本文主要是介绍Machine Learning in Action Chapter2，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
python 3.6
此代码取自书中。
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@file:KNN.py
@time:2019/1/1 14:10
@author:Victor
@site:https://blog.csdn.net/sumup
"""from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt# def 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]##输出dataSet的行数print(dataSet.shape[1])##2列print(dataSet.shape[0])##4行##构造(dataSetSize,1)规摸的数组，并inX数组与dataSet相减# [[-1. -1.1]# [-1. -1. ]# [ 0. 0. ]# [ 0. -0.1]]diffMat = tile(inX, (dataSetSize,1)) - dataSetprint(diffMat)##矩阵平方# [[1. 1.21]# [1. 1. ]# [0. 0. ]# [0. 0.01]]sqDiffMat = diffMat**2print(sqDiffMat)##矩阵每行求和# [2.21 2. 0. 0.01]sqDistances = sqDiffMat.sum(axis=1)print(sqDistances)# print(sqDistances.shape[0]) ##4##每行的和开方# [1.48660687 1.41421356 0. 0.1 ]distances = sqDistances**0.5print(distances)##从小到大排序，输出下标# [2 3 1 0]sortedDistIndicies = distances.argsort()print(sortedDistIndicies)classCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]]print(voteIlabel)##在classCount中查找voteIlabel，若没，赋值0，并+1classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1# print(classCount.items()) ##dict_items([('B', 2), ('A', 1)])##key 为classCount的valuesortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)return sortedClassCount[0][0]##Input filename
##Output returnMat classLabelVector
def file2matrix(filename):fr = open(filename)arrayOLines = fr.readlines()numberOfLines = len(arrayOLines)##创建(numberOfLines,3)矩阵，并初始化0returnMat = zeros((numberOfLines,3))classLabelVector = []index = 0for line in arrayOLines:##默认删除空白符（包括'\n', '\r', '\t', ' ')line = line.strip()##根据‘\t’划分开listFromLine = line.split('\t')returnMat[index,:] = listFromLine[0:3]# print(returnMat[index,:])classLabelVector.append(int(listFromLine[-1]))index += 1return returnMat,classLabelVector##newvalue = (oldValue-min)/(max-min)
def autoNorm(dataSet):##返回矩阵中每一列的最小值minVals = dataSet.min(0)##返回矩阵中每一列的最大值maxVals = dataSet.max(0)ranges = maxVals - minValsnormDataSet = zeros(shape(dataSet))m = dataSet.shape[0]normDataSet = dataSet - tile(minVals, (m,1))normDataSet = normDataSet/tile(ranges, (m,1))return normDataSet, ranges, minValsdef datingClassTest():#10%用于求结果hoRatio = 0.10datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')normMat,ranges,minVals = autoNorm(datingDataMat)m = normMat.shape[0]numTestVecs = int(m*hoRatio)errorCount = 0.0for i in range(numTestVecs):classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)print('the classfier came back with: %d, the real answer is: %d' %(classifierResult,datingLabels[i]))if (classifierResult != datingLabels[i]):errorCount += 1.0print('the total error rate is: %f' %(errorCount/float(numTestVecs)))def classifyPerson():resultList =['not at all','in small does','in large does']percentTats = float(input("percentage of time spent playing video games?"))ffMiles = float(input("frequent flier miles earned per year"))iceCream = float(input("liters of iceam consumed per year?"))datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')normMat, ranges, minVals = autoNorm(datingDataMat)inArr = array([ffMiles, percentTats,iceCream])classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels,3)print("you will probably link this person: ",resultList[classifierResult-1])# group,labels = createDataSet()# print(group)
# print(labels)
# print(classify0([0,0],group,labels,3))
# datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')# fig = plt.figure()
#add_subplot(mnp)#添加子轴、图。subplot（m,n,p）或者subplot（mnp）此函数最常用：
#subplot是将多个图画到一个平面上的工具。其中，m表示是图排成m行，n表示图排成n列，也就是整个figure中有n个图是排成一行的，一共m行，如果第一个数字是2就是表示2行图。
#p是指你现在要把曲线画到figure中哪个图上，最后一个如果是1表示是从左到右第一个位置。
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels))
# print(autoNorm(datingDataMat))
# plt.show()
# datingClassTest()
classifyPerson()
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