import numpy as np import pandas as pd from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error import skfuzzy as fuzz from sklearn.model_selection import train_test_split

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]])

计算熵序列

entropy_sequence = -np.sum(data * np.log2(data), axis=1)

TSK模型训练和预测

X = np.arange(len(entropy_sequence)).reshape(-1, 1) # 构造横坐标 X_train, X_test, y_train, y_test = train_test_split(X, entropy_sequence, test_size=0.2, random_state=42)

定义模糊集合

x_universe = np.arange(len(X)) y_universe = np.arange(0, 20, 0.1)

构建模糊集合

x_fuzzy = fuzz.trimf(x_universe, [0, len(X) // 2, len(X)]) y_fuzzy = fuzz.trimf(y_universe, [0, 10, 20])

训练模糊系统

fuzzy_model = fuzz.TSKRegressor() fuzzy_model.fit(X_train, y_train, X=X, y=entropy_sequence)

预测

y_pred = fuzzy_model.predict(X_test)

计算CRMSE和CMAPE

crmse = np.sqrt(mean_squared_error(y_test, y_pred)) cmape = mean_absolute_percentage_error(y_test, y_pred)

print('CRMSE:', crmse) print('CMAPE:', cmape)