def test_run_chain(self):
fwm = vfm.VisionForwardModel(render_size=(50, 50))
s = shape.Shape(forward_model=fwm, viewpoint=[(3.0, 45.0, 45.0)],
params={'LL_VARIANCE': 1.0, 'MAX_PIXEL_VALUE': 175.0})
data = np.zeros((1, 50, 50))
kernel = proposal.RandomMixtureProposal(moves={'aaa': shape.shape_change_part_size_local},
params={'CHANGE_SIZE_VARIANCE': 1.0})
params = {'name': 'unittest', 'results_folder': '.', 'sampler': 'xxx', 'burn_in': 0, 'sample_count': 1,
'best_sample_count': 1, 'thinning_period': 10, 'data': data, 'kernel': kernel, 'initial_h': s,
'report_period': 10}
# wrong sampler
self.assertRaises(ValueError, run_chain, **params)
# need to supply temperatures if sampler is pt
params['sampler'] = 'pt'
self.assertRaises(ValueError, run_chain, **params)
params['temperatures'] = [2.0, 1.0]
results = run_chain(**params)
self.assertIn('run_id', results.keys())
self.assertIn('run_file', results.keys())
self.assertIn('mean_acceptance_rate', results.keys())
self.assertIn('start_time', results.keys())
self.assertIn('end_time', results.keys())
self.assertIn('duration', results.keys())
self.assertIn('best_ll', results.keys())
self.assertIn('best_posterior', results.keys())
self.assertIn('mse', results.keys())
self.assertIn('mean_best_ll', results.keys())
self.assertIn('mean_best_posterior', results.keys())
self.assertIn('mse_mean', results.keys())
self.assertIn('mean_sample_posterior', results.keys())
self.assertIn('mean_sample_ll', results.keys())
self.assertIn('mse_sample', results.keys())
# saved the right files
start = results['start_time']
time_str = time.strftime("%Y%m%d_%H%M%S", time.localtime(start))
fname = "{0:s}/{1:s}_{2:s}_{3:06d}.pkl".format(params['results_folder'], params['name'], time_str,
results['run_id'])
self.assertTrue(os.path.isfile(fname))
os.remove(fname)
fname = "{0:s}/{1:s}/s{2:d}_0.png".format(params['results_folder'], params['name'], 0)
self.assertTrue(os.path.isfile(fname))
os.remove(fname)
fname = "{0:s}/{1:s}/b{2:d}_0.png".format(params['results_folder'], params['name'], 0)
self.assertTrue(os.path.isfile(fname))
os.remove(fname)
folder = "{0:s}/{1:s}".format(params['results_folder'], params['name'])
os.rmdir(folder)
def create_shape_from_stimuli(stim):
"""
This function creates a Infer3DShape.Shape instance
from the BDAoOSSStimuli object
:param stim: BDAoOSS stimulus object
:return: Returns Infer3DShape.Shape instance
"""
sm = stim.spatial_model
parts = []
for ss in sm.spatial_states.itervalues():
pos = ss.position * SCALE_FACTOR
size = ss.size * SCALE_FACTOR
parts.append(hyp.CuboidPrimitive(position=pos, size=size))
h = hyp.Shape(forward_model=None, parts=parts)
return h
def create_shape_from_stimuli(stim, view_angle):
"""
This function creates a Infer3DShape.Shape instance
from the BDAoOSSStimuli object
:param stim: BDAoOSS stimulus object
:return: Returns Infer3DShape.Shape instance
"""
sm = stim.spatial_model
parts = []
for ss in sm.spatial_states.itervalues():
pos = ss.position * SCALE_FACTOR
size = ss.size * SCALE_FACTOR
parts.append(hyp.CuboidPrimitive(position=pos, size=size))
h = hyp.Shape(forward_model=None, parts=parts)
view_x = d * np.cos(view_angle * np.pi / 180.0)
view_y = d * np.sin(view_angle * np.pi / 180.0)
view_z = z
h.viewpoint = [(view_x, view_y, view_z)]
return h
def setUp(self):
self.fwm = vfm.VisionForwardModel(render_size=(50, 50))
self.s = shape.Shape(forward_model=self.fwm, parts=[])