k近邻算法需要注意的3大要素是，K值，距离度量和决策方式，以下代码表示的是距离度量方式，L1 L2和L3的三种计算方式
# -*-coding:utf-8-*-
import math
point_1 = [1, 1]
point_2 = [5, 1]
point_3 = [4, 4]
def get_distance_by_1():
#L1 是曼哈顿离，计算公式是 每个向量差绝对值的和
distance_1 = abs(point_2[0] - point_1[0]) + abs(point_2[1]-point_1[1])
distance_2 = abs(point_3[0] - point_1[0]) + abs(point_3[1]-point_1[1])
if distance_1>distance_2:
print("L1点3比较近")
else:
print("L1点2比较近")
def get_distance_by_2():
distance_1 = abs(point_2[0] - point_1[0])*abs(point_2[0] - point_1[0]) + abs(point_2[1] - point_1[1])*abs(point_2[1] - point_1[1])
distance_1 = math.sqrt(distance_1)
distance_2 = abs(point_3[0] - point_1[0])*abs(point_3[0] - point_1[0]) + abs(point_3[1] - point_1[1])*abs(point_3[1] - point_1[1])
distance_2 = math.sqrt(distance_2)
if(distance_1>distance_2):
print("L2点3比较近")
else:
print("L2点2比较近")
def get_distance_by_3():
if abs(point_2[0] - point_1[0]) >abs(point_2[1] - point_1[1]):
distance_1 = abs(point_2[0] - point_1[0])
else:
distance_1 = abs(point_2[1] - point_1[1])
if abs(point_3[0] - point_1[0]) > abs(point_3[1] - point_1[1]):
distance_2 = abs(point_3[0] - point_1[0])
else:
distance_2 = abs(point_3[1] - point_1[1])
if distance_1>distance_2:
print("L3 点3比较近")
else:
print("L3 点2比较近")
get_distance_by_1()
get_distance_by_2()
get_distance_by_3()
自己写了半天还是看了牛逼人的代码觉得比较好
粘贴如下
# --*-- 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))
