我尝试使用Python和Scipy.opt来编写Andrew NG的逻辑回归问题以优化函数。然而，我遇到了一个值错误，提示我有不匹配的维度。我尝试使用flatten()函数处理我的theta数组，因为Scipy.opt似乎不太适合处理单列/行向量，但问题仍然存在。

请指导我解决问题的方向以及如何避免此类问题。

非常感谢！

使用的数据集：ex2.txt

34.62365962451697,78.0246928153624,030.28671076822607,43.89499752400101,035.84740876993872,72.90219802708364,060.18259938620976,86.30855209546826,179.0327360507101,75.3443764369103,145.08327747668339,56.3163717815305,061.10666453684766,96.51142588489624,175.02474556738889,46.55401354116538,176.09878670226257,87.42056971926803,184.43281996120035,43.53339331072109,195.86155507093572,38.22527805795094,075.01365838958247,30.60326323428011,082.30705337399482,76.48196330235604,169.36458875970939,97.71869196188608,139.53833914367223,76.03681085115882,053.9710521485623,89.20735013750205,169.07014406283025,52.74046973016765,167.94685547711617,46.67857410673128,070.66150955499435,92.92713789364831,176.97878372747498,47.57596364975532,167.37202754570876,42.83843832029179,089.67677575072079,65.79936592745237,150.534788289883,48.85581152764205,034.21206097786789,44.20952859866288,077.9240914545704,68.9723599933059,162.27101367004632,69.95445795447587,180.1901807509566,44.82162893218353,193.114388797442,38.80067033713209,061.83020602312595,50.25610789244621,038.78580379679423,64.99568095539578,061.379289447425,72.80788731317097,185.40451939411645,57.05198397627122,152.10797973193984,63.12762376881715,052.04540476831827,69.43286012045222,140.23689373545111,71.16774802184875,054.63510555424817,52.21388588061123,033.91550010906887,98.86943574220611,064.17698887494485,80.90806058670817,174.78925295941542,41.57341522824434,034.1836400264419,75.2377203360134,083.90239366249155,56.30804621605327,151.54772026906181,46.85629026349976,094.44336776917852,65.56892160559052,182.36875375713919,40.61825515970618,051.04775177128865,45.82270145776001,062.22267576120188,52.06099194836679,077.19303492601364,70.45820000180959,197.77159928000232,86.7278223300282,162.07306379667647,96.76882412413983,191.56497449807442,88.69629254546599,179.94481794066932,74.16311935043758,199.2725269292572,60.99903099844988,190.54671411399852,43.39060180650027,134.52451385320009,60.39634245837173,050.2864961189907,49.80453881323059,049.58667721632031,59.80895099453265,097.64563396007767,68.86157272420604,132.57720016809309,95.59854761387875,074.24869136721598,69.82457122657193,171.79646205863379,78.45356224515052,175.3956114656803,85.75993667331619,135.28611281526193,47.02051394723416,056.25381749711624,39.26147251058019,030.05882244669796,49.59297386723685,044.66826172480893,66.45008614558913,066.56089447242954,41.09209807936973,040.45755098375164,97.53518548909936,149.07256321908844,51.88321182073966,080.27957401466998,92.11606081344084,166.74671856944039,60.99139402740988,132.72283304060323,43.30717306430063,064.0393204150601,78.03168802018232,172.34649422579923,96.22759296761404,160.45788573918959,73.09499809758037,158.84095621726802,75.85844831279042,199.82785779692128,72.36925193383885,147.26426910848174,88.47586499559782,150.45815980285988,75.80985952982456,160.45555629271532,42.50840943572217,082.22666157785568,42.71987853716458,088.9138964166533,69.80378889835472,194.83450672430196,45.69430680250754,167.31925746917527,66.58935317747915,157.23870631569862,59.51428198012956,180.36675600171273,90.96014789746954,168.46852178591112,85.59430710452014,142.0754545384731,78.84478600148043,075.47770200533905,90.42453899753964,178.63542434898018,96.64742716885644,152.34800398794107,60.76950525602592,094.09433112516793,77.15910509073893,190.44855097096364,87.50879176484702,155.48216114069585,35.57070347228866,074.49269241843041,84.84513684930135,189.84580670720979,45.35828361091658,183.48916274498238,48.38028579728175,142.2617008099817,87.10385094025457,199.31500880510394,68.77540947206617,155.34001756003703,64.9319380069486,174.77589300092767,89.52981289513276,1

回答：

好的！经过在Github上深入搜索后，我自己找到了答案。这个值错误与数组的形状无关。首先，我需要修改我的优化函数为：

from scipy.optimize import minimizeresults = minimize(cost, b, args = (x,y),                   method = 'CG', jac = compute_gradient,                    options = {"maxiter": 400, "disp" : True})

代码仍然无法工作，因为我的函数参数顺序是（X，y，theta）。为了使函数正确工作，我必须将参数顺序更改为（theta，X，y）。这让我开始思考这种顺序是否重要。于是我对函数进行了这样的更改，优化立即生效了！

回顾起来，我明白了为什么theta必须是传递给成本和梯度函数的第一个参数。这是因为scipy.optimize中的minimize函数的接口期望其x0参数是初始猜测，即初始化的参数值。