TSK 模糊推理模型预测 天然气产量变化 - Python 代码实现

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
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(LCC方法将1改成234）
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(LCC方法将1改成234）
target = data[1:, 3]  # 使用第一列作为目标变量，下次改2、3、4

# 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)

# TSK模型参数设置
N = 5  # 集群数
cntr, u_orig, _, _, _, _, _ = fuzz.cluster.cmeans(X_train.T, N, 2, error=0.005, maxiter=1000, init=None)

# TSK模型预测
target_values = np.zeros_like(y_train)
for i in range(len(y_train)):
    alphas = u_orig[:, i]  # 获取隶属度
    rules = np.argmax(u_orig, axis=0)[i]  # 获取控制规则
    target_values[i] = np.mean(y_train[i]) + alphas[rules] * (np.std(y_train[i]) / np.std(y_train)) * (y_train[i] - np.mean(y_train))

# CRMSE计算
crmse = np.sqrt(np.mean((y_train - target_values)**2))

# CMAPE计算
cmape = np.mean(np.abs(y_train - target_values) / y_train) * 100

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

将随机森林方法替换为TSK方法后，代码首先使用fuzz.cluster.cmeans()函数进行模糊聚类，然后使用隶属度矩阵进行预测。最后计算CRMSE和CMAPE来评估模型的性能。请确保已经安装了NumPy、Pandas和scikit-fuzzy库，并运行以上改好的完整代码。