Function k_function

svm.go:230–276

(x, y []SVM_Node, param *SVM_Parameter)

}

func k_function(x, y []SVM_Node, param *SVM_Parameter) float64 {
	switch param.kernel_type {
	case LINEAR:
		return dot(x, y)
	case POLY:
		return powi(param.gamma*dot(x, y)+param.coef0, param.degree)
	case RBF:
		{
			var sum float64
			sum = 0
			xlen := len(x)
			ylen := len(y)
			i := 0
			j := 0
			for i < xlen && j < ylen {
				if x[i].index == y[j].index {
					d := x[i].value - y[j].value
					i++
					j++
					sum += d * d
				} else if x[i].index > y[j].index {
					sum += y[j].value * y[j].value
					j++
				} else {
					sum += x[i].value * x[i].value
					i++
				}
			}

			for i < xlen {
				sum += x[i].value * x[i].value
				i++
			}

			for j < ylen {
				sum += y[j].value * y[j].value
				j++
			}
			return math.Exp(-param.gamma * sum)
		}
	case SIGMOID:
		return math.Tanh(param.gamma*dot(x, y) + param.coef0)
	case PRECOMPUTED:
		return x[int(y[0].value)].value
	}
	return 0
}

// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
// Solves: