SHOGUN
v3.2.0
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00001 /* 00002 * This program is free software; you can redistribute it and/or modify 00003 * it under the terms of the GNU General Public License as published by 00004 * the Free Software Foundation; either version 3 of the License, or 00005 * (at your option) any later version. 00006 * 00007 * Written (W) 2012 Jacob Walker 00008 * 00009 * Adapted from WeightedDegreeRBFKernel.cpp 00010 */ 00011 00012 #include <shogun/lib/common.h> 00013 #include <shogun/kernel/GaussianARDKernel.h> 00014 #include <shogun/features/Features.h> 00015 #include <shogun/io/SGIO.h> 00016 00017 using namespace shogun; 00018 00019 CGaussianARDKernel::CGaussianARDKernel() : CLinearARDKernel() 00020 { 00021 init(); 00022 } 00023 00024 00025 CGaussianARDKernel::CGaussianARDKernel(int32_t size, float64_t width) 00026 : CLinearARDKernel(size), m_width(width) 00027 { 00028 init(); 00029 } 00030 00031 CGaussianARDKernel::CGaussianARDKernel(CDenseFeatures<float64_t>* l, 00032 CDenseFeatures<float64_t>* r, int32_t size, float64_t width) 00033 : CLinearARDKernel(size), m_width(width) 00034 { 00035 init(); 00036 } 00037 00038 bool CGaussianARDKernel::init(CFeatures* l, CFeatures* r) 00039 { 00040 return CLinearARDKernel::init(l,r); 00041 } 00042 00043 void CGaussianARDKernel::init() 00044 { 00045 m_width=1.0; 00046 00047 SG_ADD(&m_width, "width", "Kernel width", MS_AVAILABLE, GRADIENT_AVAILABLE); 00048 } 00049 00050 CGaussianARDKernel::~CGaussianARDKernel() 00051 { 00052 } 00053 00054 CGaussianARDKernel* CGaussianARDKernel::obtain_from_generic(CKernel* kernel) 00055 { 00056 if (kernel->get_kernel_type()!=K_GAUSSIANARD) 00057 { 00058 SG_SERROR("Provided kernel is not of type CGaussianARDKernel!\n"); 00059 } 00060 00061 /* since an additional reference is returned */ 00062 SG_REF(kernel); 00063 return (CGaussianARDKernel*)kernel; 00064 } 00065 00066 float64_t CGaussianARDKernel::compute(int32_t idx_a, int32_t idx_b) 00067 { 00068 REQUIRE(lhs && rhs, "Features not set!\n") 00069 00070 SGVector<float64_t> avec= 00071 ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(idx_a); 00072 SGVector<float64_t> bvec= 00073 ((CDenseFeatures<float64_t>*) rhs)->get_feature_vector(idx_b); 00074 00075 REQUIRE(avec.vlen==bvec.vlen, "Number of right and left hand " 00076 "features must be the same\n"); 00077 00078 float64_t result=0; 00079 00080 for (index_t i = 0; i < avec.vlen; i++) 00081 result += CMath::pow((avec[i]-bvec[i])*m_weights[i], 2); 00082 00083 return CMath::exp(-result/m_width); 00084 } 00085 00086 SGMatrix<float64_t> CGaussianARDKernel::get_parameter_gradient( 00087 const TParameter* param, index_t index) 00088 { 00089 REQUIRE(lhs && rhs, "Features not set!\n") 00090 00091 if (!strcmp(param->m_name, "weights")) 00092 { 00093 SGMatrix<float64_t> derivative=get_kernel_matrix(); 00094 00095 for (index_t j=0; j<num_lhs; j++) 00096 { 00097 for (index_t k=0; k<num_rhs; k++) 00098 { 00099 SGVector<float64_t> avec= 00100 ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(j); 00101 SGVector<float64_t> bvec= 00102 ((CDenseFeatures<float64_t>*) rhs)->get_feature_vector(k); 00103 00104 REQUIRE(avec.vlen==bvec.vlen, "Number of right and left hand " 00105 "features must be the same\n"); 00106 00107 float64_t element=compute(j,k); 00108 float64_t product=CMath::pow((avec[index]-bvec[index]), 2) 00109 *(m_weights[index]/m_width); 00110 00111 derivative(j,k)=-2*element*product; 00112 } 00113 } 00114 00115 return derivative; 00116 } 00117 else if (!strcmp(param->m_name, "width")) 00118 { 00119 SGMatrix<float64_t> derivative(num_lhs, num_rhs); 00120 00121 for (index_t j=0; j<num_lhs; j++) 00122 { 00123 for (index_t k=0; k<num_rhs; k++) 00124 { 00125 SGVector<float64_t> avec= 00126 ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(j); 00127 SGVector<float64_t> bvec= 00128 ((CDenseFeatures<float64_t>*) rhs)->get_feature_vector(k); 00129 00130 REQUIRE(avec.vlen==bvec.vlen, "Number of right and left hand " 00131 "features must be the same\n"); 00132 00133 float64_t result=0; 00134 00135 for (index_t i=0; i<avec.vlen; i++) 00136 result+=CMath::pow((avec[i]-bvec[i])*m_weights[i], 2); 00137 00138 derivative(j,k)=CMath::exp(-result/m_width)* 00139 result/(m_width*m_width); 00140 } 00141 } 00142 00143 return derivative; 00144 } 00145 else 00146 { 00147 SG_ERROR("Can't compute derivative wrt %s parameter\n", param->m_name); 00148 return SGMatrix<float64_t>(); 00149 } 00150 }