2.1 k-近邻算法概述

机器学习: KNN学习笔记

1. 1 numpy 库

 数组：array 矩阵：matrix

1. array.shape 获取数组行列数

2. tile() 函数

   a = [0, 1, 2]
   b = tile(a, (3, 2)) #重复3行， 2列

3. 代码说明

#!/usr/bin/env python# -*- coding: utf-8 -*from numpy import *import operatorfrom os import listdirimport matplotlibimport matplotlib.pyplot as pltdef createDataSet():    group = array([        [1.0, 1.1],        [1.0, 1.0],        [0, 0],        [0, 0.1]])    labels = ['A', 'A', 'B', 'B']    return group, labelsdef classify0(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0] #获取dataSet数据的行数    diffMat = tile(inX, (dataSetSize, 1)) - dataSet  # tile: 重复行列, diffMat 得到差值   #通过三角函数公式计算距离    sqDiffMat = diffMat**2 # X²    print sqDiffMat    sqDistances = sqDiffMat.sum(axis = 1) # X² + Y²    print sqDistances    #开根获取所有点的距离    distances = sqDistances**0.5 #    print distances    #排序获取距离值从小到大的索引    sortedDistIndicies = distances.argsort()    print sortedDistIndicies      #根据距离和类别,计算出以类别key,距离为value的字典,确定出类别的频率    classCount = {}    print "---------------------------"    for i in range(k):        votellabel = labels[sortedDistIndicies[i]]        classCount[votellabel] = classCount.get(votellabel, 0) + 1    print classCount    #按照距离排序类别字典,     sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)    print sortedClassCount    print sortedClassCount[0][0]    return sortedClassCount[0][0]if __name__ == "__main__":    group, labels = createDataSet()    classify0([0,0], group, labels, 3)

代码运行图示

第2节 数据准备

解析文本数据,输出训练样本矩阵和类标签向量
下面只是截取了部分数据

#!/usr/bin/env python# -*- coding: utf-8 -*# author: [e-mail]# KNN 从文件中解析海伦的约会数据from numpy import *import operatorfrom os import listdirimport matplotlibimport matplotlib.pyplot as pltdef file2matrix(filename):    fr = open(filename)    arrayOLines = fr.readlines()    numbersOfLine = len(arrayOLines)    returnMat = zeros((numbersOfLine, 3)) #构造空的array    print(returnMat)    classLabelVector = []    fr = open(filename)    index = 0    for line in fr.readlines():        line = line.strip()        listFromLine = line.split('\t')        returnMat[index,:] = listFromLine[0:3] #提取约会特征数据        classLabelVector.append(int(listFromLine[-1])) #提取分类标签        index += 1    print returnMat    print classLabelVector    return returnMat,classLabelVectorif __name__ == "__main__":    file2matrix("datingTestSet2_MyTest.txt")

运行图示