机器学习实战（6）：SVM-SMO-核函数手写识别

SVM判断模型只与支持向量有关：

# coding=utf-8
#Created on Nov 4, 2010
#Chapter 5 source file for Machine Learing in Action
#@author: Peter
##########简单的SMO程序#########################################################################
from numpy import *def loadDataSet(fileName):dataMat = []; labelMat = []fr = open(fileName)for line in fr.readlines():lineArr = line.strip().split('\t')dataMat.append([float(lineArr[0]), float(lineArr[1])])labelMat.append(float(lineArr[2]))return dataMat,labelMatdef selectJrand(i,m):j=i #we want to select any J not equal to iwhile (j==i):j = int(random.uniform(0,m))return jdef clipAlpha(aj,H,L):if aj > H: aj = Hif L > aj:aj = Lreturn ajdef smoSimple(dataMatIn, classLabels, C, toler, maxIter): #toler:容错率-KKT条件判定精度dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).transpose()b = 0; m,n = shape(dataMatrix)alphas = mat(zeros((m,1)))iter = 0while (iter < maxIter):alphaPairsChanged = 0for i in range(m):fXi = float(multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[i,:].T)) + bEi = fXi - float(labelMat[i])#if checks if an example violates KKT conditionsif ((labelMat[i]*Ei < -toler) and (alphas[i] < C)) or ((labelMat[i]*Ei > toler) and (alphas[i] > 0)):j = selectJrand(i,m)fXj = float(multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[j,:].T)) + bEj = fXj - float(labelMat[j])alphaIold = alphas[i].copy(); alphaJold = alphas[j].copy();if (labelMat[i] != labelMat[j]):L = max(0, alphas[j] - alphas[i])H = min(C, C + alphas[j] - alphas[i])else:L = max(0, alphas[j] + alphas[i] - C)H = min(C, alphas[j] + alphas[i])if L==H: print ("L==H"); continueeta = 2.0 * dataMatrix[i,:]*dataMatrix[j,:].T - dataMatrix[i,:]*dataMatrix[i,:].T - dataMatrix[j,:]*dataMatrix[j,:].Tif eta >= 0: print ("eta>=0"); continuealphas[j] -= labelMat[j]*(Ei - Ej)/etaalphas[j] = clipAlpha(alphas[j],H,L)if (abs(alphas[j] - alphaJold) < 0.00001): print ("j not moving enough"); continuealphas[i] += labelMat[j]*labelMat[i]*(alphaJold - alphas[j])#update i by the same amount as j#the update is in the oppostie directionb1 = b - Ei- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[i,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[i,:]*dataMatrix[j,:].Tb2 = b - Ej- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[j,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[j,:]*dataMatrix[j,:].Tif (0 < alphas[i]) and (C > alphas[i]): b = b1elif (0 < alphas[j]) and (C > alphas[j]): b = b2else: b = (b1 + b2)/2.0alphaPairsChanged += 1print ("iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged))if (alphaPairsChanged == 0): iter += 1else: iter = 0print ("iteration number: %d" % iter)return b,alphas#dataArr,labelArr=loadDataSet(r'C:\Users\li\Downloads\machinelearninginaction\Ch06\testSet.txt')
#b,alphas=smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
#print(b)
#print(alphas[alphas>0])
#print(shape(alphas[alphas>0]))
#for i in range(100):
#    if alphas[i]>0.0: print(dataArr[i],labelArr[i])###############################################################################################
##完整的 Platt SMO程序 增加误差缓存、寻值优化，采用核函数
##外循环，对alpha1,采用两种方式交替进行，1、对所有数据集上进行单遍扫描；2、在非边界alpha（0和C之间）中实现单遍扫描，
##先建立非边界alpha值的列表，再遍历，并跳过那些已知的不会改变的alpha值。
##内循环，对alpha2，建立全局缓存用于保存误差值，并从中选择使得步长或者说E1-E2最大的alpha值
###############################################################################################
def kernelTrans(X, A, kTup): #calc the kernel or transform data to a higher dimensional spacem,n = shape(X)K = mat(zeros((m,1)))if kTup[0]=='lin': K = X * A.T   #linear kernelelif kTup[0]=='rbf':             #radial basis functionfor j in range(m):deltaRow = X[j,:] - AK[j] = deltaRow*deltaRow.TK = exp(K/(-1*kTup[1]**2)) #divide in NumPy is element-wise not matrix like Matlab  ??why not -2else: raise NameError('Houston We Have a Problem -- \That Kernel is not recognized')return Kclass optStruct:def __init__(self,dataMatIn, classLabels, C, toler, kTup):  # Initialize the structure with the parameters self.X = dataMatInself.labelMat = classLabelsself.C = Cself.tol = tolerself.m = shape(dataMatIn)[0]self.alphas = mat(zeros((self.m,1)))self.b = 0self.eCache = mat(zeros((self.m,2))) #first column is valid flagself.K = mat(zeros((self.m,self.m)))for i in range(self.m):self.K[:,i] = kernelTrans(self.X, self.X[i,:], kTup)def calcEk(oS, k):fXk = float(multiply(oS.alphas,oS.labelMat).T*oS.K[:,k] + oS.b)Ek = fXk - float(oS.labelMat[k])return Ekdef selectJ(i, oS, Ei):         #this is the second choice -heurstic, and calcs EjmaxK = -1; maxDeltaE = 0; Ej = 0oS.eCache[i] = [1,Ei]  #set valid #choose the alpha that gives the maximum delta EvalidEcacheList = nonzero(oS.eCache[:,0].A)[0]if (len(validEcacheList)) > 1:for k in validEcacheList:   #loop through valid Ecache values and find the one that maximizes delta Eif k == i: continue #don't calc for i, waste of timeEk = calcEk(oS, k)deltaE = abs(Ei - Ek)if (deltaE > maxDeltaE):maxK = k; maxDeltaE = deltaE; Ej = Ekreturn maxK, Ejelse:   #in this case (first time around) we don't have any valid eCache valuesj = selectJrand(i, oS.m)Ej = calcEk(oS, j)return j, Ejdef updateEk(oS, k):#after any alpha has changed update the new value in the cacheEk = calcEk(oS, k)oS.eCache[k] = [1,Ek]def innerL(i, oS):Ei = calcEk(oS, i)if ((oS.labelMat[i]*Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or ((oS.labelMat[i]*Ei > oS.tol) and (oS.alphas[i] > 0)):j,Ej = selectJ(i, oS, Ei) #this has been changed from selectJrandalphaIold = oS.alphas[i].copy(); alphaJold = oS.alphas[j].copy();if (oS.labelMat[i] != oS.labelMat[j]):L = max(0, oS.alphas[j] - oS.alphas[i])H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])else:L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)H = min(oS.C, oS.alphas[j] + oS.alphas[i])if L==H: print ("L==H"); return 0eta = 2.0 * oS.K[i,j] - oS.K[i,i] - oS.K[j,j] #changed for kernelif eta >= 0: print ("eta>=0"); return 0oS.alphas[j] -= oS.labelMat[j]*(Ei - Ej)/etaoS.alphas[j] = clipAlpha(oS.alphas[j],H,L)updateEk(oS, j) #added this for the Ecacheif (abs(oS.alphas[j] - alphaJold) < 0.00001): print ("j not moving enough"); return 0oS.alphas[i] += oS.labelMat[j]*oS.labelMat[i]*(alphaJold - oS.alphas[j])#update i by the same amount as jupdateEk(oS, i) #added this for the Ecache                    #the update is in the oppostie directionb1 = oS.b - Ei- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.K[i,i] - oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.K[i,j]b2 = oS.b - Ej- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.K[i,j]- oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.K[j,j]if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]): oS.b = b1elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]): oS.b = b2else: oS.b = (b1 + b2)/2.0return 1else: return 0def smoP(dataMatIn, classLabels, C, toler, maxIter,kTup=('lin', 0)):    #full Platt SMOoS = optStruct(mat(dataMatIn),mat(classLabels).transpose(),C,toler, kTup)iter = 0entireSet = True; alphaPairsChanged = 0while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):alphaPairsChanged = 0if entireSet:   #go over allfor i in range(oS.m):        alphaPairsChanged += innerL(i,oS)print ("fullSet, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged))iter += 1else:#go over non-bound (railed) alphasnonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]for i in nonBoundIs:alphaPairsChanged += innerL(i,oS)print ("non-bound, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged))iter += 1if entireSet: entireSet = False #toggle entire set loopelif (alphaPairsChanged == 0): entireSet = True  print ("iteration number: %d" % iter)return oS.b,oS.alphas
##############################################
####SVM SMO求解手写识别问题
##############################################
def img2vector(filename):returnVect = zeros((1,1024))fr = open(filename)for i in range(32):lineStr = fr.readline()for j in range(32):returnVect[0,32*i+j] = int(lineStr[j])return returnVectdef loadImages(dirName):from os import listdirhwLabels = []trainingFileList = listdir(dirName)           #load the training setm = len(trainingFileList)trainingMat = zeros((m,1024))for i in range(m):fileNameStr = trainingFileList[i]fileStr = fileNameStr.split('.')[0]     #take off .txtclassNumStr = int(fileStr.split('_')[0])if classNumStr == 9: hwLabels.append(-1)else: hwLabels.append(1)trainingMat[i,:] = img2vector('%s/%s' % (dirName, fileNameStr))return trainingMat, hwLabels    def testDigits(kTup=('rbf', 10)):dataArr,labelArr = loadImages(r'C:\Users\li\Downloads\machinelearninginaction\Ch06\trainingDigits')b,alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)datMat=mat(dataArr); labelMat = mat(labelArr).transpose()svInd=nonzero(alphas.A>0)[0]sVs=datMat[svInd] labelSV = labelMat[svInd];print ("there are %d Support Vectors" % shape(sVs)[0])m,n = shape(datMat)errorCount = 0for i in range(m):kernelEval = kernelTrans(sVs,datMat[i,:],kTup)predict=kernelEval.T * multiply(labelSV,alphas[svInd]) + bif sign(predict)!=sign(labelArr[i]): errorCount += 1print ("the training error rate is: %f" % (float(errorCount)/m))dataArr,labelArr = loadImages(r'C:\Users\li\Downloads\machinelearninginaction\Ch06\testDigits')errorCount = 0datMat=mat(dataArr); labelMat = mat(labelArr).transpose()m,n = shape(datMat)for i in range(m):kernelEval = kernelTrans(sVs,datMat[i,:],kTup)predict=kernelEval.T * multiply(labelSV,alphas[svInd]) + bif sign(predict)!=sign(labelArr[i]): errorCount += 1    print ("the test error rate is: %f" % (float(errorCount)/m))testDigits(('lin',20))

机器学习实战（6）：SVM-SMO-核函数手写识别相关推荐

机器学习实战——2.3 示例：手写识别系统
目录 1.准备数据:将图像转换为测试向量 2.测试算法:使用K-近邻算法识别手写数字 1.准备数据:将图像转换为测试向量为了使用之前编辑好的classify0()分类器,我们必须将图像格式化处理为一 ...
OpenCV-python:SVM(支持向量机)OCR手写识别
1.SVM理解 SVM按照百度百科的解释,他属于一种监督分类的方法,对数据进行二元分类,分类的标准就是决策边界. 看下面的图片就很容易能够理解.如果说要给红点和蓝点分类的话,以绿线作为分界线,那么找到 ...
机器学习实战：k-近邻算法(手写数字识别)
k近邻法(k-nearest neighbor, k-NN)是1967年由Cover T和Hart P提出的一种基本分类与回归方法.它的工作原理是:存在一个样本数据集合,也称作为训练样本集,并且样本集 ...
深入浅出python机器学习_8.3_神经网络实例_手写识别_MNIST数据集的使用
# 导入数据集获取工具 # from sklearn.datasets import fetch_mldata# 加载MNIST手写数字数据集 # mnist=fetch_mldata('MNIST ...
机器学习实战之SVM
一引言: 支持向量机这部分确实很多,想要真正的去理解它,不仅仅知道理论,还要进行相关的代码编写和测试,二者想和结合,才能更好的帮助我们理解SVM这一非常优秀的分类算法支持向量机是一种二类分类算法,假 ...
转载-机器学习实战之SVM
出处:https://www.cnblogs.com/zy230530/p/6901277.html 机器学习实战之SVM 一引言: 支持向量机这部分确实很多,想要真正的去理解它,不仅仅知道理论,还要 ...
数字手写识别——Java实现KNN算法
引言手写识别也是当前机器学习的一大热点,数字手写识别是手写识别中的基础,我们用到的是knn算法,今天给大家讲一下我的实现方法: 环境 IDE:Eclipse 语言:Java 项目:数字手写识别思路 ...
机器学习实战2.3. k-近邻算法例子-识别手写数字
机器学习实战2.3. k-近邻算法例子-识别手写数字搜索微信公众号:'AI-ming3526'或者'计算机视觉这件小事' 获取更多机器学习干货 csdn:https://blog.csdn.net/ ...
机器学习实战-手写识别系统
在实现了K近邻算法后,书中给出了一个实例,今天来学习一下使用K近邻分类器的手写识别系统.书中原带的文件已经过处理转换为了文本格式,方便了许多. 先看一下原带文件准备数据 #将图像转换为测试向量 de ...

机器学习实战（6）：SVM-SMO-核函数手写识别

机器学习实战（6）：SVM-SMO-核函数手写识别相关推荐

最新文章

热门文章

机器学习实战（6）：SVM-SMO-核函数 手写识别

机器学习实战（6）：SVM-SMO-核函数 手写识别相关推荐

最新文章

热门文章

机器学习实战（6）：SVM-SMO-核函数手写识别

机器学习实战（6）：SVM-SMO-核函数手写识别相关推荐