import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA import skfuzzy as fuzz

成分数据矩阵

data = np.array([[0.758, 0.171, 0.049, 0.022], [0.758, 0.172, 0.047, 0.023], [0.762, 0.17, 0.047, 0.021], [0.762, 0.17, 0.047, 0.021], [0.76, 0.171, 0.047, 0.021], [0.762, 0.166, 0.051, 0.021], [0.761, 0.171, 0.048, 0.02], [0.757, 0.175, 0.049, 0.019], [0.747, 0.182, 0.052, 0.019], [0.75, 0.174, 0.057, 0.019], [0.746, 0.175, 0.061, 0.018], [0.747, 0.18, 0.055, 0.018], [0.715, 0.204, 0.062, 0.017], [0.696, 0.215, 0.067, 0.022], [0.68, 0.232, 0.066, 0.022], [0.661, 0.246, 0.068, 0.025], [0.653, 0.243, 0.077, 0.027], [0.661, 0.234, 0.078, 0.027], [0.702, 0.201, 0.074, 0.023], [0.702, 0.199, 0.076, 0.023], [0.724, 0.178, 0.074, 0.024], [0.724, 0.175, 0.074, 0.027], [0.725, 0.17, 0.075, 0.03], [0.715, 0.167, 0.084, 0.034], [0.716, 0.164, 0.085, 0.035], [0.692, 0.174, 0.094, 0.04], [0.702, 0.168, 0.084, 0.046], [0.685, 0.17, 0.097, 0.048], [0.674, 0.171, 0.102, 0.053], [0.658, 0.173, 0.113, 0.056], [0.638, 0.184, 0.12, 0.058], [0.622, 0.187, 0.13, 0.061], [0.606, 0.189, 0.136, 0.069], [0.59, 0.189, 0.145, 0.076], [0.577, 0.19, 0.153, 0.08], [0.569, 0.188, 0.159, 0.084], [0.559, 0.186, 0.167, 0.088], [0.562, 0.179, 0.175, 0.084]])

转换为特征矩阵

feature_matrix = np.zeros((len(data) - 1, len(data[0]))) for i in range(len(data) - 1): feature_matrix[i] = data[i + 1] - data[i]

构建特征矩阵的DataFrame

df = pd.DataFrame(feature_matrix, columns=['Coal', 'Petroleum', 'Others', 'Gas'])

目标变量

target = data[1:, 1] # 使用第2列作为目标变量

数据归一化

scaler = MinMaxScaler() df_scaled = scaler.fit_transform(df)

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(df_scaled, target, test_size=0.13, random_state=42)

构建TSK模型管道

model = Pipeline([ ('pca', PCA(n_components=2)), # 使用PCA进行特征降维 ('regressor', RandomForestRegressor(n_estimators=100)) # 使用随机森林回归器作为模型 ])

拟合模型

model.fit(X_train, y_train)

在测试集上进行预测

y_pred = model.predict(X_test)

计算CRMSE和CMAPE

crmse = np.sqrt(mean_squared_error(y_test, y_pred)) cmape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100

print("CRMSE:", crmse) # 输出CRMSE print("CMAPE:", cmape) # 输出CMAPE