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 /* ******************************************************* 00039 * $Id: SparseIncrementalAffineTransformVariable.h 1442 2004-04-27 15:58:16Z morinf $ 00040 * This file is part of the PLearn library. 00041 ******************************************************* */ 00042 00043 #ifndef SparseIncrementalAffineTransformVariable_INC 00044 #define SparseIncrementalAffineTransformVariable_INC 00045 00046 #include "BinaryVariable.h" 00047 #include <plearn/math/StatsCollector.h> 00048 00049 namespace PLearn { 00050 using namespace std; 00051 00052 00063 class SparseIncrementalAffineTransformVariable: public BinaryVariable 00064 { 00065 typedef BinaryVariable inherited; 00066 00067 private: 00068 Mat temp_grad; 00069 00070 protected: 00071 //TVec<TVec<int> > positions; 00072 TVec<StatsCollector> sc_input; 00073 TVec<StatsCollector> sc_grad; 00074 TMat<StatsCollector> sc_input_grad; 00075 Mat positions; 00076 Mat sums; 00077 Vec input_average; 00078 int n_grad_samples; 00079 bool has_seen_input; 00080 int n_weights; 00081 00082 public: 00083 00084 int add_n_weights; 00085 real start_grad_prop; 00086 real running_average_prop; 00087 00089 SparseIncrementalAffineTransformVariable(): add_n_weights(0), start_grad_prop(1){} 00090 SparseIncrementalAffineTransformVariable(Variable* vec, Variable* transformation, real the_running_average_prop, real the_start_grad_prop); 00091 00092 PLEARN_DECLARE_OBJECT(SparseIncrementalAffineTransformVariable); 00093 00094 virtual void build(); 00095 00096 static void declareOptions(OptionList & ol); 00097 00098 virtual void recomputeSize(int& l, int& w) const; 00099 virtual void fprop(); 00100 virtual void bprop(); 00101 virtual void symbolicBprop(); 00102 virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies); 00103 void reset(); 00104 00105 protected: 00106 void build_(); 00107 }; 00108 00109 DECLARE_OBJECT_PTR(SparseIncrementalAffineTransformVariable); 00110 00112 inline Var sparse_incremental_affine_transform(Var vec, Var transformation, real the_running_average_prop=0.01, real the_start_grad_prop=1) 00113 { 00114 return new SparseIncrementalAffineTransformVariable(vec,transformation,the_running_average_prop,the_start_grad_prop); 00115 } 00116 00117 } // end of namespace PLearn 00118 00119 #endif