(轉載請注明出處：http://blog.csdn.net/buptgshengod)

1.背景知識

? ? ?通過上一節我們通過引入拉格朗日乗子得到支持向量機變形公式。詳細變法可以參考這位大神的博客——地址? 參照拉格朗日公式F(x1,x2,...λ)=f(x1,x2,...)-λg(x1,x2...)。我們把上面的式子變型為：
?約束條件就變成了：下面就根據最小優化算法SMO(Sequential Minimal Optimization)。找出距離分隔面最近的點，也就是支持向量集。如下圖的藍色點所示。

2.代碼

import matplotlib.pyplot as plt from numpy import * from time import sleepdef 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):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 += 1 # print "iter: %d i:%d, pairs changed %d" % (iter,i,alphaPairsChanged)if (alphaPairsChanged == 0): iter += 1else: iter = 0 # print "iteration number: %d" % iterreturn b,alphasdef matplot(dataMat,lableMat):xcord1 = []; ycord1 = []xcord2 = []; ycord2 = []xcord3 = []; ycord3 = []for i in range(100):if lableMat[i]==1:xcord1.append(dataMat[i][0])ycord1.append(dataMat[i][1])else:xcord2.append(dataMat[i][0])ycord2.append(dataMat[i][1]) b,alphas=smoSimple(dataMat,labelMat,0.6,0.001,40)for j in range(100):if alphas[j]>0:xcord3.append(dataMat[j][0])ycord3.append(dataMat[j][1]) fig = plt.figure()ax = fig.add_subplot(111)ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')ax.scatter(xcord2, ycord2, s=30, c='green')ax.scatter(xcord3, ycord3, s=80, c='blue')ax.plot()plt.xlabel('X1'); plt.ylabel('X2');plt.show() if __name__=='__main__':dataMat,labelMat=loadDataSet('/Users/hakuri/Desktop/testSet.txt') # b,alphas=smoSimple(dataMat,labelMat,0.6,0.001,40) # print b,alphas[alphas>0]matplot(dataMat,labelMat)


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