PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // PLearn (A C++ Machine Learning Library) 00004 // Copyright (C) 1998 Pascal Vincent 00005 // Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal 00006 // Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal 00007 // Copyright (C) 2002 Xiangdong Wang, Christian Dorion 00008 00009 // Redistribution and use in source and binary forms, with or without 00010 // modification, are permitted provided that the following conditions are met: 00011 // 00012 // 1. Redistributions of source code must retain the above copyright 00013 // notice, this list of conditions and the following disclaimer. 00014 // 00015 // 2. Redistributions in binary form must reproduce the above copyright 00016 // notice, this list of conditions and the following disclaimer in the 00017 // documentation and/or other materials provided with the distribution. 00018 // 00019 // 3. The name of the authors may not be used to endorse or promote 00020 // products derived from this software without specific prior written 00021 // permission. 00022 // 00023 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR 00024 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00025 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN 00026 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00027 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 00028 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00029 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00030 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00031 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00032 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00033 // 00034 // This file is part of the PLearn library. For more information on the PLearn 00035 // library, go to the PLearn Web site at www.plearn.org 00036 00037 00038 #ifndef NllSemisphericalGaussianVariable_INC 00039 #define NllSemisphericalGaussianVariable_INC 00040 00041 #include <plearn/var/NaryVariable.h> 00042 00043 namespace PLearn { 00044 using namespace std; 00045 00055 00056 class NllSemisphericalGaussianVariable: public NaryVariable 00057 { 00058 typedef NaryVariable inherited; 00059 00060 public: 00061 int n; // dimension of the vectors 00062 bool use_subspace_distance; // use subspace distance instead of distance to targets 00063 bool use_noise; // Indication that noise on the data should be used to learn the mu parameter 00064 real epsilon; // cut-off of singular values to regularize linear system solution 00065 real min_p_x; 00066 int n_dim; // nb of vectors in f 00067 int n_neighbors; // nb of neighbors 00068 int mu_n_neighbors; // nb of neighbors to learn mu 00069 Vec mu, sm, sn, S, noise, mu_noisy; 00070 Mat F, diff_y_x, z, B, Ut, V, zn, zm, z_noisy, zn_noisy, zm_noisy; 00071 Vec p_neighbors, p_target; 00072 Mat w; // weights in the above minimization, in each row for each t_j 00073 00075 NllSemisphericalGaussianVariable() {} 00076 NllSemisphericalGaussianVariable(const VarArray& the_varray, bool that_use_noise, real theepsilon, real min_p_x, int mu_n_neighbors); 00077 00078 PLEARN_DECLARE_OBJECT(NllSemisphericalGaussianVariable); 00079 00080 virtual void build(); 00081 00082 virtual void recomputeSize(int& l, int& w) const; 00083 virtual void fprop(); 00084 virtual void bprop(); 00085 virtual void symbolicBprop(); 00086 00087 protected: 00088 void build_(); 00089 }; 00090 00091 DECLARE_OBJECT_PTR(NllSemisphericalGaussianVariable); 00092 00093 inline Var nll_semispherical_gaussian(Var tangent_plane_var, Var mu_var, Var sm_var, Var sn_var, Var neighbors_dist_var, 00094 Var p_target_var, Var p_neighbors_var, Var noise, Var mu_noisy, bool use_noise=false, real epsilon=1e-6, real min_p_x=0, int mu_n_neighbors=-1) 00095 { 00096 return new NllSemisphericalGaussianVariable(tangent_plane_var & mu_var & sm_var & sn_var & neighbors_dist_var & p_target_var & p_neighbors_var & noise & mu_noisy,use_noise, epsilon, min_p_x, mu_n_neighbors); 00097 } 00098 00099 00100 } // end of namespace PLearn 00101 00102 #endif 00103 00104 00105 /* 00106 Local Variables: 00107 mode:c++ 00108 c-basic-offset:4 00109 c-file-style:"stroustrup" 00110 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00111 indent-tabs-mode:nil 00112 fill-column:79 00113 End: 00114 */ 00115 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :