go fmt

Author: datastream

svm_model.go

@@ -1,15 +1,15 @@
 package libsvm
 
 type SVM_Model struct {
-param *SVM_Parameter // parameter
-nr_class int // number of classes, = 2 in regression/one class svm
-l int // total #SV
-SV [][]SVM_Node // SVs (SV[l])
-sv_coef [][]float64 // coefficients for SVs in decision functions (sv_coef[k-1][l])
-rho []float64 // constants in decision functions (rho[k*(k-1)/2])
-probA []float64 // pariwise probability information
-probB []float64
+param  *SVM_Parameter // parameter
+nr_class int  // number of classes, = 2 in regression/one class svm
+l  int  // total #SV
+SV  [][]SVM_Node  // SVs (SV[l])
+sv_coef  [][]float64  // coefficients for SVs in decision functions (sv_coef[k-1][l])
+rho  []float64  // constants in decision functions (rho[k*(k-1)/2])
+probA  []float64  // pariwise probability information
+probB  []float64
 
-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
+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
 }

svm_node.go

@@ -4,7 +4,8 @@ type SVM_Node struct {
 index int
 value float64
 }
-func (this *SVM_Node)clone() *SVM_Node {
+
+func (this *SVM_Node) clone() *SVM_Node {
 rst := new(SVM_Node)
 *rst = *this
 return rst

svm_parameter.go

@@ -2,40 +2,41 @@ package libsvm
 
 const (
 /* svm_type */
-C_SVC = 0
-NU_SVC = 1
-ONE_CLASS = 2
+C_SVC  = 0
+NU_SVC  = 1
+ONE_CLASS  = 2
 EPSILON_SVR = 3
-NU_SVR = 4
+NU_SVR  = 4
 
 /* kernel_type */
-LINEAR = 0
-POLY = 1
-RBF = 2
-SIGMOID = 3
+LINEAR  = 0
+POLY  = 1
+RBF  = 2
+SIGMOID  = 3
 PRECOMPUTED = 4
 )
 
 type SVM_Parameter struct {
-svm_type int
+svm_type  int
 kernel_type int
-degree int// for poly
-gamma float64// for poly/rbf/sigmoid
-coef0 float64// for poly/sigmoid
+degree  int // for poly
+gamma  float64 // for poly/rbf/sigmoid
+coef0  float64 // for poly/sigmoid
 
 // these are for training only
-cache_size float64 // in MB
-eps float64// stopping criteria
-C float64// for C_SVC, EPSILON_SVR and NU_SVR
-nr_weight int// for C_SVC
-weight_label []int// for C_SVC
-weight []float64// for C_SVC
-nu float64// for NU_SVC, ONE_CLASS, and NU_SVR
-p float64// for EPSILON_SVR
-shrinking int// use the shrinking heuristics
-probability int // do probability estimates
+cache_size  float64  // in MB
+eps  float64 // stopping criteria
+C  float64 // for C_SVC, EPSILON_SVR and NU_SVR
+nr_weight  int // for C_SVC
+weight_label []int // for C_SVC
+weight  []float64 // for C_SVC
+nu  float64 // for NU_SVC, ONE_CLASS, and NU_SVR
+p  float64 // for EPSILON_SVR
+shrinking  int // use the shrinking heuristics
+probability  int  // do probability estimates
 
 }
+
 func (this *SVM_Parameter) Clone() (rst *SVM_Parameter) {
 rst = new(SVM_Parameter)
 *rst = *this