【机器学习算法】之KNN算法的实现

来源：互联网发布：欢聚时代程序员的工资编辑：程序博客网时间：2024/06/05 04:59

为了加深对机器学习算法的理解，以及熟悉python,pandas,scikit-learn。现在自己实现一下主要的机器学习算法，程序记录如下：

knn类的实现程序：

import numpy as np  import pandas as pd  import random as rd  import csvfrom sklearn import preprocessingclass knn:    def __init__(self, name):        self.train_file = name        self.feature=[]        self.label=[]    def train(self):        self.feature,self.label=gen_model(self.train_file)           def test(self,x,k):        result=knn_classify(self.feature,self.label,k,x)        return resultdef knn_classify(train_set,train_label,k,x):    data_mat=(train_set)    data_len = len(data_mat)    diff_mat = (np.tile(x,(data_len,1))-data_mat)**2    dist=diff_mat.sum(axis=1)    sorted_idx = dist.argsort()    label_count={}    curr_max_vote= 0;curr_max_label=0    for i in range(k):        curr_label=label[sorted_idx[i]]        label_count[curr_label]=label_count.get(curr_label,0)+1        if(label_count[curr_label] > curr_max_vote):            curr_max_vote = label_count[curr_label]            curr_max_label = curr_label    return curr_max_labeldef gen_model(name):    #df = pd.read_csv('datingTestSet.txt')     txt_name = name +".txt"    csv_name = name + ".csv"      file = open(csv_name,'wb')     my_save=csv.writer(file)     fr = open(txt_name)    line_mat = fr.readlines()    data_mat=[]    for line in line_mat:        line=line.strip()        line_list = line.split('\t')        data_mat.append(line_list)        my_save.writerow(line_list)    file.close()    feature,label=pre_data(csv_name)    return feature,labeldef pre_data(csv_name):    df=pd.read_csv(csv_name,names=['f1','f2','f3','label'])    df['label_id']=pd.factorize(df['label'])[0]    df.drop(['label'],axis=1)    data_mat = df.values    train_data = data_mat[:,0:3]    label = data_mat[:,-1]    min_max_scaler = preprocessing.MinMaxScaler()    feature = min_max_scaler.fit_transform(train_data)    return feature,label

测试程序：

import knnmodel = knn('datingTestSet')model.train()x=[1,1,1];k=3res=model.test(x,k)

数据集采用的是《机器学习实战》的数据集，算法原理介绍参见：http://blog.csdn.net/messiran10/article/details/49333641

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