下列代码隐藏层用的函数是什么：import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error, mean_absolute_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=(12, 12), 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("预测值:", y_pred)

给下列代码增加预测的误差分析：import numpy as np from sklearn.neural_network import MLPRegressor #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)

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...

增加代码分析下列代码的预测的误差：import numpy as np from sklearn.neural_network import MLPRegressor #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)

from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error, mean_absolute_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 =...

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)

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...

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)

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...

from sklearn.datasets import load_boston import numpy as np import pandas as pd data=pd.read_csv('./data_picture/chapter1/boston_house_prices.csv') data.head() from sklearn.model_selection import train_test_split X=data.drop('MEDV',axis=1) y=data['MEDV'] X=X.values y=y.values X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25) from sklearn.preprocessing import StandardScaler ss_X=StandardScaler() scaler_X=ss_X.fit(X_train) X_train=scaler_X.transform(X_train) X_test=scaler_X.transform(X_test) from sklearn.neural_network import MLPRegressor model = MLPRegressor(solver='lbfgs', hidden_layer_sizes=(15,15), random_state=1) model.fit(X_train, y_train) from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error print('训练集回归评估指标：') model_score1=model.score(X_train,y_train) print('The accuracy of train data is',model_score1) print('测试集回归评估指标：') model_score2=model.score(X_test,y_test) print('The accuracy of test data is',model_score2) y_test_predict=model.predict(X_test) mse=mean_squared_error(y_test,y_test_predict) print('The value of mean_squared_error:',mse) mae=mean_absolute_error(y_test,y_test_predict) print('The value of mean_absolute_error:',mae)解析一下这段代码

9. 从sklearn.metrics模块中导入r2_score、mean_squared_error和mean_absolute_error函数，用于评估模型的性能。 10. 分别使用score()函数计算训练集和测试集的R2得分，并输出结果。 11. 使用predict()函数对测试集...

# -- coding: utf-8 -- """ Created on 2023-10-16 @author: Your Name 本代码用于对房屋交易数据进行分析，包括数据预处理、特征工程、模型训练与评估等步骤。目标是研究影响城市住宅价格的因素，并评估不同机器学习模型在预测房价方面的表现。 """ # 导入必要的库 import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.svm import SVR from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_absolute_error, mean_squared_error import shap import matplotlib.pyplot as plt import warnings import re # 忽略警告信息 warnings.filterwarnings("ignore") # 1. 数据加载与初步查看 def load_data(file_path): """ 加载数据并进行初步查看 """ data = pd.read_excel(file_path) print("数据集基本信息：") print(data.info()) print("\n数据集前5行：") print(data.head()) print("\n数据集描述性统计：") print(data.describe()) return data # 2. 数据预处理 def data_preprocessing(data): """ 数据预处理，包括删除无效数据、变量转化或拆分、数据集整合、删除极端值等 """ # 删除无效数据（如缺失值过多的行） data = data.dropna(thresh=len(data.columns) * 2 / 3, axis=0) # 变量转化或拆分 # 拆分户型信息，适应不同的格式 def extract_room_hall(x): if pd.isna(x): return np.nan, np.nan # 尝试匹配常见的格式，如 "3室1厅"、"3房间1厅"、"3房1厅" 等 room = 0 hall = 0 if '室' in x and '厅' in x: parts = x.split('厅') if len(parts) >= 2: room_part = parts[0] hall_part = parts[1].split(' ')[0] if ' ' in parts[1] else parts[1] room = room_part.split('室')[0] if '室' in room_part else 0 hall = hall_part.split('厅')[0] if '厅' in hall_part else 0 elif '房间' in x and '厅' in x: parts = x.split('厅') if len(parts) >= 2: room_part = parts[0] hall_part = parts[1].split(' ')[0] if ' ' in parts[1] else parts[1] room = room_part.sp

from sklearn.metrics import mean_squared_error, r2_score X = df.drop('PRICE', axis=1) y = df['PRICE'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # ...

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from pathlib import Path excel_path = Path.cwd() / "C:/Users/Administrator/Desktop/augmented_data2.xlsx" data = pd.read_excel(excel_path, sheet_name='Sheet1') x = data[['掺氨比', '过量空气系数', '燃尽风位置', '主燃区温度']] y = data['NO排放浓度'] data[['主燃区温度']] = data[['主燃区温度']].astype(str) le = LabelEncoder() cat_cols = data.select_dtypes(include=['object']).columns for col in cat_cols: data[col] = le.fit_transform(data[col]) X = data.drop('NO排放浓度', axis=1) y = data['NO排放浓度'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) import xgboost as xgb dtrain = xgb.DMatrix(X_train, label=y_train) dtest = xgb.DMatrix(X_test, label=y_test) params = { 'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'eta': 0.1, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8 } model = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, 'train'), (dtest, 'test')], early_stopping_rounds=10) y_pred = model.predict(dtest) from sklearn.metrics import mean_squared_error, r2_score from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split print(f"MSE: {mean_squared_error(y_test, y_pred):.2f}") print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.2f}") print(f"R²: {r2_score(y_test, y_pred):.2%}") import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体为黑体 plt.rcParams['axes.unicode_minus'] = False xgb.plot_importance(model) plt.show() param_constraints = { '掺氨比': (0.0, 0.5), # 氨燃料掺混比例 '过量空气系数': (1.0, 1.5), # 空燃比 '燃尽风位置': (0.0, 1.0), '主燃区温度': (1200, 1600) # 温度范围(℃) } import numpy as np from scipy.optimize import minimize def predict_no_emission(params): """ 将优化参数转换为模型输入格式 """ input_df = pd.DataFrame([params], columns=X.columns) return model.predict(xgb.DMatrix(input_df))[0] print("现有参数约束键:", param_constraints.keys()) # 初始猜测值（取训练数据均值） initial_guess = X.mean().values bounds = [ param_constraints['掺氨比'], param_constraints['过量空气系数'], (min(X['燃尽风位置']), max(X['燃尽风位置'])), # 离散型参数的编码范围 param_constraints['主燃区温度'] ] # 使用SLSQP算法进行优化 result = minimize( fun=predict_no_emission, x0=initial_guess, method='SLSQP', bounds=bounds, options={'maxiter': 100, 'ftol': 1e-6} ) # 将编码后的类别值还原 optimized_params = result.x # 燃尽风位置解码 if '燃尽风位置' in cat_cols: position_idx = X.columns.get_loc('燃尽风位置') optimized_params[position_idx] = le.inverse_transform( [int(round(optimized_params[position_idx]))] )[0] # 显示最终优化结果 print(f""" 最小NO排放浓度预测值：{result.fun:.2f} mg/m³ 对应最优参数组合：掺氨比：{optimized_params[0]:.3f} 过量空气系数：{optimized_params[1]:.2f} 燃尽风位置：{optimized_params[2]} 主燃区温度：{optimized_params[3]:.0f}℃ """) 改成其他算法

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