def test_proposal_generation_snooker(self):
"""Test that proposal generation with a snooker update returns values of the expected shape."""
self.param, self.like = multidmodel()
model = Model(self.like, self.param)
step = Dream(model=model)
history_arr = mp.Array('d', list(range(120)))
n = mp.Value('i', 0)
pydream.Dream_shared_vars.history = history_arr
pydream.Dream_shared_vars.count = n
step.nseedchains = 20
q0 = np.array([2, 3, 4, 5])
proposed_pt, snooker_logp, z = step.generate_proposal_points(
n_proposed_pts=1,
q0=q0,
CR=1,
DEpairs=1,
gamma_level=1,
snooker=True)
self.assertEqual(len(proposed_pt), step.total_var_dimension)
proposed_pts, snooker_logp, z = step.generate_proposal_points(
n_proposed_pts=5,
q0=q0,
CR=1,
DEpairs=1,
gamma_level=1,
snooker=True)
self.assertEqual(len(proposed_pts), 5)
def test_proposal_generation_nosnooker_CR66(self):
"""Test proposal generation without a snooker update with a single or multiple proposed points and a crossover value of 2/3 gives 2/3 of all dimensions changed on average as expected."""
self.param, self.like = multidmodel()
model = Model(self.like, self.param)
step = Dream(model=model)
history_arr = mp.Array('d', list(range(120)))
n = mp.Value('i', 0)
pydream.Dream_shared_vars.history = history_arr
pydream.Dream_shared_vars.count = n
step.nseedchains = 20
q0 = np.array([2, 3, 4, 5])
dims_kept = 0
for iteration in range(100000):
proposed_pt = step.generate_proposal_points(n_proposed_pts=1, q0=q0, CR=.66, DEpairs=1, gamma_level=1, snooker=False)
if iteration == 1:
self.assertEqual(len(proposed_pt), 1)
dims_change_vec = np.squeeze(q0 == proposed_pt)
for dim in dims_change_vec:
if dim:
dims_kept += 1
frac_kept = dims_kept/(step.total_var_dimension*100000.0)
self.assertAlmostEqual(frac_kept, 1-.66, places=1)
dims_kept = 0
for iteration in range(10000):
proposed_pts = step.generate_proposal_points(n_proposed_pts=5, q0=q0, CR=.66, DEpairs=1, gamma_level=1, snooker=False)
if iteration == 1:
self.assertEqual(len(proposed_pts), 5)
for pt in proposed_pts:
dims_change_vec = (q0 == pt)
for dim in dims_change_vec:
if dim:
dims_kept += 1
frac_kept = dims_kept/(step.total_var_dimension*10000.0*5)
self.assertAlmostEqual(frac_kept, 1-.66, places=1)