PLearn 0.1
NllSemisphericalGaussianVariable.h
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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 :
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