题解 | 特征扩展实现

import numpy as np

def feature_scaling(data):
    # 补全代码
    # 标准化 (Z-score 标准化)
    mean = np.mean(data, axis=0)
    std = np.std(data, axis=0)
    standardized_data = (data - mean) / std

    # 最小-最大标准化
    min_vals = np.min(data, axis=0)
    max_vals = np.max(data, axis=0)
    min_max_scaled_data = (data - min_vals) / (max_vals - min_vals)

    # 四舍五入保留小数点后四位
    standardized_data = np.round(standardized_data, 4)
    min_max_scaled_data = np.round(min_max_scaled_data, 4)
    
    return standardized_data.tolist(),min_max_scaled_data.tolist()


# 主程序
if __name__ == "__main__":
    # 输入数组
    data = input()

    # 处理输入
    import ast
    data = ast.literal_eval(data)

    # 调用函数计算
    output = feature_scaling(data)
    
    # 输出结果
    print(output)