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 14:31:06 浏览: 70
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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改正下列代码: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)
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)
import pandas as pd import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn import preprocessing import matplotlib.pyplot as plt data_tr = pd.read_csv('BPdata_tr.txt') # 训练集样本 data_te = pd.read_csv('BPdata_te.txt') # 测试集样本 X=np.array([[0.0499853495508432],[0.334657078469172]]).reshape(1, -1) model = MLPRegressor(hidden_layer_sizes=(10,), activation='relu',random_state=10, max_iter=8000, learning_rate_init=0.3) # 构建模型,调用sklearn实现神经网络算法 model.fit(data_tr.iloc[:, :2], data_tr.iloc[:, 2]) # 模型训练 pre = model.predict(data_te.iloc[:, :2]) # 模型预测 pre1 = model.predict(X) err = np.abs(pre - data_te.iloc[:, 2]).mean() # 模型预测误差 print(pre,end='\n______________________________\n') print(pre1,end='\n++++++++++++++++++++++++++++++++\n') print('误差:',err)
这段代码是一个简单的使用sklearn库实现的多层感知器神经网络回归算法的示例。具体实现步骤如下:
1. 导入相关的库:
``` python
import pandas as pd
import numpy as np
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import matplotlib.pyplot as plt
```
2. 读取训练集和测试集数据,并将数据分为输入特征和目标输出两部分:
``` python
data_tr = pd.read_csv('BPdata_tr.txt') # 训练集样本
data_te = pd.read_csv('BPdata_te.txt') # 测试集样本
X = np.array([[0.0499853495508432],[0.334657078469172]]).reshape(1, -1) # 预测数据
```
3. 构建模型,设置神经网络的参数:
``` python
model = MLPRegressor(hidden_layer_sizes=(10,), activation='relu',random_state=10, max_iter=8000, learning_rate_init=0.3)
```
其中,hidden_layer_sizes表示隐藏层的神经元个数为10,activation表示激活函数使用ReLU,random_state表示随机种子,max_iter表示最大迭代次数,learning_rate_init表示初始学习率。
4. 使用训练集数据训练模型:
``` python
model.fit(data_tr.iloc[:, :2], data_tr.iloc[:, 2])
```
其中,data_tr.iloc[:, :2]表示取训练集数据的前两列作为输入特征,data_tr.iloc[:, 2]表示取训练集数据的第三列作为目标输出。
5. 使用测试集数据预测模型的输出:
``` python
pre = model.predict(data_te.iloc[:, :2])
```
其中,data_te.iloc[:, :2]表示取测试集数据的前两列作为输入特征。
6. 对预测结果进行评估,计算预测误差:
``` python
err = np.abs(pre - data_te.iloc[:, 2]).mean()
print('误差:',err)
```
其中,data_te.iloc[:, 2]表示取测试集数据的第三列作为目标输出。最后输出预测结果和预测误差。
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