def _logpmf(self, x, mu, alpha, p, w): s, p = self.convert_params(mu, alpha, p) return _lazywhere(x != 0, (x, s, p, w), (lambda x, s, p, w: np.log(1. - w) + nbinom.logpmf(x, s, p)), np.log(w + (1. - w) * nbinom.pmf(x, s, p)))
def _logpmf(self, x, mu, alpha, p, w): s, p = self.convert_params(mu, alpha, p) return _lazywhere(x != 0, (x, s, p, w), (lambda x, s, p, w: np.log(1. - w) + nbinom.logpmf(x, s, p)), np.log(w + (1. - w) * nbinom.pmf(x, s, p)))
def _logpmf(self, x, mu, alpha, p, w): return _lazywhere( x != 0, (x, mu, alpha, p, w), (lambda x, mu, alpha, p, w: np.log(1. - w) + genpoisson_p.logpmf( x, mu, alpha, p)), np.log(w + (1. - w) * genpoisson_p.pmf(x, mu, alpha, p)))
def _logpmf(self, x, mu, w): return _lazywhere(x != 0, (x, mu, w), (lambda x, mu, w: np.log( 1. - w) + x * np.log(mu) - gammaln(x + 1.) - mu), np.log(w + (1. - w) * np.exp(-mu)))
def _logpmf(self, x, mu, alpha, p, w): return _lazywhere(x != 0, (x, mu, alpha, p, w), (lambda x, mu, alpha, p, w: np.log(1. - w) + genpoisson_p.logpmf(x, mu, alpha, p)), np.log(w + (1. - w) * genpoisson_p.pmf(x, mu, alpha, p)))
def _logpmf(self, x, mu, w): return _lazywhere(x != 0, (x, mu, w), (lambda x, mu, w: np.log(1. - w) + x * np.log(mu) - gammaln(x + 1.) - mu), np.log(w + (1. - w) * np.exp(-mu)))