python无法输出结果

为什么会无法输出呢？哪里出错了呀？
SystemModel文件下的__init__.py文件

 
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from Environment import alpha,num_iters
 
class System_Model():
    def __init__(
            self,
    ):
        #future是一个矩阵，每一行代表一个样本的特征向量
        # self.future.iloc[0, :] = [1,1,1,1] #X的第一列都是1，用于表示截距项
        # self.future.iloc[1, :] = [3,4,2,3] #市场需求（供应商数目多少，少-多）：分为5档
        # self.future.iloc[2, :] = [3,5,2,4] #平均服务质量（坏-好）：分为5档
        future = np.array([[1, 3, 5], [1, 4, 6], [1, 2, 3], [1, 5, 7]])
        goal = np.array([8, 10, 6, 12])
 
        self.alpha = alpha
        self.num_iters = num_iters
 
 
 
 
    # 定义梯度下降函数
    def Get_S2A_bandwithPrice(future, goal, alpha, num_iters):
        m = len(goal)  # 样本数量
        n = future.shape[1]  # 特征数量
        theta = np.zeros(n)  # 初始化参数向量
        J_history = []  # 保存每次迭代的损失函数值
 
        for i in range(num_iters):
            # 计算预测值
            h = np.dot(future, theta)
 
            # 梯度下降更新参数
            theta = theta - alpha * (1 / m) * np.dot(future.T, h - goal)
 
            # 计算损失函数值（均方误差）
            J = np.sum((h - goal) ** 2) / (2 * m)
            J_history.append(J)
 
        # 绘制损失函数值的变化
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')
 
        # 将迭代次数转换为数组
        iterations = np.arange(1, num_iters + 1)
 
        # 将数据转换成网格数据
        theta0_vals, theta1_vals = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))
        J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))
 
        # 计算不同参数组合下的损失函数值
        for i in range(len(theta0_vals)):
            for j in range(len(theta1_vals)):
                t = [theta0_vals[i, j], theta1_vals[i, j]]
                J_vals[i, j] = np.sum((np.dot(future, t) - goal) ** 2) / (2 * m)
 
        return theta, J_history
 

RunModel文件下的__init__.py文件

from SystemModel import System_Model
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
def main():
    #调用梯度下降函数进行训练
    env = System_Model()
    theta, J_history = env.Get_S2A_bandwithPrice(env.future, env.goal, env.alpha, env.num_iters)
    print("最优参数:", theta, J_history)
 
    # 绘制损失函数曲面
    env.ax.plot_surface(env.theta0_vals, env.theta1_vals, env.J_vals, cmap='viridis')
    env.ax.set_xlabel('Theta 0')
    env.ax.set_ylabel('Theta 1')
    env.ax.set_zlabel('Cost')
 
    # 绘制梯度下降过程中的参数更新点
    env.ax.scatter(env.theta[0], env.theta[1], env.J_history[-1], c='r', marker='o')
 
    plt.show()
 
 

Environment_init_.py

# 设置学习率和迭代次数
alpha = 0.01
num_iters = 1000
 

可能是缩进问题导致的错误,代码修改为

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from Environment import alpha, num_iters


class System_Model():
    def __init__(self):
        # future是一个矩阵，每一行代表一个样本的特征向量
        # self.future.iloc[0, :] = [1,1,1,1] #X的第一列都是1，用于表示截距项
        # self.future.iloc[1, :] = [3,4,2,3] #市场需求（供应商数目多少，少-多）：分为5档
        # self.future.iloc[2, :] = [3,5,2,4] #平均服务质量（坏-好）：分为5档
        future = np.array([[1, 3, 5], [1, 4, 6], [1, 2, 3], [1, 5, 7]])
        goal = np.array([8, 10, 6, 12])

        self.alpha = alpha
        self.num_iters = num_iters

    # 定义梯度下降函数
    def Get_S2A_bandwithPrice(future, goal, alpha, num_iters):
        m = len(goal)  # 样本数量
        n = future.shape[1]  # 特征数量
        theta = np.zeros(n)  # 初始化参数向量
        J_history = []  # 保存每次迭代的损失函数值

        for i in range(num_iters):
            # 计算预测值
            h = np.dot(future, theta)

            # 梯度下降更新参数
            theta = theta - alpha * (1 / m) * np.dot(future.T, h - goal)

            # 计算损失函数值（均方误差）
            J = np.sum((h - goal) ** 2) / (2 * m)
            J_history.append(J)

        # 绘制损失函数值的变化
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')

        # 将迭代次数转换为数组
        iterations = np.arange(1, num_iters + 1)

        # 将数据转换成网格数据
        theta0_vals, theta1_vals = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))
        J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))

        # 计算不同参数组合下的损失函数值
        for i in range(len(theta0_vals)):
            for j in range(len(theta1_vals)):
                t = [theta0_vals[i, j], theta1_vals[i, j]]
                J_vals[i, j] = np.sum((np.dot(future, t) - goal) ** 2) / (2 * m)

        return theta, J_history

亲，别放截图好吗，把代码贴出来