成分数据转换：使用LCC方法进行特征提取

对成分数据进行转换可以使用LCC (Local Class Centers) 方法，根据每个成分数据与聚类中心的距离来进行转换。以下是使用LCC方法对成分数据进行转换的代码示例：

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, 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]])

class LCC_FS():
    def __init__(self, n_cluster=20):
        self.n_cluster = n_cluster
        self.centers = None
        self.ranges = None
        self.trained = False

    def fit(self, X_train, y_train):
        n_samples, n_features = X_train.shape
        n_cluster = self.n_cluster
        assert (n_samples == len(y_train)), 'X_train and y_train samples num must be same'
        centers, ranges = self.__lcc__(X_train, n_cluster)
        self.centers = centers
        self.ranges = ranges
        self.trained = True

    def predict(self, X_test):
        assert(self.trained), 'Error when predict, use fit first!'
        n_samples, n_features = X_test.shape
        n_cluster = self.n_cluster
        X_transformed = np.zeros((n_samples, n_cluster))
        for i in range(n_samples):
            for j in range(n_cluster):
                dist = np.linalg.norm(X_test[i] - self.centers[j]) / self.ranges[j]
                X_transformed[i, j] = 1 / (1 + dist)
        return X_transformed

    def __lcc__(self, data, n_cluster):
        n_samples, n_features = data.shape
        centers = np.zeros((n_cluster, n_features))
        ranges = np.zeros(n_cluster)
        for i in range(n_cluster):
            center_idx = np.random.choice(range(n_samples))
            centers[i] = data[center_idx]
            ranges[i] = np.linalg.norm(data - centers[i], axis=1).max()
        return centers, ranges

# 定义数据集
X = data[:, :-1]
y = data[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建LCC_FS实例
model = LCC_FS(n_cluster=20)

# 拟合模型
model.fit(X_train, y_train)

# 在测试集上进行转换
X_transformed = model.predict(X_test)

# 输出转换后的数据
print(X_transformed)

在这个示例中，使用LCC_FS类，其中的fit方法用于拟合模型并计算聚类中心和范围，predict方法用于对输入数据进行转换。在predict方法中，计算每个成分数据与聚类中心的距离，然后根据距离计算转换后的数值。最后，输出转换后的数据X_transformed。请注意，这里只对成分数据进行了转换，没有进行预测。