机器学习实战之knn算法pandas


						机器学习实战之knn算法pandas，供大家参考，具体内容如下

开始学习机器学习实战这本书，打算看完了再回头看 周志华的 机器学习。机器学习实战的代码都是用numpy写的，有些麻烦，所以考虑用pandas来实现代码，也能回顾之前学的 用python进行数据分析。感觉目前章节的测试方法太渣，留着以后学了更多再回头写。





# coding: gbk

import pandas as pd

import numpy as np





def getdata(path):

 data = pd.read_csv(path, header=None, sep='\t')

 character = data.iloc[:, :-1]

 label = data.iloc[:, -1]

 chara_max = character.max()

 chara_min = character.min()

 chara_range = chara_max - chara_min

 normal_chara = (character - chara_min) / chara_range

 return normal_chara, label # 获得归一化特征值和标记





def knn(inX, normal_chara, label, k):

 data_sub = normal_chara - inX

 data_square = data_sub.applymap(np.square)

 data_sum = data_square.sum(axis=1)

 data_sqrt = data_sum.map(np.sqrt)

 dis_sort = data_sqrt.argsort()

 k_label = label[dis_sort[:k]]

 label_sort = k_label.value_counts()

 res_label = label_sort.index[0]

 return res_label # knn算法分类



小编为大家分享一段代码：机器学习--KNN基本实现





# _*_ coding _*_

import numpy as np

import math

import operator

 

def get_data(dataset):

 x = dataset[:,:-1].astype(np.float)

 y = dataset[:,-1]

 return x,y

# def cal_dis(a,b):

# x1,y1 = a[:]

# x2,y2 = b[:]

# dist = math.sqrt(math.pow(2,x2)-math.pow(2,x1))

 

def knnclassifer(dataset,predict,k=3):

 x,y = get_data(dataset)

 dic = {}

 distince = np.sum((predict-x)**2,axis=1)**0.5

 sorted_dict = np.argsort(distince)#[2 1 0 3 4]

 countLabel = {}

 for i in range(k):

 label = y[sorted_dict[i]]

 # print(i,sorted_dict[i],label)

 countLabel[label] = countLabel.get(label,0)+1

 new_dic = sorted(countLabel,key=operator.itemgetter(0),reverse=True)

 return new_dic[0][0]

 

if __name__ == '__main__':

 dataset = np.loadtxt("dataset.txt",dtype=np.str,delimiter=",")

 

 predict = [2,2]

 label = knnclassifer(dataset,predict,3)

 print(label)



以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持中文源码网。