如何解决AttributeError：''LogisticRegressionGD''对象没有属性''net_input''为什么会出现此错误？我定义了net_input？

from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt

# Assigning the petal length and petal width of the 150 flower samples to Matrix X 
# Class labels of the flower to vector y 

iris = datasets.load_iris()
X = iris.data[:,[2,3]]
y = iris.target
print(''Class labels:'',np.unique(y))

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=1,stratify=y)

print(''Labels counts in y:'',np.bincount(y))
print(''Labels counts in y_train:'',np.bincount(y_train))
print (''Labels counts in y_test:'',np.bincount(y_test))

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

class LogisticRegressionGD(object):
    """Logistic Regression Classifier using gradient descent.

    Parameters
    ------------
    eta : float
        Learning rate (between 0.0 and 1.0)
    n_iter : int
        Passes over the training dataset
    random_state : int
        Random number generator seed for random weight initialization

    Attributes
    ------------
    w_ : 1d-array
        Weights after fitting.
    cost_ : list
        Sum-of-squares cost function value in each epoch.

    """
    def __init__(self,eta=0.05,n_iter=100,random_state=1):
        self.eta = eta
        self.n_iter = n_iter
        self.random_state = random_state
    
    def fit(self,X,y):
        """ Fit training data.

        Parameters
        ------------
        X : {array-like},shape = [n_samples,n_features]
            Training vectors,where n_samples is the number of samples and 
            n_features is the number of features.
        y : array-like,shape = [n_samples]
            Target values

        Returns
        ------------
        self : object

        """
        rgen = np.random.RandomState(self.random_state)
        self.w_ = rgen.normal(loc=0.0,scale=0.01,size=1 + X.shape[1])
        self.cost_ = []

        for i in range(self.n_iter):
            net_input = self.net_input(X)
            output = self.activation(net_input)
            errors = (y - output)
            self.w_[1:] += self.eta * X.T.dot(errors)
            self.w_[0] += self.eta * errors.sum()

            # note that we compute the logistic ''cost'' Now
            # instead of the sum of the squared errors cost
            cost = (-y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output))))
            self.cost_.append(cost)
            return self
        
        def net_input(self,X):
            """Calculate net input"""
            return np.dot(X,self.w_[1:]) + self.w_[0]

        def activation(self,z):
            """Compute logistic sigmoid activation"""
            return 1. / (1. + (np.clip(z,-250,250)))

        def predict(self,X):
            """Return class label after unit step"""
            return np.where(self.net_input(X) >= 0.0,1,0)
            # equivalent to: 
            # return np.where(self.activation(self.net_input(X)) >= 0.5,0)

# Considering only Iris-setosa and Iris-versicolor (Classes 0 and 1)
X_train_01_subset = X_train[(y_train == 0) | (y_train == 1)]
y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)]
lrgd = LogisticRegressionGD(eta=0.05,n_iter=1000,random_state=1)
lrgd.fit(X_train_01_subset,y_train_01_subset)
plot_decision_regions(X=X_train_01_subset,y=y_train_01_subset,classifier=lrgd)
plt.xlabel(''petal length [standardized]'')
plt.ylabel(''petal width [standardized]'')
plt.legend(loc=''upper left'')
plt.show()

运行该命令后，出现以下错误消息：

Traceback (most recent call last):
  File "c:/Users/Desfios 5/Desktop/Python/Ch3LogisticRegressionGD.py",line 102,in <module>
    lrgd.fit(X_train_01_subset,y_train_01_subset)
  File "c:/Users/Desfios 5/Desktop/Python/Ch3LogisticRegressionGD.py",line 72,in fit
    net_input = self.net_input(X)
AttributeError: ''LogisticRegressionGD'' object has no attribute ''net_input''

我看着其他问类似问题的人，发现通常由于未定义属性而出现此错误消息。但是，第72行-net_input = self.net_input（X）定义了net_input吗？

解决方法

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