public List <List <Vector> > Cluster(Vector[] vectors)
{
List <Vector> centers = new List <Vector>();
List <List <Vector> > clusters = null;
//任选K个初始聚类中心
for (int i = 0; i < K; i++)
{
centers.Add(vectors[i]);
}
bool end = false;
while (!end)
{
//分配所有样本
clusters = DistanceOperations.Sort(centers, vectors);
end = true;
//计算新聚类中心
for (int i = 0; i < centers.Count; i++)
{
Vector newCenter = DistanceOperations.CalculateCenter(clusters[i]);
if (!newCenter.ValueEquals(centers[i]))
{
centers[i] = newCenter;
end = false;
}
}
}
return(clusters);
}
public List <List <Vector> > Cluster(Vector[] vectors)
{
int N = vectors.Length;
List <Vector> centers = new List <Vector>(Nc);
List <List <Vector> > clusters = null;
//1 预选Nc个聚类中心
for (int i = 0; i < Nc; i++)
{
centers.Add(vectors[i]);
}
int times = 1;
bool end2_14 = false;
while (!end2_14)
{
bool end2_10 = false;
while (!end2_10)
{
end2_10 = true;
//2 分配所有样本
clusters = DistanceOperations.Sort(centers, vectors);
//3 取消样本数太少的类
clusters.RemoveAll(new Predicate <List <Vector> >((vs) => vs.Count < Theta_N));
Nc = clusters.Count;
//4 修正各聚类中心值
centers.Clear();
for (int i = 0; i < Nc; i++)
{
centers.Add(DistanceOperations.CalculateCenter(clusters[i]));
}
//5 计算类内平均距离
decimal[] averageDistances = new decimal[Nc];
for (int i = 0; i < Nc; i++)
{
averageDistances[i] = DistanceOperations.CalculateAverageDistance(centers[i], clusters[i]);
}
//6 计算全部样本的总体平均距离
decimal averageDistanceOfAll = 0;
for (int i = 0; i < Nc; i++)
{
averageDistanceOfAll += averageDistances[i] * clusters[i].Count;
}
averageDistanceOfAll /= N;
const int NextIs8 = 8;
const int NextIs11 = 11;
int nextStep;//下一步
//7
//如果迭代已达I次，置Theta_C=0，跳到第11步
if (times >= I)
{
Theta_C = 0;
nextStep = NextIs11;
}
//如果Nc<=K/2，进入第八步
if (Nc <= K / 2)
{
nextStep = NextIs8;
}
//如果迭代次数是偶数，或Nc>=2K，跳到11；否则进入第八步。
if (times % 2 == 0 || Nc >= 2 * K)
{
nextStep = NextIs11;
}
else
{
nextStep = NextIs8;
}
if (nextStep == NextIs11)
{
break;// End While 2-10
}
//8 计算每个聚类中样本的标准差向量
Vector[] ss = new Vector[Nc];
for (int i = 0; i < Nc; i++)
{
ss[i] = DistanceOperations.CalculateS(centers[i], clusters[i]);
}
//9 求每个标准差向量的最大分量对应的的维数
int[] sMaxDimentions = new int[Nc];
for (int i = 0; i < Nc; i++)
{
sMaxDimentions[i] = DistanceOperations.GetMaxValueIndex(ss[i].Numbers);
}
//10 判断是否分裂
for (int i = 0; i < Nc; i++)
{
if (ss[i][sMaxDimentions[i]] > Theta_S)
{
if ((averageDistances[i] > averageDistanceOfAll &&
clusters[i].Count > 2 * (Theta_N + 1))
||
(Nc <= K / 2))
{
//分裂
Vector oldCenter = centers[i];
decimal delta = ss[i][sMaxDimentions[i]] * SplitK;
Vector newCenter1 = oldCenter.CloneVector();
newCenter1.Numbers[sMaxDimentions[i]] += delta;
Vector newCenter2 = oldCenter.CloneVector();
newCenter2.Numbers[sMaxDimentions[i]] -= delta;
centers[i] = newCenter1;
centers.Add(newCenter2);
Nc = centers.Count;
times++;
end2_10 = false;
}
}
}
}//End While 2-10
//11 计算所有聚类中心间的距离，
// (i , j) 对应 i*Nc+j ; n 对应 ( n/Nc , n%Nc )
List <KeyValuePair <int, decimal> > ds = DistanceOperations.CalculateAllDistances(centers);
//12 将小于Theta_C的距离升序排序
ds.RemoveAll((kv) => { return(kv.Value >= Theta_C); });
ds.Sort((kv1, kv2) => { return(decimal.Compare(kv1.Value, kv2.Value)); });
//13 合并
List <Vector> newCenters = new List <Vector>();
List <List <Vector> > newClusters = new List <List <Vector> >();
foreach (var kv in ds)
{
int i = kv.Key / Nc;
int j = kv.Key % Nc;
int Ni = clusters[i].Count;
int Nj = clusters[j].Count;
Vector newCenter = (centers[i] * Ni + centers[j] * Nj) / (Ni + Nj);
List <Vector> newCluster = new List <Vector>(Ni + Nj);
newCluster.AddRange(clusters[i]);
newCluster.AddRange(clusters[j]);
newCenters.Add(newCenter);
newClusters.Add(newCluster);
centers[i] = null;
centers[j] = null;
clusters[i] = null;
clusters[j] = null;
}
centers.RemoveAll((v) => v == null);
clusters.RemoveAll((vl) => vl == null);
Nc = clusters.Count;
//14
if (times >= I)
{
end2_14 = true;
}
else
{
times++;
end2_14 = false;
}
}// End While 2-14
return(clusters);
}
