PLearn 0.1
GaussianContinuum.h
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00001 // -*- C++ -*-
00002 
00003 // GaussianContinuum.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: GaussianContinuum.h 3994 2005-08-25 13:35:03Z chapados $
00037  ******************************************************* */
00038 
00039 // Authors: Yoshua Bengio & Martin Monperrus
00040 
00044 #ifndef GaussianContinuum_INC
00045 #define GaussianContinuum_INC
00046 #include <plearn/io/PStream.h>
00047 #include <plearn_learners/generic/PLearner.h>
00048 #include <plearn/var/Func.h>
00049 #include <plearn/opt/Optimizer.h>
00050 #include <plearn_learners/distributions/PDistribution.h>
00051 #include <plearn/ker/DistanceKernel.h>
00052 
00053 namespace PLearn {
00054 using namespace std;
00055 
00056 class GaussianContinuum: public PLearner
00057 {
00058 
00059 private:
00060 
00061     typedef PLearner inherited;
00062   
00063 protected:
00064     // NON-OPTION FIELDS
00065     int n;
00066     Func cost_of_one_example;
00067     //Func verify_gradient_func;
00068     Var x, noise_var; // input vector
00069     Var b, W, c, V, muV, smV, smb, snV, snb; // explicit view of the parameters (also in parameters field).
00070     //Var W_src, c_src, V_src, muV_src, smV_src, smb_src, snV_src, snb_src; 
00071     //VarArray mu_neighbors, sm_neighbors, sn_neighbors, hidden_neighbors, input_neighbors, index_neighbors, tangent_plane_neighbors;
00072     Var tangent_targets, tangent_targets_and_point; // target for the tangent vectors for one example 
00073     Var tangent_plane;
00074     Var mu, sm, sn, mu_noisy; // parameters of the conditional models
00075     Var p_x, p_target, p_neighbors, p_neighbors_and_point, target_index, neigbor_indexes;
00076     Var sum_nll;
00077     Var min_sig, min_d;
00078 
00079     PP<PDistribution> dist;
00080 
00081     VMat valid_set;
00082 
00083     // Random walk fields
00084     Array<VMat> ith_step_generated_set;
00085 
00086     // p(x) computation fields
00087     VMat train_and_generated_set;
00088     VMat reference_set;
00089     TMat<int> train_nearest_neighbors;
00090     TMat<int> validation_nearest_neighbors;
00091     TVec< Mat > Bs, Fs;
00092     Mat mus;
00093     Vec sms;
00094     Vec sns;
00095 
00096     Mat Ut_svd, V_svd;  // for SVD computation
00097     Vec S_svd;      // idem
00098     mutable Vec z, zm, zn, x_minus_neighbor, w;
00099     mutable Vec t_row, neighbor_row;
00100     mutable TVec<int> t_nn;
00101     mutable Vec t_dist;
00102     mutable Mat distances;
00103 
00104     mutable DistanceKernel dk;
00105 
00106     real best_validation_cost;
00107 
00108     // *********************
00109     // * protected options *
00110     // *********************
00111 
00112     // ### declare protected option fields (such as learnt parameters) here
00113     VarArray parameters;
00114 
00115 public:
00116 
00117     // ************************
00118     // * public build options *
00119     // ************************
00120 
00121     // ### declare public option fields (such as build options) here
00122 
00123     real weight_mu_and_tangent;
00124     bool include_current_point;
00125     real random_walk_step_prop;
00126     bool use_noise;
00127     bool use_noise_direction;
00128     real noise;
00129     string noise_type;
00130     int n_random_walk_step;
00131     int n_random_walk_per_point;
00132     bool save_image_mat;
00133     bool walk_on_noise;
00134     VMat image_points_vmat;
00135     Mat image_points_mat;
00136     Mat image_prob_mat;
00137     TMat<int> image_nearest_neighbors;
00138     real upper_y;
00139     real lower_y;
00140     real upper_x;
00141     real lower_x;
00142     int points_per_dim;
00143     real min_sigma;
00144     real min_diff;
00145     real min_p_x;
00146     bool print_parameters;
00147     bool sm_bigger_than_sn;
00148     int n_neighbors; // number of neighbors used for gradient descent
00149     int n_neighbors_density; // number of neighbors for the p(x) density estimation
00150     int mu_n_neighbors; // number of neighbors to learn the mus
00151     int n_dim; // number of reduced dimensions (number of tangent vectors to compute)
00152     int compute_cost_every_n_epochs;
00153     string variances_transfer_function; // "square", "exp" or "softplus"
00154     real validation_prop;
00155     PP<Optimizer> optimizer; // to estimate the function that predicts local tangent vectors given the input
00156     Var embedding;
00157     Func output_f;
00158     Func output_f_all;
00159     Func predictor; // predicts everything about the gaussian
00160     Func projection_error_f; // map output to projection error
00161     Func noisy_data;
00162 
00163     // manual construction of the tangent_predictor
00164     string architecture_type; // "neural_network" or "linear" or "" or "embedding_neural_nework" or "embedding_quadratic" 
00165     string output_type; // "tangent_plane", "embedding", or "tangent_plane+embedding".
00166     int n_hidden_units;
00167 
00168     int batch_size;
00169 
00170     real norm_penalization; // penalizes sum_i (||f_i||^2-1)^2
00171     real svd_threshold;
00172 
00173     // ****************
00174     // * Constructors *
00175     // ****************
00176 
00178     // (Make sure the implementation in the .cc
00179     // initializes all fields to reasonable default values)
00180     GaussianContinuum();
00181 
00182 
00183     // ********************
00184     // * PLearner methods *
00185     // ********************
00186 
00187 private: 
00188 
00190     // (Please implement in .cc)
00191     void build_();
00192 
00193     void compute_train_and_validation_costs();
00194 
00195     void make_random_walk();
00196   
00197     void update_reference_set_parameters();
00198 
00199     void knn(const VMat& vm, const Vec& x, const int& k, TVec<int>& neighbors, bool sortk) const; 
00200 
00201     void get_image_matrix(VMat points, VMat image_points_vmat, int begin, string file_path, int n_near_neigh);
00202 
00203     real get_nll(VMat points, VMat image_points_vmat, int begin, int n_near_neigh);
00204 
00205 protected: 
00206   
00208     // (Please implement in .cc)
00209     static void declareOptions(OptionList& ol);
00210 
00211 public:
00212 
00213     // ************************
00214     // **** Object methods ****
00215     // ************************
00216 
00218     virtual void build();
00219 
00221     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00222 
00223     // Declares other standard object methods.
00224     // If your class is not instantiatable (it has pure virtual methods)
00225     // you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS.
00226     PLEARN_DECLARE_OBJECT(GaussianContinuum);
00227 
00228 
00229     // **************************
00230     // **** PLearner methods ****
00231     // **************************
00232 
00235     virtual int outputsize() const;
00236 
00239     virtual void forget();
00240     virtual void initializeParams();
00241 
00242     
00245     virtual void train();
00246 
00247 
00249     // (PLEASE IMPLEMENT IN .cc)
00250     virtual void computeOutput(const Vec& input, Vec& output) const;
00251 
00253     // (PLEASE IMPLEMENT IN .cc)
00254     virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 
00255                                          const Vec& target, Vec& costs) const;
00256                                 
00257 
00259     // (PLEASE IMPLEMENT IN .cc)
00260     virtual TVec<string> getTestCostNames() const;
00261 
00264     // (PLEASE IMPLEMENT IN .cc)
00265     virtual TVec<string> getTrainCostNames() const;
00266 
00267 
00268     // *** SUBCLASS WRITING: ***
00269     // While in general not necessary, in case of particular needs 
00270     // (efficiency concerns for ex) you may also want to overload
00271     // some of the following methods:
00272     // virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const;
00273     // virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const;
00274     // virtual void test(VMat testset, PP<VecStatsCollector> test_stats, VMat testoutputs=0, VMat testcosts=0) const;
00275     // virtual int nTestCosts() const;
00276     // virtual int nTrainCosts() const;
00277 
00278 };
00279 
00280 // Declares a few other classes and functions related to this class.
00281 DECLARE_OBJECT_PTR(GaussianContinuum);
00282   
00283 } // end of namespace PLearn
00284 
00285 #endif
00286 
00287 
00288 /*
00289   Local Variables:
00290   mode:c++
00291   c-basic-offset:4
00292   c-file-style:"stroustrup"
00293   c-file-offsets:((innamespace . 0)(inline-open . 0))
00294   indent-tabs-mode:nil
00295   fill-column:79
00296   End:
00297 */
00298 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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