clc,clear
fid=fopen('txt3.txt','r');
n1=6;n2=3;
a=[];
for i=1:n1
    tmp=str2num(fgetl(fid));
    a=[a;tmp]; %读准则层判断矩阵
end
for i=1:n1
    str1=char(['b',int2str(i),'=[];']);
    str2=char(['b',int2str(i),'=[b',int2str(i),';tmp];']);
    eval(str1);
    for j=1:n2
        tmp=str2num(fgetl(fid));
        eval(str2); %读方案层的判断矩阵
    end
end
ri=[0,0,0.58,0.90,1.12,1.24,1.32,1.41,1.45]; %一致性指标
[x,y]=eig(a);  % matlab eig(a) 返回矩阵的特征值和特征向量， 这里的 x 为矩阵 a 的 n 个特征向量， y 为矩阵 a 的 n 个特征值
lamda=max(diag(y));  %  eig 函数返回的 y 是矩阵形式保存的， dig(y) 提取对角线上的n 个特征值到一个数组中， 求出最大特征值 lamda
num=find(diag(y)==lamda);  % 返回最大特征的索引
w0=x(:,num)/sum(x(:,num));  % x( :num) 为最大特征值所对应的那一列特征向量。 w0 中准则层计算出的 包含归一化后的n 个权重值
cr0=(lamda-n1)/(n1-1)/ri(n1)

for i=1:n1 % 循环 n 个维度， 针对每个维度， 都计算一次方案层的比较矩阵及其权重值
    [x,y]=eig(eval(char(['b',int2str(i)])));
    lamda=max(diag(y));
    num=find(diag(y)==lamda);
    w1(:,i)=x(:,num)/sum(x(:,num));
    cr1(i)=(lamda-n2)/(n2-1)/ri(n2);
end
cr1, ts=w1*w0, cr=cr1*w0

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 3 1
1 1/4 1/2
4 1 3
2 1/3 1
1 1/4 1/5
4 1 1/2
5 2 1
1 3 1/3
1/3 1 1/7
3 7 1
1 1/3 5
3 1 7
1/5 1/7 1
1 1 7
1 1 7
1/7 1/7 1
1 7 9
1/7 1 1
1/9 1 1

import org.apache.commons.math3.linear.*;


public class MatrixTester {
    public static void main(String[] args) {

        // Create a real matrix with two rows and three columns, using a factory
        // method that selects the implementation class for us.
        double[][] matrixData = {   {1d,    1d,     1d,     4d,     1d,     1d/2d},
                                    {1d,    1d,     2d,     4d,     1d,     1d/2d},
                                    {1d,    1d/2d,  1d,     5d,     3d,     1d/2d },
                                    {1d/4d, 1d/4d,  1d/5d,  1d,     1d/3d,  1d/3d },
                                    {1d,   1d,     1d/3d,  3d,     1d,     1d },
                                    {2d,    2d,     2d,     3d,     3d,     1d },
                                };
        RealMatrix m = MatrixUtils.createRealMatrix(matrixData);



        // One more with three rows, two columns, this time instantiating the
        // RealMatrix implementation class directly.
        double[][] matrixData2 = {{1d, 2d}, {2d, 5d}, {1d, 7d}};
        RealMatrix n = new Array2DRowRealMatrix(matrixData2);

        // Note: The constructor copies  the input double[][] array in both cases.
        // Now multiply m by n
//        RealMatrix p = m.multiply(n);
//        System.out.println(p.getRowDimension());    // 2
//        System.out.println(p.getColumnDimension()); // 2
//
//        // Invert p, using LU decomposition
//        RealMatrix pInverse = new LUDecomposition(p).getSolver().getInverse();


        RealMatrix D = new EigenDecomposition(m).getD();
        RealMatrix V = new EigenDecomposition(m).getV();

        for(int i=0; i<D.getRowDimension();i++)
        {
            System.out.println(D.getRowMatrix(i));
        }

        for(int i=0; i<V.getRowDimension();i++)
        {
            System.out.println(V.getRowMatrix(i));
        }

        // 特征值
        double maxLamda;
        int columIndexForMaxLamda=0;
        maxLamda=D.getEntry(0,0);

        for(int i =0, j=0; i<D.getRowDimension()&&j<D.getColumnDimension();i++,j=i)
        {
            double lamda = D.getEntry(i,j);
            if(maxLamda<lamda)
            {
                maxLamda=lamda;
                columIndexForMaxLamda = j;
            }
            System.out.println(lamda);
        }

        // 输出尚未做归一化 w1, w2, w3, w4, w5, w6 ， 
        System.out.println(V.getColumnMatrix(columIndexForMaxLamda));

    }
}