Method SVM_group_classes

svm.go:1820–1875

label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data perm, length l, must be allocated before calling this subroutine

(prob *SVM_Problem, nr_class_ret []int, label_ret, start_ret, count_ret [][]int, perm []int)

// perm, length l, must be allocated before calling this subroutine

func (this *SVM) SVM_group_classes(prob *SVM_Problem, nr_class_ret []int, label_ret, start_ret, count_ret [][]int, perm []int) {
	l := prob.l
	max_nr_class := 16
	nr_class := 0
	label := make([]int, max_nr_class)
	count := make([]int, max_nr_class)
	data_label := make([]int, l)
	var i int

	for i = 0; i < l; i++ {
		this_label := int(prob.y[i])
		var j int
		for j = 0; j < nr_class; j++ {
			if this_label == label[j] {
				count[j]++
				break
			}
		}
		data_label[i] = j
		if j == nr_class {
			if nr_class == max_nr_class {
				max_nr_class *= 2
				new_data := make([]int, max_nr_class)
				//System.arraycopy(label,0,new_data,0,label.length);
				copy(new_data, label)
				label = new_data
				new_data = make([]int, max_nr_class)
				//System.arraycopy(count,0,new_data,0,count.length);
				copy(new_data, count)
				count = new_data
			}
			label[nr_class] = this_label
			count[nr_class] = 1
			nr_class++
		}
	}

	start := make([]int, nr_class)
	start[0] = 0
	for i = 1; i < nr_class; i++ {
		start[i] = start[i-1] + count[i-1]
	}
	for i = 0; i < l; i++ {
		perm[start[data_label[i]]] = i
		start[data_label[i]]++
	}
	start[0] = 0
	for i = 1; i < nr_class; i++ {
		start[i] = start[i-1] + count[i-1]
	}

	nr_class_ret[0] = nr_class
	label_ret[0] = label
	start_ret[0] = start
	count_ret[0] = count
}

func (this *SVM) SVM_train(prob *SVM_Problem, param *SVM_Parameter) *SVM_Model {