def _log_hazard(self, params, times):
# mu_, ln_sigma_, lambda_ = params # 変更前
mu_, lambda_ = params # 変更後
ilambda_2 = 1 / lambda_ ** 2
# Z = (log(times) - mu_) / safe_exp(ln_sigma_) # 変更前
Z = (log(times) - mu_) / lambda_ # 変更後
if lambda_ > 0:
v = (
# log(lambda_) # 削除
- log(times)
# - ln_sigma_ # 削除
- gammaln(ilambda_2)
+ (lambda_ * Z - safe_exp(lambda_ * Z) - 2 * log(lambda_)) * ilambda_2
- gammainccln(ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(lambda_)))
)
else:
v = (
# log(-lambda_) # 削除
- log(times)
# - ln_sigma_ # 削除
- gammaln(ilambda_2)
+ (lambda_ * Z - safe_exp(lambda_ * Z) - 2 * log(-lambda_)) * ilambda_2
- gammaincln(ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(-lambda_)))
)
return v
def _log_1m_sf(self, params, times):
mu_, ln_sigma_, lambda_ = params
sigma_ = safe_exp(ln_sigma_)
Z = (log(times) - mu_) / sigma_
if lambda_ > 0:
v = gammaincln(1 / lambda_ ** 2, safe_exp(lambda_ * Z - 2 * np.log(lambda_)))
else:
v = gammainccln(1 / lambda_ ** 2, safe_exp(lambda_ * Z - 2 * np.log(-lambda_)))
return v
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
if lambda_ > 0:
v = -gammainccln(ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(lambda_)))
else:
v = -gammaincln(ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(-lambda_)))
return v
def _log_1m_sf(self, params, times):
mu_, ln_sigma_, lambda_ = params
sigma_ = exp(ln_sigma_)
Z = (log(times) - mu_) / sigma_
if lambda_ > 0:
v = gammaincln(1 / lambda_**2, exp(lambda_ * Z) / lambda_**2)
else:
v = gammainccln(1 / lambda_**2, exp(lambda_ * Z) / lambda_**2)
return v
def _cumulative_hazard(self, params, times):
mu_, ln_sigma_, lambda_ = params
sigma_ = exp(ln_sigma_)
Z = (log(times) - mu_) / sigma_
ilambda_2 = 1 / lambda_**2
if lambda_ > 0:
v = -gammainccln(ilambda_2, exp(lambda_ * Z) * ilambda_2)
else:
v = -gammaincln(ilambda_2, exp(lambda_ * Z) * ilambda_2)
return v
def _cumulative_hazard(self, params, T, Xs):
lambda_ = Xs["lambda_"] @ params["lambda_"]
sigma_ = safe_exp(Xs["sigma_"] @ params["sigma_"])
mu_ = Xs["mu_"] @ params["mu_"]
ilambda_2 = 1 / lambda_ ** 2
Z = (log(T) - mu_) / np.clip(sigma_, 1e-10, 1e20)
exp_term = np.clip(safe_exp(lambda_ * Z - 2 * log(np.abs(lambda_))), 1e-300, np.inf)
return -np.where(lambda_ > 0, gammainccln(ilambda_2, exp_term), gammaincln(ilambda_2, exp_term))
def _log_hazard(self, params, T, Xs):
lambda_ = Xs["lambda_"] @ params["lambda_"]
ln_sigma_ = Xs["sigma_"] @ params["sigma_"]
mu_ = Xs["mu_"] @ params["mu_"]
ilambda_2 = 1 / lambda_**2
Z = (log(T) - mu_) / np.clip(safe_exp(ln_sigma_), 0, 1e20)
exp_term = np.clip(safe_exp(lambda_ * Z) * ilambda_2, 1e-300, 1e25)
return (log(np.abs(lambda_)) - log(T) - ln_sigma_ -
gammaln(ilambda_2) +
(lambda_ * Z - 2 * log(np.abs(lambda_))) * ilambda_2 -
exp_term -
np.where(lambda_ > 0, gammainccln(ilambda_2, exp_term),
gammaincln(ilambda_2, exp_term)))
def _log_1m_sf(self, params, times):
mu_, ln_sigma_, lambda_ = params
sigma_ = safe_exp(ln_sigma_)
ilambda_2 = 1 / lambda_**2
Z = (log(times) - mu_) / sigma_
clipped_exp = np.clip(safe_exp(lambda_ * Z) * ilambda_2, 1e-15, 1e20)
if lambda_ > 0:
v = gammaincln(1 / lambda_**2, clipped_exp)
elif lambda_ < 0:
v = gammainccln(1 / lambda_**2, clipped_exp)
else:
v = norm.logcdf(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_)
if lambda_ > 0:
v = (log(lambda_) - log(times) - ln_sigma_ - gammaln(ilambda_2) +
(lambda_ * Z - safe_exp(lambda_ * Z) - 2 * log(lambda_)) *
ilambda_2 - gammainccln(
ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(lambda_))))
else:
v = (log(-lambda_) - log(times) - ln_sigma_ - gammaln(ilambda_2) +
(lambda_ * Z - safe_exp(lambda_ * Z) - 2 * log(-lambda_)) *
ilambda_2 - gammaincln(
ilambda_2, safe_exp(lambda_ * Z - 2 * np.log(-lambda_))))
return v
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 test_log_gamma():
gammaincln(1., 1.)
grad(gammaincln)(1., 1.)
grad(gammaincln, argnum=1)(1., 1.)
