def core(p, betaL, betaH, mL, mH):
CL = inttail(betaL, mL)
CH = inttail(betaH, mH)
C = CL + CH
N = 1 / (C + intcore(betaL, betaH))
return _norm_ppf(
_norm_cdf(-betaL) + (1 / _norm_pdf_C) * (p / N - CL))
def _ppf(self, q, a, b):
output = np.zeros_like(self.a)
idx = self.a > 0
if np.any(idx):
output[idx] = _norm_isf(q[idx] * self._sb[idx] + self._sa[idx] * (-q[idx] + 1.0))
idx = np.logical_not(idx)
if np.any(idx):
output[idx] = _norm_ppf(q[idx] * self._nb[idx] + self._na[idx] * (-q[idx] + 1.0))
return output
def _ppf_version_131(self, q, a, b):
with np.errstate(divide='raise',
over='raise',
under='ignore',
invalid='raise'):
ans = self._get_norms(a, b)
_na, _nb, _sa, _sb = ans[:4]
ppf = np.where(a > 0, _norm_isf(q * _sb + _sa * (1.0 - q)),
_norm_ppf(q * _nb + _na * (1.0 - q)))
return ppf
def _ppf(self, q, a, b):
output = np.zeros_like(self.a)
idx = self.a > 0
if np.any(idx):
output[idx] = _norm_isf(q[idx] * self._sb[idx] +
self._sa[idx] * (-q[idx] + 1.0))
idx = np.logical_not(idx)
if np.any(idx):
output[idx] = _norm_ppf(q[idx] * self._nb[idx] +
self._na[idx] * (-q[idx] + 1.0))
return output
def _ppf(self, q, a, b):
return np.where(
self.a > 0,
-crv_helper._norm_ppf(q * self._cdfminb + self._cdfmina * (1.0 - q)),
crv_helper._norm_ppf(q * self._cdfb + self._cdfa * (1.0 - q)),
)
