kd树之最近邻的搜索Java实现

本算法思想来自于李航的《统计学习方法》本文主要实现其kd树最近邻的搜索

构造代码已经发表过了，现在也还是写一下，

//为了方便储存数据
public class Data {
    public double x1;
    public double x2;
}

//kd树的代码
public class Tree {
    public Tree left;//左节点
    public Tree father;//父节点
    public Tree right;//右节点
    public Data mData;//节点的数据
    public int split;//判断维数

    public void setSplit(int split) {
        this.split = split;
    }

    public int getSplit() {
        return split;
    }
}

//构造kd树
private static void builtTree(Tree root, Data[] datas) {
    if (datas == null) return;//当没有的时候说明这个不能成为节点
    if (datas.length == 1) {  //只有一个就是他自己了，它和第一个判断不能换位置
        root.mData = datas[0];//设置数据
        return;
    } else {
        dataSort(datas, root.getSplit() % 2); //进行数据的排列，注意传参判断其是第几维划分
        root.left = new Tree();root.left.father = root;

        root.left.setSplit(root.getSplit() + 1);//不要忘记写其左右节点的维度

        int middle = datas.length / 2;           //进行数据的对分

        root.mData = datas[middle];
        Data leftData[] = new Data[middle];
        for (int j = 0; j < middle; j++) {
            leftData[j] = datas[j];
        }
        Data rightData[];
        if (datas.length == 2) {
            rightData = null;
        } else {
            root.right = new Tree();root.right.father = root;
            root.right.setSplit(root.getSplit() + 1);
            rightData = new Data[datas.length - 1 - middle];
            for (int k = middle + 1, j = 0; k < datas.length; k++, j++) {
                rightData[j] = datas[k];
            }
        }
        builtTree(root.left, leftData); //递归
        builtTree(root.right, rightData);
    }
}
private static void dataSort(Data[] datas, int i) {//冒泡排序法
    if (i == 0) {
        for (int k = 0; k < datas.length - 1; k++) {
            for (int j = 0; j < datas.length - 1 - k; j++) {
                if (datas[j].x1 > datas[j + 1].x1) {
                    Data temp = datas[j];
                    datas[j] = datas[j + 1];
                    datas[j + 1] = temp;
                }
            }
        }
    } else {
        for (int k = 0; k < datas.length - 1; k++) {
            for (int j = 0; j < datas.length - 1 - k; j++) {
                if (datas[j].x2 > datas[j + 1].x2) {
                    Data temp = datas[j];
                    datas[j] = datas[j + 1];
                    datas[j + 1] = temp;
                }
            }
        }
    }
}

//寻找叶节点
private static Tree find(Tree root, Data testData) {//查找叶节点
    int s = root.split%2;
    if(root.left==null)return root;
    else {
        if(s==0){
            if(root.mData.x1>testData.x1)root = find(root.left,testData);
            else root = find(root.right,testData);
        }else{
            if(root.mData.x2>testData.x2)root = find(root.left,testData);
            else root = find(root.right,testData);
        }
    }
 return root;
}

private static double range(Data mData, Data testData) {//计算欧式距离
    double sum = (mData.x1 - testData.x1)*(mData.x1 - testData.x1)+(mData.x2 - testData.x2)*(mData.x2 - testData.x2);
    return Math.sqrt(sum);
}

private static Data findmin(Tree test, Data testData) {
    double minran = range(test.mData,testData);
    Tree minTree = test;
    Tree nowTree = test;
    while(nowTree.father!=null){
        int s = nowTree.split%2;
        double nowran = range(nowTree.mData,testData);//判断当前节点距离，如果小于就取当前点
        if(nowran