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

Adding gaussian noise to a dataset of floating points is a common task in data analysis and machine learning. In Python, there are several ways to achieve this. In this article, we will explore three different approaches to solve this problem.

Approach 1: Using NumPy

NumPy is a powerful library for scientific computing in Python. It provides a wide range of mathematical functions, including the ability to generate random numbers from various distributions.

import numpy as np

def add_gaussian_noise(data, mean, std_dev):
    noise = np.random.normal(mean, std_dev, data.shape)
    noisy_data = data + noise
    return noisy_data

# Example usage
data = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
mean = 0.0
std_dev = 0.1
noisy_data = add_gaussian_noise(data, mean, std_dev)
print(noisy_data)

In this approach, we use the np.random.normal function from NumPy to generate random numbers from a Gaussian distribution with the specified mean and standard deviation. We then add this noise to the original data to obtain the noisy data.

Approach 2: Using the random module

If you don’t want to use NumPy, you can achieve the same result using the built-in random module in Python.

import random

def add_gaussian_noise(data, mean, std_dev):
    noisy_data = []
    for value in data:
        noise = random.gauss(mean, std_dev)
        noisy_data.append(value + noise)
    return noisy_data

# Example usage
data = [1.0, 2.0, 3.0, 4.0, 5.0]
mean = 0.0
std_dev = 0.1
noisy_data = add_gaussian_noise(data, mean, std_dev)
print(noisy_data)

In this approach, we use the random.gauss function from the random module to generate random numbers from a Gaussian distribution. We iterate over each value in the data and add the generated noise to it.

Approach 3: Using the statistics module

If you only need to add noise to a single value, you can use the statistics.NormalDist class from the statistics module in Python.

import statistics

def add_gaussian_noise(value, mean, std_dev):
    noise = statistics.NormalDist(mean, std_dev).samples(1)[0]
    noisy_value = value + noise
    return noisy_value

# Example usage
value = 1.0
mean = 0.0
std_dev = 0.1
noisy_value = add_gaussian_noise(value, mean, std_dev)
print(noisy_value)

In this approach, we use the statistics.NormalDist class to generate a single random number from a Gaussian distribution. We then add this noise to the original value to obtain the noisy value.

After comparing these three approaches, it is clear that Approach 1 using NumPy is the most efficient and concise solution. NumPy provides a wide range of mathematical functions and is optimized for numerical operations, making it the ideal choice for this task. However, if you prefer to avoid external libraries, Approach 2 using the random module is a viable alternative. Approach 3 using the statistics module is suitable only if you need to add noise to a single value.

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