

@classmethod
def from_parameters(cls, N, data_params=default_data_parameters, hypers=None, gen_seed=0):
    '\n        Initialize a continuous component model with sufficient statistics\n        generated from random data.\n        Inputs:\n          N: the number of data points\n          data_params: a dict with the following keys\n              mu: the mean of the data\n              kappa: the precision of the data\n          hypers: a dict with the following keys\n              a: the prior precision of the mean\n              b: the prior mean of the\n              kappa: precision parameter\n          gen_seed: an integer from which the rng is seeded\n        '
    check_model_params_dict(data_params)
    data_kappa = data_params['kappa']
    data_mean = data_params['mu']
    rng = random.Random(gen_seed)
    X = [[(rng.vonmisesvariate((gen_seed - math.pi), data_kappa) + math.pi)] for i in range(N)]
    X = numpy.array(X)
    check_data_type_column_data(X)
    if (hypers is None):
        hypers = cls.draw_hyperparameters(X, n_draws=1, gen_seed=next_seed(rng))[0]
    check_hyperparams_dict(hypers)
    sum_sin_x = numpy.sum(numpy.sin(X))
    sum_cos_x = numpy.sum(numpy.cos(X))
    hypers['fixed'] = 0.0
    return cls(hypers, float(N), sum_sin_x, sum_cos_x)

dataset/ETHPy150Open probcomp/crosscat/src/tests/component_model_extensions/CyclicComponentModel.py p_CyclicComponentModel.from_parameters/VarMisuse@67/283 `data_mean`->`gen_seed`