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

I'm working on classification problem where i need to add different levels of gaussian noise to my dataset and do classification experiments until my ML algorithms can't classify the dataset. unfortunately i have no idea how to do that. any advise or coding tips on how to add the gaussian noise?

1 Answer

You can follow these steps:

1. Load the data into a pandas dataframe clean_signal = pd.read_csv("data_file_name")
2. Use numpy to generate Gaussian noise with the same dimension as the dataset.
3. Add gaussian noise to the clean signal with signal = clean_signal + noise

Here's a reproducible example:

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 """ 

Overall code without the comments and print statements:

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 

To save the file back to csv

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