上节基本完成了SVM的理论推倒,寻找最大化间隔的目标最终转换成求解拉格朗日乘子变量alpha的求解问题,求出了alpha即可求解出SVM的权重W,有了权重也就有了最大间隔距离,但是其实上节我们有个假设:就是训练集是线性可分的,这样求出的alpha在[0,infinite]。但是如果数据不是线性可分的呢?此时我们就要允许部分的样本可以越过分类器,这样优化的目标函数就可以不变,只要引入松弛变量即可,它表示错分类样本点的代价,分类正确时它等于0,当分类错误时,其中Tn表示样本的真实标签-1或者1,回顾上节中,我们把支持向量到分类器的距离固定为1,因此两类的支持向量间的距离肯定大于1的,当分类错误时肯定也大于1,如(图五)所示(这里公式和图标序号都接上一节)。
(图五)
这样有了错分类的代价,我们把上节(公式四)的目标函数上添加上这一项错分类代价,得到如(公式八)的形式:
(公式八)
重复上节的拉格朗日乘子法步骤,得到(公式九):
(公式九)
多了一个Un乘子,当然我们的工作就是继续求解此目标函数,继续重复上节的步骤,求导得到(公式十):
(公式十)
又因为alpha大于0,而且Un大于0,所以0<alpha<C,为了解释的清晰一些,我们把(公式九)的KKT条件也发出来(上节中的第三类优化问题),注意Un是大于等于0:
推导到现在,优化函数的形式基本没变,只是多了一项错分类的价值,但是多了一个条件,0<alpha<C,C是一个常数,它的作用就是在允许有错误分类的情况下,控制最大化间距,它太大了会导致过拟合,太小了会导致欠拟合。接下来的步骤貌似大家都应该知道了,多了一个C常量的限制条件,然后继续用SMO算法优化求解二次规划,但是我想继续把核函数也一次说了,如果样本线性不可分,引入核函数后,把样本映射到高维空间就可以线性可分,如(图六)所示的线性不可分的样本:
(图六)
在(图六)中,现有的样本是很明显线性不可分,但是加入我们利用现有的样本X之间作些不同的运算,如(图六)右边所示的样子,而让f作为新的样本(或者说新的特征)是不是更好些?现在把X已经投射到高维度上去了,但是f我们不知道,此时核函数就该上场了,以高斯核函数为例,在(图七)中选几个样本点作为基准点,来利用核函数计算f,如(图七)所示:
(图七)
这样就有了f,而核函数此时相当于对样本的X和基准点一个度量,做权重衰减,形成依赖于x的新的特征f,把f放在上面说的SVM中继续求解alpha,然后得出权重就行了,原理很简单吧,为了显得有点学术味道,把核函数也做个样子加入目标函数中去吧,如(公式十一)所示:
(公式十一)
其中K(Xn,Xm)是核函数,和上面目标函数比没有多大的变化,用SMO优化求解就行了,代码如下:
- 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
下面演示一个小例子,手写识别。
(1)收集数据:提供文本文件
(2)准备数据:基于二值图像构造向量
(3)分析数据:对图像向量进行目测
(4)训练算法:采用两种不同的核函数,并对径向基函数采用不同的设置来运行SMO算法。
(5)测试算法:编写一个函数来测试不同的核函数,并计算错误率
(6)使用算法:一个图像识别的完整应用还需要一些图像处理的只是,此demo略。
完整代码如下:
- 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
运行结果如(图八)所示:
(图八)
上面代码有兴趣的可以读读,用的话,建议使用libsvm。
参考文献:
[1]machine learning in action. PeterHarrington
[2] pattern recognition and machinelearning. Christopher M. Bishop
[3]machine learning.Andrew Ng
转载请注明来源:http://blog.csdn.net/cuoqu/article/details/9305497
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