帮我找找实现信用整形（CBS）算法的代码

以下是Python实现的CBS算法代码示例：

import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin

class CBS(BaseEstimator, TransformerMixin):
    def __init__(self, threshold=0.1):
        """
        threshold: 阈值，用于确定要修正的异常值的最大值
        """
        self.threshold = threshold
        
    def fit(self, X, y=None):
        """
        计算每个特征的中位数和标准差
        """
        self.medians_ = np.median(X, axis=0)
        self.mads_ = np.median(np.abs(X - self.medians_), axis=0) * 1.4826
        return self
    
    def transform(self, X):
        """
        用CBS算法修正异常值
        """
        X_corrected = X.copy()
        for i in range(X.shape[1]):
            median = self.medians_[i]
            mad = self.mads_[i]
            diff = np.abs(X[:, i] - median)
            y = diff / mad
            mask = y > self.threshold
            X_corrected[mask, i] = median + np.sign(X[mask, i] - median) * self.threshold * mad
        return X_corrected

使用示例：

# 导入数据
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target

# 使用CBS算法修正异常值
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

cbs = CBS(threshold=3)
scaler = StandardScaler()
pipeline = Pipeline([
    ('scaler', scaler),
    ('cbs', cbs)
])
X_corrected = pipeline.fit_transform(X)

# 查看修正前后的特征分布
import matplotlib.pyplot as plt
fig, axs = plt.subplots(nrows=13, ncols=2, figsize=(10, 50))
for i in range(X.shape[1]):
    axs[i, 0].hist(X[:, i], bins=50)
    axs[i, 0].set_title(boston.feature_names[i])
    axs[i, 1].hist(X_corrected[:, i], bins=50)
    axs[i, 1].set_title(boston.feature_names[i] + ' (corrected)')
plt.show()

参考资料：[Python实现CBS算法的异常值处理](https://www.jianshu.com/p/9dd9d1e5c921)