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using System;
using System.Collections.Generic;
using System.Text; namespace AHP
{
public static class Formulas
{
//经验RI数组
public static double[] RI = new double[15] { 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41, 1.45, 1.49, 1.52, 1.54, 1.56, 1.58, 1.59 }; //对比较矩阵进行归一化，返回特征向量W
public static double[] normalize(double[][] matrix)
{
int row = matrix.Length;
int column = matrix[0].Length;
double[] Sum_column = new double[column];
for (int i = 0; i < column; i++)
{
Sum_column[i] = 0;
for (int j = 0; j < row; j++)
{
Sum_column[i] += matrix[j][i];
}
} //进行归一化,计算特征向量W
double[] w = new double[row];
for (int i = 0; i < row; i++)
{
w[i] = 0;
for (int j = 0; j < column; j++)
{
w[i] += matrix[i][j] / Sum_column[j];
}
w[i] /= row;
} return w;
} //进行一致性校验
public static checkResult checkCR(double[][] matrix)
{
checkResult res = new checkResult(); int row = matrix.Length;
int column = matrix[0].Length; res.w = Formulas.normalize(matrix); //计算AW
double[] aw = new double[row];
for (int i = 0; i < row; i++)
{
aw[i] = 0;
for (int j = 0; j < column; j++)
{
aw[i] += matrix[i][j] * res.w[j];
}
} res.body = matrix; //求和
double sum = 0;
for (int i = 0; i < row; i++)
{
sum += aw[i] / res.w[i];
} //最大特征根
res.r = sum / column; //一致性指标
res.CI = (res.r - column) / (column - 1); //当前节点的检验系数
res.CR = res.CI / Formulas.RI[column - 1]; if (res.CR < 0.1)
{
res.success = true;
} return res;
} //将类似1/3格式的字符串转为数字
public static double CalculationFormula(string formula)
{
if (formula.IndexOf('/') > 0)
{
string[] kt = formula.Split('/');
return Double.Parse(kt[0]) / Double.Parse(kt[1]);
}
return Double.Parse(formula);
} //代码结束
}
} | 
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