Create Kd-tree nearest neighbor searcher（创建kd-树最近邻搜索器）。
Description
KDTreeSearcher model objects store the results of a nearest neighbor search that uses the Kd-tree algorithm. Results include the training data, distance metric and its parameters, and maximum number of data points in each leaf node (that is, the bucket size). The Kd-tree algorithm partitions an n-by-K data set by recursively splitting n points in K-dimensional space into a binary tree.
KDrelesearcher模型对象存储最近邻搜索的结果，该搜索使用kd-tree算法。结果包括训练数据、距离及其参数，以及每个叶子节点中的最大数据点数目(即BucketSize)。KD-树算法通过递归地将k维空间中的n个点分割为二叉树来分割n行k列数据集。
Once you create a KDTreeSearcher model object, you can search the stored tree to find all neighboring points to the query data by performing a nearest neighbor search using knnsearch or a radius search usingrangesearch. The Kd-tree algorithm is more efficient than the exhaustive search algorithm when K is small (that is, K ≤ 10), the training and query sets are not sparse, and the training and query sets have many observations.
创建KDrelesearcher模型对象后，可以通过使用knnsearch()函数或使用rangesearch()函数搜索执行最近的邻居搜索来搜索所存储的树，以查找查询数据的所有相邻点。当维数K比较小的时候(如k≤10)，Kd-tree算法比穷举搜索算法更有效，训练和查询集并不稀疏，训练和查询集具有许多观察结果。
Syntax
Mdl = KDTreeSearcher(X)Mdl = KDTreeSearcher(X,Name,Value)Description
Mdl = KDTreeSearcher(X)Mdl) using the n-by-K numeric matrix of training data (X).
KDTreeSearcher(X)使用训练数据(X)的n行k列数字矩阵生长默认的kd-树(Mdl)。
Mdl = KDTreeSearcher(X,Name,Value)DistParameter) property. For example,KDTreeSearcher(X,'Distance','minkowski','BucketSize',10) specifies to use the Minkowski distance when searching for nearest neighbors and to use 10 for the bucket size. To specify DistParameter, use the P name-value pair argument.
mdl=kdtreesearcher(x，name，value)使用一个或多个名称-值对参数指定附加选项。您可以在每个叶子节点(即bucket size)和距离度量中指定数据点的最大数量，并设置距离度量参数属性。例如，KDTreeSearcher(x，‘Distance’，‘Minkowski’，‘BucketSize’，10)指定在搜索最近的邻居时使用Minkowski距离，bucket size指定为10。若要指定DistParameter参数，请使用p name-value对参数。
Object Functions 对象函数
knnsearch |
Find k-nearest neighbors using searcher object |
rangesearch |
Find all neighbors within specified distance using searcher object |
knnsearch:用搜索器对象Mdl寻找k个临近点。
rangesearch:使用搜索器对象Mdl以指定的距离范围寻找所有临近点。
举例：
load fisheriris;
X = meas;
[n,k] = size(X);
Mdl1 = KDTreeSearcher(X);
X has 150 observations and 4 predictors.
n=150
k=4
X有150个观测值和4个预测器。（predictors翻译成预测器好像有些别扭！）
Grow a four-dimensional Kd-tree using the entire data set as training data.
使用整个数据集作为训练样本来生成四维KD-树。
总结：Mdl1 = KDTreeSearcher(X);这一句中的原始样本数据和利用原始样本数据建立的KDTreeSearcher(kd树搜索器)是完全一样的，只不过一个是离散的点，一个是对象。