def compute_post(self):
self.prop.lpost[0] = self.prior.logpdf(self.prop.theta)
if np.isfinite(self.prop.lpost[0]):
ssm_kwargs_dict = ssp.rec_to_dict(self.prop.theta[0])
pf = self.alg_instance(ssm_kwargs_dict)
pf.run()
self.prop_history = pf.hist # store SMC sampler history
self.prop.lpost[0] += pf.logLt
def loglik(self, theta, t=None):
# Note: for simplicity we ignore argument t here,
# and compute the full log-likelihood
ll = np.zeros(theta.shape[0])
for n, th in enumerate(theta):
mod = ReparamLinGauss(**smc_samplers.rec_to_dict(th))
exact = mod.kalman_filter(data)
ll[n] = np.sum(exact.logpyts)
return ll
def compute_post(self):
self.prop.lpost[0] = self.prior.logpdf(self.prop.theta)
if np.isfinite(self.prop.lpost[0]):
pf = self.alg_instance(ssp.rec_to_dict(self.prop.theta[0]))
pf.run()
self.prop.lpost[0] += pf.logLt
if self.keep_states:
#self.states.append(np.array([m['mean'] for m in pf.summaries.moments]))
self.states.append(np.array(pf.hist.extract_one_trajectory()))
def update_states(self, theta, x):
if x is None:
new_x, _ = ssm_cls(**smc_samplers.rec_to_dict(theta)).simulate(
self.T)
else:
new_x = x[:]
xa = np.array(new_x).flatten()
m = np.matmul(self.A, self.tod * xa + self.grad_log_lik(xa))
xp = stats.multivariate_normal.rvs(mean=m, cov=self.cov)
mh_log_ratio = (self.log_lik(xp) - self.log_lik(xa) - self.h(xp, xa) +
self.h(xa, xp))
if np.log(random.uniform()) <= mh_log_ratio:
self.nacc += 1
return list(xp)
else:
return new_x
def update_states(self, theta, x):
dt = smc_samplers.rec_to_dict(theta)
T = len(self.data)
if x is None:
new_x, _ = ssm_cls(**dt).simulate(T)
else:
new_x = x[:]
mu, sigma, rho = dt['mu'], dt['sigma'], dt['rho']
a = 1. + rho**2
sp = sigma / np.sqrt(a)
c = rho / a
for t, yt in enumerate(self.data):
if t == 0:
m = mu + rho * (new_x[1] - mu)
s = sigma
elif t == T - 1:
m = mu + rho * (new_x[-2] - mu)
s = sigma
else:
m = mu + c * (new_x[t - 1] + new_x[t + 1] - 2. * mu)
s = sp
new_x[t] = reject_sv(m, s, yt)
return new_x
def fk_mod(self, theta):
ssm = self.ssm_cls(**ssp.rec_to_dict(theta))
return self.fk_cls(ssm=ssm, data=self.data)
def compute_post(self):
self.prop.lpost[0] = self.prior.logpdf(self.prop.theta)
if np.isfinite(self.prop.lpost[0]):
pf = self.alg_instance(ssp.rec_to_dict(self.prop.theta[0]))
pf.run()
self.prop.lpost[0] += pf.logLt
Nx=Nx, niter=niter, adaptive=False, rw_cov=rw_cov,
verbose=10)
# Run the algorithms
####################
for alg_name, alg in algos.items():
print('\nRunning ' + alg_name)
alg.run()
print('CPU time: %.2f min' % (alg.cpu_time / 60))
# Compute variances
###################
thin = int(niter / 100) # compute average (of variances) over 100 points
thetas = algos['mh'].chain.theta[(burnin - 1)::thin]
fks = {k: ssm.Bootstrap(ssm=ReparamLinGauss(**smc_samplers.rec_to_dict(th)), data=data)
for k, th in enumerate(thetas)}
outf = lambda pf: pf.logLt
print('Computing variances of log-lik estimates as a function of N')
results = particles.multiSMC(fk=fks, N=Nxs, nruns=4, nprocs=0, out_func=outf)
df = pandas.DataFrame(results)
df_var = df.groupby(['fk', 'N']).var() # variance as a function of fk and N
df_var = df_var.reset_index()
df_var_mean = df_var.groupby('N').mean() # mean variance as function of N
# Plots
#######
savefigs = False
plt.style.use('ggplot')
def msjd(theta):
verbose=10)
# Run the algorithms
####################
for alg_name, alg in algos.items():
print('\nRunning ' + alg_name)
alg.run()
print('CPU time: %.2f min' % (alg.cpu_time / 60))
# Compute variances
###################
thin = int(niter / 100) # compute average (of variances) over 100 points
thetas = algos['mh'].chain.theta[(burnin - 1)::thin]
fks = {
k: ssms.Bootstrap(ssm=ReparamLinGauss(**smc_samplers.rec_to_dict(th)),
data=data)
for k, th in enumerate(thetas)
}
outf = lambda pf: pf.logLt
print('Computing variances of log-lik estimates as a function of N')
results = particles.multiSMC(fk=fks, N=Nxs, nruns=4, nprocs=0, out_func=outf)
df = pandas.DataFrame(results)
df_var = df.groupby(['fk', 'N']).var() # variance as a function of fk and N
df_var = df_var.reset_index()
df_var_mean = df_var.groupby('N').mean() # mean variance as function of N
# Plots
#######
savefigs = True # False if you don't want to save plots as pdfs
plt.style.use('ggplot')
