| svm_problem* Impala::Core::Training::TrainDataSrcKernelMatrix::MakeProblem | ( | int | start, | |
| int | end | |||
| ) | [inline, private] |
- Note:
- this code is suspiciously similar to TrainDataSrcKernelTable::MakeProblem I guess some refeactoring is in order
The resulting svm_problem can only be used for prediction, not for training.
This code wants to be compatible to how libsvm handles kernel matrices so that's why we add an empty column in the matrix where libsvm normally has a column with indices for training.
Definition at line 85 of file TrainDataSrcKernelMatrix.h.
References Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CPB(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CW(), ILOG_VAR, svm_node::index, svm_problem::l, mMatrix, svm_node::value, svm_problem::x, and svm_problem::y.
Referenced by MakeSvmProblem().
00086 { 00087 ILOG_VAR(Core.Training.Svm.KernelSvmProblem); 00088 int vectorLength = mMatrix->CW(); 00089 svm_problem* problem = new svm_problem; 00090 problem->l = end - start; 00091 problem->y = new double[problem->l]; 00092 problem->x = new struct svm_node *[problem->l]; 00093 struct svm_node* nodes = new struct svm_node[problem->l*(2+vectorLength)]; 00094 for(int i=0 ; i<problem->l ; i++) 00095 { 00096 problem->y[i] = 0; 00097 problem->x[i] = &nodes[i*(vectorLength+2)]; 00098 problem->x[i][0].index = 0; 00099 problem->x[i][0].value = 0; // empty column expected by libsvm 00100 const double* values = mMatrix->CPB(0, start+i); 00101 int j; 00102 for(j=0 ; j<vectorLength ; j++) 00103 { 00104 problem->x[i][j+1].index = j+1; 00105 problem->x[i][j+1].value = values[j]; 00106 } 00107 problem->x[i][j+1].index = -1; 00108 } 00109 return problem; 00110 }
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