按步在shell中调试logistic例程
>>> for line in fr.readlines(): line.strip().split() print line结果：
>>> line'0.317029\t14.739025\t0\n'这是最后一行line中两个特征值加一个类值
question1：运行代码如下，怎么查看最后得到的两个数组
>>> for line in fr.readlines(): lineArr=line.strip().split() dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])]) labelMat.append(int(lineArr[2]))result：
>>> dataMat[]>>> labelMat[]answer of question1：
dataMat和labelMat是临时变量。>>> def loadDataSet(): dataMat = [];labelMat=[] fr=open('F:/temp/testSet.txt') for line in fr.readlines(): lineArr=line.strip().split() dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])]) labelMat.append(int(lineArr[2])) return dataMat,labelMat>>> dataArr[[1.0, -0.017612, 14.053064], [1.0, -1.395634, 4.662541], [1.0, -0.752157, 6.53862], [1.0, -1.322371, 7.152853], [1.0, 0.423363, 11.054677], [1.0, 0.406704, 7.067335], [1.0, 0.667394, 12.741452], [1.0, -2.46015, 6.866805], [1.0, 0.569411, 9.548755], [1.0, -0.026632, 10.427743], [1.0, 0.850433, 6.920334], [1.0, 1.347183, 13.1755], [1.0, 1.176813, 3.16702], [1.0, -1.781871, 9.097953], [1.0, -0.566606, 5.749003], [1.0, 0.931635, 1.589505], [1.0, -0.024205, 6.151823], [1.0, -0.036453, 2.690988], [1.0, -0.196949, 0.444165], [1.0, 1.014459, 5.754399], [1.0, 1.985298, 3.230619], [1.0, -1.693453, -0.55754], [1.0, -0.576525, 11.778922], [1.0, -0.346811, -1.67873], [1.0, -2.124484, 2.672471], [1.0, 1.217916, 9.597015], [1.0, -0.733928, 9.098687], [1.0, -3.642001, -1.618087], [1.0, 0.315985, 3.523953], [1.0, 1.416614, 9.619232], [1.0, -0.386323, 3.989286], [1.0, 0.556921, 8.294984], [1.0, 1.224863, 11.58736], [1.0, -1.347803, -2.406051], [1.0, 1.196604, 4.951851], [1.0, 0.275221, 9.543647], [1.0, 0.470575, 9.332488], [1.0, -1.889567, 9.542662], [1.0, -1.527893, 12.150579], [1.0, -1.185247, 11.309318], [1.0, -0.445678, 3.297303], [1.0, 1.042222, 6.105155], [1.0, -0.618787, 10.320986], [1.0, 1.152083, 0.548467], [1.0, 0.828534, 2.676045], [1.0, -1.237728, 10.549033], [1.0, -0.683565, -2.166125], [1.0, 0.229456, 5.921938], [1.0, -0.959885, 11.555336], [1.0, 0.492911, 10.993324], [1.0, 0.184992, 8.721488], [1.0, -0.355715, 10.325976], [1.0, -0.397822, 8.058397], [1.0, 0.824839, 13.730343], [1.0, 1.507278, 5.027866], [1.0, 0.099671, 6.835839], [1.0, -0.344008, 10.717485], [1.0, 1.785928, 7.718645], [1.0, -0.918801, 11.560217], [1.0, -0.364009, 4.7473], [1.0, -0.841722, 4.119083], [1.0, 0.490426, 1.960539], [1.0, -0.007194, 9.075792], [1.0, 0.356107, 12.447863], [1.0, 0.342578, 12.281162], [1.0, -0.810823, -1.466018], [1.0, 2.530777, 6.476801], [1.0, 1.296683, 11.607559], [1.0, 0.475487, 12.040035], [1.0, -0.783277, 11.009725], [1.0, 0.074798, 11.02365], [1.0, -1.337472, 0.468339], [1.0, -0.102781, 13.763651], [1.0, -0.147324, 2.874846], [1.0, 0.518389, 9.887035], [1.0, 1.015399, 7.571882], [1.0, -1.658086, -0.027255], [1.0, 1.319944, 2.171228], [1.0, 2.056216, 5.019981], [1.0, -0.851633, 4.375691], [1.0, -1.510047, 6.061992], [1.0, -1.076637, -3.181888], [1.0, 1.821096, 10.28399], [1.0, 3.01015, 8.401766], [1.0, -1.099458, 1.688274], [1.0, -0.834872, -1.733869], [1.0, -0.846637, 3.849075], [1.0, 1.400102, 12.628781], [1.0, 1.752842, 5.468166], [1.0, 0.078557, 0.059736], [1.0, 0.089392, -0.7153], [1.0, 1.825662, 12.693808], [1.0, 0.197445, 9.744638], [1.0, 0.126117, 0.922311], [1.0, -0.679797, 1.22053], [1.0, 0.677983, 2.556666], [1.0, 0.761349, 10.693862], [1.0, -2.168791, 0.143632], [1.0, 1.38861, 9.341997], [1.0, 0.317029, 14.739025]]>>> labelMat[0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0]go on
显示转换成矩阵的训练数据
>>> dataMatrix=mat(dataArr);labelMat=mat(labelMat).transpose()>>> dataMatrix,labelMat(matrix([[ 1.00000000e+00, -1.76120000e-02, 1.40530640e+01], [ 1.00000000e+00, -1.39563400e+00, 4.66254100e+00], [ 1.00000000e+00, -7.52157000e-01, 6.53862000e+00], [ 1.00000000e+00, -1.32237100e+00, 7.15285300e+00], [ 1.00000000e+00, 4.23363000e-01, 1.10546770e+01], [ 1.00000000e+00, 4.06704000e-01, 7.06733500e+00], [ 1.00000000e+00, 6.67394000e-01, 1.27414520e+01], [ 1.00000000e+00, -2.46015000e+00, 6.86680500e+00], [ 1.00000000e+00, 5.69411000e-01, 9.54875500e+00], [ 1.00000000e+00, -2.66320000e-02, 1.04277430e+01], [ 1.00000000e+00, 8.50433000e-01, 6.92033400e+00], [ 1.00000000e+00, 1.34718300e+00, 1.31755000e+01], [ 1.00000000e+00, 1.17681300e+00, 3.16702000e+00], [ 1.00000000e+00, -1.78187100e+00, 9.09795300e+00], [ 1.00000000e+00, -5.66606000e-01, 5.74900300e+00], [ 1.00000000e+00, 9.31635000e-01, 1.58950500e+00], [ 1.00000000e+00, -2.42050000e-02, 6.15182300e+00], [ 1.00000000e+00, -3.64530000e-02, 2.69098800e+00], [ 1.00000000e+00, -1.96949000e-01, 4.44165000e-01], [ 1.00000000e+00, 1.01445900e+00, 5.75439900e+00], [ 1.00000000e+00, 1.98529800e+00, 3.23061900e+00], [ 1.00000000e+00, -1.69345300e+00, -5.57540000e-01], [ 1.00000000e+00, -5.76525000e-01, 1.17789220e+01], [ 1.00000000e+00, -3.46811000e-01, -1.67873000e+00], [ 1.00000000e+00, -2.12448400e+00, 2.67247100e+00], [ 1.00000000e+00, 1.21791600e+00, 9.59701500e+00], [ 1.00000000e+00, -7.33928000e-01, 9.09868700e+00], [ 1.00000000e+00, -3.64200100e+00, -1.61808700e+00], [ 1.00000000e+00, 3.15985000e-01, 3.52395300e+00], [ 1.00000000e+00, 1.41661400e+00, 9.61923200e+00], [ 1.00000000e+00, -3.86323000e-01, 3.98928600e+00], [ 1.00000000e+00, 5.56921000e-01, 8.29498400e+00], [ 1.00000000e+00, 1.22486300e+00, 1.15873600e+01], [ 1.00000000e+00, -1.34780300e+00, -2.40605100e+00], [ 1.00000000e+00, 1.19660400e+00, 4.95185100e+00], [ 1.00000000e+00, 2.75221000e-01, 9.54364700e+00], [ 1.00000000e+00, 4.70575000e-01, 9.33248800e+00], [ 1.00000000e+00, -1.88956700e+00, 9.54266200e+00], [ 1.00000000e+00, -1.52789300e+00, 1.21505790e+01], [ 1.00000000e+00, -1.18524700e+00, 1.13093180e+01], [ 1.00000000e+00, -4.45678000e-01, 3.29730300e+00], [ 1.00000000e+00, 1.04222200e+00, 6.10515500e+00], [ 1.00000000e+00, -6.18787000e-01, 1.03209860e+01], [ 1.00000000e+00, 1.15208300e+00, 5.48467000e-01], [ 1.00000000e+00, 8.28534000e-01, 2.67604500e+00], [ 1.00000000e+00, -1.23772800e+00, 1.05490330e+01], [ 1.00000000e+00, -6.83565000e-01, -2.16612500e+00], [ 1.00000000e+00, 2.29456000e-01, 5.92193800e+00], [ 1.00000000e+00, -9.59885000e-01, 1.15553360e+01], [ 1.00000000e+00, 4.92911000e-01, 1.09933240e+01], [ 1.00000000e+00, 1.84992000e-01, 8.72148800e+00], [ 1.00000000e+00, -3.55715000e-01, 1.03259760e+01], [ 1.00000000e+00, -3.97822000e-01, 8.05839700e+00], [ 1.00000000e+00, 8.24839000e-01, 1.37303430e+01], [ 1.00000000e+00, 1.50727800e+00, 5.02786600e+00], [ 1.00000000e+00, 9.96710000e-02, 6.83583900e+00], [ 1.00000000e+00, -3.44008000e-01, 1.07174850e+01], [ 1.00000000e+00, 1.78592800e+00, 7.71864500e+00], [ 1.00000000e+00, -9.18801000e-01, 1.15602170e+01], [ 1.00000000e+00, -3.64009000e-01, 4.74730000e+00], [ 1.00000000e+00, -8.41722000e-01, 4.11908300e+00], [ 1.00000000e+00, 4.90426000e-01, 1.96053900e+00], [ 1.00000000e+00, -7.19400000e-03, 9.07579200e+00], [ 1.00000000e+00, 3.56107000e-01, 1.24478630e+01], [ 1.00000000e+00, 3.42578000e-01, 1.22811620e+01], [ 1.00000000e+00, -8.10823000e-01, -1.46601800e+00], [ 1.00000000e+00, 2.53077700e+00, 6.47680100e+00], [ 1.00000000e+00, 1.29668300e+00, 1.16075590e+01], [ 1.00000000e+00, 4.75487000e-01, 1.20400350e+01], [ 1.00000000e+00, -7.83277000e-01, 1.10097250e+01], [ 1.00000000e+00, 7.47980000e-02, 1.10236500e+01], [ 1.00000000e+00, -1.33747200e+00, 4.68339000e-01], [ 1.00000000e+00, -1.02781000e-01, 1.37636510e+01], [ 1.00000000e+00, -1.47324000e-01, 2.87484600e+00], [ 1.00000000e+00, 5.18389000e-01, 9.88703500e+00], [ 1.00000000e+00, 1.01539900e+00, 7.57188200e+00], [ 1.00000000e+00, -1.65808600e+00, -2.72550000e-02], [ 1.00000000e+00, 1.31994400e+00, 2.17122800e+00], [ 1.00000000e+00, 2.05621600e+00, 5.01998100e+00], [ 1.00000000e+00, -8.51633000e-01, 4.37569100e+00], [ 1.00000000e+00, -1.51004700e+00, 6.06199200e+00], [ 1.00000000e+00, -1.07663700e+00, -3.18188800e+00], [ 1.00000000e+00, 1.82109600e+00, 1.02839900e+01], [ 1.00000000e+00, 3.01015000e+00, 8.40176600e+00], [ 1.00000000e+00, -1.09945800e+00, 1.68827400e+00], [ 1.00000000e+00, -8.34872000e-01, -1.73386900e+00], [ 1.00000000e+00, -8.46637000e-01, 3.84907500e+00], [ 1.00000000e+00, 1.40010200e+00, 1.26287810e+01], [ 1.00000000e+00, 1.75284200e+00, 5.46816600e+00], [ 1.00000000e+00, 7.85570000e-02, 5.97360000e-02], [ 1.00000000e+00, 8.93920000e-02, -7.15300000e-01], [ 1.00000000e+00, 1.82566200e+00, 1.26938080e+01], [ 1.00000000e+00, 1.97445000e-01, 9.74463800e+00], [ 1.00000000e+00, 1.26117000e-01, 9.22311000e-01], [ 1.00000000e+00, -6.79797000e-01, 1.22053000e+00], [ 1.00000000e+00, 6.77983000e-01, 2.55666600e+00], [ 1.00000000e+00, 7.61349000e-01, 1.06938620e+01], [ 1.00000000e+00, -2.16879100e+00, 1.43632000e-01], [ 1.00000000e+00, 1.38861000e+00, 9.34199700e+00], [ 1.00000000e+00, 3.17029000e-01, 1.47390250e+01]]), matrix([[0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0]]))new problem1:
labelMat格式不对 -> 在shell中多调用transpose()一次new problem2：
计算结果不对 -> sigmoid函数符号敲错版权声明：本文为博主原创文章，未经博主允许不得转载。