def quantile(self, *q):
# check array for numpy structure
q = check_array(q, reduce_args=True, ensure_1d=True)
# get the upper value of X (ceiling)
X_up = np.ceil(sc.nbdtrik(q, self.n_success, self.bias))
# get the lower value of X (floor)
X_down = np.maximum(X_up - 1, 0)
# recompute quantiles to validate transformation
q_test = sc.nbdtr(X_down, self.n_success, self.bias)
# when q_test is greater than true, shift output down
out = np.where(q_test >= q, X_down, X_up).astype(int)
# return only in-bound values
return np.where(self.support.contains(out), out, np.nan)
def _ppf(self, q, n, p):
vals = ceil(special.nbdtrik(q, n, p))
vals1 = (vals - 1).clip(0.0, np.inf)
temp = self._cdf(vals1, n, p)
return np.where(temp >= q, vals1, vals)
def nbdtrik_comp(y, n, p):
return nbdtrik(1-y, n, p)
def nbdtrik_comp(y, n, p):
return nbdtrik(1-y, n, p)
def _ppf(self, q, n, p):
vals = ceil(special.nbdtrik(q, n, p))
vals1 = (vals-1).clip(0.0, np.inf)
temp = self._cdf(vals1, n, p)
return np.where(temp >= q, vals1, vals)