def test_value_and_grad_argnums(self):
def f(x, y, z, flag=False):
assert flag
return 1.0 * x + 2.0 * y + 3.0 * z
y = f(1.0, 1.0, 1.0, flag=True)
assert api.value_and_grad(f)(1.0, 1.0, 1.0, flag=True) == (y, 1.0)
assert api.value_and_grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == (y, 2.0)
assert api.value_and_grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (y, (3.0, 1.0))
def test_defvjp_all_const(self):
foo_p = Primitive('foo')
def foo(x): return foo_p.bind(x)
ad.defvjp_all(foo_p, lambda x: (x**2, lambda g: (12.,)))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, 9., check_dtypes=False)
self.assertAllClose(grad_ans, 12., check_dtypes=True)
def test_defvjp_all(self):
foo_p = Primitive('foo')
def foo(x): return 2. * foo_p.bind(x)
ad.defvjp_all(foo_p, lambda x: (x**2, lambda g: (4 * g * np.sin(x),)))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, 2 * 3.**2, check_dtypes=False)
self.assertAllClose(grad_ans, 4 * 2 * onp.sin(3.), check_dtypes=False)
def test_defvjp_all(self):
@api.custom_transforms
def foo(x):
return np.sin(x)
api.defvjp_all(foo, lambda x: (np.sin(x), lambda g: (g * x, )))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, onp.sin(3.), check_dtypes=False)
self.assertAllClose(grad_ans, 3., check_dtypes=False)
def test_defvjp_use_ans(self):
@api.custom_transforms
def foo(x, y):
return np.sin(x * y)
api.defvjp(foo, None, lambda g, ans, x, y: g * x * y + np.cos(ans))
val_ans, grad_ans = api.value_and_grad(foo, 1)(3., 4.)
self.assertAllClose(val_ans, onp.sin(3. * 4.), check_dtypes=False)
self.assertAllClose(grad_ans, 3. * 4. + onp.cos(onp.sin(3. * 4)),
check_dtypes=False)
def update(i, opt_state, batch, rng, no_batch):
params = get_params(opt_state)
loss_, gradient = value_and_grad(elbo)(
params,
batch,
predict_apply_fn,
rng,
kl_scale,
loss_fn,
noise_std=noise_std) # scalar, (1, hidden_dim)
return opt_update(i, gradient, opt_state), loss_
def test_defvjp(self):
@api.custom_transforms
def foo(x, y):
return np.sin(x * y)
api.defvjp(foo, None, lambda g, _, x, y: g * x * y)
val_ans, grad_ans = api.value_and_grad(foo)(3., 4.)
self.assertAllClose(val_ans, onp.sin(3. * 4.), check_dtypes=False)
self.assertAllClose(grad_ans, 0., check_dtypes=False)
ans_0, ans_1 = api.grad(foo, (0, 1))(3., 4.)
self.assertAllClose(ans_0, 0., check_dtypes=False)
self.assertAllClose(ans_1, 3. * 4., check_dtypes=False)
def __init__(self,
forward,
backward,
domain,
image,
val_and_grad_forward=None):
self._forward = forward
self._backward = backward
self.domain = domain
self.image = image
if val_and_grad_forward is None:
val_and_grad_forward = api.value_and_grad(forward)
self._val_and_grad_forward = val_and_grad_forward
def test_custom_root_scalar(self):
# TODO(shoyer): Figure out why this fails and re-enable it, if possible. My
# best guess is that TPUs use less stable numerics for pow().
if jtu.device_under_test() == "tpu":
raise SkipTest("Test fails on TPU")
def scalar_solve(f, y):
return y / f(1.0)
def binary_search(func, x0, low=0.0, high=100.0, tolerance=1e-6):
del x0 # unused
def cond(state):
low, high = state
return high - low > tolerance
def body(state):
low, high = state
midpoint = 0.5 * (low + high)
update_upper = func(midpoint) > 0
low = np.where(update_upper, low, midpoint)
high = np.where(update_upper, midpoint, high)
return (low, high)
solution, _ = lax.while_loop(cond, body, (low, high))
return solution
def sqrt_cubed(x, tangent_solve=scalar_solve):
f = lambda y: y ** 2. - np.array(x) ** 3.
return lax.custom_root(f, 0.0, binary_search, tangent_solve)
value, grad = api.value_and_grad(sqrt_cubed)(5.0)
self.assertAllClose(value, 5 ** 1.5, check_dtypes=False, rtol=1e-6)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False,
rtol=1e-7)
jtu.check_grads(sqrt_cubed, (5.0,), order=2, rtol=1e-3)
# TODO(shoyer): reenable when batching works
# inputs = np.array([4.0, 5.0])
# results = api.vmap(sqrt_cubed)(inputs)
# self.assertAllClose(results, inputs ** 1.5, check_dtypes=False)
results = api.jit(sqrt_cubed)(5.0)
self.assertAllClose(results, 5.0 ** 1.5, check_dtypes=False,
rtol={onp.float64:1e-7})
def test_defvjp_all_multiple_arguments(self):
# also tests passing in symbolic zero tangents b/c we differentiate wrt only
# the first argument in one case
foo_p = Primitive('foo')
def foo(x, y): return foo_p.bind(x, y)
def vjpfun(x, y):
out = x**2 + y**3
vjp = lambda g: (g + x + y, g * x * 9.)
return out, vjp
ad.defvjp_all(foo_p, vjpfun)
val_ans, grad_ans = api.value_and_grad(foo)(3., 4.)
self.assertAllClose(val_ans, 3.**2 + 4.**3, check_dtypes=False)
self.assertAllClose(grad_ans, 1. + 3. + 4., check_dtypes=False)
ans = api.grad(foo, (0, 1))(3., 4.)
self.assertAllClose(ans, (1. + 3. + 4., 1. * 3. * 9.), check_dtypes=False)
def test_root_scalar(self):
def scalar_solve(f, y):
return y / f(1.0)
def binary_search(func, x0, low=0.0, high=100.0, tolerance=1e-6):
del x0 # unused
def cond(state):
low, high = state
return high - low > tolerance
def body(state):
low, high = state
midpoint = 0.5 * (low + high)
update_upper = func(midpoint) > 0
low = np.where(update_upper, low, midpoint)
high = np.where(update_upper, midpoint, high)
return (low, high)
solution, _ = lax.while_loop(cond, body, (low, high))
return solution
def sqrt_cubed(x, tangent_solve=scalar_solve):
f = lambda y: y ** 2 - x ** 3
return lax.root(f, 0.0, binary_search, tangent_solve)
value, grad = api.value_and_grad(sqrt_cubed)(5.0)
self.assertAllClose(value, 5 ** 1.5, check_dtypes=False)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False)
jtu.check_grads(sqrt_cubed, (5.0,), order=2, rtol=1e-3)
# TODO(shoyer): reenable when batching works
# inputs = np.array([4.0, 5.0])
# results = api.vmap(sqrt_cubed)(inputs)
# self.assertAllClose(results, inputs ** 1.5, check_dtypes=False)
results = api.jit(sqrt_cubed)(5.0)
self.assertAllClose(results, 5.0 ** 1.5, check_dtypes=False)
def test_custom_implicit_solve(self):
def scalar_solve(f, y):
return y / f(1.0)
def _binary_search(func, params, low=0.0, high=100.0, tolerance=1e-6):
def cond(state):
low, high = state
return high - low > tolerance
def body(state):
low, high = state
midpoint = 0.5 * (low + high)
update_upper = func(midpoint, params) > 0
low = np.where(update_upper, low, midpoint)
high = np.where(update_upper, midpoint, high)
return (low, high)
solution, _ = lax.while_loop(cond, body, (low, high))
return solution
binary_search = api._custom_implicit_solve(_binary_search,
scalar_solve)
sqrt_cubed = lambda y, x: y**2 - x**3
value, grad = api.value_and_grad(binary_search, argnums=1)(sqrt_cubed,
5.0)
self.assertAllClose(value, 5**1.5, check_dtypes=False)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False)
def scalar_solve2(f, y):
y_1d = y[np.newaxis]
return np.linalg.solve(api.jacobian(f)(y_1d), y_1d).squeeze()
binary_search = api._custom_implicit_solve(_binary_search,
scalar_solve2)
grad = api.grad(binary_search, argnums=1)(sqrt_cubed, 5.0)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False)
elif args.optimizer == 'momentum':
opt_init, opt_apply, get_params = myopt.momentum(
lr, args.momentum, weight_decay=args.weight_decay)
elif args.optimizer == 'adagrad':
opt_init, opt_apply, get_params = optimizers.adagrad(lr, args.momentum)
elif args.optimizer == 'adam':
opt_init, opt_apply, get_params = optimizers.adam(lr)
state = opt_init(params)
if args.loss == 'logistic':
loss = lambda fx, y: np.mean(-np.sum(logsoftmax(fx) * y, axis=1))
elif args.loss == 'squared':
loss = lambda fx, y: np.mean(np.sum((fx - y)**2, axis=1))
value_and_grad_loss = jit(
value_and_grad(lambda params, x, y: loss(f(params, x), y)))
loss_fn = jit(lambda params, x, y: loss(f(params, x), y))
accuracy_sum = jit(
lambda fx, y: np.sum(np.argmax(fx, axis=1) == np.argmax(y, axis=1)))
# Create tensorboard writer
writer = SummaryWriter(logdir=args.logdir)
# Train the network
global_step, running_count = 0, 0
running_loss, running_loss_g = 0., 0.
if args.save_path is not None:
save_path = os.path.join(args.save_path, f'{global_step}.npy')
save_jax_params(params, save_path)
test_logits = onp.zeros((n_test, args.num_classes), dtype=onp.float32)
test_loader = tfds.as_numpy(test_data.batch(args.batch_size_test))
compute_score=True,
tol=1e-3 * tau,
alpha_1=hyper_prior[0],
alpha_2=hyper_prior[1],
threshold_lambda=1e6,
)
reg.fit(theta_normed, dt)
# %%
loss, mn, prior, metric = SBL(theta_normed, dt, beta_prior=hyper_prior, tol=1e-3 * tau)
# %%
# hyper_prior = (n_samples / 2, n_samples / 2 * 1 / (1e-6))
hyper_prior = (1e-6, 1e-6)
f = lambda x, y: SBL(x, y, beta_prior=hyper_prior, tol=1e-3)[0]
grad_fn = value_and_grad(f)
grad_fn(theta_normed, dt)
# %%
# %%
hyper_prior = (1e-6, 1e-6)
f = lambda x, y: SBL(x, y, beta_prior=hyper_prior, tol=1e-3)[0].sum()
grad_fn = value_and_grad(f)
grad_fn(theta_normed, dt)
# %%
n_samples, n_features = theta.shape
prior_init = jnp.concatenate(
[jnp.ones((n_features,)), (1.0 / (jnp.var(y) + 1e-7))[jnp.newaxis]], axis=0
)
# %%
def __init__(self,
obs_interval,
num_steps_per_obs,
num_obs_per_subseq,
y_seq,
dim_z,
dim_x,
dim_v,
forward_func,
generate_x_0,
generate_z,
obs_func,
metric=None):
"""
Args:
obs_interval (float): Interobservation time interval.
num_steps_per_obs (int): Number of discrete time steps to simulate
between each observation time.
num_obs_per_subseq (int): Average number of observations per
partitioned subsequence. Must be a factor of `len(y_obs_seq)`.
y_seq (array): Two-dimensional array containing observations at
equally spaced time intervals, with first axis of array
corresponding to observation time index (in order of increasing
time) and second axis corresponding to dimension of each
(vector-valued) observation.
dim_z(int): Dimension of parameter vector `z`.
dim_x (int): Dimension of state vector `x`.
dim_v (int): Dimension of noise vector `v` consumed by
`forward_func` to approximate time step.
forward_func (Callable[[array, array, array, float], array]):
Function implementing forward step of time-discretisation of
diffusion such that `forward_func(z, x, v, δ)` for parameter
vector `z`, current state `x` at time `t`, standard normal
vector `v` and small timestep `δ` and is distributed
approximately according to `X(t + δ) | X(t) = x, Z = z`.
generate_x_0 (Callable[[array, array], array]): Generator function
for the initial state such that `generator_x_0(z, v_0)` for
parameter vector `z` and standard normal vector `v_0` is
distributed according to prior distribution on `X(0) | Z = z`.
generate_z (Callable[[array], array]): Generator function
for parameter vector such that `generator_z(u)` for standard
normal vector `u` is distributed according to prior distribution
on parameter vector `Z`.
obs_func (Callable[[array], array]): Function mapping from state
vector `x` at an observation time to the corresponding observed
vector `y = obs_func(x)`.
metric (Matrix): Metric matrix representation. Should be either an
`mici.matrices.IdentityMatrix` or
`mici.matrices.SymmetricBlockDiagonalMatrix` instance, with in
the latter case the matrix having two blocks on the diagonal,
the left most of size `dim_z x dim_z`, and the rightmost being
positive diagonal. Defaults to `mici.matrices.IdentityMatrix`.
"""
if metric is None or isinstance(metric, IdentityMatrix):
metric_1 = np.eye(dim_z)
log_det_sqrt_metric_1 = 0
elif (isinstance(metric, SymmetricBlockDiagonalMatrix)
and isinstance(metric.blocks[1], PositiveDiagonalMatrix)):
metric_1 = metric.blocks[0].array
log_det_sqrt_metric_1 = metric.blocks[0].log_abs_det_sqrt
metric_2_diag = metric.blocks[1].diagonal
else:
raise NotImplementedError(
'Only identity and block diagonal metrics with diagonal lower '
'right block currently supported.')
num_obs, dim_y = y_seq.shape
δ = obs_interval / num_steps_per_obs
dim_q = dim_z + dim_x + num_obs * dim_v * num_steps_per_obs
if num_obs % num_obs_per_subseq != 0:
raise NotImplementedError(
'Only cases where num_obs_per_subseq is a factor of num_obs '
'supported.')
num_subseq = num_obs // num_obs_per_subseq
obs_indices = slice(num_steps_per_obs - 1, None, num_steps_per_obs)
num_step_per_subseq = num_obs_per_subseq * num_steps_per_obs
y_subseqs_p0 = np.reshape(y_seq, (num_subseq, num_obs_per_subseq, -1))
y_subseqs_p1 = split(
y_seq, (num_obs_per_subseq // 2, num_obs - num_obs_per_subseq))
y_subseqs_p1[1] = np.reshape(
y_subseqs_p1[1], (num_subseq - 1, num_obs_per_subseq, dim_y))
super().__init__(neg_log_dens=standard_normal_neg_log_dens,
grad_neg_log_dens=standard_normal_grad_neg_log_dens,
metric=metric)
@api.jit
def step_func(z, x, v):
x_n = forward_func(z, x, v, δ)
return (x_n, x_n)
@api.jit
def generate_x_obs_seq(q):
u, v_0, v_seq_flat = split(q, (dim_z, dim_x))
z = generate_z(u)
x_0 = generate_x_0(z, v_0)
v_seq = np.reshape(v_seq_flat, (-1, dim_v))
_, x_seq = lax.scan(lambda x, v: step_func(z, x, v), x_0, v_seq)
return x_seq[obs_indices]
@api.partial(api.jit, static_argnums=(3, ))
def partition_into_subseqs(v_seq, v_0, x_obs_seq, partition=0):
"""Partition noise increment and observation sequences.
Partitition sequences in to either `num_subseq` equally sized
subsequences (`partition == 0`) or `num_subseq - 1` equally sized
subsequences plus initial and final 'half' subsequences.
"""
if partition == 0:
v_subseqs = v_seq.reshape(
(num_subseq, num_step_per_subseq, dim_v))
v_subseqs = (v_subseqs[0], v_subseqs[1:-1], v_subseqs[-1])
x_obs_subseqs = x_obs_seq.reshape(
(num_subseq, num_obs_per_subseq, dim_x))
w_inits = (v_0, x_obs_subseqs[:-2, -1], x_obs_subseqs[-2, -1])
y_bars = (np.concatenate(
(y_subseqs_p0[0, :-1].flatten(), x_obs_subseqs[0, -1])),
np.concatenate((y_subseqs_p0[1:-1, :-1].reshape(
(num_subseq - 2, -1)), x_obs_subseqs[1:-1, -1]),
-1), y_subseqs_p0[-1].flatten())
else:
v_subseqs = split(
v_seq, ((num_obs_per_subseq // 2) * num_steps_per_obs,
num_step_per_subseq * (num_subseq - 1)))
v_subseqs[1] = v_subseqs[1].reshape(
(num_subseq - 1, num_step_per_subseq, dim_v))
x_obs_subseqs = split(
x_obs_seq,
(num_obs_per_subseq // 2, num_obs - num_obs_per_subseq))
x_obs_subseqs[1] = x_obs_subseqs[1].reshape(
(num_subseq - 1, num_obs_per_subseq, dim_x))
w_inits = (v_0,
np.concatenate(
(x_obs_subseqs[0][-1:], x_obs_subseqs[1][:-1,
-1]),
0), x_obs_subseqs[1][-1, -1])
y_bars = (np.concatenate(
(y_subseqs_p1[0][:-1].flatten(), x_obs_subseqs[0][-1])),
np.concatenate((
y_subseqs_p1[1][:, :-1].reshape(
(num_subseq - 1, -1)),
x_obs_subseqs[1][:, -1],
), -1), y_subseqs_p1[2].flatten())
return v_subseqs, w_inits, y_bars
def generate_y_bar(z, w_0, v_seq, b):
x_0 = generate_x_0(z, w_0) if b == 0 else w_0
_, x_seq = lax.scan(lambda x, v: step_func(z, x, v), x_0, v_seq)
y_seq = obs_func(x_seq[obs_indices])
return y_seq.flatten() if b == 2 else np.concatenate(
(y_seq[:-1].flatten(), x_seq[-1]))
@api.partial(api.jit, static_argnums=(2, ))
def constr(q, x_obs_seq, partition=0):
"""Calculate constraint function for current partition."""
u, v_0, v_seq_flat = split(q, (
dim_z,
dim_x,
))
v_seq = v_seq_flat.reshape((-1, dim_v))
z = generate_z(u)
(v_subseqs, w_inits,
y_bars) = partition_into_subseqs(v_seq, v_0, x_obs_seq, partition)
gen_funcs = (generate_y_bar,
api.vmap(generate_y_bar,
(None, 0, 0, None)), generate_y_bar)
return np.concatenate([
(gen_funcs[b](z, w_inits[b], v_subseqs[b], b) -
y_bars[b]).flatten() for b in range(3)
])
@api.jit
def init_objective(q, x_obs_seq, reg_coeff):
"""Optimisation objective to find initial state on manifold."""
u, v_0, v_seq_flat = split(q, (
dim_z,
dim_x,
))
v_subseqs = v_seq_flat.reshape((num_obs, num_steps_per_obs, dim_v))
z = generate_z(u)
x_0 = generate_x_0(z, v_0)
x_inits = np.concatenate((x_0[None], x_obs_seq[:-1]), 0)
def generate_final_state(z, v_seq, x_0):
_, x_seq = lax.scan(lambda x, v: step_func(z, x, v), x_0,
v_seq)
return x_seq[-1]
c = api.vmap(generate_final_state, in_axes=(None, 0, 0))(
z, v_subseqs, x_inits) - x_obs_seq
return 0.5 * np.mean(c**2) + 0.5 * reg_coeff * np.mean(q**2), c
@api.partial(api.jit, static_argnums=(2, ))
def jacob_constr_blocks(q, x_obs_seq, partition=0):
"""Return non-zero blocks of constraint function Jacobian.
Input state q can be decomposed into q = [u, v₀, v₁, v₂]
where global latent state (parameters) are determined by u,
initial subsequence by v₀, middle subsequences by v₁ and final
subsequence by v₂.
Constraint function can then be decomposed as
c(q) = [c₀(u, v₀), c₁(u, v₁), c₂(u, v₂)]
Constraint Jacobian ∂c(q) has block structure
∂c(q) = [[∂₀c₀(u, v₀), ∂₁c₀(u, v₀), 0, , 0 ]
[∂₀c₁(u, v₁), 0 , ∂₁c₁(u, v₁), 0 ]
[∂₀c₂(u, v₀), 0 , 0 , ∂₁c₂(u, v₂)]]
"""
def g_y_bar(u, v, w_0, b):
z = generate_z(u)
if b == 0:
w_0, v = split(v, (dim_x, ))
v_seq = np.reshape(v, (-1, dim_v))
return generate_y_bar(z, w_0, v_seq, b)
u, v_0, v_seq_flat = split(q, (
dim_z,
dim_x,
))
v_seq = np.reshape(v_seq_flat, (-1, dim_v))
(v_subseqs, w_inits,
y_bars) = partition_into_subseqs(v_seq, v_0, x_obs_seq, partition)
v_bars = (np.concatenate([v_0, v_subseqs[0].flatten()]),
np.reshape(v_subseqs[1], (v_subseqs[1].shape[0], -1)),
v_subseqs[2].flatten())
jac_g_y_bar = api.jacrev(g_y_bar, (0, 1))
jacob_funcs = (jac_g_y_bar,
api.vmap(jac_g_y_bar,
(None, 0, 0, None)), jac_g_y_bar)
return tuple(
zip(*[
jacob_funcs[b](u, v_bars[b], w_inits[b], b)
for b in range(3)
]))
@api.jit
def chol_gram_blocks(dc_du, dc_dv):
"""Calculate Cholesky factors of decomposition of Gram matrix. """
if isinstance(metric, IdentityMatrix):
D = tuple(
np.einsum('...ij,...kj', dc_dv[i], dc_dv[i])
for i in range(3))
else:
m_v = split(
metric_2_diag,
(dc_dv[0].shape[1], dc_dv[1].shape[0] * dc_dv[1].shape[2]))
m_v[1] = m_v[1].reshape((dc_dv[1].shape[0], dc_dv[1].shape[2]))
D = tuple(
np.einsum('...ij,...kj', dc_dv[i] /
m_v[i][..., None, :], dc_dv[i])
for i in range(3))
chol_D = tuple(nla.cholesky(D[i]) for i in range(3))
D_inv_dc_du = tuple(
sla.cho_solve((chol_D[i], True), dc_du[i]) for i in range(3))
chol_C = nla.cholesky(metric_1 + (
dc_du[0].T @ D_inv_dc_du[0] +
np.einsum('ijk,ijl->kl', dc_du[1], D_inv_dc_du[1]) +
dc_du[2].T @ D_inv_dc_du[2]))
return chol_C, chol_D
@api.jit
def log_det_sqrt_gram_from_chol(chol_C, chol_D):
"""Calculate log-det of Gram matrix from Cholesky factors."""
return (sum(
np.log(np.abs(chol_D[i].diagonal(0, -2, -1))).sum()
for i in range(3)) + np.log(np.abs(chol_C.diagonal())).sum() -
log_det_sqrt_metric_1)
@api.partial(api.jit, static_argnums=(2, ))
def log_det_sqrt_gram(q, x_obs_seq, partition=0):
"""Calculate log-determinant of constraint Jacobian Gram matrix."""
dc_du, dc_dv = jacob_constr_blocks(q, x_obs_seq, partition)
chol_C, chol_D = chol_gram_blocks(dc_du, dc_dv)
return (log_det_sqrt_gram_from_chol(chol_C, chol_D),
((dc_du, dc_dv), (chol_C, chol_D)))
@api.jit
def lmult_by_jacob_constr(dc_du, dc_dv, vct):
"""Left-multiply vector by constraint Jacobian matrix."""
vct_u, vct_v = split(vct, (dim_z, ))
j0, j1, j2 = dc_dv[0].shape[1], dc_dv[1].shape[0], dc_dv[2].shape[
1]
return (np.vstack((dc_du[0], dc_du[1].reshape(
(-1, dim_z)), dc_du[2])) @ vct_u + np.concatenate(
(dc_dv[0] @ vct_v[:j0],
np.einsum('ijk,ik->ij', dc_dv[1],
np.reshape(vct_v[j0:-j2], (j1, -1))).flatten(),
dc_dv[2] @ vct_v[-j2:])))
@api.jit
def rmult_by_jacob_constr(dc_du, dc_dv, vct):
"""Right-multiply vector by constraint Jacobian matrix."""
vct_parts = split(
vct,
(dc_du[0].shape[0], dc_du[1].shape[0] * dc_du[1].shape[1]))
vct_parts[1] = np.reshape(vct_parts[1], dc_du[1].shape[:2])
return np.concatenate([
vct_parts[0] @ dc_du[0] +
np.einsum('ij,ijk->k', vct_parts[1], dc_du[1]) +
vct_parts[2] @ dc_du[2], vct_parts[0] @ dc_dv[0],
np.einsum('ij,ijk->ik', vct_parts[1],
dc_dv[1]).flatten(), vct_parts[2] @ dc_dv[2]
])
@api.jit
def lmult_by_inv_gram(dc_du, dc_dv, chol_C, chol_D, vct):
"""Left-multiply vector by inverse Gram matrix."""
vct_parts = split(
vct,
(dc_du[0].shape[0], dc_du[1].shape[0] * dc_du[1].shape[1]))
vct_parts[1] = np.reshape(vct_parts[1], dc_du[1].shape[:2])
D_inv_vct = [
sla.cho_solve((chol_D[i], True), vct_parts[i])
for i in range(3)
]
dc_du_T_D_inv_vct = sum(
np.einsum('...jk,...j->k', dc_du[i], D_inv_vct[i])
for i in range(3))
C_inv_dc_du_T_D_inv_vct = sla.cho_solve((chol_C, True),
dc_du_T_D_inv_vct)
return np.concatenate([
sla.cho_solve((chol_D[i], True), vct_parts[i] -
dc_du[i] @ C_inv_dc_du_T_D_inv_vct).flatten()
for i in range(3)
])
@api.jit
def normal_space_component(vct, dc_du, dc_dv, chol_C, chol_D):
return rmult_by_jacob_constr(
dc_du, dc_dv,
lmult_by_inv_gram(dc_du, dc_dv, chol_C, chol_D,
lmult_by_jacob_constr(dc_du, dc_dv, vct)))
@api.partial(api.jit, static_argnums=(2, 7, 8, 9, 10))
def quasi_newton_projection(q, x_obs_seq, partition, dc_du_prev,
dc_dv_prev, chol_C_prev, chol_D_prev,
convergence_tol, position_tol,
divergence_tol, max_iters):
norm = lambda x: np.max(np.abs(x))
def body_func(val):
q, i, _, _ = val
c = constr(q, x_obs_seq, partition)
error = norm(c)
delta_q = rmult_by_jacob_constr(
dc_du_prev, dc_dv_prev,
lmult_by_inv_gram(dc_du_prev, dc_dv_prev, chol_C_prev,
chol_D_prev, c))
q -= delta_q
i += 1
return q, i, norm(delta_q), error
def cond_func(val):
q, i, norm_delta_q, error, = val
diverged = np.logical_or(error > divergence_tol,
np.isnan(error))
converged = np.logical_and(error < convergence_tol,
norm_delta_q < position_tol)
return np.logical_not(
np.logical_or((i >= max_iters),
np.logical_or(diverged, converged)))
return lax.while_loop(cond_func, body_func, (q, 0, np.inf, -1.))
self._generate_x_obs_seq = generate_x_obs_seq
self._constr = constr
self._jacob_constr_blocks = jacob_constr_blocks
self._chol_gram_blocks = chol_gram_blocks
self._log_det_sqrt_gram_from_chol = log_det_sqrt_gram_from_chol
self._grad_log_det_sqrt_gram = api.jit(
api.value_and_grad(log_det_sqrt_gram, has_aux=True), (2, ))
self.value_and_grad_init_objective = api.jit(
api.value_and_grad(init_objective, (0, ), has_aux=True))
self._normal_space_component = normal_space_component
self.quasi_newton_projection = quasi_newton_projection
