scipy.optimize.fmin_bfgs优化提供了与简单函数调用不同的结果

我正在探索一些numpy / scipy函数，并且我注意到scipy.optimize.fmin_bfgs要求对被调用的函数进行更改以提供与直接函数调用相比正确的结果。我对fnRSS函数的第一个定义在调用函数时返回了正确的值，但拒绝进行优化。我的第二个定义在调用函数时给出了错误的结果，但在运行优化时给出了正确的结果。有人可以告诉我，为vY优化而调换参数有何关键？它应该已经是164x1。

import numpy as np
import scipy as sp
import pandas as pd
from scipy import optimize

if __name__ == "__main__":
    urlSheatherData = "http://www.stat.tamu.edu/~sheather/book/docs/datasets/MichelinNY.csv"
    data = pd.read_csv(urlSheatherData)
    Xs = np.vstack(data[['Service','Decor', 'Food', 'Price']].values)
    Xs = np.concatenate((np.vstack(np.ones(Xs.shape[0])),Xs), axis=1)
    Ys = np.vstack(data[['InMichelin']].values)
    # optimal solution (given)
    vBeta = np.array([-1.49209249,  -0.01117662,  0.044193, 0.05773374, 0.00179794]).reshape(5,1)

    print Ys.shape, Xs.shape, vBeta.shape 
    # first definition of function
    def fnRSS(vBeta, vY, mX):
      return np.sum((vY - np.dot(mX, vBeta))**2) 
    print fnRSS(vBeta, Ys, Xs) # correct value
    print np.linalg.lstsq(Xs, Ys)[1] # confirm correct value
    print sp.optimize.fmin_bfgs(fnRSS, x0=vBeta, args=(Ys,Xs)) # wrong value
    # second definition
    def fnRSS(vBeta, vY, mX):
      return np.sum((vY.T - np.dot(mX, vBeta))**2) 
    print fnRSS(vBeta, Ys, Xs) # incorrect value
    print sp.optimize.fmin_bfgs(fnRSS, x0=vBeta, args=(Ys,Xs)) # correct convergence but simple call gives different value

我的输出：

(164, 1) (164, 5) (5, 1)
26.3239061505
[ 26.32390615]
Warning: Desired error not necessarily achieved due to precision loss.
         Current function value: 6660.000000
         Iterations: 39
         Function evaluations: 3558
         Gradient evaluations: 480
[  4.51220111e-01   1.32711255e-07   8.09143368e-08  -1.06633003e-07
  -5.18448332e-08]
9002.87916028
Warning: Desired error not necessarily achieved due to precision loss.
         Current function value: 26.323906
         Iterations: 29
         Function evaluations: 1954
         Gradient evaluations: 260
[-1.49209095 -0.0111764   0.04419313  0.05773347  0.00179789]