SHOGUN
v2.0.0
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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 * (W) 2012 Jacob Walker 00008 * 00009 * Adapted from WeightedDegreeRBFKernel.cpp 00010 * 00011 */ 00012 00013 #include <shogun/lib/common.h> 00014 #include <shogun/kernel/LinearARDKernel.h> 00015 #include <shogun/features/Features.h> 00016 #include <shogun/io/SGIO.h> 00017 00018 using namespace shogun; 00019 00020 CLinearARDKernel::CLinearARDKernel() 00021 : CDotKernel() 00022 { 00023 init(); 00024 } 00025 00026 00027 CLinearARDKernel::CLinearARDKernel(int32_t size) 00028 : CDotKernel(size) 00029 { 00030 init(); 00031 } 00032 00033 CLinearARDKernel::CLinearARDKernel(CDenseFeatures<float64_t>* l, 00034 CDenseFeatures<float64_t>* r, 00035 int32_t size) 00036 : CDotKernel(size) 00037 { 00038 init(); 00039 init(l,r); 00040 } 00041 00042 void CLinearARDKernel::init() 00043 { 00044 m_weights = SGVector<float64_t>(); 00045 SG_ADD(&m_weights, "weights", "Feature Weights", MS_AVAILABLE); 00046 } 00047 00048 CLinearARDKernel::~CLinearARDKernel() 00049 { 00050 CKernel::cleanup(); 00051 } 00052 00053 bool CLinearARDKernel::init(CFeatures* l, CFeatures* r) 00054 { 00055 CDotKernel::init(l, r); 00056 00057 init_ft_weights(); 00058 00059 return init_normalizer(); 00060 } 00061 00062 void CLinearARDKernel::init_ft_weights() 00063 { 00064 if (!lhs || !rhs) 00065 return; 00066 00067 int32_t alen, blen; 00068 00069 alen = ((CDenseFeatures<float64_t>*) lhs)->get_num_features(); 00070 blen = ((CDenseFeatures<float64_t>*) rhs)->get_num_features(); 00071 00072 REQUIRE(alen==blen, "Number of Right and Left Hand "\ 00073 "Features Must be the Same./n"); 00074 00075 if (m_weights.vlen != alen) 00076 { 00077 m_weights = SGVector<float64_t>(alen); 00078 00079 for (int32_t i=0; i < alen; i++) 00080 m_weights[i]=1.0; 00081 } 00082 00083 SG_DEBUG("Initialized weights for LinearARDKernel (%p).\n", this); 00084 00085 } 00086 00087 void CLinearARDKernel::set_weight(float64_t w, index_t i) 00088 { 00089 if (i >= m_weights.vlen) 00090 { 00091 SG_ERROR("Index %i out of range for LinearARDKernel."\ 00092 "Number of features is %i.\n", i, m_weights.vlen); 00093 } 00094 00095 m_weights[i]=w; 00096 } 00097 00098 float64_t CLinearARDKernel::get_weight(index_t i) 00099 { 00100 if (i >= m_weights.vlen) 00101 { 00102 SG_ERROR("Index %i out of range for LinearARDKernel."\ 00103 "Number of features is %i.\n", i, m_weights.vlen); 00104 } 00105 00106 return m_weights[i]; 00107 } 00108 00109 float64_t CLinearARDKernel::compute(int32_t idx_a, int32_t idx_b) 00110 { 00111 if (!lhs || !rhs) 00112 SG_ERROR("Features not set!\n"); 00113 00114 SGVector<float64_t> avec 00115 = ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(idx_a); 00116 SGVector<float64_t> bvec 00117 = ((CDenseFeatures<float64_t>*) rhs)->get_feature_vector(idx_b); 00118 00119 REQUIRE(avec.vlen==bvec.vlen, "Number of Right and Left Hand "\ 00120 "Features Must be the Same./n"); 00121 00122 float64_t result=0; 00123 00124 for (index_t i = 0; i < avec.vlen; i++) 00125 result += avec[i]*bvec[i]*m_weights[i]*m_weights[i]; 00126 00127 return result; 00128 } 00129 00130 SGMatrix<float64_t> CLinearARDKernel::get_parameter_gradient(TParameter* param, 00131 CSGObject* obj, index_t index) 00132 { 00133 if (!lhs || !rhs) 00134 SG_ERROR("Features not set!\n"); 00135 00136 if (!strcmp(param->m_name, "weights") && obj == this) 00137 { 00138 SGMatrix<float64_t> derivative(num_lhs, num_rhs); 00139 00140 for (index_t j = 0; j < num_lhs; j++) 00141 { 00142 for (index_t k = 0; k < num_rhs; k++) 00143 { 00144 SGVector<float64_t> avec 00145 = ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(j); 00146 SGVector<float64_t> bvec 00147 = ((CDenseFeatures<float64_t>*) rhs)->get_feature_vector(k); 00148 00149 REQUIRE(avec.vlen==bvec.vlen, "Number of Right and Left Hand "\ 00150 "Features Must be the Same./n"); 00151 00152 derivative(j,k) = avec[index]*bvec[index]*m_weights[index]; 00153 } 00154 } 00155 return derivative; 00156 } 00157 00158 else 00159 return SGMatrix<float64_t>(); 00160 } 00161 00162