首页修改下列代码第20行的错误：import numpy as np import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error #输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape(-1, 1) y = np.array([7.37, 7.37, 7.39, 7.4, 7.41]) #创建并训练人工神经网络模型 model = MLPRegressor(hidden_layer_sizes=(50, 50), activation='relu', solver='adam') model.fit(X, y) #预测新的自变量对应的因变量 X_new = np.array([7.41]).reshape(-1, 1) y_pred = model.predict(X_new) print(y_pred) #计算均方误差（MSE） mse = mean_squared_error(y, y_pred) #计算均方根误差（RMSE） rmse = np.sqrt(mse) print("均方误差（MSE）：", mse) print("均方根误差（RMSE）：", rmse)

修改下列代码第20行的错误：import numpy as np import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error #输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape(-1, 1) y = np.array([7.37, 7.37, 7.39, 7.4, 7.41]) #创建并训练人工神经网络模型 model = MLPRegressor(hidden_layer_sizes=(50, 50), activation='relu', solver='adam') model.fit(X, y) #预测新的自变量对应的因变量 X_new = np.array([7.41]).reshape(-1, 1) y_pred = model.predict(X_new) print(y_pred) #计算均方误差（MSE） mse = mean_squared_error(y, y_pred) #计算均方根误差（RMSE） rmse = np.sqrt(mse) print("均方误差（MSE）：", mse) print("均方根误差（RMSE）：", rmse)

时间: 2024-04-09 11:31:06 浏览: 66

import numpy as np import matplotlib.pyplot as plt import math

import numpy as np import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 #原数据 data=np.array([[72.03,241.2,1592.74],[73.84,241.2,1855.36],[74.49,244.8,2129.60],[76.68,250.9,2486.86],[78.00,250.9,2728.94],[79.68,252.2,3038.90]]) #要预测数据的真实值 data_T=np.array([[81.21,256.5,3458.05],[82.84,259.4,3900.27],[84.5,262.4,4399.06],[86.19,265.3,4961.62],[87

import numpy as np import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error # 输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape(-1, 1) y = np.array([7.37, 7.37, 7.39, 7.4, 7.41]) # 创建并训练人工神经网络模型 model = MLPRegressor(hidden_layer_sizes=(50, 50), activation='relu', solver='adam') model.fit(X, y) # 预测新的自变量对应的因变量 X_new = np.array([[7.41]]) # 修改错误：将数组的形状由 (-1, 1) 改为 (1, 1) y_pred = model.predict(X_new) print(y_pred) # 计算均方误差（MSE） mse = mean_squared_error(y, y_pred) # 计算均方根误差（RMSE） rmse = np.sqrt(mse) print("均方误差（MSE）：", mse) print("均方根误差（RMSE）：", rmse)

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