private double ValueStandarization(List<CriteriaDividing> criteriaValues, double indexValue, IndexOptimizationType optimType)
{
double xmax=criteriaValues[criteriaValues.Count-1].DividingValue;//todo:需要细化
double xmin=criteriaValues[0].DividingValue;
if (optimType == IndexOptimizationType.Undefined)
{
MessageBox.Show("optimType is null!");
}
if (indexValue==double.MaxValue)//todo:
{
return 100;
}
if(optimType==IndexOptimizationType.Negative)
{
if (indexValue > xmax)
{
return 0;
}
if (indexValue < xmin)
{
MessageBox.Show("the range of criteria is wrong!");
}
double result = 100 - (indexValue - xmin) / (xmax - xmin) * 100;
return result;
}
else if(optimType==IndexOptimizationType.Positive)
{
if (indexValue < xmin)
{
return 0;
}
if (indexValue >xmax)
{
MessageBox.Show("the range of criteria is wrong!");
}
double result = (indexValue - xmin) / (xmax - xmin) * 100;
return result;
}
else if (optimType == IndexOptimizationType.Middle)
{
double middle = criteriaValues[1].DividingValue;
if (indexValue > xmax)
{
return 0;
}
if (indexValue < xmin)
{
MessageBox.Show("the range of criteria is wrong!");
}
double result = 100 - (indexValue - xmin) / (xmax - xmin) * 100;
return result;
}
else
{
MessageBox.Show("optimType is Undefined!");
return 0;
}
}
//public DenseVector CalculateFuzzyVector(string index,double originValue)
//{
//MemberShipFun membershipFun = new MemberShipFun();
//membershipFun.ValueDivision = FiveLevelCriteria[index];
//var gradeFuzzyVector= membershipFun.TrapezoiMebership(originValue);
//return gradeFuzzyVector;
//}
public double CalculateStandardGrade(string index, IndexOptimizationType optimType,double originValue)
{
var indexCriteria = criterias.Find(c => c.IndexName == index);
if(indexCriteria==null)
{
MessageBox.Show("there is no criteria for this index");
}
return ValueStandarization(indexCriteria.CriteriaValues, originValue, optimType);
}
