def test_flow(self):
log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
x0 = np.array([2, 2])
# Test initial proposal is first point
mcmc = pints.MonomialGammaHamiltonianMCMC(x0)
self.assertTrue(np.all(mcmc.ask() == mcmc._x0))
# Repeated asks
self.assertRaises(RuntimeError, mcmc.ask)
# Tell without ask
mcmc = pints.MonomialGammaHamiltonianMCMC(x0)
self.assertRaises(RuntimeError, mcmc.tell, 0)
# Repeated tells should fail
x = mcmc.ask()
mcmc.tell(log_pdf.evaluateS1(x))
self.assertRaises(RuntimeError, mcmc.tell, log_pdf.evaluateS1(x))
# Bad starting point
mcmc = pints.MonomialGammaHamiltonianMCMC(x0)
mcmc.ask()
self.assertRaises(ValueError, mcmc.tell,
(float('-inf'), np.array([1, 1])))
def test_method(self):
# Create log pdf
log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
# Create mcmc
x0 = np.array([2, 2])
sigma = [[3, 0], [0, 3]]
mcmc = pints.MonomialGammaHamiltonianMCMC(x0, sigma)
# This method needs sensitivities
self.assertTrue(mcmc.needs_sensitivities())
# Set number of leapfrog steps
ifrog = 10
mcmc.set_leapfrog_steps(ifrog)
# Perform short run
chain = []
for i in range(100 * ifrog):
x = mcmc.ask()
fx, gr = log_pdf.evaluateS1(x)
sample = mcmc.tell((fx, gr))
if i >= 50 * ifrog and sample is not None:
chain.append(sample)
if np.all(sample == x):
self.assertEqual(mcmc.current_log_pdf(), fx)
chain = np.array(chain)
self.assertEqual(chain.shape[0], 50)
self.assertEqual(chain.shape[1], len(x0))
def test_set_hyper_parameters(self):
"""
Tests the parameter interface for this sampler.
"""
x0 = np.array([2, 2])
mcmc = pints.MonomialGammaHamiltonianMCMC(x0)
# Test leapfrog parameters
n = mcmc.leapfrog_steps()
d = mcmc.leapfrog_step_size()
self.assertIsInstance(n, int)
self.assertTrue(len(d) == mcmc._n_parameters)
mcmc.set_leapfrog_steps(n + 1)
self.assertEqual(mcmc.leapfrog_steps(), n + 1)
self.assertRaises(ValueError, mcmc.set_leapfrog_steps, 0)
mcmc.set_leapfrog_step_size(0.5)
self.assertEqual(mcmc.leapfrog_step_size()[0], 0.5)
self.assertRaises(ValueError, mcmc.set_leapfrog_step_size, -1)
self.assertEqual(mcmc.n_hyper_parameters(), 5)
mcmc.set_hyper_parameters([n + 2, 2, 0.4, 2.3, 1.3])
self.assertEqual(mcmc.leapfrog_steps(), n + 2)
self.assertEqual(mcmc.leapfrog_step_size()[0], 2)
self.assertEqual(mcmc.a(), 0.4)
self.assertEqual(mcmc.c(), 2.3)
self.assertEqual(mcmc.mass(), 1.3)
mcmc.set_epsilon(0.4)
self.assertEqual(mcmc.epsilon(), 0.4)
self.assertRaises(ValueError, mcmc.set_epsilon, -0.1)
mcmc.set_leapfrog_step_size(1)
self.assertEqual(len(mcmc.scaled_epsilon()), 2)
self.assertEqual(mcmc.scaled_epsilon()[0], 0.4)
self.assertEqual(len(mcmc.divergent_iterations()), 0)
self.assertRaises(ValueError, mcmc.set_leapfrog_step_size, [1, 2, 3])
mcmc.set_leapfrog_step_size([1.5, 3])
self.assertEqual(mcmc.leapfrog_step_size()[0], 1.5)
self.assertEqual(mcmc.leapfrog_step_size()[1], 3)
a = 1.9
mcmc.set_a(a)
self.assertEqual(mcmc.a(), a)
self.assertRaises(ValueError, mcmc.set_a, -0.9)
c = 3.5
mcmc.set_c(c)
self.assertEqual(mcmc.c(), c)
self.assertRaises(ValueError, mcmc.set_c, -0.1)
m = 2.9
mcmc.set_mass(m)
self.assertEqual(mcmc.mass(), m)
self.assertRaises(ValueError, mcmc.set_mass, -1.8)
def test_other_setters(self):
# Tests other setters and getters.
x0 = np.array([2, 2])
mcmc = pints.MonomialGammaHamiltonianMCMC(x0)
self.assertRaises(ValueError, mcmc.set_hamiltonian_threshold, -0.3)
threshold1 = mcmc.hamiltonian_threshold()
self.assertEqual(threshold1, 10**3)
threshold2 = 10
mcmc.set_hamiltonian_threshold(threshold2)
self.assertEqual(mcmc.hamiltonian_threshold(), threshold2)
def test_kinetic_energy(self):
# Tests kinetic energy values and derivatives
x0 = np.array([2, 2])
model = pints.MonomialGammaHamiltonianMCMC(x0)
model.ask()
# kinetic energy
ke = model._K([-3.5, 0.5], 1.5, 1.7, 0.7)
self.assertAlmostEqual(4.4280785731741243, ke)
# derivatives
self.assertAlmostEqual(model._K_deriv_indiv(1.3, 0.5, 0.3, 1.3),
0.38513079718715132)
derivs = model._K_deriv_indiv([-3.5, 0.5], 1.5, 1.7, 0.7)
self.assertAlmostEqual(derivs[0], -0.62264078154277391)
self.assertAlmostEqual(derivs[1], 0.77273306758997307)
self.assertAlmostEqual(len(derivs), 2)
# sample momentum from ke function
mom = model._sample_momentum()
self.assertEqual(len(mom), 2)
self.assertTrue(mom[0] != mom[1])
def test_method(self):
# Create log pdf
log_pdf = pints.toy.GaussianLogPDF([5, 5], [[4, 1], [1, 3]])
# Create mcmc
x0 = np.array([2, 2])
sigma = [[3, 0], [0, 3]]
mcmc = pints.MonomialGammaHamiltonianMCMC(x0, sigma)
# This method needs sensitivities
self.assertTrue(mcmc.needs_sensitivities())
# Set number of leapfrog steps
ifrog = 4
mcmc.set_leapfrog_steps(ifrog)
# Perform short run
chain = []
for i in range(50 * ifrog):
x = mcmc.ask()
fx, gr = log_pdf.evaluateS1(x)
reply = mcmc.tell((fx, gr))
if reply is not None:
y, fy, ac = reply
if i >= 25 * ifrog:
chain.append(y)
self.assertTrue(isinstance(ac, bool))
if ac:
self.assertTrue(np.all(x == y))
self.assertEqual(fx, fy[0])
self.assertTrue(np.all(gr == fy[1]))
chain = np.array(chain)
self.assertEqual(chain.shape[0], 25)
self.assertEqual(chain.shape[1], len(x0))