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)))