def test_optimize_new_scipy():
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='Powell')
npt.assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
npt.assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8})
npt.assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
npt.assert_almost_equal(opt.fopt, 0)
npt.assert_equal(opt.evolution, None)
npt.assert_equal(opt.evolution, None)
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8},
evolution=False)
npt.assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
npt.assert_almost_equal(opt.fopt, 0)
opt.print_summary()
opt = Optimizer(fun=func2, x0=np.array([1., 1., 1., 5.]),
method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8},
evolution=True)
npt.assert_equal(opt.evolution.shape, (opt.nit, 4))
opt = Optimizer(fun=func2, x0=np.array([1., 1., 1., 5.]),
method='Powell',
options={'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6},
evolution=True)
npt.assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]))
def test_optimize_new_scipy():
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='Powell')
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8})
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
assert_equal(opt.evolution, None)
assert_equal(opt.evolution, None)
opt = Optimizer(fun=func, x0=np.array([1., 1., 1.]), method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8},
evolution=False)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
opt.print_summary()
opt = Optimizer(fun=func2, x0=np.array([1., 1., 1., 5.]),
method='L-BFGS-B',
options={'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8},
evolution=True)
assert_equal(opt.evolution.shape, (opt.nit, 4))
opt = Optimizer(fun=func2, x0=np.array([1., 1., 1., 5.]),
method='Powell',
options={'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6},
evolution=True)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]))
def optimize(self, static, moving, mat=None):
""" Find the minimum of the provided metric.
Parameters
----------
static : streamlines
Reference or fixed set of streamlines.
moving : streamlines
Moving set of streamlines.
mat : array
Transformation (4, 4) matrix to start the registration. ``mat``
is applied to moving. Default value None which means that initial
transformation will be generated by shifting the centers of moving
and static sets of streamlines to the origin.
Returns
-------
map : StreamlineRegistrationMap
"""
msg = 'need to have the same number of points. Use '
msg += 'set_number_of_points from dipy.tracking.streamline'
if not np.all(np.array(list(map(len, static))) == static[0].shape[0]):
raise ValueError('Static streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == moving[0].shape[0]):
raise ValueError('Moving streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == static[0].shape[0]):
raise ValueError('Static and moving streamlines ' + msg)
if mat is None:
static_centered, static_shift = center_streamlines(static)
moving_centered, moving_shift = center_streamlines(moving)
static_mat = compose_matrix44([static_shift[0], static_shift[1],
static_shift[2], 0, 0, 0])
moving_mat = compose_matrix44([-moving_shift[0], -moving_shift[1],
-moving_shift[2], 0, 0, 0])
else:
static_centered = static
moving_centered = transform_streamlines(moving, mat)
static_mat = np.eye(4)
moving_mat = mat
self.metric.setup(static_centered, moving_centered)
distance = self.metric.distance
if self.method == 'Powell':
if self.options is None:
self.options = {'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6}
opt = Optimizer(distance, self.x0.tolist(),
method=self.method, options=self.options,
evolution=self.evolution)
if self.method == 'L-BFGS-B':
if self.options is None:
self.options = {'maxcor': 10, 'ftol': 1e-7,
'gtol': 1e-5, 'eps': 1e-8,
'maxiter': 100}
opt = Optimizer(distance, self.x0.tolist(),
method=self.method,
bounds=self.bounds, options=self.options,
evolution=self.evolution)
if self.verbose:
opt.print_summary()
opt_mat = compose_matrix44(opt.xopt)
mat = compose_transformations(moving_mat, opt_mat, static_mat)
mat_history = []
if opt.evolution is not None:
for vecs in opt.evolution:
mat_history.append(
compose_transformations(moving_mat,
compose_matrix44(vecs),
static_mat))
srm = StreamlineRegistrationMap(mat, opt.xopt, opt.fopt,
mat_history, opt.nfev, opt.nit)
del opt
return srm
def optimize(self, static, moving, mat=None):
""" Find the minimum of the provided metric.
Parameters
----------
static : streamlines
Reference or fixed set of streamlines.
moving : streamlines
Moving set of streamlines.
mat : array
Transformation (4, 4) matrix to start the registration. ``mat``
is applied to moving. Default value None which means that initial
transformation will be generated by shifting the centers of moving
and static sets of streamlines to the origin.
Returns
-------
map : StreamlineRegistrationMap
"""
msg = 'need to have the same number of points. Use '
msg += 'set_number_of_points from dipy.tracking.streamline'
if not np.all(np.array(list(map(len, static))) == static[0].shape[0]):
raise ValueError('Static streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == moving[0].shape[0]):
raise ValueError('Moving streamlines ' + msg)
if not np.all(np.array(list(map(len, moving))) == static[0].shape[0]):
raise ValueError('Static and moving streamlines ' + msg)
if mat is None:
static_centered, static_shift = center_streamlines(static)
moving_centered, moving_shift = center_streamlines(moving)
static_mat = compose_matrix44(
[static_shift[0], static_shift[1], static_shift[2], 0, 0, 0])
moving_mat = compose_matrix44([
-moving_shift[0], -moving_shift[1], -moving_shift[2], 0, 0, 0
])
else:
static_centered = static
moving_centered = transform_streamlines(moving, mat)
static_mat = np.eye(4)
moving_mat = mat
self.metric.setup(static_centered, moving_centered)
distance = self.metric.distance
if self.method == 'Powell':
if self.options is None:
self.options = {'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6}
opt = Optimizer(distance,
self.x0.tolist(),
method=self.method,
options=self.options,
evolution=self.evolution)
if self.method == 'L-BFGS-B':
if self.options is None:
self.options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': 100
}
opt = Optimizer(distance,
self.x0.tolist(),
method=self.method,
bounds=self.bounds,
options=self.options,
evolution=self.evolution)
if self.verbose:
opt.print_summary()
opt_mat = compose_matrix44(opt.xopt)
mat = compose_transformations(moving_mat, opt_mat, static_mat)
mat_history = []
if opt.evolution is not None:
for vecs in opt.evolution:
mat_history.append(
compose_transformations(moving_mat, compose_matrix44(vecs),
static_mat))
srm = StreamlineRegistrationMap(mat, opt.xopt, opt.fopt, mat_history,
opt.nfev, opt.nit)
del opt
return srm
def test_optimize_old_scipy():
opt = Optimizer(fun=func,
x0=np.array([1., 1., 1.]),
method='L-BFGS-B',
options={
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8
})
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func2,
x0=np.array([1., 1., 1., 5.]),
method='Powell',
options={
'xtol': 1e-6,
'ftol': 1e-6,
'maxiter': 1e6
},
evolution=True)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]))
opt = Optimizer(fun=func,
x0=np.array([1., 1., 1.]),
method='L-BFGS-B',
options={
'maxcor': 10,
'eps': 1e-8
})
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func,
x0=np.array([1., 1., 1.]),
method='L-BFGS-B',
options=None)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0]))
assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func2,
x0=np.array([1., 1., 1., 5.]),
method='L-BFGS-B',
options={
'gtol': 1e-7,
'ftol': 1e-7,
'maxiter': 10000
})
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]), 4)
assert_almost_equal(opt.fopt, 0)
opt = Optimizer(fun=func2,
x0=np.array([1., 1., 1., 5.]),
method='Powell',
options={'maxiter': 1e6},
evolution=True)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]))
opt = Optimizer(fun=func2,
x0=np.array([1., 1., 1., 5.]),
method='Powell',
options={'maxiter': 1e6},
evolution=True)
assert_array_almost_equal(opt.xopt, np.array([0, 0, 0, 0.]))
moving_clusters = KMeans(n_clusters=num).fit(np.concatenate(moving))
#x = np.array([[i,i,i,i,i,i,i] for i in range(1,9)])
x0 = np.array([[0, 0, 0, 0, 0, 0, 1] for _ in range(num)])
options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8
} #,'maxiter': 10000000}#,'maxfun':200}
start = time()
m = Optimizer(cost_fun,
x0,
args=(static_clusters.cluster_centers_,
moving_clusters.cluster_centers_, 7),
method='L-BFGS-B',
options=options)
end = time()
m.print_summary()
#np.save('out/new_x0_00.npy',m.xopt)
x1 = np.reshape(m.xopt, (num, 7))
new_moving = transform(x1, moving, moving_clusters.cluster_centers_)
draw_bundles([static, moving], [[1, 0, 0], [0, 0, 1]])
#draw_bundles([new_moving])
hours = int((end - start) / 3600)
minutes = int(((end - start) % 3600) / 60)
elif init_from == 'random':
f0 = np.random.uniform(0, 1, len(fmean_t1lab))
else:
raise ValueError('Unknown initial point type:%s' % (init_from, ))
ofname = 'fopt_t1lab_' + str(radius) + '_' + init_from + '.npy'
fopt_t1lab = None
if os.path.isfile(ofname):
print('Found precomputed t1lab transfer. Skipping optimization.')
fopt_t1lab = np.load(ofname)
else:
print('Computing optimal llr transfer, t1lab. Radius=%d, init:%s' %
(radius, init_from))
opt_t1lab = Optimizer(value_and_gradient_t1lab,
f0,
"BFGS",
jac=True,
options=options)
fopt_t1lab = opt_t1lab.xopt
np.save(ofname, fopt_t1lab)
options = {'maxiter': 20}
if init_from == 'mean':
f0 = fmean_t2lab
elif init_from == 'random':
f0 = np.random.uniform(0, 1, len(fmean_t2lab))
else:
raise ValueError('Unknown initial point type:%s' % (init_from, ))
ofname = 'fopt_t2lab_' + str(radius) + '_' + init_from + '.npy'
fopt_t2lab = None
def registration_icp(static,
moving,
points=20,
pca=True,
maxiter=100000,
affine=[0, 0, 0, 0, 0, 0, 1],
clustering=None,
medoids=[0, 1, 2],
k=3,
beta=999,
max_dist=40,
dist='pc'):
options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': maxiter
}
#options1 = {'xtol': 1e-6, 'ftol': 1e-6, 'maxiter': 1e6}
if pca:
moving = pca_transform_norm(static, moving, max_dist)
else:
mean_m = np.mean(np.concatenate(moving), axis=0)
mean_s = np.mean(np.concatenate(static), axis=0)
moving = [i - mean_m + mean_s for i in moving]
original_moving = moving.copy()
static = set_number_of_points(static, points)
moving = set_number_of_points(moving, points)
if clustering == 'kmeans':
kmeans = KMeans(k).fit(np.concatenate(moving))
idx = {i: np.where(kmeans.labels_ == i)[0] for i in range(k)}
#dist = Clustering().distance_pc_clustering_mean
if dist == 'pc':
dist_fun = distance_pc_clustering_mean
else:
dist_fun = distance_tract_clustering_mean
args = (static, moving, kmeans, idx, beta, max_dist)
print('kmeans')
elif clustering == 'kmedoids':
k_medoids = kmedoids(np.concatenate(moving), medoids)
k_medoids.process()
#dist = Clustering().distance_pc_clustering_medoids
if dist == 'pc':
dist_fun = distance_pc_clustering_medoids
else:
dist_fun = distance_tract_clustering_medoids
args = (static, moving, k_medoids, beta, max_dist)
print('kmedoids')
else:
if dist == 'pc':
dist_fun = distance_pc
args = (static, moving, beta, max_dist)
else:
dist_fun = distance_mdf
args = (static, moving)
print('Without Clustering')
'L-BFGS-B,Powell'
m = Optimizer(dist_fun,
affine,
args=args,
method='L-BFGS-B',
options=options)
#m = Optimizer(dist, affine,args=args,method='Powell',options=options1)
m.print_summary()
mat = compose_matrix44(m.xopt)
return transform_streamlines(original_moving, mat)
pca_moving = pca_transform_norm(static, moving)
max_range = 46
x0 = [0, 0, 0, 0, 0, 0, 1]
options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': 1000
}
start = time()
m = Optimizer(distance_pc,
x0,
args=(static, pca_moving, 1, 50),
method='L-BFGS-B',
options=options)
end = time()
aff = compose_matrix44(m.xopt)
new_moving = transform_streamlines(pca_moving, aff)
draw_bundles([new_moving, static], [[0, 0, 1], [1, 0, 0]])
''' Build KDTree '''
kdtree = KDTree(np.concatenate(static))
distances = kdtree.query(np.concatenate(new_moving), k=1)[0]
hours = int((end - start) / 3600)
minutes = int(((end - start) % 3600) / 60)
seconds = int(((end - start) % 3600) % 60)
print("Duration: {:02}:{}:{}".format(hours, minutes, seconds))
length = 5
x0 = np.array([[0, 0, 0, 0, 0, 0, 1] for _ in range(length)])
lnk_cost = link_cost(transform(x0, pca_moving))
options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': 1000
} #,'maxfun':200}
start = time()
m = Optimizer(dist_new,
x0,
args=(static, pca_moving, length, lnk_cost, 50, 10),
method='L-BFGS-B',
options=options)
end = time()
m.print_summary()
np.save('out/dist_link11.npy', m.xopt)
np.save('out/costs11.npy', costs)
x1 = np.reshape(m.xopt, (5, 7))
new_moving = transform(x1, moving)
#draw_bundles([moving,new_moving],[[1,0,0],[0,0,1]])
#draw_bundles([new_moving])
hours = int((end - start) / 3600)
