/// <summary>
/// 从cur节点开始向子树中寻找x的最近邻
/// </summary>
/// <param name="cur">开始查找的根节点</param>
private void Query(KD_TreeNode cur)
{
if (cur == null) return;
//求出目标x到当前节点的距离
double dist = Dist(cur.data.value, curQuery.value);
if (check.Contains(cur.data.index)==false && dist<curNearestDist)
{
//当前节点未被提取过且dist小于当前最近距离
curNearestDist = dist;
curNearestNode = cur.data.index;
}
//计算x到分裂平面的距离
double radius = Math.Pow(curQuery.value[cur.split] - cur.data.value[cur.split], 2);
//对子区间进行查询
if (curQuery.value[cur.split]<cur.data.value[cur.split])
{
Query(cur.left);
if (radius <= curNearestDist) Query(cur.right);
}
else
{
Query(cur.right);
if (radius <= curNearestDist) Query(cur.left);
}
}
public KD_Tree(KD_DataType[] s)
{
dataset = s;
dimension = s[0].value.Count();
root = BuildTree(0, s.Count() - 1);
}
/// <summary>
/// 对区间head到tail之间的点建树
/// </summary>
/// <param name="head"></param>
/// <param name="tail"></param>
/// <returns>当前节点</returns>
private KD_TreeNode BuildTree(int head, int tail)
{
if (head > tail) return null;
//计算每一维上的方差，并找到方差最大的维度
double maxVariance = 0;
int bestDimension = 0;
for (int s = 0; s < dimension; s++)
{
double variance = 0;
double sum = 0;
for (int i = head; i <= tail; i++) sum += dataset[i].value[s];
double avr = sum / (double)(tail - head + 1);
for(int i=head;i<=tail; i++) variance += Math.Pow(dataset[i].value[s] - avr, 2);
variance /= (double)(tail - head + 1);
if (variance>maxVariance)
{
maxVariance = variance;
bestDimension = s;
}
}
//新节点
KD_TreeNode newNode = new KD_TreeNode();
newNode.split = bestDimension;
//将区间内的点按best dimension排序
Dictionary<KD_DataType, double> unsort = new Dictionary<KD_DataType, double>();
for (int i = head; i <= tail; i++)
{
unsort.Add(dataset[i], dataset[i].value[bestDimension]);
}
var sorted = from pair in unsort
orderby pair.Value ascending
select pair;
var arrSorted = sorted.ToArray();
for(int i = head;i<=tail;i++)
{
dataset[i] = arrSorted[i - head].Key;
}
//建立左右子树
//sort(head, tail, bestDimension);
int mid = head + (tail - head) / 2;
newNode.data = dataset[mid];
newNode.left = BuildTree(head, mid - 1);
newNode.right = BuildTree(mid + 1, tail);
return newNode;
}
