class LogisticRegression:
def init(self):
self.coef = None
self.intercept = None
self.theta = None

def sigmoid(t):
    return 1/(1+exp(-t))

def fit(self,X_train,y_train,eta=0.01,n_iters=1e4):
    
    def J(theta,X_b,y):
        y_hat = sigmoid(X_b.dot(theta))
        return -np.sum(y*np.log(y_hat)+(1-y)*np.log(1-y_hat))/len(y)

    def dJ(theta,X_b,y):
        return X_b.dot(sigmoid(X_b.dot(theat)-y)/len(X_b))


    def gradient_descent(X_b,y,initial_theta,eta,n_iters=1e4,epsilon=1e-8):
        theta = initial_theta
        cur_iter =0
        while cur_iter<n_iters:
            gradient = dJ(theta,X_b,y)
            last_theta = theta
            theta = theta-eta*gradient
            if(abs(J(theta,X_b,y)-J(last_theta,X_b,y))<epsilon):
                break
            cur_iter +=1
        return theta
    
    X_b = np.hstack([np.ones((len(X_train),1)),X_train])
    initial_theta = np.zeros(X_b.shape[1])
    self.theta = gradient_descent(X_b,y_train,initial_theta,eta,n_iters)
    self.intercept=self.theta[0]
    self.coef =self.theta[1:]
    
    return self

def predict_prob(self,X_predict):
    X_b = np.hstack([np.ones((len(X_predict),1)),X_predict])
    return self.sigmoid(X_b.dot(self.theta))

def predict(self,X_predict):
    prob = self.predict_prob(X_predict)
    return np.array(prob>=0.5,dtype='int')

def score(self,X_test,y_test):
    y_predict=self.predict(X_test)
    return np.sum(y_test==y_predict)/len(y_test)

老师您好，按照视频的逻辑写的报错未定义，从您给你的代码直接运行也是提示这个未定义的错误，不知道问题出在哪里了。。