def poisson_sample(
    offset,
    G,
    heritability=0.5,
    causal_variants=None,
    causal_variance=0,
    random_state=None,
):
    """Poisson likelihood sampling.

    Parameters
    ----------
    random_state : random_state
        Set the initial random state.

    Example
    -------

    .. doctest::

        >>> from glimix_core.random import poisson_sample
        >>> from numpy.random import RandomState
        >>> offset = -0.5
        >>> G = [[0.5, -1], [2, 1]]
        >>> poisson_sample(offset, G, random_state=RandomState(0))
        array([0, 6])
    """
    mean, cov = _mean_cov(
        offset, G, heritability, causal_variants, causal_variance, random_state
    )
    link = LogLink()
    lik = PoissonProdLik(link)
    sampler = GGPSampler(lik, mean, cov)

    return sampler.sample(random_state)
def poisson_sample(
    offset,
    G,
    heritability=0.5,
    causal_variants=None,
    causal_variance=0,
    random_state=None,
):
    
    mean, cov = _mean_cov(
        offset, G, heritability, causal_variants, causal_variance, random_state
    )
    link = LogLink()
    lik = PoissonProdLik(link)
    sampler = GGPSampler(lik, mean, cov)

    return sampler.sample(random_state)
def poisson_sample ( offset , G , heritability = 0.5 , causal_variants = None , causal_variance = 0 , random_state = None , ) : mean , cov = _mean_cov ( offset , G , heritability , causal_variants , causal_variance , random_state ) link = LogLink ( ) lik = PoissonProdLik ( link ) sampler = GGPSampler ( lik , mean , cov ) return sampler . sample ( random_state )
Poisson likelihood sampling.

    Parameters
    ----------
    random_state : random_state
        Set the initial random state.

    Example
    -------

    .. doctest::

        >>> from glimix_core.random import poisson_sample
        >>> from numpy.random import RandomState
        >>> offset = -0.5
        >>> G = [[0.5, -1], [2, 1]]
        >>> poisson_sample(offset, G, random_state=RandomState(0))
        array([0, 6])
Poisson likelihood sampling .