start import 3.14

Author: datastream

svm.go

@@ -1907,11 +1907,13 @@ func (this *SVM) SVM_train(prob *SVM_Problem, param *SVM_Parameter) *SVM_Model {
 model.l = nSV
 model.SV = make([][]SVM_Node, nSV)
 model.sv_coef[0] = make([]float64, nSV)
+model.sv_indices = make([]int, nSV)
 j := 0
 for i = 0; i < prob.l; i++ {
 if math.Abs(f.alpha[i]) > 0 {
 model.SV[j] = prob.x[i]
 model.sv_coef[0][j] = f.alpha[i]
+model.sv_indices[j] = i+1
 j++
 }
 }
@@ -2065,10 +2067,12 @@ func (this *SVM) SVM_train(prob *SVM_Problem, param *SVM_Parameter) *SVM_Model {
 
 model.l = nnz
 model.SV = make([][]SVM_Node, nnz)
+model.sv_indices = make([]int, nnz)
 p = 0
 for i = 0; i < l; i++ {
 if nonzero[i] {
 model.SV[p] = x[i]
+model.sv_indices[p] = perm[i] + 1
 p++
 }
 }
@@ -2244,6 +2248,18 @@ func (this *SVM) SVM_get_labels(model *SVM_Model, label []int) {
 }
 }
 
+func (this *SVM) SVM_get_sv_indices(model SVM_Model, indices []int) {
+ if model.sv_indices != nil {
+ for i:=0; i<model.l; i++ {
+ indices[i] = model.sv_indices[i]
+}
+}
+}
+
+func (this *SVM) SVM_get_nr_sv(model SVM_Model) int {
+return model.l
+}
+
 func (this *SVM) SVM_get_svr_probability(model *SVM_Model) float64 {
 var rst float64
 if (model.param.svm_type == EPSILON_SVR || model.param.svm_type == NU_SVR) && model.probA != nil {

svm_model.go

@@ -9,6 +9,7 @@ type SVM_Model struct {
 rho []float64 // constants in decision functions (rho[k*(k-1)/2])
 probA []float64 // pariwise probability information
 probB []float64
+sv_indices []int // sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set
 
 label []int // label of each class (label[k])
 nSV []int // number of SVs for each class (nSV[k]) nSV[0] + nSV[1] + ... + nSV[k-1] = l