def __call__(self , i_particle):
rho = 1.e37
prior_obj = Prior(self.prior_dict)
while rho > self.eps_t:
theta_star = utils.weighted_sampling(self.theta_t, self.w_t)
np.random.seed()
theta_starstar = np.random.multivariate_normal(theta_star, self.sig_t, 1)[0]
#theta_starstar = multivariate_normal(theta_star, self.sig_t).rvs(size=1)
model_starstar = self.model(theta_starstar)
rho = self.distance(self.data, model_starstar)
p_theta = prior_obj.pi_priors(theta_starstar)
w_starstar = p_theta / np.sum(self.w_t * \
utils.better_multinorm(theta_starstar,
self.theta_t,
self.sig_t) )
pool_list = [np.int(i_particle)]
theta_starstar = np.atleast_1d(theta_starstar)
for i_param in xrange(self.n_params):
pool_list.append(theta_starstar[i_param])
pool_list.append(w_starstar)
pool_list.append(rho)
return pool_list
def __call__(self , i_particle):
rho = 1.e100
prior_obj = Prior(self.prior_dict)
while np.all(rho < self.eps_t)==False:
theta_star = utils.weighted_sampling(self.theta_t, self.w_t)
theta_starstar = np.random.multivariate_normal(theta_star, self.sig_t, 1)[0]
while np.all((prior_range[:,0] < theta_starstar)&(theta_starstar < prior_range[:,1]))==False:
theta_star = utils.weighted_sampling(self.theta_t, self.w_t)
theta_starstar = np.random.multivariate_normal(theta_star, self.sig_t).rvs(size=1)
model_starstar = self.model(theta_starstar)
rho = self.distance(self.data, model_starstar)
p_theta = prior_obj.pi_priors(theta_starstar)
w_starstar = p_theta / np.sum(self.w_t * \
utils.better_multinorm(theta_starstar,
self.theta_t,
self.sig_t) )
pool_list = [np.int(i_particle)]
theta_starstar = np.atleast_1d(theta_starstar)
for i_param in xrange(self.n_params):
pool_list.append(theta_starstar[i_param])
pool_list.append(w_starstar)
for r in rho:
pool_list.append(r)
return np.array(pool_list)
