedyStep( const Vec& predictor, int params_index );
00274     virtual void jointGreedyStep( const Vec& input );
00275     virtual void fineTuneByGradientDescent( const Vec& input );
00276 
00278     static void declareOptions(OptionList& ol);
00279 
00280 private:
00281     //#####  Private Member Functions  ########################################
00282 
00284     void build_();
00285 
00287     void build_layers();
00288 
00290     void build_params();
00291 
00292 private:
00293     //#####  Private Data Members  ############################################
00294 
00295     // The rest of the private stuff goes here
00296 };
00297 
00298 // Declares a few other classes and functions related to this class
00299 DECLARE_OBJECT_PTR(GaussianDBNClassification);
00300 
00301 } // end of namespace PLearn
00302 
00303 #endif
00304 
00305 
00306 /*
00307   Local Variables:
00308   mode:c++
00309   c-basic-offset:4
00310   c-file-style:"stroustrup"
00311   c-file-offsets:((innamespace . 0)(inline-open . 0))
00312   indent-tabs-mode:nil
00313   fill-column:79
00314   End:
00315 */
00316 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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