def log_likelihood(self, x):
if isinstance(x, np.ndarray):
x0 = x[:self.nb_lags]
log_lik = np.zeros((x0.shape[0], self.nb_states))
for k in range(self.nb_states):
log_lik[:, k] = lg_mvn(x0, self.mean(k), self.sigma[k])
return log_lik
else:
return list(map(self.log_likelihood, x))
def log_likelihood(self, x, u=None):
if isinstance(x, np.ndarray) and isinstance(u, np.ndarray):
log_lik = np.zeros((x.shape[0], self.nb_states))
for k in range(self.nb_states):
log_lik[:, k] = lg_mvn(x, self.mean(k), self.sigma[k])
return log_lik
else:
def inner(x, u):
return self.log_likelihood.__wrapped__(self, x, u)
return list(map(inner, x, u))
def log_likelihood(self, x, u):
if isinstance(x, np.ndarray) and isinstance(u, np.ndarray):
xr = arstack(x, self.nb_lags + 1)
ur = u[self.nb_lags:]
log_lik = np.zeros((ur.shape[0], self.nb_states))
for k in range(self.nb_states):
log_lik[:, k] = lg_mvn(ur, self.mean(k, xr), self.sigma[k])
return log_lik
else:
def inner(x, u):
return self.log_likelihood.__wrapped__(self, x, u)
return list(map(inner, x, u))
def log_likelihood(self, x, u):
if isinstance(x, np.ndarray) and isinstance(u, np.ndarray):
xr = arstack(x, self.nb_lags)[:-1]
ur = u[self.nb_lags - 1:-1]
xn = x[self.nb_lags:]
log_lik = np.zeros((xr.shape[0], self.nb_states))
for k in range(self.nb_states):
mu = self.mean(k, xr, ur)
log_lik[:, k] = lg_mvn(xn, mu, self.sigma[k])
return log_lik
else:
def inner(x, u):
return self.log_likelihood.__wrapped__(self, x, u)
return list(map(inner, x, u))