9a8f5127d38037de5d56ba2279fbaff6d8374960,pymc3/distributions/discrete.py,BetaBinomial,_random,#BetaBinomial#Any#Any#Any#Any#,155

Before Change

    def _random(self, alpha, beta, n, size=None):
        size = size or 1
        p = np.atleast_1d(stats.beta.rvs(a=alpha, b=beta, size=np.prod(size)))
        // Sometimes scipy.beta returns nan. Ugh.
        while np.any(np.isnan(p)):
            i = np.isnan(p)

After Change

            i = np.isnan(p)
            p[i] = stats.beta.rvs(a=alpha, b=beta, size=np.sum(i))
        // Sigh...
        _n, _p, _size = np.atleast_1d(n).flatten(), p.flatten(), p.shape[0]

        quotient, remainder = divmod(_p.shape[0], _n.shape[0])
        if remainder != 0:
            raise TypeError("n has a bad size! Was cast to {}, must evenly divide {}".format(

Instances