数据挖掘领域十大经典算法之—K-邻近算法/kNN（超详细附代码）

# -*- coding: utf-8 -*-
"""
Created on Tue Sep 15 20:53:14 2020

@author: Administrator
"""

# coding:utf-8

import numpy as np

def createDataset():
    '''
    创建训练集,特征值分别为搞笑镜头、拥抱镜头、打斗镜头的数量
    '''
    learning_dataset = {"宝贝当家": [45, 2, 9, "喜剧片"],
              "美人鱼": [21, 17, 5, "喜剧片"],
              "澳门风云3": [54, 9, 11, "喜剧片"],
              "功夫熊猫3": [39, 0, 31, "喜剧片"],
              "谍影重重": [5, 2, 57, "动作片"],
              "叶问3": [3, 2, 65, "动作片"],
              "伦敦陷落": [2, 3, 55, "动作片"],
              "我的特工爷爷": [6, 4, 21, "动作片"],
              "奔爱": [7, 46, 4, "爱情片"],
              "夜孔雀": [9, 39, 8, "爱情片"],
              "代理情人": [9, 38, 2, "爱情片"],
              "新步步惊心": [8, 34, 17, "爱情片"]}
    return learning_dataset

def kNN(learning_dataset,dataPoint,k):
    '''
    kNN算法,返回k个邻居的类别和得到的测试数据的类别
    '''
    # s1:计算一个新样本与数据集中所有数据的距离
    disList=[]
    for key,v in learning_dataset.items():
       #对距离进行平方和开根号
       d=np.linalg.norm(np.array(v[:3])-np.array(dataPoint))
       #round四舍五入保留两位小数，并添加到集合中
       disList.append([key,round(d,2)])

    # s2:按照距离大小进行递增排序
    disList.sort(key=lambda dis: dis[1]) 
    # s3:选取距离最小的k个样本
    disList=disList[:k]
    # s4:确定前k个样本所在类别出现的频率，并输出出现频率最高的类别
    labels = {"喜剧片":0,"动作片":0,"爱情片":0}
    #从k个中进行统计哪个类别标签最多
    for s in disList:  
        #取出对应标签
        label = learning_dataset[s[0]] 
        labels[label[len(label)-1]] += 1
    labels =sorted(labels.items(),key=lambda asd: asd[1],reverse=True)

    return labels,labels[0][0]


if __name__ == '__main__':

    learning_dataset=createDataset()
    testData={"唐人街探案": [23, 3, 17, "？片"]}
    dataPoint=list(testData.values())[0][:3]
    
    k=6

    labels,result=kNN(learning_dataset,dataPoint,k)
    print(labels,result,sep='\n')