noise
GaussianNoiseClassesGenerator
Bases: NoiseGenerator
A generator that adds Gaussian white noise to each class separately.
Source code in badgers/generators/tabular_data/noise.py
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__init__(random_generator=default_rng(seed=0), repeat=1, noise_std_per_class=None)
:param random_generator: A random generator :param noise_std_per_class: A dictionary giving the standard deviation of the noise to be added for each class key = class labels, values = noise std for this given class
Source code in badgers/generators/tabular_data/noise.py
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generate(X, y, **params)
Add Gaussian white noise to the data.
the data is first standardized (each column has a mean = 0 and variance = 1).
The noise is generated from a normal distribution with standard deviation = noise_std
.
The noise is added to the data.
:param X: the input :param y: the target :param params: optional parameters :return: Xt, yt
Source code in badgers/generators/tabular_data/noise.py
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GaussianNoiseGenerator
Bases: NoiseGenerator
A generator that adds Gaussian white noise to the tabular data
Source code in badgers/generators/tabular_data/noise.py
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__init__(random_generator=default_rng(seed=0), noise_std=0.1, repeat=1)
:param random_generator: A random generator :param noise_std: The standard deviation of the noise to be added
Source code in badgers/generators/tabular_data/noise.py
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generate(X, y, **params)
Adds Gaussian white noise to the data.
The data is first standardized (each column has a mean = 0 and variance = 1).
The noise is generated from a normal distribution with standard deviation = noise_std
.
The noise is added to the data.
:param X: the input :param y: the target :param params: optional parameters :return: Xt, yt
Source code in badgers/generators/tabular_data/noise.py
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NoiseGenerator
Bases: GeneratorMixin
Base class for generators that add noise to tabular data
Source code in badgers/generators/tabular_data/noise.py
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__init__(random_generator=default_rng(seed=0), repeat=1)
:param random_generator: A random generator :param repeat: number of times a noisy point is generated from the original. repeat = 1 means that Xt.shape[0] == X.shape[0], repeat = 10 means that Xt.shape[0] == 10 * X.shape[0]
Source code in badgers/generators/tabular_data/noise.py
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