上節(jié)基本完成了SVM的理論推倒，尋找最大化間隔的目標(biāo)最終轉(zhuǎn)換成求解拉格朗日乘子變量alpha的求解問(wèn)題，求出了alpha即可求解出SVM的權(quán)重W，有了權(quán)重也就有了最大間隔距離，但是其實(shí)上節(jié)我們有個(gè)假設(shè)：就是訓(xùn)練集是線性可分的，這樣求出的alpha在[0,infinite]。但是如果數(shù)據(jù)不是線性可分的呢？此時(shí)我們就要允許部分的樣本可以越過(guò)分類器，這樣優(yōu)化的目標(biāo)函數(shù)就可以不變，只要引入松弛變量
即可，它表示錯(cuò)分類樣本點(diǎn)的代價(jià)，分類正確時(shí)它等于0，當(dāng)分類錯(cuò)誤時(shí)
，其中Tn表示樣本的真實(shí)標(biāo)簽-1或者1，回顧上節(jié)中，我們把支持向量到分類器的距離固定為1，因此兩類的支持向量間的距離肯定大于1的，當(dāng)分類錯(cuò)誤時(shí)
肯定也大于1，如（圖五）所示（這里公式和圖標(biāo)序號(hào)都接上一節(jié)）。
（圖五）
這樣有了錯(cuò)分類的代價(jià)，我們把上節(jié)（公式四）的目標(biāo)函數(shù)上添加上這一項(xiàng)錯(cuò)分類代價(jià)，得到如（公式八）的形式：
（公式八）
重復(fù)上節(jié)的拉格朗日乘子法步驟，得到（公式九）：
（公式九）
多了一個(gè)Un乘子，當(dāng)然我們的工作就是繼續(xù)求解此目標(biāo)函數(shù)，繼續(xù)重復(fù)上節(jié)的步驟，求導(dǎo)得到（公式十）：
（公式十）
又因?yàn)閍lpha大于0，而且Un大于0，所以0<alpha<C,為了解釋的清晰一些，我們把（公式九）的KKT條件也發(fā)出來(lái)（上節(jié)中的第三類優(yōu)化問(wèn)題），注意Un是大于等于0：
推導(dǎo)到現(xiàn)在，優(yōu)化函數(shù)的形式基本沒(méi)變，只是多了一項(xiàng)錯(cuò)分類的價(jià)值，但是多了一個(gè)條件，0<alpha<C，C是一個(gè)常數(shù)，它的作用就是在允許有錯(cuò)誤分類的情況下，控制最大化間距，它太大了會(huì)導(dǎo)致過(guò)擬合，太小了會(huì)導(dǎo)致欠擬合。接下來(lái)的步驟貌似大家都應(yīng)該知道了，多了一個(gè)C常量的限制條件，然后繼續(xù)用SMO算法優(yōu)化求解二次規(guī)劃，但是我想繼續(xù)把核函數(shù)也一次說(shuō)了，如果樣本線性不可分，引入核函數(shù)后，把樣本映射到高維空間就可以線性可分，如（圖六）所示的線性不可分的樣本：
（圖六）
在（圖六）中，現(xiàn)有的樣本是很明顯線性不可分，但是加入我們利用現(xiàn)有的樣本X之間作些不同的運(yùn)算，如（圖六）右邊所示的樣子，而讓f作為新的樣本（或者說(shuō)新的特征）是不是更好些？現(xiàn)在把X已經(jīng)投射到高維度上去了，但是f我們不知道，此時(shí)核函數(shù)就該上場(chǎng)了，以高斯核函數(shù)為例，在（圖七）中選幾個(gè)樣本點(diǎn)作為基準(zhǔn)點(diǎn)，來(lái)利用核函數(shù)計(jì)算f,如（圖七）所示：
（圖七）
這樣就有了f,而核函數(shù)此時(shí)相當(dāng)于對(duì)樣本的X和基準(zhǔn)點(diǎn)一個(gè)度量，做權(quán)重衰減，形成依賴于x的新的特征f,把f放在上面說(shuō)的SVM中繼續(xù)求解alpha，然后得出權(quán)重就行了，原理很簡(jiǎn)單吧，為了顯得有點(diǎn)學(xué)術(shù)味道，把核函數(shù)也做個(gè)樣子加入目標(biāo)函數(shù)中去吧，如（公式十一）所示：
（公式十一）
其中K(Xn,Xm)是核函數(shù)，和上面目標(biāo)函數(shù)比沒(méi)有多大的變化，用SMO優(yōu)化求解就行了，代碼如下：
def smoPK(dataMatIn, classLabels, C, toler, maxIter): #full Platt SMO oS = optStruct(mat(dataMatIn),mat(classLabels).transpose(),C,toler) iter = 0 entireSet = True; alphaPairsChanged = 0 while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)): alphaPairsChanged = 0 if entireSet: #go over all for i in range(oS.m): alphaPairsChanged += innerL(i,oS) print "fullSet, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged) iter += 1 else:#go over non-bound (railed) alphas nonBoundIs = 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 += 1 if entireSet: entireSet = False #toggle entire set loop elif (alphaPairsChanged == 0): entireSet = True print "iteration number: %d" % iter return oS.b,oS.alphas
下面演示一個(gè)小例子，手寫識(shí)別。
(1)收集數(shù)據(jù)：提供文本文件
(2)準(zhǔn)備數(shù)據(jù)：基于二值圖像構(gòu)造向量
(3)分析數(shù)據(jù)：對(duì)圖像向量進(jìn)行目測(cè)
(4)訓(xùn)練算法：采用兩種不同的核函數(shù)，并對(duì)徑向基函數(shù)采用不同的設(shè)置來(lái)運(yùn)行SMO算法。
(5)測(cè)試算法：編寫一個(gè)函數(shù)來(lái)測(cè)試不同的核函數(shù)，并計(jì)算錯(cuò)誤率
(6)使用算法：一個(gè)圖像識(shí)別的完整應(yīng)用還需要一些圖像處理的只是，此demo略。
完整代碼如下：
from numpy import * from time import sleep 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,labelMat def selectJrand(i,m): j=i #we want to select any J not equal to i while (j==i): j = int(random.uniform(0,m)) return j def clipAlpha(aj,H,L): if aj > H: aj = H if L > aj: aj = L return aj def smoSimple(dataMatIn, classLabels, C, toler, maxIter): dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).transpose() b = 0; m,n = shape(dataMatrix) alphas = mat(zeros((m,1))) iter = 0 while (iter < maxIter): alphaPairsChanged = 0 for i in range(m): fXi = float(multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[i,:].T)) + b Ei = fXi - float(labelMat[i])#if checks if an example violates KKT conditions if ((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)) + b Ej = 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"; continue eta = 2.0 * dataMatrix[i,:]*dataMatrix[j,:].T - dataMatrix[i,:]*dataMatrix[i,:].T - dataMatrix[j,:]*dataMatrix[j,:].T if eta >= 0: print "eta>=0"; continue alphas[j] -= labelMat[j]*(Ei - Ej)/eta alphas[j] = clipAlpha(alphas[j],H,L) if (abs(alphas[j] - alphaJold) < 0.00001): print "j not moving enough"; continue alphas[i] += labelMat[j]*labelMat[i]*(alphaJold - alphas[j])#update i by the same amount as j #the update is in the oppostie direction b1 = b - Ei- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[i,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[i,:]*dataMatrix[j,:].T b2 = b - Ej- labelMat[i]*(alphas[i]-alphaIold)*dataMatrix[i,:]*dataMatrix[j,:].T - labelMat[j]*(alphas[j]-alphaJold)*dataMatrix[j,:]*dataMatrix[j,:].T if (0 < alphas[i]) and (C > alphas[i]): b = b1 elif (0 < alphas[j]) and (C > alphas[j]): b = b2 else: b = (b1 + b2)/2.0 alphaPairsChanged += 1 print "iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged) if (alphaPairsChanged == 0): iter += 1 else: iter = 0 print "iteration number: %d" % iter return b,alphas def kernelTrans(X, A, kTup): #calc the kernel or transform data to a higher dimensional space m,n = shape(X) K = mat(zeros((m,1))) if kTup[0]=='lin': K = X * A.T #linear kernel elif kTup[0]=='rbf': for j in range(m): deltaRow = X[j,:] - A K[j] = deltaRow*deltaRow.T K = exp(K/(-1*kTup[1]**2)) #divide in NumPy is element-wise not matrix like Matlab else: raise NameError('Houston We Have a Problem -- / That Kernel is not recognized') return K class optStruct: def __init__(self,dataMatIn, classLabels, C, toler, kTup): # Initialize the structure with the parameters self.X = dataMatIn self.labelMat = classLabels self.C = C self.tol = toler self.m = shape(dataMatIn)[0] self.alphas = mat(zeros((self.m,1))) self.b = 0 self.eCache = mat(zeros((self.m,2))) #first column is valid flag self.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 Ek def selectJ(i, oS, Ei): #this is the second choice -heurstic, and calcs Ej maxK = -1; maxDeltaE = 0; Ej = 0 oS.eCache[i] = [1,Ei] #set valid #choose the alpha that gives the maximum delta E validEcacheList = 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 E if k == i: continue #don't calc for i, waste of time Ek = calcEk(oS, k) deltaE = abs(Ei - Ek) if (deltaE > maxDeltaE): maxK = k; maxDeltaE = deltaE; Ej = Ek return maxK, Ej else: #in this case (first time around) we don't have any valid eCache values j = selectJrand(i, oS.m) Ej = calcEk(oS, j) return j, Ej def updateEk(oS, k):#after any alpha has changed update the new value in the cache Ek = 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 selectJrand alphaIold = 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 0 eta = 2.0 * oS.K[i,j] - oS.K[i,i] - oS.K[j,j] #changed for kernel if eta >= 0: print "eta>=0"; return 0 oS.alphas[j] -= oS.labelMat[j]*(Ei - Ej)/eta oS.alphas[j] = clipAlpha(oS.alphas[j],H,L) updateEk(oS, j) #added this for the Ecache if (abs(oS.alphas[j] - alphaJold) < 0.00001): print "j not moving enough"; return 0 oS.alphas[i] += oS.labelMat[j]*oS.labelMat[i]*(alphaJold - oS.alphas[j])#update i by the same amount as j updateEk(oS, i) #added this for the Ecache #the update is in the oppostie direction b1 = 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 = b1 elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]): oS.b = b2 else: oS.b = (b1 + b2)/2.0 return 1 else: return 0 def smoP(dataMatIn, classLabels, C, toler, maxIter,kTup=('lin', 0)): #full Platt SMO oS = optStruct(mat(dataMatIn),mat(classLabels).transpose(),C,toler, kTup) iter = 0 entireSet = True; alphaPairsChanged = 0 while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)): alphaPairsChanged = 0 if entireSet: #go over all for i in range(oS.m): alphaPairsChanged += innerL(i,oS) print "fullSet, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged) iter += 1 else:#go over non-bound (railed) alphas nonBoundIs = 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 += 1 if entireSet: entireSet = False #toggle entire set loop elif (alphaPairsChanged == 0): entireSet = True print "iteration number: %d" % iter return oS.b,oS.alphas def calcWs(alphas,dataArr,classLabels): X = mat(dataArr); labelMat = mat(classLabels).transpose() m,n = shape(X) w = zeros((n,1)) for i in range(m): w += multiply(alphas[i]*labelMat[i],X[i,:].T) return w def testRbf(k1=1.3): dataArr,labelArr = loadDataSet('testSetRBF.txt') b,alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) #C=200 important datMat=mat(dataArr); labelMat = mat(labelArr).transpose() svInd=nonzero(alphas.A>0)[0] sVs=datMat[svInd] #get matrix of only support vectors labelSV = labelMat[svInd]; print "there are %d Support Vectors" % shape(sVs)[0] m,n = shape(datMat) errorCount = 0 for i in range(m): kernelEval = kernelTrans(sVs,datMat[i,:],('rbf', k1)) predict=kernelEval.T * multiply(labelSV,alphas[svInd]) + b if sign(predict)!=sign(labelArr[i]): errorCount += 1 print "the training error rate is: %f" % (float(errorCount)/m) dataArr,labelArr = loadDataSet('testSetRBF2.txt') errorCount = 0 datMat=mat(dataArr); labelMat = mat(labelArr).transpose() m,n = shape(datMat) for i in range(m): kernelEval = kernelTrans(sVs,datMat[i,:],('rbf', k1)) predict=kernelEval.T * multiply(labelSV,alphas[svInd]) + b if sign(predict)!=sign(labelArr[i]): errorCount += 1 print "the test error rate is: %f" % (float(errorCount)/m) 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 returnVect def loadImages(dirName): from os import listdir hwLabels = [] trainingFileList = listdir(dirName) #load the training set m = len(trainingFileList) trainingMat = zeros((m,1024)) for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] #take off .txt classNumStr = 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('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 = 0 for i in range(m): kernelEval = kernelTrans(sVs,datMat[i,:],kTup) predict=kernelEval.T * multiply(labelSV,alphas[svInd]) + b if sign(predict)!=sign(labelArr[i]): errorCount += 1 print "the training error rate is: %f" % (float(errorCount)/m) dataArr,labelArr = loadImages('testDigits') errorCount = 0 datMat=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]) + b if sign(predict)!=sign(labelArr[i]): errorCount += 1 print "the test error rate is: %f" % (float(errorCount)/m) '''''#######******************************** Non-Kernel VErsions below '''#######******************************** class optStructK: def __init__(self,dataMatIn, classLabels, C, toler): # Initialize the structure with the parameters self.X = dataMatIn self.labelMat = classLabels self.C = C self.tol = toler self.m = shape(dataMatIn)[0] self.alphas = mat(zeros((self.m,1))) self.b = 0 self.eCache = mat(zeros((self.m,2))) #first column is valid flag def calcEkK(oS, k): fXk = float(multiply(oS.alphas,oS.labelMat).T*(oS.X*oS.X[k,:].T)) + oS.b Ek = fXk - float(oS.labelMat[k]) return Ek def selectJK(i, oS, Ei): #this is the second choice -heurstic, and calcs Ej maxK = -1; maxDeltaE = 0; Ej = 0 oS.eCache[i] = [1,Ei] #set valid #choose the alpha that gives the maximum delta E validEcacheList = 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 E if k == i: continue #don't calc for i, waste of time Ek = calcEk(oS, k) deltaE = abs(Ei - Ek) if (deltaE > maxDeltaE): maxK = k; maxDeltaE = deltaE; Ej = Ek return maxK, Ej else: #in this case (first time around) we don't have any valid eCache values j = selectJrand(i, oS.m) Ej = calcEk(oS, j) return j, Ej def updateEkK(oS, k):#after any alpha has changed update the new value in the cache Ek = calcEk(oS, k) oS.eCache[k] = [1,Ek] def innerLK(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 selectJrand alphaIold = 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 0 eta = 2.0 * oS.X[i,:]*oS.X[j,:].T - oS.X[i,:]*oS.X[i,:].T - oS.X[j,:]*oS.X[j,:].T if eta >= 0: print "eta>=0"; return 0 oS.alphas[j] -= oS.labelMat[j]*(Ei - Ej)/eta oS.alphas[j] = clipAlpha(oS.alphas[j],H,L) updateEk(oS, j) #added this for the Ecache if (abs(oS.alphas[j] - alphaJold) < 0.00001): print "j not moving enough"; return 0 oS.alphas[i] += oS.labelMat[j]*oS.labelMat[i]*(alphaJold - oS.alphas[j])#update i by the same amount as j updateEk(oS, i) #added this for the Ecache #the update is in the oppostie direction b1 = oS.b - Ei- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.X[i,:]*oS.X[i,:].T - oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.X[i,:]*oS.X[j,:].T b2 = oS.b - Ej- oS.labelMat[i]*(oS.alphas[i]-alphaIold)*oS.X[i,:]*oS.X[j,:].T - oS.labelMat[j]*(oS.alphas[j]-alphaJold)*oS.X[j,:]*oS.X[j,:].T if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]): oS.b = b1 elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]): oS.b = b2 else: oS.b = (b1 + b2)/2.0 return 1 else: return 0 def smoPK(dataMatIn, classLabels, C, toler, maxIter): #full Platt SMO oS = optStruct(mat(dataMatIn),mat(classLabels).transpose(),C,toler) iter = 0 entireSet = True; alphaPairsChanged = 0 while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)): alphaPairsChanged = 0 if entireSet: #go over all for i in range(oS.m): alphaPairsChanged += innerL(i,oS) print "fullSet, iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged) iter += 1 else:#go over non-bound (railed) alphas nonBoundIs = 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 += 1 if entireSet: entireSet = False #toggle entire set loop elif (alphaPairsChanged == 0): entireSet = True print "iteration number: %d" % iter return oS.b,oS.alphas 運(yùn)行結(jié)果如（圖八）所示：
（圖八）
上面代碼有興趣的可以讀讀，用的話，建議使用libsvm。
參考文獻(xiàn)：
[1]machine learning in action. PeterHarrington
[2] pattern recognition and machinelearning. Christopher M. Bishop
[3]machine learning.Andrew Ng
以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持VEVB武林網(wǎng)。
新聞熱點(diǎn)
疑難解答
圖片精選
