def optimize(self, search='rigid', method='powell', start=None,
radius=BRAIN_RADIUS_MM, tol=1e-1, ftol=1e-2):
"""
radius: a parameter for the 'typical size' in mm of the object
being registered. This is used to reformat the parameter
vector (translation+rotation+scaling+shearing) so that each
element roughly represents a variation in mm.
"""
if start == None:
T = Affine(subtype=search, radius=radius)
else:
T = Affine(subtype=search, vec12=start.vec12, radius=radius)
tc0 = T.to_param()
# Loss function to minimize
def loss(tc):
T.from_param(tc)
return -self.eval(T)
def callback(tc):
T.from_param(tc)
print(T)
print(self.similarity + ' = %s' % self.eval(T))
print('')
# Switching to the appropriate optimizer
print('Initial guess...')
print(T)
if method=='simplex':
print ('Optimizing using the simplex method...')
tc = sp.optimize.fmin(loss, tc0, callback=callback, xtol=tol, ftol=ftol)
elif method=='powell':
print ('Optimizing using Powell method...')
tc = sp.optimize.fmin_powell(loss, tc0, callback=callback, xtol=tol, ftol=ftol)
elif method=='conjugate_gradient':
print ('Optimizing using conjugate gradient descent...')
tc = sp.optimize.fmin_cg(loss, tc0, callback=callback, gtol=ftol)
else:
raise ValueError('Unrecognized optimizer')
# Output
T.from_param(tc)
return T
