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更新完：SVM。手写识别案例
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jiangzhonglian
@@ -66,6 +66,7 @@ def kernelTrans(X, A, kTup): # calc the kernel or transform data to a higher di
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for j in range(m):
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deltaRow = X[j, :] - A
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K[j] = deltaRow * deltaRow.T
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# 径向基函数的高斯版本
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K = exp(K / (-1 * kTup[1] ** 2)) # divide in NumPy is element-wise not matrix like Matlab
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else:
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raise NameError('Houston We Have a Problem -- That Kernel is not recognized')
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@@ -360,6 +361,100 @@ def calcWs(alphas, dataArr, classLabels):
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return w
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def testRbf(k1=1.3):
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dataArr, labelArr = loadDataSet('input/6.SVM/testSetRBF.txt')
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b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) # C=200 important
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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svInd = nonzero(alphas.A > 0)[0]
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sVs = datMat[svInd] # get matrix of only support vectors
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labelSV = labelMat[svInd]
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print("there are %d Support Vectors" % shape(sVs)[0])
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m, n = shape(datMat)
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errorCount = 0
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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# 和这个svm-simple类似： fXi = float(multiply(alphas, labelMat).T*(dataMatrix*dataMatrix[i, :].T)) + b
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]):
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errorCount += 1
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print("the training error rate is: %f" % (float(errorCount) / m))
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dataArr, labelArr = loadDataSet('input/6.SVM/testSetRBF2.txt')
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errorCount = 0
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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m, n = shape(datMat)
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]):
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errorCount += 1
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print("the test error rate is: %f" % (float(errorCount) / m))
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def img2vector(filename):
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returnVect = zeros((1, 1024))
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fr = open(filename)
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for i in range(32):
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lineStr = fr.readline()
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for j in range(32):
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returnVect[0, 32 * i + j] = int(lineStr[j])
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return returnVect
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def loadImages(dirName):
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from os import listdir
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hwLabels = []
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print(dirName)
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trainingFileList = listdir(dirName) # load the training set
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m = len(trainingFileList)
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trainingMat = zeros((m, 1024))
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for i in range(m):
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fileNameStr = trainingFileList[i]
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fileStr = fileNameStr.split('.')[0] # take off .txt
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classNumStr = int(fileStr.split('_')[0])
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if classNumStr == 9:
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hwLabels.append(-1)
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else:
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hwLabels.append(1)
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trainingMat[i, :] = img2vector('%s/%s' % (dirName, fileNameStr))
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return trainingMat, hwLabels
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def testDigits(kTup=('rbf', 10)):
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# 1. 导入训练数据
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dataArr, labelArr = loadImages('input/6.SVM/trainingDigits')
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b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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svInd = nonzero(alphas.A > 0)[0]
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sVs = datMat[svInd]
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labelSV = labelMat[svInd]
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print("there are %d Support Vectors" % shape(sVs)[0])
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m, n = shape(datMat)
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errorCount = 0
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the training error rate is: %f" % (float(errorCount) / m))
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# 2. 导入测试数据
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dataArr, labelArr = loadImages('input/6.SVM/testDigits')
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errorCount = 0
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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m, n = shape(datMat)
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the test error rate is: %f" % (float(errorCount) / m))
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def plotfig_SVM(xArr, yArr, ws, b, alphas):
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"""
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参考地址：
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@@ -400,134 +495,28 @@ def plotfig_SVM(xArr, yArr, ws, b, alphas):
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if __name__ == "__main__":
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# 获取特征和目标变量
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dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
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# print labelArr
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# b是常量值， alphas是拉格朗日乘子
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b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
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print '/n/n/n'
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print 'b=', b
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print 'alphas[alphas>0]=', alphas[alphas > 0]
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print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
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for i in range(100):
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if alphas[i] > 0:
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print dataArr[i], labelArr[i]
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# 画图
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ws = calcWs(alphas, dataArr, labelArr)
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plotfig_SVM(dataArr, labelArr, ws, b, alphas)
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# # 无核函数的测试
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# # 获取特征和目标变量
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# dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
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# # print labelArr
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# # b是常量值， alphas是拉格朗日乘子
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# b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
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# print '/n/n/n'
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# print 'b=', b
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# print 'alphas[alphas>0]=', alphas[alphas > 0]
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# print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
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# for i in range(100):
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# if alphas[i] > 0:
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# print dataArr[i], labelArr[i]
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# # 画图
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# ws = calcWs(alphas, dataArr, labelArr)
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# plotfig_SVM(dataArr, labelArr, ws, b, alphas)
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# # 有核函数的测试
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# testRbf(1)
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def testRbf(k1=1.3):
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dataArr, labelArr = loadDataSet('testSetRBF.txt')
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b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) # C=200 important
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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svInd = nonzero(alphas.A > 0)[0]
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sVs = datMat[svInd] # get matrix of only support vectors
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labelSV = labelMat[svInd]
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print("there are %d Support Vectors" % shape(sVs)[0])
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m, n = shape(datMat)
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errorCount = 0
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the training error rate is: %f" % (float(errorCount) / m))
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dataArr, labelArr = loadDataSet('testSetRBF2.txt')
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errorCount = 0
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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m, n = shape(datMat)
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the test error rate is: %f" % (float(errorCount) / m))
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def img2vector(filename):
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returnVect = zeros((1, 1024))
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fr = open(filename)
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for i in range(32):
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lineStr = fr.readline()
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for j in range(32):
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returnVect[0, 32 * i + j] = int(lineStr[j])
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return returnVect
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def loadImages(dirName):
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from os import listdir
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hwLabels = []
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print(dirName)
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trainingFileList = listdir(dirName) # load the training set
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m = len(trainingFileList)
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trainingMat = zeros((m, 1024))
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for i in range(m):
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fileNameStr = trainingFileList[i]
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fileStr = fileNameStr.split('.')[0] # take off .txt
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classNumStr = int(fileStr.split('_')[0])
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if classNumStr == 9:
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hwLabels.append(-1)
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else:
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hwLabels.append(1)
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trainingMat[i, :] = img2vector('%s/%s' % (dirName, fileNameStr))
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return trainingMat, hwLabels
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def testDigits(kTup=('rbf', 10)):
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dataArr, labelArr = loadImages('trainingDigits')
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b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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svInd = nonzero(alphas.A > 0)[0]
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sVs = datMat[svInd]
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labelSV = labelMat[svInd]
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print("there are %d Support Vectors" % shape(sVs)[0])
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m, n = shape(datMat)
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errorCount = 0
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the training error rate is: %f" % (float(errorCount) / m))
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dataArr, labelArr = loadImages('testDigits')
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errorCount = 0
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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m, n = shape(datMat)
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the test error rate is: %f" % (float(errorCount) / m))
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# 项目实战
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# 示例：手写识别问题回顾
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testDigits(('rbf', 20))
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