def test_method(self):
# Create mcmc
xs = [
self.real_parameters * 1.1,
self.real_parameters * 1.05,
self.real_parameters * 0.9,
self.real_parameters * 0.95,
]
mcmc = pints.DifferentialEvolutionMCMC(4, xs)
# Perform short run
chains = []
for i in range(100):
xs = mcmc.ask()
fxs = np.array([self.log_posterior(x) for x in xs])
ys, fys, ac = mcmc.tell(fxs)
if i >= 50:
chains.append(ys)
if np.any(ac):
self.assertTrue(np.all(xs[ac] == ys[ac]))
self.assertTrue(np.all(fys[ac] == fxs[ac]))
chains = np.array(chains)
self.assertEqual(chains.shape[0], 50)
self.assertEqual(chains.shape[1], len(xs))
self.assertEqual(chains.shape[2], len(xs[0]))
def test_set_hyper_parameters(self):
"""
Tests the hyper-parameter interface for this sampler.
"""
n = 3
x0 = [self.real_parameters] * n
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
self.assertEqual(mcmc.n_hyper_parameters(), 5)
mcmc.set_hyper_parameters([0.5, 0.6, 20, 0, 0])
self.assertEqual(mcmc._gamma, 0.5)
self.assertEqual(mcmc._b, 0.6)
self.assertEqual(mcmc._gamma_switch_rate, 20)
self.assertTrue(not mcmc._gaussian_error)
self.assertTrue(not mcmc._relative_scaling)
mcmc.set_gamma(0.5)
self.assertEqual(mcmc._gamma, 0.5)
self.assertRaisesRegex(ValueError, 'non-negative', mcmc.set_gamma, -1)
mcmc.set_scale_coefficient(1)
self.assertTrue(not mcmc._relative_scaling)
self.assertRaisesRegex(ValueError, 'non-negative',
mcmc.set_scale_coefficient, -1)
mcmc.set_gamma_switch_rate(11)
self.assertEqual(mcmc._gamma_switch_rate, 11)
self.assertRaisesRegex(ValueError, 'integer',
mcmc.set_gamma_switch_rate, 11.5)
self.assertRaisesRegex(ValueError, 'exceed 1',
mcmc.set_gamma_switch_rate, 0)
mcmc.set_gaussian_error(False)
self.assertTrue(not mcmc._gaussian_error)
mcmc.set_relative_scaling(0)
self.assertTrue(
np.array_equal(mcmc._b_star, np.repeat(mcmc._b,
mcmc._n_parameters)))
mcmc.set_relative_scaling(1)
self.assertTrue(np.array_equal(mcmc._b_star, mcmc._mu * mcmc._b))
# test implicit conversion to int
mcmc.set_hyper_parameters([0.5, 0.6, 20.2, 0, 0])
self.assertEqual(mcmc._gamma_switch_rate, 20)
self.assertRaisesRegex(ValueError, 'convertable to an integer',
mcmc.set_hyper_parameters,
(0.5, 0.6, 'sdf', 0, 0))
def test_flow(self):
# Test we have at least 3 chains
n = 2
x0 = [self.real_parameters] * n
self.assertRaises(ValueError, pints.DifferentialEvolutionMCMC, n, x0)
# Test initial proposal is first point
n = 3
x0 = [self.real_parameters] * n
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
self.assertTrue(mcmc.ask() is mcmc._x0)
# Double initialisation
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
mcmc.ask()
self.assertRaises(RuntimeError, mcmc._initialise)
# Tell without ask
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
self.assertRaises(RuntimeError, mcmc.tell, 0)
# Repeated asks should return same point
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
# Get into accepting state
for i in range(100):
mcmc.tell([self.log_posterior(x) for x in mcmc.ask()])
x = mcmc.ask()
for i in range(10):
self.assertTrue(x is mcmc.ask())
# Repeated tells should fail
mcmc.tell([1, 1, 1])
self.assertRaises(RuntimeError, mcmc.tell, [1, 1, 1])
# Bad starting point
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
mcmc.ask()
self.assertRaises(ValueError, mcmc.tell, float('-inf'))
# Use uniform error
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
mcmc.set_gaussian_error(False)
for i in range(10):
mcmc.tell([self.log_posterior(x) for x in mcmc.ask()])
# Use absolute scaling
mcmc = pints.DifferentialEvolutionMCMC(n, x0)
mcmc.set_relative_scaling(False)
for i in range(10):
mcmc.tell([self.log_posterior(x) for x in mcmc.ask()])