def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.RaoBlackwellACMC(x0)
# Configure
mcmc.set_target_acceptance_rate(0.3)
mcmc.set_initial_phase(True)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(sample)
rate.append(mcmc.acceptance_rate())
if np.all(sample == x):
self.assertEqual(mcmc.current_log_pdf(), fx)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)
def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.RaoBlackwellACMC(x0)
# Configure
mcmc.set_target_acceptance_rate(0.3)
mcmc.set_initial_phase(True)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
y, fy, ac = mcmc.tell(fx)
if i == 20:
mcmc.set_initial_phase(False)
if i >= 50:
chain.append(y)
rate.append(mcmc.acceptance_rate())
self.assertTrue(isinstance(ac, bool))
if ac:
self.assertTrue(np.all(x == y))
self.assertEqual(fx, fy)
chain = np.array(chain)
rate = np.array(rate)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
self.assertEqual(rate.shape[0], 100)
def test_name(self):
# Test name method
mcmc = pints.RaoBlackwellACMC(self.real_parameters)
self.assertEqual(mcmc.name(), 'Rao-Blackwell adaptive covariance MCMC')
def test_hyperparameters(self):
# Hyperparameters unchanged from base class
mcmc = pints.RaoBlackwellACMC(self.real_parameters)
self.assertEqual(mcmc.n_hyper_parameters(), 1)