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: NegLogProbCostFunction.cc 6128 2006-07-26 01:41:36Z lamblin $ 00040 * This file is part of the PLearn library. 00041 ******************************************************* */ 00042 00043 #include "NegLogProbCostFunction.h" 00044 #include <plearn/math/TMat_maths.h> 00045 00046 #if USING_MPI 00047 #include <plearn/sys/PLMPI.h> 00048 #endif 00049 00050 namespace PLearn { 00051 using namespace std; 00052 00053 00054 00055 PLEARN_IMPLEMENT_OBJECT(NegLogProbCostFunction, "ONE LINE DESCR", "NO HELP"); 00056 00057 00058 #define smoothmap sigmoid 00059 real NegLogProbCostFunction::evaluate(const Vec& output, const Vec& target) const 00060 { 00061 real prob = 0.; 00062 int desired_class = int(target[0]); 00063 if (desired_class == -1) desired_class=0; 00064 if(output.length()==1) // we assume output[0] gives the probability of having class 1 00065 { 00066 prob = output[0]; 00067 if(smooth_map_outputs) 00068 prob = smoothmap(prob); 00069 if(desired_class==0) 00070 prob = 1-prob; 00071 } 00072 else 00073 { 00074 if(!normalize) // we assume output gives a real probability for each class 00075 #if USING_MPI 00076 #define SEND_PROB_TAG 981 00077 { 00078 // EACH CPU ONLY CARRIES THE CORRECT outputs IN THE INTERVAL 00079 // GIVEN BY [out_start,out_end). 00080 // THE RESULT WILL BE THAT CPU 0 WILL HAVE 00081 // THE PROBABILITY OF desired_class IN prob 00082 // (and the other CPUs will have some dummy value) 00083 if (PLMPI::size>1 && out_end>=0) 00084 { 00085 if (desired_class>=out_start && desired_class<out_end) 00086 { 00087 prob = output[desired_class]; 00088 if (PLMPI::rank>0) // send it to CPU 0 00089 { 00090 MPI_Send(&prob,1,PLMPI_REAL,0,SEND_PROB_TAG,MPI_COMM_WORLD); 00091 } 00092 } 00093 else 00094 { 00095 if (PLMPI::rank==0) 00096 { 00097 MPI_Status status; 00098 MPI_Recv(&prob,1,PLMPI_REAL,MPI_ANY_SOURCE,SEND_PROB_TAG,MPI_COMM_WORLD,&status); 00099 } 00100 else 00101 { 00102 prob = 1; // dummy value (whose log exists) 00103 } 00104 } 00105 } 00106 else 00107 prob = output[desired_class]; 00108 } 00109 #else 00110 prob = output[desired_class]; 00111 #endif 00112 else // outputs may not sum to 1, so we'll normalize them 00113 { 00114 #if USING_MPI 00115 if (PLMPI::size>1 && out_end>=0) 00116 PLERROR("condprob used in parallel mode: normalize not implemented"); 00117 #endif 00118 real* outputdata = output.data(); 00119 if(smooth_map_outputs) // outputs may be slightly smaller than 0 or slightly larger than 1 00120 { // so we'll smooth-map them to fit in range [0,1] before normalizing 00121 real outputsum = 0.0; 00122 for(int i=0; i<output.length(); i++) 00123 outputsum += smoothmap(outputdata[i]); 00124 prob = smoothmap(outputdata[desired_class])/outputsum; 00125 } 00126 else 00127 prob = output[desired_class]/sum(output, false); 00128 } 00129 } 00130 return -safeflog(prob); 00131 } 00132 00133 void NegLogProbCostFunction::declareOptions(OptionList &ol) 00134 { 00135 declareOption(ol, "normalize", &NegLogProbCostFunction::normalize, OptionBase::buildoption, 00136 "TODO: Some comments"); 00137 declareOption(ol, "smooth_map_outputs", &NegLogProbCostFunction::smooth_map_outputs, OptionBase::buildoption, 00138 "TODO: Some comments"); 00139 inherited::declareOptions(ol); 00140 } 00141 00142 } // end of namespace PLearn 00143 00144 00145 /* 00146 Local Variables: 00147 mode:c++ 00148 c-basic-offset:4 00149 c-file-style:"stroustrup" 00150 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00151 indent-tabs-mode:nil 00152 fill-column:79 00153 End: 00154 */ 00155 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :