public static DecisionMatrix ApprovedNormalize(DecisionMatrix toBeStandardize)
{
//归一化之后的矩阵
DecisionMatrix standardized = new DecisionMatrix(toBeStandardize.Level, toBeStandardize.X);
//将原始数据头填充到新数据中，所有操作都不改变原来的判断矩阵
standardized.InsertFromMatrix(toBeStandardize);
var factors = toBeStandardize.Level.Factors;
int m = standardized.X;
//标准化按照指标值，逐列进行的
for (int j = 0; j < factors.Count; j++)
{
//每一列的最大值和最小值
double columnMax = toBeStandardize.GetColumnMaxValue(j);
double columnMin = toBeStandardize.GetColumnMinValue(j);
//1. 如果是逆向指标，就执行
if (factors[j].Direction == FactorDirection.Negative)
{
for (int i = 0; i < toBeStandardize.X; i++)
{
standardized[i, j] = columnMax - toBeStandardize[i, j] + columnMin;
}
}
//2. 如果指标值包含负数，就执行
bool containsNegative = false;
for (int i = 0; i < standardized.X; i++)
{
if (standardized[i, j] < 0)
{
containsNegative = true;
break;
}
}
//如果包含负数
if (containsNegative)
{
for (int i = 0; i < standardized.X; i++)
{
standardized[i, j] = standardized[i, j] - columnMin;
}
}
//3. 执行标准的归一化操作
//考虑到如果m太大，会造成标准化之太小，所以统一都乘以m
double columnSum = standardized.GetColumnSum(j);
for (int i = 0; i < standardized.X; i++)
{
standardized[i, j] = (standardized[i, j] / columnSum) * m;
}
}
return standardized;
}
public static DecisionMatrix Normalize(DecisionMatrix toBeStandardize)
{
//归一化之后的矩阵
DecisionMatrix standardized = new DecisionMatrix(toBeStandardize.Level, toBeStandardize.X);
//将原始数据头填充到新数据中，所有操作都不改变原来的判断矩阵
standardized.InsertFromMatrix(toBeStandardize);
var factors = toBeStandardize.Level.Factors;
for (int j = 0; j < factors.Count; j++)
{
//执行标准的归一化操作
//获取一列的和
double columnSum = standardized.GetColumnSum(j);
for (int i = 0; i < standardized.X; i++)
{
standardized[i, j] = (standardized[i, j] / columnSum);
}
}
return standardized;
}
