def test_flow(self):
# Test initial proposal is first point
x0 = self.real_parameters
mcmc = pints.MetropolisRandomWalkMCMC(x0)
self.assertTrue(mcmc.ask() is mcmc._x0)
# Double initialisation
mcmc = pints.MetropolisRandomWalkMCMC(x0)
mcmc.ask()
self.assertRaises(RuntimeError, mcmc._initialise)
# Tell without ask
mcmc = pints.MetropolisRandomWalkMCMC(x0)
self.assertRaises(RuntimeError, mcmc.tell, 0)
# Repeated asks should return same point
mcmc = pints.MetropolisRandomWalkMCMC(x0)
# Get nearer accepting state
for i in range(100):
mcmc.tell(self.log_posterior(mcmc.ask()))
x = mcmc.ask()
for i in range(10):
self.assertTrue(x is mcmc.ask())
# Repeated tells should fail
mcmc.tell(1)
self.assertRaises(RuntimeError, mcmc.tell, 1)
# Bad starting point
mcmc = pints.MetropolisRandomWalkMCMC(x0)
mcmc.ask()
self.assertRaises(ValueError, mcmc.tell, float('-inf'))
def test_replace(self):
x0 = self.real_parameters * 1.1
mcmc = pints.MetropolisRandomWalkMCMC(x0)
# One round of ask-tell must have been run
self.assertRaisesRegex(RuntimeError, 'already running', mcmc.replace,
x0, 1)
mcmc.ask()
# One round of ask-tell must have been run
self.assertRaises(RuntimeError, mcmc.replace, x0, 1)
mcmc.tell(0.5)
mcmc.replace([1, 2, 3], 10)
mcmc.replace([1, 2, 3], 10)
# New position must have correct size
self.assertRaisesRegex(ValueError,
'`current` has the wrong dimensions',
mcmc.replace, [1, 2], 1)
# Proposal can be changed too
mcmc.ask()
mcmc.replace([1, 2, 3], 10, [3, 4, 5])
# New proposal must have correct size
self.assertRaisesRegex(ValueError,
'`proposed` has the wrong dimensions',
mcmc.replace, [1, 2, 3], 3, [3, 4])
def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.MetropolisRandomWalkMCMC(x0)
# 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 >= 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_set_hyper_parameters(self):
# Tests the hyper-parameter interface for this optimiser.
x0 = self.real_parameters
mcmc = pints.MetropolisRandomWalkMCMC(x0)
self.assertEqual(mcmc.n_hyper_parameters(), 0)
mcmc.set_hyper_parameters([])
def test_replace(self):
x0 = self.real_parameters * 1.1
mcmc = pints.MetropolisRandomWalkMCMC(x0)
self.assertRaises(RuntimeError, mcmc.replace, x0, 1)
mcmc.ask()
self.assertRaises(RuntimeError, mcmc.replace, x0, 1)
mcmc.tell(0.5)
mcmc.replace([1, 2, 3], 10)
mcmc.replace([1, 2, 3], 10)
self.assertRaises(ValueError, mcmc.replace, [1, 2], 1)
def test_method(self):
# Create mcmc
x0 = self.real_parameters * 1.1
mcmc = pints.MetropolisRandomWalkMCMC(x0)
# Perform short run
rate = []
chain = []
for i in range(100):
x = mcmc.ask()
fx = self.log_posterior(x)
sample = mcmc.tell(fx)
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)