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generators.utils.noises

Data - Generators - VAR¤

GaussianNoise ¤

1 D Gaussian noise

:param mu: mean of the Gaussian distribution :param std: standard deviation of the Gaussian distribution :param seed: seed of the RNG for reproducibility

Source code in eerily/generators/utils/noises.py 
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class GaussianNoise:
    """1 D Gaussian noise

    :param mu: mean of the Gaussian distribution
    :param std: standard deviation of the Gaussian distribution
    :param seed: seed of the RNG for reproducibility
    """

    def __init__(self, mu: float, std: float, seed: Optional[float] = None):
        self.mu = mu
        self.std = std
        self.rng = np.random.default_rng(seed=seed)

    def __iter__(self):
        return self

    def __next__(self) -> float:
        return self.rng.normal(self.mu, self.std)

LogNormalNoise ¤

1 D lognormal noise

:param mu: mean of the Gaussian distribution :param std: standard deviation of the Gaussian distribution :param seed: seed of the RNG for reproducibility

Source code in eerily/generators/utils/noises.py 
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class LogNormalNoise:
    """1 D lognormal noise

    :param mu: mean of the Gaussian distribution
    :param std: standard deviation of the Gaussian distribution
    :param seed: seed of the RNG for reproducibility
    """

    def __init__(self, mu: float, std: float, seed: Optional[float] = None):
        self.mu = mu
        self.std = std
        self.rng = np.random.default_rng(seed=seed)

    def __iter__(self):
        return self

    def __next__(self) -> float:
        return self.rng.lognormal(self.mu, self.std)

MultiGaussianNoise ¤

A multivariate Gaussian noise

To generate constants,

mge = MultiGaussianEpsilon(
    mu=np.array([1,2]), cov=np.array([
        [0, 0],
        [0, 0]
    ])
)

To generate independent noises,

mge = MultiGaussianEpsilon(
    mu=np.array([1,2]), cov=np.array([
        [1, 0],
        [0, 1]
    ])
)

:param mu: means of the variables :param cov: covariance of the variables :param seed: seed of the random number generator for reproducibility

Source code in eerily/generators/utils/noises.py 
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class MultiGaussianNoise:
    """A multivariate Gaussian noise

    To generate constants,

    ```python
    mge = MultiGaussianEpsilon(
        mu=np.array([1,2]), cov=np.array([
            [0, 0],
            [0, 0]
        ])
    )
    ```

    To generate independent noises,

    ```python
    mge = MultiGaussianEpsilon(
        mu=np.array([1,2]), cov=np.array([
            [1, 0],
            [0, 1]
        ])
    )
    ```

    :param mu: means of the variables
    :param cov: covariance of the variables
    :param seed: seed of the random number generator for reproducibility
    """

    def __init__(self, mu: np.ndarray, cov: np.ndarray, seed: Optional[float] = None):
        self.mu = mu
        self.cov = cov
        self.rng = np.random.default_rng(seed=seed)

    def __next__(self) -> np.ndarray:
        return self.rng.multivariate_normal(self.mu, self.cov)