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SpectralClustering.cc

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00001 // -*- C++ -*- 00002 00003 // SpectralClustering.cc 00004 // 00005 // Copyright (C) 2004 Olivier Delalleau 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: SpectralClustering.cc,v 1.3 2004/07/21 16:30:59 chrish42 Exp $ 00037 ******************************************************* */ 00038 00039 // Authors: Olivier Delalleau 00040 00043 #include <plearn/ker/DivisiveNormalizationKernel.h> 00044 #include "SpectralClustering.h" 00045 00046 namespace PLearn { 00047 using namespace std; 00048 00050 // SpectralClustering // 00052 SpectralClustering::SpectralClustering() 00053 : remove_bias(false) 00054 { 00055 // Usually, one will want only positive eigenvalues. 00056 min_eigenvalue = 0; 00057 } 00058 00059 PLEARN_IMPLEMENT_OBJECT(SpectralClustering, 00060 "Spectral Clustering dimensionality reduction.", 00061 "The current code only performs dimensionality reduction, and does not do\n" 00062 "clustering." 00063 ); 00064 00066 // declareOptions // 00068 void SpectralClustering::declareOptions(OptionList& ol) 00069 { 00070 declareOption(ol, "remove_bias", &SpectralClustering::remove_bias, OptionBase::buildoption, 00071 "If set to 1, the (divisively) normalized kernel will not take into account terms\n" 00072 "of the form K(x_i,x_i), in order to remove bias induced by those terms."); 00073 00074 // Now call the parent class' declareOptions 00075 inherited::declareOptions(ol); 00076 00077 // Redirect the 'kernel' option toward sc_kernel. 00078 redeclareOption(ol, "kernel", &SpectralClustering::sc_kernel, OptionBase::buildoption, 00079 "The kernel used to (implicitly) project the data in feature space."); 00080 00081 redeclareOption(ol, "ignore_n_first", &SpectralClustering::ignore_n_first, OptionBase::nosave, 00082 "In Spectral clustering, no eigenvector is ignored."); 00083 00084 // And declare the normalized kernel so that it can be saved. 00085 declareOption(ol, "normalized_kernel", &KernelProjection::kernel, OptionBase::learntoption, 00086 "The normalized kernel."); 00087 } 00088 00090 // build // 00092 void SpectralClustering::build() 00093 { 00094 inherited::build(); 00095 build_(); 00096 } 00097 00099 // build_ // 00101 void SpectralClustering::build_() 00102 { 00103 // Obtain the "real" kernel by divisive normalization of 'sc_kernel'. 00104 // We have to do this iff: 00105 // 1. A 'sc_kernel' is provided, and 00106 // 2. either: 00107 // 2.a. the 'kernel' option is not set, or 00108 // 2.b. the 'kernel' option is not a DivisiveNormalizationKernel acting on 'sc_kernel'. 00109 // This is to ensure that a loaded 'kernel' won't be overwritten. 00110 if (sc_kernel && 00111 (!kernel || 00112 (dynamic_cast<DivisiveNormalizationKernel*>((Kernel*) kernel))->source_kernel != sc_kernel)) { 00113 this->kernel = new DivisiveNormalizationKernel 00114 (sc_kernel, remove_bias); 00115 } 00116 } 00117 00119 // forget // 00121 void SpectralClustering::forget() 00122 { 00123 inherited::forget(); 00124 } 00125 00127 // makeDeepCopyFromShallowCopy // 00129 void SpectralClustering::makeDeepCopyFromShallowCopy(map<const void*, void*>& copies) 00130 { 00131 inherited::makeDeepCopyFromShallowCopy(copies); 00132 00133 // ### Call deepCopyField on all "pointer-like" fields 00134 // ### that you wish to be deepCopied rather than 00135 // ### shallow-copied. 00136 // ### ex: 00137 // deepCopyField(trainvec, copies); 00138 00139 // ### Remove this line when you have fully implemented this method. 00140 PLERROR("SpectralClustering::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); 00141 } 00142 00143 } // end of namespace PLearn 00144

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