将高斯噪声添加到浮点数据集并保存 (python)

Question

我正在处理分类问题，我需要向我的数据集添加不同级别的高斯噪声并进行分类实验，直到我的 ML 算法无法对数据集进行分类。不幸的是我不知道该怎么做。关于如何添加高斯噪声的任何建议或编码技巧？

Answer 1

您可以按照以下步骤操作：

将数据加载到 pandas 数据帧中 clean_signal = pd.read_csv("data_file_name")
使用numpy生成与数据集同维的高斯噪声。
使用 signal = clean_signal + noise

这是一个可重现的例子：

import pandas as pd
# create a sample dataset with dimension (2,2)
# in your case you need to replace this with 
# clean_signal = pd.read_csv("your_data.csv")   
clean_signal = pd.DataFrame([[1,2],[3,4]], columns=list('AB'), dtype=float) 
print(clean_signal)
"""
print output: 
    A    B
0  1.0  2.0
1  3.0  4.0
"""
import numpy as np 
mu, sigma = 0, 0.1 
# creating a noise with the same dimension as the dataset (2,2) 
noise = np.random.normal(mu, sigma, [2,2]) 
print(noise)

"""
print output: 
array([[-0.11114313,  0.25927152],
       [ 0.06701506, -0.09364186]])
"""
signal = clean_signal + noise
print(signal)
"""
print output: 
          A         B
0  0.888857  2.259272
1  3.067015  3.906358
"""

没有注释和打印语句的整体代码：

import pandas as pd
# clean_signal = pd.read_csv("your_data.csv")
clean_signal = pd.DataFrame([[1,2],[3,4]], columns=list('AB'), dtype=float) 
import numpy as np 
mu, sigma = 0, 0.1 
noise = np.random.normal(mu, sigma, [2,2])
signal = clean_signal + noise

将文件保存回 csv

signal.to_csv("output_filename.csv", index=False)

将高斯噪声添加到浮点数据集并保存 (python)

Adding gaussian noise to a dataset of floating points and save it (python)

python-3.x

machine-learning

classification

noise