机器学习—python手动实现KD树算法

# --*-- coding:utf-8 --*-- import numpy as np class Node: # 结点 def __init__(self, data, lchild=None, rchild=None): self.data = data self.lchild = lchild self.rchild = rchild class KdTree: # kd树 def __init__(self): self.kdTree = None def create(self, dataSet, depth): # 创建kd树，返回根结点 if (len(dataSet) > 0): m, n = np.shape(dataSet) # 求出样本行，列 midIndex = int(m / 2) # 中间数的索引位置 axis = depth % n # 判断以哪个轴划分数据 sortedDataSet = self.sort(dataSet, axis) # 进行排序 node = Node(sortedDataSet[midIndex]) # 将节点数据域设置为中位数，具体参考下书本 # print sortedDataSet[midIndex] leftDataSet = sortedDataSet[: midIndex] # 将中位数的左边创建2改副本 rightDataSet = sortedDataSet[midIndex + 1:] # print(leftDataSet) # print(rightDataSet) node.lchild = self.create(leftDataSet, depth + 1) # 将中位数左边样本传入来递归创建树 node.rchild = self.create(rightDataSet, depth + 1) return node else: return None def sort(self, dataSet, axis): # 采用冒泡排序，利用aixs作为轴进行划分 sortDataSet = dataSet[:] # 由于不能破坏原样本，此处建立一个副本 m, n = np.shape(sortDataSet) for i in range(m): for j in range(0, m - i - 1): if (sortDataSet[j][axis] > sortDataSet[j + 1][axis]): temp = sortDataSet[j] sortDataSet[j] = sortDataSet[j + 1] sortDataSet[j + 1] = temp # print(sortDataSet) return sortDataSet def preOrder(self, node): # 前序遍历 if node != None: print("tttt->%s" % node.data) self.preOrder(node.lchild) self.preOrder(node.rchild) def search(self, tree, x): # 搜索 self.nearestPoint = None # 保存最近的点 self.nearestValue = 0 # 保存最近的值 def travel(node, depth=0): # 递归搜索 if node != None: # 递归终止条件 n = len(x) # 特征数 axis = depth % n # 计算轴 if x[axis] < node.data[axis]: # 如果数据小于结点，则往左结点找 travel(node.lchild, depth + 1) else: travel(node.rchild, depth + 1) # 以下是递归完毕后，往父结点方向回朔，对应算法3.3(3) distNodeAndX = self.dist(x, node.data) # 目标和节点的距离判断 if (self.nearestPoint == None): # 确定当前点，更新最近的点和最近的值，对应算法3.3(3)(a) self.nearestPoint = node.data self.nearestValue = distNodeAndX elif (self.nearestValue > distNodeAndX): self.nearestPoint = node.data self.nearestValue = distNodeAndX # print(node.data, depth, self.nearestValue, node.data[axis], x[axis]) if (abs(x[axis] - node.data[axis]) <= self.nearestValue): # 确定是否需要去子节点的区域去找（圆的判断），对应算法3.3(3)(b) if x[axis] < node.data[axis]: travel(node.rchild, depth + 1) else: travel(node.lchild, depth + 1) travel(tree) return self.nearestPoint def dist(self, x1, x2): # 欧式距离的计算 return ((np.array(x1) - np.array(x2)) ** 2).sum() ** 0.5 if __name__ == '__main__': dataSet = [[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]] x = [5, 3] kdtree = KdTree() tree = kdtree.create(dataSet, 0) kdtree.preOrder(tree) # print(kdtree.search(tree, x))