PLearn 0.1
GaussianContinuumDistribution.h
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00001 // -*- C++ -*-
00002 
00003 // GaussianContinuumDistribution.h
00004 //
00005 // Copyright (C) 2004 Yoshua Bengio & 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: GaussianContinuumDistribution.h 9418 2008-09-02 15:33:46Z nouiz $
00037  ******************************************************* */
00038 
00039 // Authors: Yoshua Bengio & Martin Monperrus
00040 
00044 #ifndef GaussianContinuumDistribution_INC
00045 #define GaussianContinuumDistribution_INC
00046 
00047 #include "UnconditionalDistribution.h"
00048 #include <plearn/io/PStream.h>
00049 #include <plearn_learners/generic/PLearner.h>
00050 #include <plearn/var/Func.h>
00051 #include <plearn/opt/Optimizer.h>
00052 #include <plearn_learners/distributions/PDistribution.h>
00053 #include <plearn/ker/DistanceKernel.h>
00054 
00055 namespace PLearn {
00056 using namespace std;
00057 
00058 class GaussianContinuumDistribution: public UnconditionalDistribution
00059 {
00060 
00061 private:
00062 
00063     typedef UnconditionalDistribution inherited;
00064   
00065 protected:
00066     // NON-OPTION FIELDS
00067     int n;
00068     Func cost_of_one_example;
00069     //Func verify_gradient_func;
00070     Var x, noise_var; // input vector
00071     Var b, W, c, V, muV, smV, smb, snV, snb; // explicit view of the parameters (also in parameters field).
00072     //Var W_src, c_src, V_src, muV_src, smV_src, smb_src, snV_src, snb_src; 
00073     //VarArray mu_neighbors, sm_neighbors, sn_neighbors, hidden_neighbors, input_neighbors, index_neighbors, tangent_plane_neighbors;
00074     Var tangent_targets, tangent_targets_and_point; // target for the tangent vectors for one example 
00075     Var tangent_plane;
00076     Var mu, sm, sn, mu_noisy; // parameters of the conditional models
00077     Var p_x, p_target, p_neighbors, p_neighbors_and_point, target_index, neigbor_indexes;
00078     Var sum_nll;
00079     Var min_sig, min_d;
00080     Var fixed_min_sig, fixed_min_d;
00081 
00082     PP<PDistribution> dist;
00083 
00084     // Random walk fields
00085     Array<VMat> ith_step_generated_set;
00086 
00087     // p(x) computation fields
00088     VMat train_and_generated_set;
00089     TMat<int> train_nearest_neighbors;
00090     TVec< Mat > Bs, Fs;
00091     Mat mus;
00092     Vec sms;
00093     Vec sns;
00094 
00095     Mat Ut_svd, V_svd;  // for SVD computation
00096     Vec S_svd;      // idem
00097     mutable Vec z, zm, zn, x_minus_neighbor, w;
00098     mutable Vec t_row, neighbor_row;
00099     mutable Vec t_dist;
00100     mutable Mat distances;
00101 
00102     mutable DistanceKernel dk;
00103 
00104     real best_validation_cost;
00105 
00106     // *********************
00107     // * protected options *
00108     // *********************
00109 
00110     // ### declare protected option fields (such as learnt parameters) here
00111     VarArray parameters;
00112 
00113 public:
00114 
00115     mutable TVec<int> t_nn;
00116     mutable Vec log_gauss;                        
00117     mutable Mat w_mat;                            
00118     VMat reference_set;
00119 
00120     // ************************
00121     // * public build options *
00122     // ************************
00123 
00124     // ### declare public option fields (such as build options) here
00125 
00126     real weight_mu_and_tangent;
00127     bool include_current_point;
00128     real random_walk_step_prop;
00129     bool use_noise;
00130     bool use_noise_direction;
00131     real noise;
00132     string noise_type;
00133     int n_random_walk_step;
00134     int n_random_walk_per_point;
00135     bool save_image_mat;
00136     bool walk_on_noise;
00137     real min_sigma;
00138     real min_diff;
00139     real fixed_min_sigma;
00140     real fixed_min_diff;
00141     real min_p_x;
00142     bool sm_bigger_than_sn;
00143     int n_neighbors; // number of neighbors used for gradient descent
00144     int n_neighbors_density; // number of neighbors for the p(x) density estimation
00145     int mu_n_neighbors; // number of neighbors to learn the mus
00146     int n_dim; // number of reduced dimensions (number of tangent vectors to compute)
00147     real sigma_grad_scale_factor;
00148     int update_parameters_every_n_epochs;
00149     string variances_transfer_function; // "square", "exp" or "softplus"
00150     PP<Optimizer> optimizer; // to estimate the function that predicts local tangent vectors given the input
00151     Var embedding;
00152     Func output_embedding;
00153     Func output_f;
00154     Func output_f_all;
00155     Func predictor; // predicts everything about the gaussian
00156     Func projection_error_f; // map output to projection error
00157     Func noisy_data;
00158 
00159     // manual construction of the tangent_predictor
00160     string architecture_type; // "neural_network" or "linear" or "" or "embedding_neural_nework" or "embedding_quadratic" 
00161     string output_type; // "tangent_plane", "embedding", or "tangent_plane+embedding".
00162     int n_hidden_units;
00163 
00164     int batch_size;
00165 
00166     real norm_penalization; // penalizes sum_i (||f_i||^2-1)^2
00167     real svd_threshold;
00168 
00169     // ****************
00170     // * Constructors *
00171     // ****************
00172 
00174     GaussianContinuumDistribution();
00175 
00176 
00177     // ********************
00178     // * PLearner methods *
00179     // ********************
00180 
00181 private: 
00182 
00184     void build_();
00185 
00186     void compute_train_and_validation_costs();
00187 
00188     void make_random_walk();
00189   
00190     void update_reference_set_parameters();
00191 
00192     void knn(const VMat& vm, const Vec& x, const int& k, TVec<int>& neighbors, bool sortk) const; 
00193 
00194 protected: 
00195   
00197     static void declareOptions(OptionList& ol);
00198 
00201     virtual void forget();
00202     virtual void initializeParams();
00203 
00204 public:
00205 
00206     // ************************
00207     // **** Object methods ****
00208     // ************************
00209 
00211     virtual void build();
00212 
00214     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00215 
00216     // Declares other standard object methods.
00217     // If your class is not instantiatable (it has pure virtual methods)
00218     // you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS.
00219     PLEARN_DECLARE_OBJECT(GaussianContinuumDistribution);
00220 
00221     // *******************************
00222     // **** PDistribution methods ****
00223     // *******************************
00224 
00226     virtual real log_density(const Vec& x) const;
00227 
00229     real log_density(int i);
00230 
00233     virtual void train();
00234 
00235     /* Not implemented for now
00237     virtual void expectation(Vec& mu) const;
00238 
00240     virtual void variance(Mat& cov) const;
00241 
00243     virtual void generate(Vec& y) const;
00244 
00246     virtual void resetGenerator(long g_seed) const;
00247     */
00248 
00250     virtual void computeOutput(const Vec& input, Vec& output) const;
00251 
00253     virtual int outputsize() const;
00254 
00255     /* Not needed anymore
00258     virtual int outputsize() const;
00259     */
00260 
00261 
00262     // *** SUBCLASS WRITING: ***
00263     // While in general not necessary, in case of particular needs 
00264     // (efficiency concerns for ex) you may also want to overload
00265     // some of the following methods:
00266     // virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const;
00267     // virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const;
00268     // virtual void test(VMat testset, PP<VecStatsCollector> test_stats, VMat testoutputs=0, VMat testcosts=0) const;
00269     // virtual int nTestCosts() const;
00270     // virtual int nTrainCosts() const;
00271 
00272     Mat getEigenvectors(int j) const;
00273   
00274     Vec getTrainPoint(int j) const;
00275 
00276 };
00277 
00278 // Declares a few other classes and functions related to this class.
00279 DECLARE_OBJECT_PTR(GaussianContinuumDistribution);
00280   
00281 } // end of namespace PLearn
00282 
00283 #endif
00284 
00285 
00286 /*
00287   Local Variables:
00288   mode:c++
00289   c-basic-offset:4
00290   c-file-style:"stroustrup"
00291   c-file-offsets:((innamespace . 0)(inline-open . 0))
00292   indent-tabs-mode:nil
00293   fill-column:79
00294   End:
00295 */
00296 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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