KNN总结一
即在样本空间中，计算目标与所有样本的距离，并选取k个来进行投票
投票最多的类即为目标所属类，k为超参数。
from numpy import *
import operator
# 创建一个数据集，包含2个类别共4个样本
def createDataSet():
# 生成一个矩阵，每行表示一个样本
group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])
# 4个样本分别所属的类别
labels = ['A', 'A', 'B', 'B']
return group, labels
# KNN分类算法函数定义
def kNNClassify(newInput, dataSet, labels, k):
numSamples = dataSet.shape[0] # shape[0]表示行数
# # step 1: 计算距离[
# 假如：
# Newinput：[1,0,2]
# Dataset:
# [1,0,1]
# [2,1,3]
# [1,0,2]
# 计算过程即为：
# 1、求差
# [1,0,1] [1,0,2]
# [2,1,3] -- [1,0,2]
# [1,0,2] [1,0,2]
# =
# [0,0,-1]
# [1,1,1]
# [0,0,-1]
# 2、对差值平方
# [0,0,1]
# [1,1,1]
# [0,0,1]
# 3、将平方后的差值累加
# [1]
# [3]
# [1]
# 4、将上一步骤的值求开方，即得距离
# [1]
# [1.73]
# [1]
#
# ]
# tile(A, reps): 构造一个矩阵，通过A重复reps次得到
# the following copy numSamples rows for dataSet
diff = tile(newInput, (numSamples, 1)) - dataSet # 按元素求差值
squaredDiff = diff ** 2 # 将差值平方
squaredDist = sum(squaredDiff, axis = 1) # 按行累加
distance = squaredDist ** 0.5 # 将差值平方和求开方，即得距离
# # step 2: 对距离排序
# argsort() 返回排序后的索引值
sortedDistIndices = argsort(distance)
classCount = {} # define a dictionary (can be append element)
for i in range(k):
# # step 3: 选择k个最近邻
voteLabel = labels[sortedDistIndices[i]]
# # step 4: 计算k个最近邻中各类别出现的次数
# when the key voteLabel is not in dictionary classCount, get()
# will return 0
classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
# # step 5: 返回出现次数最多的类别标签
maxCount = 0
for key, value in classCount.items():
if value > maxCount:
maxCount = value
maxIndex = key
return maxIndex
###进阶 kd树