Method SVM_train_one

svm.go:1473–1516

(prob *SVM_Problem, param *SVM_Parameter, Cp, Cn float64)

}

func (this *SVM) SVM_train_one(prob *SVM_Problem, param *SVM_Parameter, Cp, Cn float64) *decision_function {
	alpha := make([]float64, prob.l)
	si := new(SolutionInfo)
	switch param.svm_type {
	case C_SVC:
		this.Solve_c_svc(prob, param, alpha, si, Cp, Cn)
	case NU_SVC:
		this.Solve_nu_svc(prob, param, alpha, si)
	case ONE_CLASS:
		this.Solve_one_class(prob, param, alpha, si)
	case EPSILON_SVR:
		this.Solve_epsilon_svr(prob, param, alpha, si)
	case NU_SVR:
		this.Solve_nu_svr(prob, param, alpha, si)
	}

	log.Println("obj = ", si.obj, ", rho = ", si.rho)

	// output SVs

	nSV := 0
	nBSV := 0
	for i := 0; i < prob.l; i++ {
		if math.Abs(alpha[i]) > 0 {
			nSV++
			if prob.y[i] > 0 {
				if math.Abs(alpha[i]) >= si.upper_bound_p {
					nBSV++
				}
			} else {
				if math.Abs(alpha[i]) >= si.upper_bound_n {
					nBSV++
				}
			}
		}
	}

	log.Println("nSV = ", nSV, ", nBSV = ", nBSV)

	f := new(decision_function)
	f.alpha = alpha
	f.rho = si.rho
	return f
}

func (this *SVM) sigmoid_train(l int, dec_values, labels, probAB []float64) {
	var A, B float64