AHP算法--java版

import java.util.Scanner;

public class AHP {

/*
1	2	3
1/2	1	2/3
1/3	3/2	1

*
6
1  1  1  4  1  1/2
1  1  2  4  1  1/2
1  1/2  1  5  3  1/2
1/4  1/4  1/5  1  1/3  1/3
1  1  1/3  3  1  1
2  2  2  3  1  1
*/
public static void main(String[] args) {
System.out.println();
Scanner scanner = new Scanner(System.in);
int n = scanner.nextInt();
double d[][] = new double

;
double temp[][] = new double

;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
double sToD = SToD(scanner.next());
d[i][j] = sToD;
temp[i][j] = sToD;
}
}

System.out.println("以下是判断矩阵");
// 得到判断矩阵
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
System.out.printf("%.2f\t", d[i][j]);
}
System.out.println();
}

// 1.对判断矩阵进行求和
System.out.println("列相加结果");

double w1[] = new double
;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
w1[i] = w1[i] + d[j][i];
}
System.out.printf("%.2f\t", w1[i]);
}

// 2.相除
System.out.println();
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
d[i][j] = d[i][j] / w1[j];
}
}
System.out.println("和积法第一步求出的结果");
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
System.out.printf("%.2f\t", d[i][j]);
}
System.out.println();
}

System.out.println("对第一步求出的结果进行 行相加");

System.out.println("行相加结果");

double w2[] = new double
;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
w2[i] = w2[i] + d[i][j];
}
System.out.printf("\t%.2f\n", w2[i]);
}

System.out.println("特征向量求解第一步 ： 将上面的行相加的所有结果相加：");
double sum = 0.0;
for (int i = 0; i < n; i++) {
sum += w2[i];
}
System.out.printf("结果为sum = \t%.2f\n", sum);

System.out.println("将行结果与 sum 相除 得出结果为 ：  ");
double w3[] = new double
;
for (int i = 0; i < n; i++) {
w3[i] = w2[i] / sum;
System.out.printf("\t%.2f\n", w3[i]);
}

System.out.println();
System.out.println("用和积法计算其最大特征向量为：W = ");
// 以下是校验
System.out.printf("(");
for (int i = 0; i < n; i++) {
System.out.printf("%.2f ,", w3[i]);
}
System.out.printf(")");

System.out.println("\nBW = ");
double w4[] = new double
;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
w4[i] = w4[i] + temp[i][j] * w3[j];
}
System.out.printf("%.3f \t", w4[i]);
}
System.out.println("\n----------------------------------------");
double sum2 = 0.0;
for (int i = 0; i < n; i++) {
sum2 = sum2 + w4[i];
}
System.out.printf("得到sum2\t %.2f\n", sum2);

System.out.println("最大的特征向量为 ：  ");

double result = 0.0;
for (int i = 0; i < n; i++) {
result = result + w4[i] / (6 * w3[i]);
}
System.out.printf(" %.2f \n", result);

System.out.println("\n判断矩阵一致性指标C.I.(Consistency Index)");
double CI = (result - n) / (n - 1);
System.out.printf(" %.2f \n", CI);

System.out.println("随机一致性比率C.R.(Consistency Ratio)。");

/*
*
n  1  2  3  	4  		5	  6	  7		  8		 9	  10	  11
RI  0  0  0.58  0.90  1.12  1.24  1.32  1.41  1.45  1.49  1.51
一般，当一致性比率 <0.1 时
的不一致程度在容许范围之内，可用其归一化特征向量
作为权向量，否则要重新构造成对比较矩阵，对  加
以调整
*/
double RI = 0.0;
switch (n) {
case 0:
RI=0;
break;
case 1:
RI=0;
break;
case 2:
RI=0;
break;
case 3:
RI=0.58;
break;
case 4:
RI=0.90;
break;
case 5:
RI=1.12;
break;
case 6:
RI=1.24;
break;
case 7:
RI=1.32;
break;

default:
break;
}
System.out.printf("C.R =  %.2f \n", CI / RI);

scanner.cl
4000
ose();
}

public static double SToD(String s) {
String[] p = s.split("/");
if (p.length > 1) {
return Double.parseDouble(p[0]) / Double.parseDouble(p[1]);
}
return Double.parseDouble(s);
}
}