今天开始学习机器学习，第一章是K-近邻算法，有不对的地方请指正
大概总结一下近邻算法写分类器步骤：
1. 计算测试数据与已知数据的特征值的距离，离得越近越相似
2. 取距离最近的K个已知数据的所属分类
3. 最后统计K个值的分类分别出现的概率，返回最多的一个属性，即为测试数据的所属分类
4. 至于怎么把文本转换成numpy的类型，需要学习numpy模块的相关知识，附上
numpy学习连接 http://old.sebug.net/paper/books/scipydoc/numpy_intro.html

#-*- coding:utf-8 *-*-from numpy import *
import operator   #计算模块
import matplotlib
import matplotlib.pyplot as plt
import time
import random
from mpl_toolkits.mplot3d import Axes3D
from os import listdir
import timedef createDataSet():group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])labels = ['A','A','B','B']return group,labels#A,B分类
def classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]diffMat = tile(inX,(dataSetSize,1)) - dataSet #tile函数把inx复制datasetsize行1列sqDiffMat = diffMat**2#print "sqDiffMat : ",sqDiffMatsqDistance = sqDiffMat.sum(axis = 1)distance = sqDistance**0.5#print "distance : ",distancesortedDistIndicies = distance.argsort()  #返回从小到大的元素的下标，比如[1 3 2 4].argsort()返回[0 2 1 3]#print "****",sortedDistIndiciesclassCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]]   #统计各个现有值所属的特征向量#print sortedDistIndicies[i],voteIlabelclassCount[voteIlabel] = classCount.get(voteIlabel,0)+1  #统计各个特征向量出现的次数sortedClassCount = sorted(classCount.iteritems(),key = operator.itemgetter(1),reverse = True) #operator.itemgetter()从小到大排序#print "sortedClassCount : ",sortedClassCountreturn sortedClassCount[0][0]group,labels =  createDataSet()#print classify0([0,0], group, labels, 3)# # a = [('b',2),('a',1),('c',0)]
# a=[('b',2),('a',2),('a',1),('c',0)]
# b = sorted(a,key =  operator.itemgetter(0)) #优先根据第一个元素排序
# print b
# b = sorted(a,key =  operator.itemgetter(1)) #优先根据第二个元素排序
# print b
# b = sorted(a,key =  operator.itemgetter(1,0)) #优先根据第二个元素排序，当第二个元素相等的情况下根据第一个元素排序
# print b#解析数据
def file2matrix(filename):with open(filename) as f:lines = f.readlines()matrixNumber = len(lines)print 'the all lines is :',matrixNumber#matrix = zeros((matrixNumber,3),dtype = 'int') #生成空的n行3列的矩阵matrix = zeros((matrixNumber,2))vector = []index = 0     #矩阵索引for line in lines:line = line.strip()data = line.split("\t")matrix[index:] = data[0:2]   #把提取出来的复制到矩阵里面vector.append(int((data[-1])))  #最后一个特征值作为特征向量index+=1return matrix,vector#生成文本数据
def createdata(filename):with open(filename,'w') as f:for i in range(1000):r1 = int(random.random()*1000)r2 = 0if(0<=r1<=200):r2 = 1if(200<r1<=400):r2 = 2if(400<r1<=600):r2 = 3if(600<r1<=800):r2 = 4if(800<r1<=1000):r2 = 5r1 = str(r1)r2 = str(r2)#r2 = str(int(random.random()*10))r3 = str(int(random.random()*10))f.writelines(r3+'\t'+r1+'\t'+r2+'\n')#createdata(r'D:\test_packages\knntest.txt')'''
datat,labels = file2matrix(r'D:\test_packages\knntest.txt')
print datat
# print datat[:,1] #纵向的第二列
# print datat[:][1] #横向的第二列
print labels
fig = plt.figure()  #生成容器
plt.title('favorite table data')
ax = fig.add_subplot(1,1,1,projection='3d') #3D模型
ax.scatter(datat[:,0],datat[:,1],datat[:,2],array(labels),array(labels),array(labels))  #使用datat的第二列和第三列作为X轴和Y轴的值
ax.legend()
plt.show()fig = plt.figure()
ax = fig.add_subplot(1,1,1) #把容器划分为1行1列，图像画在第一格,背景颜色为axisbg = ‘’
ax.scatter(datat[:,1],datat[:,2],array(labels),array(labels))  #使用datat的第二列和第三列作为X轴和Y轴的值
#ax.grid(True) #是否显示网格
# plt.show()
plt.show()
'''#归一化，(old-min)/(max-min)
def autoNormal(dataSet):maxVals = dataSet.max(0)  #纵向找到每一个样本的最大特征值minVals = dataSet.min(0)ranges = maxVals - minVals #计算差值normalValue = zeros(shape(dataSet))m = dataSet.shape[0]normalValue = dataSet - tile(minVals,(m,1))   #计算(old-min)normalValue = normalValue/tile(ranges,(m,1))return normalValue,ranges,minVals#归一化特征值之后
datat,labels = file2matrix(r'D:\test_packages\knntest.txt')
normalValue,ranges,minVals = autoNormal(datat)
print normalValue
fig = plt.figure()
ax = fig.add_subplot(1,1,1) #把容器划分为1行1列，图像画在第一格,背景颜色为axisbg = ‘’
ax.scatter(normalValue[:,0],normalValue[:,1],array(labels),array(labels))  #使用datat的第二列和第三列作为X轴和Y轴的值
#ax.grid(True) #是否显示网格
# plt.show()
plt.show()#约会网站测试函数
def datinggTest():datat,labels = file2matrix(r'D:\test_packages\knntest.txt')normal,ranges,minvals = autoNormal(datat)testData = 0.5  #10%用来测试，90%用来训练testNumber = normal.shape[0]  #总行数numberTestValues = int(testNumber*testData)  #测试行数error = 0.0for i in range(numberTestValues):labelValue = classify0(normal[i,:], normal[numberTestValues:testNumber,:], labels[numberTestValues:testNumber], 3)if (labelValue != labels[i]):error+=1.0print "this time is error the error is %s, the right is %s"%(labelValue,labels[i])else:print "all right ,the number is %s, the right is %s"%(labelValue,labels[i])error_result = ((error/float(numberTestValues)))print "your error_result is %s"%(error_result)print 'error is :',error
datinggTest()#把二进制文件转化为np.array
def img2Vector(filename):with open(filename) as f:vector = zeros((1,1024))for i in range(32):line = f.readline()for j in range(32):vector[0,32*i+j] = line[j]return vector
vector = img2Vector(r'D:\test_packages\trainingDigits\0_0.txt')
print vector[0,11:17]#手写数字识别系统测试代码
def handwritingClassTest():startTime = time.ctime()handLabels = []trainFile = listdir(r'D:\test_packages\trainingDigits')m = len(trainFile)trainMat = zeros((m,1024))for i in range(m):fileName = trainFile[i]file = fileName.split('.')[0]classNumber = file.split('_')[0]handLabels.append(classNumber)trainMat[i,:] = img2Vector(r'D:\test_packages\trainingDigits\%s'%fileName) testFiles = listdir(r'D:\test_packages\testDigits')nTest = len(testFiles)error = 0.0for i in range(nTest):fileName = testFiles[i]file = fileName.split('.')[0]classNumber = file.split('_')[0]testMat = img2Vector(r'D:\test_packages\testDigits\%s'%fileName) testLabels = classify0(testMat, trainMat, handLabels, 3)if (testLabels != classNumber):error+=1.0print 'error , error number is %s, the right number is %s'%(testLabels,classNumber)else:print 'right'error = error/float(nTest)stopTime = time.ctime()print 'all right ,the error_result is %s'%(error)print 'the process start at %s'%(startTime)print 'the process stop at %s'%(stopTime)handwritingClassTest()