Question

我一直在尝试使用python和Scipy.opt编写Andrew NG的Logistic回归问题，以优化该功能。但是，我收到一个VALUE错误，说我的尺寸不匹配。我已经尝试过将我的theta数组flatten（）用作scipy.opt在单列/行向量中似乎无法很好地工作，但是问题仍然存在。

请向正确的方向指出导致该问题的原因以及如何避免该问题。

感谢一百万！

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt

dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())

positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]

#Visualizing Dataset
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.xlabel("Exam 1 Score")
plt.ylabel("Exam 2 Score")
plt.title("Admission Graph")
plt.legend()
#plt.show()

#Preprocessing Data
dataset.insert(0, "x0", 1)
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values
y = dataset.iloc[:,col-1:col].values
b = np.zeros([1,col-1])
m = len(y)
print(f"X Shape: {x.shape}   Y Shape: {y.shape}   B Shape: {b.shape}")

#Defining Functions
def hypothesis(x, y, b):
    h = 1 / (1+np.exp(-x @ b.T))
    return h

def cost(x, y, b):
    first = (y.T @ np.log(hypothesis(x, y, b)))
    second = (1-y).T @ np.log(1 - hypothesis(x, y, b))
    j = (-1/m) * np.sum(first+second)
    return j

def gradient(x, y, b):
    grad_step = ((hypothesis(x, y, b) - y) @ x.T) / m
    return b

#Output
initial_cost = cost(x, y, b)
print(f"\nInitial Cost = {initial_cost}")
final_cost = opt.fmin_tnc(func=cost, x0=b.flatten() , fprime=gradient, args=(x,y))
print(f"Final Cost = {final_cost} \nTheta = {b}")

使用的数据集：ex2.txt

34.62365962451697,78.0246928153624,0
30.28671076822607,43.89499752400101,0
35.84740876993872,72.90219802708364,0
60.18259938620976,86.30855209546826,1
79.0327360507101,75.3443764369103,1
45.08327747668339,56.3163717815305,0
61.10666453684766,96.51142588489624,1
75.02474556738889,46.55401354116538,1
76.09878670226257,87.42056971926803,1
84.43281996120035,43.53339331072109,1
95.86155507093572,38.22527805795094,0
75.01365838958247,30.60326323428011,0
82.30705337399482,76.48196330235604,1
69.36458875970939,97.71869196188608,1
39.53833914367223,76.03681085115882,0
53.9710521485623,89.20735013750205,1
69.07014406283025,52.74046973016765,1
67.94685547711617,46.67857410673128,0
70.66150955499435,92.92713789364831,1
76.97878372747498,47.57596364975532,1
67.37202754570876,42.83843832029179,0
89.67677575072079,65.79936592745237,1
50.534788289883,48.85581152764205,0
34.21206097786789,44.20952859866288,0
77.9240914545704,68.9723599933059,1
62.27101367004632,69.95445795447587,1
80.1901807509566,44.82162893218353,1
93.114388797442,38.80067033713209,0
61.83020602312595,50.25610789244621,0
38.78580379679423,64.99568095539578,0
61.379289447425,72.80788731317097,1
85.40451939411645,57.05198397627122,1
52.10797973193984,63.12762376881715,0
52.04540476831827,69.43286012045222,1
40.23689373545111,71.16774802184875,0
54.63510555424817,52.21388588061123,0
33.91550010906887,98.86943574220611,0
64.17698887494485,80.90806058670817,1
74.78925295941542,41.57341522824434,0
34.1836400264419,75.2377203360134,0
83.90239366249155,56.30804621605327,1
51.54772026906181,46.85629026349976,0
94.44336776917852,65.56892160559052,1
82.36875375713919,40.61825515970618,0
51.04775177128865,45.82270145776001,0
62.22267576120188,52.06099194836679,0
77.19303492601364,70.45820000180959,1
97.77159928000232,86.7278223300282,1
62.07306379667647,96.76882412413983,1
91.56497449807442,88.69629254546599,1
79.94481794066932,74.16311935043758,1
99.2725269292572,60.99903099844988,1
90.54671411399852,43.39060180650027,1
34.52451385320009,60.39634245837173,0
50.2864961189907,49.80453881323059,0
49.58667721632031,59.80895099453265,0
97.64563396007767,68.86157272420604,1
32.57720016809309,95.59854761387875,0
74.24869136721598,69.82457122657193,1
71.79646205863379,78.45356224515052,1
75.3956114656803,85.75993667331619,1
35.28611281526193,47.02051394723416,0
56.25381749711624,39.26147251058019,0
30.05882244669796,49.59297386723685,0
44.66826172480893,66.45008614558913,0
66.56089447242954,41.09209807936973,0
40.45755098375164,97.53518548909936,1
49.07256321908844,51.88321182073966,0
80.27957401466998,92.11606081344084,1
66.74671856944039,60.99139402740988,1
32.72283304060323,43.30717306430063,0
64.0393204150601,78.03168802018232,1
72.34649422579923,96.22759296761404,1
60.45788573918959,73.09499809758037,1
58.84095621726802,75.85844831279042,1
99.82785779692128,72.36925193383885,1
47.26426910848174,88.47586499559782,1
50.45815980285988,75.80985952982456,1
60.45555629271532,42.50840943572217,0
82.22666157785568,42.71987853716458,0
88.9138964166533,69.80378889835472,1
94.83450672430196,45.69430680250754,1
67.31925746917527,66.58935317747915,1
57.23870631569862,59.51428198012956,1
80.36675600171273,90.96014789746954,1
68.46852178591112,85.59430710452014,1
42.0754545384731,78.84478600148043,0
75.47770200533905,90.42453899753964,1
78.63542434898018,96.64742716885644,1
52.34800398794107,60.76950525602592,0
94.09433112516793,77.15910509073893,1
90.44855097096364,87.50879176484702,1
55.48216114069585,35.57070347228866,0
74.49269241843041,84.84513684930135,1
89.84580670720979,45.35828361091658,1
83.48916274498238,48.38028579728175,1
42.2617008099817,87.10385094025457,1
99.31500880510394,68.77540947206617,1
55.34001756003703,64.9319380069486,1
74.77589300092767,89.52981289513276,1

Answer 1

好吧！因此，在深入探究Github之后，我自己找出了答案。 Value错误与数组的形状无关。首先，我必须将优化函数修改为：

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

由于我的函数的参数按（X，y，theta）顺序排列，因此代码仍然无法正常工作。为了使函数正常工作，我必须将参数的顺序更改为（theta，X，y）。这让我想知道这个顺序是否重要。因此，我将此更改应用到我的函数中，并立即进行了优化！

回想起来，我理解为什么theta必须成为传递给cost和gradient函数的第一个参数。这是因为scipy.optimize中的minimum函数的接口希望其x0参数是初始猜测，即。初始化的参数值。

使用Scipy.opt的Andrew NG Logistic回归中的形状错误

1 个答案: