public double compare(OneClass one, DataRow test, List <int> core)
{
List <Node[]> _X = new List <Node[]>();
List <double> _Y = new List <double>();
Problem.sampleSimilar.Clear();
int attrNum = core.Count;
int sampleNum = samples.Count;
for (int i = 0; i < sampleNum; i++)
{
//初始化样本权值
Problem.sampleSimilar.Add(similars[i]);
DataRow sample = samples[i];//训练样本
List <Node> sampleVal = new List <Node>();
for (int j = 0; j < attrNum; j++)
{
sampleVal.Add(new Node(j + 1, Convert.ToDouble(sample[core[j]])));
}
_X.Add(sampleVal.ToArray()); //关键属性
_Y.Add(Convert.ToDouble(sample[0])); //决策属性
}
sampleNum = one.samples.Count; //另一类样本个数
for (int i = 0; i < sampleNum; i++)
{
//初始化样本权值
Problem.sampleSimilar.Add(one.similars[i]);
DataRow sample = one.samples[i];//训练样本
List <Node> sampleVal = new List <Node>();
for (int j = 0; j < attrNum; j++)
{
sampleVal.Add(new Node(j + 1, Convert.ToDouble(sample[core[j]])));
}
_X.Add(sampleVal.ToArray()); //关键属性
_Y.Add(Convert.ToDouble(sample[0])); //决策属性
}
//构造测试样本数据
List <Node> testVal = new List <Node>();
for (int j = 0; j < attrNum; j++)
{
testVal.Add(new Node(j + 1, /*Problem.attrWeight[core[j]-1]**/ Convert.ToDouble(test[core[j]])));
}
//构造Svm测试
Svm svm = new Svm(_X.ToArray(), _Y.ToArray());
double preRes = svm.predict(testVal.ToArray());
if (preRes != label)
{
return(label);
}
else
{
return(one.label);
}
}
public int CompareTo(object o)
{
OneClass one = (OneClass)o;
if (distance > one.distance)
{
return(1);
}
else
{
return(-1);
}
}
public void classify(List <DataRow> trainCollection)
{
//将训练样本按照类别分类，标号有序
int sampleNum = ids.Count; //训练样本数
double oldLabel = -1; //临时记录标签
OneClass one = null;
for (int i = 0; i < sampleNum; i++)
{
DataRow sample = trainCollection[ids[i]]; //测试样本
double label = double.Parse(sample[0].ToString()); //类标签
if (label == oldLabel) //同类
{
one.addSample(sample, id_u[ids[i]]);
}
else //新类
{
oldLabel = label;//更新临时标签
one = new OneClass(label); //构造类别
one.addSample(sample, id_u[ids[i]]); //添加样本及其相似度
classes.Add(one);
}
}
}
