/// <summary>
/// 计算关键属性的依赖度
/// </summary>
/// <returns>依赖度集合1-13存储在0-12</returns>
public List <double> calDependance()
{
List <double> keyDependance = new List <double>(); //关键属性的依赖度
int sampleNum = trainCollection.Count; //相似样本个数
//计算决策属性的划分
Depend dec = new Depend(true);
for (int j = 0; j < sampleNum; j++)//遍历相似样本，进行属性划分
{
dec.addRecord(trainCollection[j][0].ToString(), j);
}
int keyNum = keyCollection.Count;
for (int i = 1; i < keyNum; i++)//遍历属性求出相应的依赖度
{
//计算关键属性的划分
Depend depend = new Depend(true); //不考虑属性类型，一律按照文本处理
for (int j = 0; j < sampleNum; j++) //遍历样本，进行属性划分
{
depend.addRecord(trainCollection[j][i].ToString(), j);
}
//求出依赖度
keyDependance.Add(depend.getDependance(dec, sampleNum));
}
//for (int i = 0; i < keyDependance.Count; i++)
//{
// Console.Write(keyDependance[i].ToString() + " ");
//}
return(keyDependance);
}
/// <summary>
/// 获取关系依赖度
/// </summary>
/// <param name="dec">决策划分依赖对象</param>
/// <param name="allNum">样本总数</param>
/// <returns>依赖度</returns>
public double getDependance(Depend dec, int allNum)
{
double sum = 0; //正域的数量
int divNum = divs.Count; //当前划分的个数
List <Div> decDivs = dec.divs; //决策属性的划分
int decDivNum = decDivs.Count; //决策划分的个数
for (int i = 0; i < divNum; i++) //遍历划分集合
{
Div keyDiv = divs[i];
for (int j = 0; j < decDivNum; j++)
{
Div decDiv = decDivs[j];
if (decDiv.containDiv(keyDiv))
{
sum += keyDiv.getRecNum();
break;
}
}
}
return(sum / allNum);
}
