机器学习实战（一）kNN调用scikit-learn库函数

最新推荐文章于 2024-06-03 06:00:00 发布

fullstack_lth

最新推荐文章于 2024-06-03 06:00:00 发布

阅读量632

点赞数 1

CC 4.0 BY-SA版权

分类专栏： MachineLearning 文章标签：机器学习

本文链接：https://blog.youkuaiyun.com/u014744118/article/details/77967472

本文介绍了如何在机器学习实战中运用kNN算法，重点讲解通过调用scikit-learn库函数来实现kNN的过程。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

kNN算法

略。

kNN.py

'''
edit  Sep 13, 2017
kNN: k Nearest Neighbors

Input:      inX: vector to compare to existing dataset (1xN)
            dataSet: size m data set of known vectors (NxM)
            labels: data set labels (1xM vector)
            k: number of neighbors to use for comparison (should be an odd number)
            
Output:     the most popular class label

@author: thl
'''
from numpy import *
import operator
from os import listdir

def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()     
    classCount={}          
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCoun