-
Notifications
You must be signed in to change notification settings - Fork 239
/
connection.py
643 lines (557 loc) · 24.4 KB
/
connection.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
"""Affine connections."""
from abc import ABC
from scipy.optimize import minimize
import geomstats.backend as gs
import geomstats.errors
import geomstats.vectorization
from geomstats.integrator import integrate
N_STEPS = 10
class Connection(ABC):
r"""Class for affine connections.
Parameters
----------
dim : int
Dimension of the underlying manifold.
default_point_type : str, {\'vector\', \'matrix\'}
Point type.
Optional, default: \'vector\'.
default_coords_type : str, {\'intrinsic\', \'extrinsic\', etc}
Coordinate type.
Optional, default: \'intrinsic\'.
"""
def __init__(
self, dim, default_point_type="vector", default_coords_type="intrinsic"
):
geomstats.errors.check_integer(dim, "dim")
geomstats.errors.check_parameter_accepted_values(
default_point_type, "default_point_type", ["vector", "matrix"]
)
self.dim = dim
self.default_point_type = default_point_type
self.default_coords_type = default_coords_type
def christoffels(self, base_point):
"""Christoffel symbols associated with the connection.
Parameters
----------
base_point : array-like, shape=[..., dim]
Point on the manifold.
Returns
-------
gamma : array-like, shape=[..., dim, dim, dim]
Christoffel symbols, with the contravariant index on
the first dimension.
"""
raise NotImplementedError("The Christoffel symbols are not implemented.")
def geodesic_equation(self, state, _time):
"""Compute the geodesic ODE associated with the connection.
Parameters
----------
state : array-like, shape=[..., dim]
Tangent vector at the position.
_time : array-like, shape=[..., dim]
Point on the manifold, the position at which to compute the
geodesic ODE.
Returns
-------
geodesic_ode : array-like, shape=[..., dim]
Value of the vector field to be integrated at position.
"""
position, velocity = state
gamma = self.christoffels(position)
equation = gs.einsum("...kij,...i->...kj", gamma, velocity)
equation = -gs.einsum("...kj,...j->...k", equation, velocity)
return gs.stack([velocity, equation])
def exp(
self,
tangent_vec,
base_point,
n_steps=N_STEPS,
step="euler",
point_type=None,
**kwargs
):
"""Exponential map associated to the affine connection.
Exponential map at base_point of tangent_vec computed by integration
of the geodesic equation (initial value problem), using the
christoffel symbols.
Parameters
----------
tangent_vec : array-like, shape=[..., dim]
Tangent vector at the base point.
base_point : array-like, shape=[..., dim]
Point on the manifold.
n_steps : int
Number of discrete time steps to take in the integration.
Optional, default: N_STEPS.
step : str, {'euler', 'rk4'}
The numerical scheme to use for integration.
Optional, default: 'euler'.
point_type : str, {'vector', 'matrix'}
Type of representation used for points.
Optional, default: None.
Returns
-------
exp : array-like, shape=[..., dim]
Point on the manifold.
"""
initial_state = gs.stack([base_point, tangent_vec])
flow = integrate(
self.geodesic_equation, initial_state, n_steps=n_steps, step=step
)
exp = flow[-1][0]
return exp
def log(
self,
point,
base_point,
n_steps=N_STEPS,
step="euler",
max_iter=25,
verbose=False,
tol=gs.atol,
):
"""Compute logarithm map associated to the affine connection.
Solve the boundary value problem associated to the geodesic equation
using the Christoffel symbols and conjugate gradient descent.
Parameters
----------
point : array-like, shape=[..., dim]
Point on the manifold.
base_point : array-like, shape=[..., dim]
Point on the manifold.
n_steps : int
Number of discrete time steps to take in the integration.
Optional, default: N_STEPS.
step : str, {'euler', 'rk4'}
Numerical scheme to use for integration.
Optional, default: 'euler'.
max_iter
verbose
tol
Returns
-------
tangent_vec : array-like, shape=[..., dim]
Tangent vector at the base point.
"""
max_shape = point.shape
if len(point.shape) <= len(base_point.shape):
max_shape = base_point.shape
def objective(velocity):
"""Define the objective function."""
velocity = gs.array(velocity)
velocity = gs.cast(velocity, dtype=base_point.dtype)
velocity = gs.reshape(velocity, max_shape)
delta = self.exp(velocity, base_point, n_steps, step) - point
return gs.sum(delta ** 2)
objective_with_grad = gs.autodiff.value_and_grad(objective, to_numpy=True)
tangent_vec = gs.flatten(gs.random.rand(*max_shape))
res = minimize(
objective_with_grad,
tangent_vec,
method="L-BFGS-B",
jac=True,
options={"disp": verbose, "maxiter": max_iter},
tol=tol,
)
tangent_vec = gs.array(res.x)
tangent_vec = gs.reshape(tangent_vec, max_shape)
tangent_vec = gs.cast(tangent_vec, dtype=base_point.dtype)
return tangent_vec
def _pole_ladder_step(
self, base_point, next_point, base_shoot, return_geodesics=False, **kwargs
):
"""Compute one Pole Ladder step.
One step of pole ladder scheme [LP2013a]_ using the geodesic to
transport along as main_geodesic of the parallelogram.
Parameters
----------
base_point : array-like, shape=[..., dim]
Point on the manifold, from which to transport.
next_point : array-like, shape=[..., dim]
Point on the manifold, to transport to.
base_shoot : array-like, shape=[..., dim]
Point on the manifold, end point of the geodesics starting
from the base point with initial speed to be transported.
return_geodesics : bool, optional (defaults to False)
Whether to return the geodesics of the
construction.
Returns
-------
next_step : dict of array-like and callable with following keys:
next_tangent_vec : array-like, shape=[..., dim]
Tangent vector at end point.
end_point : array-like, shape=[..., dim]
Point on the manifold, closes the geodesic parallelogram of the
construction.
geodesics : list of callable, len=3 (only if
`return_geodesics=True`)
Three geodesics of the construction.
References
----------
.. [LP2013a] Marco Lorenzi, Xavier Pennec. Efficient Parallel Transport
of Deformations in Time Series of Images: from Schild's to
Pole Ladder. Journal of Mathematical Imaging and Vision, Springer
Verlag, 2013,50 (1-2), pp.5-17. ⟨10.1007/s10851-013-0470-3⟩
"""
mid_tangent_vector_to_shoot = (
1.0 / 2.0 * self.log(base_point=base_point, point=next_point, **kwargs)
)
mid_point = self.exp(
base_point=base_point, tangent_vec=mid_tangent_vector_to_shoot, **kwargs
)
tangent_vector_to_shoot = -self.log(
base_point=mid_point, point=base_shoot, **kwargs
)
end_shoot = self.exp(
base_point=mid_point, tangent_vec=tangent_vector_to_shoot, **kwargs
)
geodesics = []
if return_geodesics:
main_geodesic = self.geodesic(
initial_point=base_point, end_point=next_point
)
diagonal = self.geodesic(initial_point=mid_point, end_point=base_shoot)
final_geodesic = self.geodesic(
initial_point=next_point, end_point=end_shoot
)
geodesics = [main_geodesic, diagonal, final_geodesic]
return {"geodesics": geodesics, "end_point": end_shoot}
def _schild_ladder_step(
self, base_point, next_point, base_shoot, return_geodesics=False, **kwargs
):
"""Compute one Schild's Ladder step.
One step of the Schild's ladder scheme [LP2013a]_ using the geodesic to
transport along as one side of the parallelogram.
Parameters
----------
base_point : array-like, shape=[..., dim]
Point on the manifold, from which to transport.
next_point : array-like, shape=[..., dim]
Point on the manifold, to transport to.
base_shoot : array-like, shape=[..., dim]
Point on the manifold, end point of the geodesics starting
from the base point with initial speed to be transported.
return_geodesics : bool
Whether to return points computed along each geodesic of the
construction.
Optional, default: False.
Returns
-------
transported_tangent_vector : array-like, shape=[..., dim]
Tangent vector at end point.
end_point : array-like, shape=[..., dim]
Point on the manifold, closes the geodesic parallelogram of the
construction.
References
----------
.. [LP2013a] Marco Lorenzi, Xavier Pennec. Efficient Parallel Transport
of Deformations in Time Series of Images: from Schild's to
Pole Ladder. Journal of Mathematical Imaging and Vision, Springer
Verlag, 2013,50 (1-2), pp.5-17. ⟨10.1007/s10851-013-0470-3⟩
"""
mid_tangent_vector_to_shoot = (
1.0 / 2.0 * self.log(base_point=base_shoot, point=next_point, **kwargs)
)
mid_point = self.exp(
base_point=base_shoot, tangent_vec=mid_tangent_vector_to_shoot, **kwargs
)
tangent_vector_to_shoot = -self.log(
base_point=mid_point, point=base_point, **kwargs
)
end_shoot = self.exp(
base_point=mid_point, tangent_vec=tangent_vector_to_shoot, **kwargs
)
geodesics = []
if return_geodesics:
main_geodesic = self.geodesic(
initial_point=base_point, end_point=next_point
)
diagonal = self.geodesic(initial_point=base_point, end_point=end_shoot)
second_diagonal = self.geodesic(
initial_point=base_shoot, end_point=next_point
)
final_geodesic = self.geodesic(
initial_point=next_point, end_point=end_shoot
)
geodesics = [main_geodesic, diagonal, second_diagonal, final_geodesic]
return {"geodesics": geodesics, "end_point": end_shoot}
def ladder_parallel_transport(
self,
tangent_vec_a,
tangent_vec_b,
base_point,
n_rungs=1,
scheme="pole",
alpha=1,
**single_step_kwargs
):
"""Approximate parallel transport using the pole ladder scheme.
Approximate Parallel transport using either the pole ladder or the
Schild's ladder scheme [LP2013b]_. Pole ladder is exact in symmetric
spaces and of order two in general while Schild's ladder is a first
order approximation [GP2020]_. Both schemes are available on any affine
connection manifolds whose exponential and logarithm maps are
implemented. `tangent_vec_a` is transported along the geodesic starting
at the base_point with initial tangent vector `tangent_vec_b`.
Parameters
----------
tangent_vec_a : array-like, shape=[..., dim]
Tangent vector at base point to transport.
tangent_vec_b : array-like, shape=[..., dim]
Tangent vector at base point, initial speed of the geodesic along
which to transport.
base_point : array-like, shape=[..., dim]
Point on the manifold, initial position of the geodesic along
which to transport.
n_rungs : int
Number of steps of the ladder.
Optional, default: 1.
scheme : str, {'pole', 'schild'}
The scheme to use for the construction of the ladder at each step.
Optional, default: 'pole'.
alpha : float
Exponent for the scaling of the vector to transport. Must be
greater or equal to 1, 2 is optimal. See [GP2020]_.
Optional, default: 2
**single_step_kwargs : keyword arguments for the step functions
Returns
-------
ladder : dict of array-like and callable with following keys
transported_tangent_vector : array-like, shape=[..., dim]
Approximation of the parallel transport of tangent vector a.
trajectory : list of list of callable, len=n_steps
List of lists containing the geodesics of the
construction, only if `return_geodesics=True` in the step
function. The geodesics are methods of the class connection.
References
----------
.. [LP2013b] Lorenzi, Marco, and Xavier Pennec. “Efficient Parallel
Transport of Deformations in Time Series of Images: From Schild to
Pole Ladder.” Journal of Mathematical Imaging and Vision 50, no. 1
(September 1, 2014): 5–17. https://doi.org/10.1007/s10851-013-0470-3.
.. [GP2020] Guigui, Nicolas, and Xavier Pennec. “Numerical Accuracy
of Ladder Schemes for Parallel Transport on Manifolds.”
Foundations of Computational Mathematics, June 18, 2021.
https://doi.org/10.1007/s10208-021-09515-x.
"""
geomstats.errors.check_integer(n_rungs, "n_rungs")
if alpha < 1:
raise ValueError("alpha must be greater or equal to one")
current_point = base_point
next_tangent_vec = tangent_vec_a / (n_rungs ** alpha)
methods = {"pole": self._pole_ladder_step, "schild": self._schild_ladder_step}
single_step = methods[scheme]
base_shoot = self.exp(base_point=current_point, tangent_vec=next_tangent_vec)
trajectory = []
for i_point in range(n_rungs):
frac_tan_vector_b = (i_point + 1) / n_rungs * tangent_vec_b
next_point = self.exp(base_point=base_point, tangent_vec=frac_tan_vector_b)
next_step = single_step(
base_point=current_point,
next_point=next_point,
base_shoot=base_shoot,
**single_step_kwargs
)
current_point = next_point
base_shoot = next_step["end_point"]
trajectory.append(next_step["geodesics"])
transported_tangent_vec = self.log(base_shoot, current_point)
if n_rungs % 2 == 1 and scheme == "pole":
transported_tangent_vec *= -1.0
transported_tangent_vec *= n_rungs ** alpha
return {
"transported_tangent_vec": transported_tangent_vec,
"end_point": current_point,
"trajectory": trajectory,
}
def curvature(self, tangent_vec_a, tangent_vec_b, tangent_vec_c, base_point):
r"""Compute the curvature.
For three vectors fields :math:`X|_P = tangent_vec_a,
Y|_P = tangent_vec_b, Z|_P = tangent_vec_c` with tangent vector
specified in argument at the base point :math:`P`,
the curvature is defined by :math:`R(X,Y)Z = \nabla_{[X,Y]}Z
- \nabla_X\nabla_Y Z + \nabla_Y\nabla_X Z`.
Parameters
----------
tangent_vec_a : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
tangent_vec_b : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
tangent_vec_c : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
base_point : array-like, shape=[..., {dim, [n, m]}]
Point on the group. Optional, default is the identity.
Returns
-------
curvature : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
"""
raise NotImplementedError("The curvature is not implemented.")
def directional_curvature(self, tangent_vec_a, tangent_vec_b, base_point):
"""Compute the directional curvature (tidal force operator).
For two vectors fields :math:`X|_P = tangent_vec_a`, and :math:
`Y|_P = tangent_vec_b` with tangent vector specified in argument at
the base point :math:`P`, the directional curvature, better known
in relativity as the tidal force operator, is defined by
:math:`R_Y(X) = R(Y,X)Y`.
Parameters
----------
tangent_vec_a : array-like, shape=[..., dim]
Tangent vector at `base_point`.
tangent_vec_b : array-like, shape=[..., dim]
Tangent vector at `base_point`.
base_point : array-like, shape=[..., dim]
Base-point on the manifold.
Returns
-------
directional_curvature : array-like, shape=[..., dim]
Tangent vector at `base_point`.
"""
return self.curvature(tangent_vec_b, tangent_vec_a, tangent_vec_b, base_point)
def curvature_derivative(
self,
tangent_vec_a,
tangent_vec_b,
tangent_vec_c,
tangent_vec_d,
base_point=None,
):
r"""Compute the covariant derivative of the curvature.
For four vectors fields :math:`H|_P = tangent_vec_a, X|_P =
tangent_vec_b, Y|_P = tangent_vec_c, Z|_P = tangent_vec_d` with
tangent vector value specified in argument at the base point `P`,
the covariant derivative of the curvature
:math:`(\nabla_H R)(X, Y) Z |_P` is computed at the base point P.
Parameters
----------
tangent_vec_a : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
tangent_vec_b : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
tangent_vec_c : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
tangent_vec_d : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at `base_point`.
base_point : array-like, shape=[..., {dim, [n, m]}]
Point on the group. Optional, default is the identity.
Returns
-------
curvature_derivative : array-like, shape=[..., dim]
Tangent vector at base-point.
"""
raise NotImplementedError("The curvature is not implemented.")
def directional_curvature_derivative(
self, tangent_vec_a, tangent_vec_b, base_point=None
):
r"""Compute the covariant derivative of the directional curvature.
For two vectors fields :math:`X|_P = tangent_vec_a, Y|_P =
tangent_vec_b` with tangent vector value specified in argument at the
base point `P`, the covariant derivative (in the direction 'X')
:math:`(\nabla_X R_Y)(X) |_P = (\nabla_X R)(Y, X) Y |_P` of the
directional curvature (in the direction `Y`)
:math:`R_Y(X) = R(Y, X) Y` is a quadratic tensor in 'X' and 'Y' that
plays an important role in the computation of the moments of the
empirical Fréchet mean.
References
----------
.. [Pennec] Pennec, xavier. Curvature effects on the empirical mean in
Riemannian and affine Manifolds: a non-asymptotic high concentration
expansion in the small-sample regime. Preprint. June 2019.
https://arxiv.org/abs/1906.07418
"""
return self.curvature_derivative(
tangent_vec_a, tangent_vec_b, tangent_vec_a, tangent_vec_b, base_point
)
def geodesic(self, initial_point, end_point=None, initial_tangent_vec=None):
"""Generate parameterized function for the geodesic curve.
Geodesic curve defined by either:
- an initial point and an initial tangent vector,
- an initial point and an end point.
Parameters
----------
initial_point : array-like, shape=[..., dim]
Point on the manifold, initial point of the geodesic.
end_point : array-like, shape=[..., dim], optional
Point on the manifold, end point of the geodesic. If None,
an initial tangent vector must be given.
initial_tangent_vec : array-like, shape=[..., dim],
Tangent vector at base point, the initial speed of the geodesics.
Optional, default: None.
If None, an end point must be given and a logarithm is computed.
Returns
-------
path : callable
Time parameterized geodesic curve. If a batch of initial
conditions is passed, the output array's first dimension
represents the different initial conditions, and the second
corresponds to time.
"""
point_type = self.default_point_type
if end_point is None and initial_tangent_vec is None:
raise ValueError(
"Specify an end point or an initial tangent "
"vector to define the geodesic."
)
if end_point is not None:
shooting_tangent_vec = self.log(point=end_point, base_point=initial_point)
if initial_tangent_vec is not None:
if not gs.allclose(shooting_tangent_vec, initial_tangent_vec):
raise RuntimeError(
"The shooting tangent vector is too"
" far from the input initial tangent vector."
)
initial_tangent_vec = shooting_tangent_vec
if point_type == "vector":
initial_point = gs.to_ndarray(initial_point, to_ndim=2)
initial_tangent_vec = gs.to_ndarray(initial_tangent_vec, to_ndim=2)
else:
initial_point = gs.to_ndarray(initial_point, to_ndim=3)
initial_tangent_vec = gs.to_ndarray(initial_tangent_vec, to_ndim=3)
n_initial_conditions = initial_tangent_vec.shape[0]
if n_initial_conditions > 1 and len(initial_point) == 1:
initial_point = gs.stack([initial_point[0]] * n_initial_conditions)
def path(t):
"""Generate parameterized function for geodesic curve.
Parameters
----------
t : array-like, shape=[n_points,]
Times at which to compute points of the geodesics.
"""
t = gs.array(t)
t = gs.cast(t, initial_tangent_vec.dtype)
t = gs.to_ndarray(t, to_ndim=1)
if point_type == "vector":
tangent_vecs = gs.einsum("i,...k->...ik", t, initial_tangent_vec)
else:
tangent_vecs = gs.einsum("i,...kl->...ikl", t, initial_tangent_vec)
points_at_time_t = [
self.exp(tv, pt) for tv, pt in zip(tangent_vecs, initial_point)
]
points_at_time_t = gs.stack(points_at_time_t, axis=0)
return (
points_at_time_t[0] if n_initial_conditions == 1 else points_at_time_t
)
return path
def parallel_transport(self, tangent_vec_a, tangent_vec_b, base_point):
r"""Compute the parallel transport of a tangent vector.
Closed-form solution for the parallel transport of a tangent vector a
along the geodesic defined by :math:`t \mapsto exp_(base_point)(t*
tangent_vec_b)`.
Parameters
----------
tangent_vec_a : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at base point to be transported.
tangent_vec_b : array-like, shape=[..., {dim, [n, m]}]
Tangent vector at base point, along which the parallel transport
is computed.
base_point : array-like, shape=[..., {dim, [n, m]}]
Point on the manifold.
Returns
-------
transported_tangent_vec: array-like, shape=[..., {dim, [n, m]}]
Transported tangent vector at `exp_(base_point)(tangent_vec_b)`.
"""
raise NotImplementedError(
"The closed-form solution of parallel transport is not known, "
"use the ladder_parallel_transport instead."
)