def _cumulative_hazard(self, params, T, Xs):
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
sigma_ = np.exp(np.dot(Xs["sigma_"], sigma_params))
Z = (np.log(T) - mu_) / sigma_
return -norm.logsf(Z)
def _cumulative_hazard(self, params, T, Xs):
sigma_2 = np.exp(np.dot(Xs["sigma_"], params["sigma_"]))
sigma_ = np.sqrt(sigma_2)
mu_ = np.dot(Xs["mu_"], params["mu_"])
Z = (np.log(T) - mu_) / sigma_
return -norm.logsf(Z)
def _cumulative_hazard(
self, params: Union[DictBox, Dict[str, ndarray]], T: Union[float, ndarray], Xs: DataframeSlicer
) -> Union[ndarray, ArrayBox]:
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
sigma_ = safe_exp(np.dot(Xs["sigma_"], sigma_params))
Z = (np.log(T) - mu_) / sigma_
return -norm.logsf(Z)
def _log_hazard(self, params, T, Xs):
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
log_sigma_ = np.dot(Xs["sigma_"], sigma_params)
sigma_ = np.exp(log_sigma_)
Z = (np.log(T) - mu_) / sigma_
return norm.logpdf(Z) - log_sigma_ - np.log(T) - norm.logsf(Z)
def _log_hazard(self, params: DictBox, T: Union[float, ndarray], Xs: DataframeSlicer) -> ArrayBox:
mu_params = params["mu_"]
mu_ = np.dot(Xs["mu_"], mu_params)
sigma_params = params["sigma_"]
log_sigma_ = np.dot(Xs["sigma_"], sigma_params)
sigma_ = safe_exp(log_sigma_)
Z = (np.log(T) - mu_) / sigma_
return norm.logpdf(Z) - log_sigma_ - np.log(T) - norm.logsf(Z)
def _cumulative_hazard(self, params, times):
mu_, ln_sigma_, lambda_ = params
sigma_ = safe_exp(ln_sigma_)
Z = (log(times) - mu_) / sigma_
ilambda_2 = 1 / lambda_**2
clipped_exp = np.clip(safe_exp(lambda_ * Z) * ilambda_2, 1e-300, 1e20)
if lambda_ > 0:
v = -gammainccln(ilambda_2, clipped_exp)
elif lambda_ < 0:
v = -gammaincln(ilambda_2, clipped_exp)
else:
v = -norm.logsf(Z)
return v
def _log_hazard(self, params, times):
mu_, ln_sigma_, lambda_ = params
ilambda_2 = 1 / lambda_**2
Z = (log(times) - mu_) / safe_exp(ln_sigma_)
clipped_exp = np.clip(safe_exp(lambda_ * Z) * ilambda_2, 1e-15, 1e20)
if lambda_ > 0:
v = (log(lambda_) - log(times) - ln_sigma_ - gammaln(ilambda_2) +
-2 * log(lambda_) * ilambda_2 - clipped_exp + Z / lambda_ -
gammainccln(ilambda_2, clipped_exp))
elif lambda_ < 0:
v = (log(-lambda_) - log(times) - ln_sigma_ - gammaln(ilambda_2) -
2 * log(-lambda_) * ilambda_2 - clipped_exp + Z / lambda_ -
gammaincln(ilambda_2, clipped_exp))
else:
v = norm.logpdf(Z, loc=0,
scale=1) - ln_sigma_ - log(times) - norm.logsf(Z)
return v
def _log_hazard(self, params, times):
mu_, sigma_ = params
Z = (np.log(times) - mu_) / sigma_
return norm.logpdf(
Z, loc=0, scale=1) - np.log(sigma_) - np.log(times) - norm.logsf(Z)
def _cumulative_hazard(self, params, times):
mu_, sigma_ = params
Z = (np.log(times) - mu_) / sigma_
return -norm.logsf(Z)
