我正在研究分类问题,我需要在我的数据集中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。有关如何添加高斯噪音的任何建议或编码提示?
您可以按照以下步骤操作:
clean_signal = pd.read_csv("data_file_name")
中signal = clean_signal + noise
添加噪音以清洁信号这是一个可重复的例子:
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
"""
没有评论和打印声明的整体代码:
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)