def compose(a1, a2):
label = "%s%s" % (a1.label, a2.label)
# This is the correct order
diffeo = Diffeomorphism2D.compose(a2.diffeo, a1.diffeo)
diffeo_inv = Diffeomorphism2D.compose(a1.diffeo_inv, a2.diffeo_inv)
# diffeo = Diffeomorphism2D.compose(a1.diffeo, a2.diffeo)
# diffeo_inv = Diffeomorphism2D.compose(a2.diffeo_inv, a1.diffeo_inv)
original_cmds = a1.get_original_cmds() + a2.get_original_cmds()
return DiffeoAction(label, diffeo, diffeo_inv, original_cmds)
def compose(a1, a2):
label = '%s%s' % (a1.label, a2.label)
# This is the correct order
diffeo = Diffeomorphism2D.compose(a2.diffeo, a1.diffeo)
diffeo_inv = Diffeomorphism2D.compose(a1.diffeo_inv, a2.diffeo_inv)
# diffeo = Diffeomorphism2D.compose(a1.diffeo, a2.diffeo)
# diffeo_inv = Diffeomorphism2D.compose(a2.diffeo_inv, a1.diffeo_inv)
original_cmds = a1.get_original_cmds() + a2.get_original_cmds()
return DiffeoAction(label, diffeo, diffeo_inv, original_cmds)
def DDSFromSymbolic(resolution, actions, topology=None): #@UnusedVariable
"""
Creates a DiffeoSystem from synthetic diffeomorphisms.
"""
config = get_current_config()
logger.info('Creating symbolic diffeomorphism (resolution = %d)' %
resolution)
diffeoactions = []
for _, action in enumerate(actions):
id_diffeo, diffeo = parse_diffeo_spec(config, action['diffeo'])
label = action.get('label', id_diffeo)
original_cmd = np.array(action['original_cmd'])
logger.info('Getting symbolic diffeomorphism %r' % id_diffeo)
shape = (resolution, resolution)
viewport = SquareDomain([[-1, +1], [-1, +1]])
manifold = diffeo.get_topology()
D, Dinfo = diffeo_from_function_viewport(diffeo, manifold, viewport,
shape)
D2d = Diffeomorphism2D(D, Dinfo)
diffeo_inv = diffeo.get_inverse()
D_inv, Dinfo_inv = diffeo_from_function_viewport(
diffeo_inv, manifold, viewport, shape)
D2d_inv = Diffeomorphism2D(D_inv, Dinfo_inv)
action = DiffeoAction(label=label,
diffeo=D2d,
diffeo_inv=D2d_inv,
original_cmd=original_cmd)
diffeoactions.append(action)
dds = DiffeoSystem('%s' % actions, actions=diffeoactions)
return dds
def make_hard(dd):
assert isinstance(dd, Diffeomorphism2D)
if use_isomorphism_heuristics:
stats = diffeo_stats(dd.d)
per = np.percentile(stats.norm, norm_percentile)
limit = per * factor
# / 3.0
#print('norm mean/mean: %g %g' % (np.mean(stats.norm), np.median(stats.norm)))
#for i in range(0, 100, 5):
# print(' %3d%% = %g' % (i, np.percentile(stats.norm, i)))
#limit = np.percentile(stats.norm, info_percentile)
#if limit <= 1:
# print('limit was %g' % limit)
# limit = 4
variance = (stats.norm > limit).astype('float')
logger.info('---hard choices---')
logger.info(' per: %g pixels * %g =' % (per, factor))
logger.info('limit: %g pixels' % limit)
logger.info(' vis: %.1f%% ' % (100 * np.mean(variance)))
else:
variance = (dd.variance > info_threshold).astype('float')
return Diffeomorphism2D(dd.d, variance)
def identity(label, shape, original_cmd):
""" Constructs the identity action of the given shape. """
diffeo = Diffeomorphism2D.identity(shape)
diffeo_inv = diffeo
return DiffeoAction(label, diffeo, diffeo_inv, original_cmd)
def identity(label, shape, original_cmd):
""" Constructs the identity action of the given shape. """
diffeo = Diffeomorphism2D.identity(shape)
diffeo_inv = diffeo
return DiffeoAction(label, diffeo, diffeo_inv, original_cmd)
