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00012 #include <shogun/lib/config.h>
00013
00014 #ifdef HAVE_LAPACK
00015 #include <shogun/regression/KernelRidgeRegression.h>
00016 #include <shogun/mathematics/lapack.h>
00017 #include <shogun/mathematics/Math.h>
00018 #include <shogun/labels/RegressionLabels.h>
00019
00020 using namespace shogun;
00021
00022 CKernelRidgeRegression::CKernelRidgeRegression()
00023 : CKernelMachine()
00024 {
00025 init();
00026 }
00027
00028 CKernelRidgeRegression::CKernelRidgeRegression(float64_t tau, CKernel* k, CLabels* lab, ETrainingType m)
00029 : CKernelMachine()
00030 {
00031 init();
00032
00033 m_tau=tau;
00034 set_labels(lab);
00035 set_kernel(k);
00036 set_epsilon(0.0001);
00037 m_train_func=m;
00038 }
00039
00040 void CKernelRidgeRegression::init()
00041 {
00042 m_tau=1e-6;
00043
00044 SG_ADD(&m_tau, "tau", "Regularization parameter", MS_AVAILABLE);
00045 }
00046
00047 bool CKernelRidgeRegression::train_machine_pinv()
00048 {
00049
00050 SGMatrix<float64_t> kernel_matrix=kernel->get_kernel_matrix<float64_t>();
00051 int32_t n = kernel_matrix.num_cols;
00052 int32_t m = kernel_matrix.num_rows;
00053 ASSERT(kernel_matrix.matrix && m>0 && n>0);
00054
00055 for(int32_t i=0; i < n; i++)
00056 kernel_matrix.matrix[i+i*n]+=m_tau;
00057
00058
00059 m_alpha=((CRegressionLabels*) m_labels)->get_labels_copy();
00060
00061
00062 m_svs=SGVector<index_t>(m_alpha.vlen);
00063 m_svs.range_fill();
00064
00065 if (get_alphas().vlen!=n)
00066 {
00067 SG_ERROR("Number of labels does not match number of kernel"
00068 " columns (num_labels=%d cols=%d\n", m_alpha.vlen, n);
00069 }
00070
00071 clapack_dposv(CblasRowMajor,CblasUpper, n, 1, kernel_matrix.matrix, n,
00072 m_alpha.vector, n);
00073
00074 return true;
00075 }
00076
00077 bool CKernelRidgeRegression::train_machine_gs()
00078 {
00079 int32_t n = kernel->get_num_vec_rhs();
00080 int32_t m = kernel->get_num_vec_lhs();
00081 ASSERT(m>0 && n>0);
00082
00083
00084 SGVector<float64_t> b;
00085 float64_t alpha_old;
00086
00087 b=((CRegressionLabels*) m_labels)->get_labels_copy();
00088 m_alpha=((CRegressionLabels*) m_labels)->get_labels_copy();
00089 m_alpha.zero();
00090
00091
00092 m_svs=SGVector<index_t>(m_alpha.vlen);
00093 m_svs.range_fill();
00094
00095 if (get_alphas().vlen!=n)
00096 {
00097 SG_ERROR("Number of labels does not match number of kernel"
00098 " columns (num_labels=%d cols=%d\n", m_alpha.vlen, n);
00099 }
00100
00101
00102 float64_t sigma, err, d;
00103 bool flag=true;
00104 while(flag)
00105 {
00106 err=0.0;
00107 for(int32_t i=0; i<n; i++)
00108 {
00109 sigma=b[i];
00110 for(int32_t j=0; j<n; j++)
00111 if (i!=j)
00112 sigma-=kernel->kernel(j, i)*m_alpha[j];
00113 alpha_old=m_alpha[i];
00114 m_alpha[i]=sigma/(kernel->kernel(i, i)+m_tau);
00115 d=fabs(alpha_old-m_alpha[i]);
00116 if(d>err)
00117 err=d;
00118 }
00119 if (err<=m_epsilon)
00120 flag=false;
00121 }
00122
00123 return true;
00124 }
00125
00126 bool CKernelRidgeRegression::train_machine(CFeatures *data)
00127 {
00128 if (!m_labels)
00129 SG_ERROR("No labels set\n");
00130
00131 if (m_labels->get_label_type() != LT_REGRESSION)
00132 SG_ERROR("Real labels needed for kernel ridge regression.\n");
00133
00134 if (data)
00135 {
00136 if (m_labels->get_num_labels() != data->get_num_vectors())
00137 SG_ERROR("Number of training vectors does not match number of labels\n");
00138 kernel->init(data, data);
00139 }
00140 ASSERT(kernel && kernel->has_features());
00141
00142 switch (m_train_func)
00143 {
00144 case PINV:
00145 return train_machine_pinv();
00146 break;
00147 case GS:
00148 return train_machine_gs();
00149 break;
00150 default:
00151 return train_machine_pinv();
00152 break;
00153 }
00154 }
00155
00156 bool CKernelRidgeRegression::load(FILE* srcfile)
00157 {
00158 SG_SET_LOCALE_C;
00159 SG_RESET_LOCALE;
00160 return false;
00161 }
00162
00163 bool CKernelRidgeRegression::save(FILE* dstfile)
00164 {
00165 SG_SET_LOCALE_C;
00166 SG_RESET_LOCALE;
00167 return false;
00168 }
00169 #endif