From b496bbdf740f4792c9eae2e618e4e6534b424e67 Mon Sep 17 00:00:00 2001 From: geekidentity Date: Mon, 27 Mar 2017 16:37:42 +0800 Subject: [PATCH] =?UTF-8?q?svmMLiA.py=20=E6=B3=A8=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/06.SVM/svmMLiA.py | 52 +++++++++++++----------------------- 1 file changed, 19 insertions(+), 33 deletions(-) diff --git a/src/python/06.SVM/svmMLiA.py b/src/python/06.SVM/svmMLiA.py index d8b2ed3c..24aa144a 100644 --- a/src/python/06.SVM/svmMLiA.py +++ b/src/python/06.SVM/svmMLiA.py @@ -80,14 +80,14 @@ 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 + iter = 0 # 没有任何alpha改变的情况下遍历数据的次数 while (iter < maxIter): - alphaPairsChanged = 0 + alphaPairsChanged = 0 #记录alpha是否已经进行优化，每次循环时设为0，然后再对整个集合顺序遍历 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 + fXi = float(multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[i,:].T)) + b # 我们预测的类别 + Ei = fXi - float(labelMat[i])#if checks if an example violates KKT conditions 误差 if ((labelMat[i]*Ei < -toler) and (alphas[i] < C)) or ((labelMat[i]*Ei > toler) and (alphas[i] > 0)): - j = selectJrand(i,m) + j = selectJrand(i,m) # 误差很大时进行优化 fXj = float(multiply(alphas,labelMat).T*(dataMatrix*dataMatrix[j,:].T)) + b Ej = fXj - float(labelMat[j]) alphaIold = alphas[i].copy(); alphaJold = alphas[j].copy() @@ -195,18 +195,15 @@ def innerL(i, oS): 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"; + 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"; + 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"; + 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 @@ -235,22 +232,19 @@ def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)): # full Pl 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) + 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) + 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 + print("iteration number: %d" % iter) return oS.b, oS.alphas @@ -272,16 +266,14 @@ def testRbf(k1=1.3): 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] + 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) + print("the training error rate is: %f" % (float(errorCount) / m)) dataArr, labelArr = loadDataSet('testSetRBF2.txt') errorCount = 0 datMat = mat(dataArr); @@ -291,8 +283,7 @@ def testRbf(k1=1.3): 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) + print("the test error rate is: %f" % (float(errorCount) / m)) def img2vector(filename): @@ -338,8 +329,7 @@ def testDigits(kTup=('rbf', 10)): 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) + print("the training error rate is: %f" % (float(errorCount) / m)) dataArr, labelArr = loadImages('testDigits') errorCount = 0 datMat = mat(dataArr); @@ -349,8 +339,7 @@ def testDigits(kTup=('rbf', 10)): 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) + print("the test error rate is: %f" % (float(errorCount) / m)) '''#######******************************** @@ -416,18 +405,15 @@ def innerLK(i, oS): 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"; + 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"; + 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"; + 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