from itertools import product
from numpy.polynomial.hermite_e import hermegauss
from jax import jacfwd, vmap, lax
import jax.numpy as jnp
from jax import lax
from tensorflow_probability.substrates.jax.distributions import MultivariateNormalFullCovariance as MVN
from jaxtyping import Array, Float
from typing import NamedTuple, Optional, Union, Callable
from dynamax.utils.utils import psd_solve
from dynamax.generalized_gaussian_ssm.models import ParamsGGSSM
from dynamax.linear_gaussian_ssm.inference import PosteriorGSSMFiltered, PosteriorGSSMSmoothed
# Helper functions
_get_params = lambda x, dim, t: x[t] if x.ndim == dim + 1 else x
_process_fn = lambda f, u: (lambda x, y: f(x)) if u is None else f
_process_input = lambda x, y: jnp.zeros((y,)) if x is None else x
_jacfwd_2d = lambda f, x: jnp.atleast_2d(jacfwd(f)(x))
class EKFIntegrals(NamedTuple):
""" Lightweight container for EKF Gaussian integrals."""
gaussian_expectation: Callable = lambda f, m, P: jnp.atleast_1d(f(m))
gaussian_cross_covariance: Callable = lambda f, g, m, P: _jacfwd_2d(f, m) @ P @ _jacfwd_2d(g, m).T
class UKFIntegrals(NamedTuple):
"""Lightweight container for UKF Gaussian integrals."""
alpha: float = jnp.sqrt(3)
beta: float = 2.0
kappa: float = 1.0
def gaussian_expectation(self, f, m, P):
w_mean, _, sigmas = self.compute_weights_and_sigmas(m, P)
return jnp.atleast_1d(jnp.tensordot(w_mean, vmap(f)(sigmas), axes=1))
def gaussian_cross_covariance(self, f, g, m, P):
_, w_cov, sigmas = self.compute_weights_and_sigmas(m, P)
_outer = vmap(lambda x, y: jnp.atleast_2d(x).T @ jnp.atleast_2d(y), 0, 0)
f_mean, g_mean = self.gaussian_expectation(f, m, P), self.gaussian_expectation(g, m, P)
return jnp.atleast_2d(jnp.tensordot(w_cov, _outer(vmap(f)(sigmas) - f_mean, vmap(g)(sigmas) - g_mean), axes=1))
def compute_weights_and_sigmas(self, m, P):
n = len(m)
lamb = self.alpha**2 * (n + self.kappa) - n
# Compute weights
factor = 1 / (2 * (n + lamb))
w_mean = jnp.concatenate((jnp.array([lamb / (n + lamb)]), jnp.ones(2 * n) * factor))
w_cov = jnp.concatenate((jnp.array([lamb / (n + lamb) + (1 - self.alpha**2 + self.beta)]), jnp.ones(2 * n) * factor))
# Compute sigmas
distances = jnp.sqrt(n + lamb) * jnp.linalg.cholesky(P)
sigma_plus = jnp.array([m + distances[:, i] for i in range(n)])
sigma_minus = jnp.array([m - distances[:, i] for i in range(n)])
sigmas = jnp.concatenate((jnp.array([m]), sigma_plus, sigma_minus))
return w_mean, w_cov, sigmas
class GHKFIntegrals(NamedTuple):
"""Lightweight container for GHKF Gaussian integrals."""
order: int = 10
def gaussian_expectation(self, f, m, P):
w_mean, _, sigmas = self.compute_weights_and_sigmas(m, P)
return jnp.atleast_1d(jnp.tensordot(w_mean, vmap(f)(sigmas), axes=1))
def gaussian_cross_covariance(self, f, g, m, P):
_, w_cov, sigmas = self.compute_weights_and_sigmas(m, P)
_outer = vmap(lambda x, y: jnp.atleast_2d(x).T @ jnp.atleast_2d(y), 0, 0)
f_mean, g_mean = self.gaussian_expectation(f, m, P), self.gaussian_expectation(g, m, P)
return jnp.atleast_2d(jnp.tensordot(w_cov, _outer(vmap(f)(sigmas) - f_mean, vmap(g)(sigmas) - g_mean), axes=1))
def compute_weights_and_sigmas(self, m, P):
n = len(m)
samples_1d, weights_1d = jnp.array(hermegauss(self.order))
weights_1d /= weights_1d.sum()
weights = jnp.prod(jnp.array(list(product(weights_1d, repeat=n))), axis=1)
unit_sigmas = jnp.array(list(product(samples_1d, repeat=n)))
sigmas = m + vmap(jnp.matmul, [None, 0], 0)(jnp.linalg.cholesky(P), unit_sigmas)
return weights, weights, sigmas
CMGFIntegrals = Union[EKFIntegrals, UKFIntegrals, GHKFIntegrals]
def _predict(m, P, f, Q, u, g_ev, g_cov):
"""Predict next mean and covariance under an additive-noise Gaussian filter
p(x_{t+1}) = N(x_{t+1} | mu_pred, Sigma_pred)
where
mu_pred = gev(f, m, P)
= \int f(x_t, u) N(x_t | m, P) dx_t
Sigma_pred = gev((f - mu_pred)(f - mu_pred)^T, m, P) + Q
= \int (f(x_t, u) - mu_pred)(f(x_t, u) - mu_pred)^T
N(x_t | m, P)dx_t + Q
Args:
m (D_hid,): prior mean.
P (D_hid,D_hid): prior covariance.
f (Callable): dynamics function.
Q (D_hid,D_hid): dynamics covariance matrix.
u (D_in,): inputs.
g_ev (Callable): Gaussian expectation value function.
g_cov (Callable): Gaussian cross covariance function.
Returns:
mu_pred (D_hid,): predicted mean.
Sigma_pred (D_hid,D_hid): predicted covariance.
cross_pred (D_hid,D_hid): cross covariance term.
"""
dynamics_fn = lambda x: f(x, u)
identity_fn = lambda x: x
mu_pred = g_ev(dynamics_fn, m, P)
Sigma_pred = g_cov(dynamics_fn, dynamics_fn, m, P) + Q
cross_pred = g_cov(identity_fn, dynamics_fn, m, P)
return mu_pred, Sigma_pred, cross_pred
def _condition_on(m, P, y_cond_mean, y_cond_cov, u, y, g_ev, g_cov, num_iter, emission_dist):
"""Condition a Gaussian potential on a new observation with arbitrary
likelihood with given functions for conditional moments and make a
Gaussian approximation.
p(x_t | y_t, u_t, y_{1:t-1}, u_{1:t-1})
propto p(x_t | y_{1:t-1}, u_{1:t-1}) p(y_t | x_t, u_t)
= N(x_t | m, P) ArbitraryDist(y_t |y_cond_mean(x_t), y_cond_cov(x_t))
\approx N(x_t | mu_cond, Sigma_cond)
where
mu_cond = m + K*(y - yhat)
yhat = gev(h, m, P)
S = gev((h - yhat)(h - yhat)^T, m, P) + R
C = gev((Identity - m)(h - yhat)^T, m, P)
K = C * S^{-1}
Sigma_cond = P - K S K'
Args:
m (D_hid,): prior mean.
P (D_hid,D_hid): prior covariance.
y_cond_mean (Callable): conditional emission mean function.
y_cond_cov (Callable): conditional emission covariance function.
u (D_in,): inputs.
y (D_obs,): observation.
g_ev (Callable): Gaussian expectation value function.
g_cov (Callable): Gaussian cross covariance function.
num_iter (int): number of re-linearizations around posterior for update step.
emission_dist: the observation pdf q. Constructed from specified mean and covariance.
Returns:
log_likelihood (Scalar): prediction log likelihood for observation y
mu_cond (D_hid,): conditioned mean.
Sigma_cond (D_hid,D_hid): conditioned covariance.
"""
m_Y = lambda x: y_cond_mean(x, u)
Cov_Y = lambda x: y_cond_cov(x, u)
identity_fn = lambda x: x
def _step(carry, _):
prior_mean, prior_cov = carry
yhat = g_ev(m_Y, prior_mean, prior_cov)
S = g_ev(Cov_Y, prior_mean, prior_cov) + g_cov(m_Y, m_Y, prior_mean, prior_cov)
log_likelihood = emission_dist(yhat, S).log_prob(jnp.atleast_1d(y)).sum()
C = g_cov(identity_fn, m_Y, prior_mean, prior_cov)
K = psd_solve(S, C.T).T
posterior_mean = prior_mean + K @ (y - yhat)
posterior_cov = prior_cov - K @ S @ K.T
return (posterior_mean, posterior_cov), log_likelihood
# Iterate re-linearization over posterior mean and covariance
carry = (m, P)
(mu_cond, Sigma_cond), lls = lax.scan(_step, carry, jnp.arange(num_iter))
return lls[0], mu_cond, Sigma_cond
def _statistical_linear_regression(mu, Sigma, m, S, C):
"""Return moment-matching affine coefficients and approximation noise variance
given joint moments.
g(x) \approx Ax + b + e where e ~ N(0, Omega)
p(x) = N(x | mu, Sigma)
m = E[g(x)]
S = Var[g(x)]
C = Cov[x, g(x)]
Args:
mu (D_hid): prior mean.
Sigma (D_hid, D_hid): prior covariance.
m (D_obs): E[g(x)].
S (D_obs, D_obs): Var[g(x)]
C (D_hid, D_obs): Cov[x, g(x)]
Returns:
A (D_obs, D_hid): _description_
b (D_obs):
Omega (D_obs, D_obs):
"""
A = psd_solve(Sigma.T, C).T
b = m - A @ mu
Omega = S - A @ Sigma @ A.T
return A, b, Omega
[docs]
def conditional_moments_gaussian_filter(
model_params: ParamsGGSSM,
inf_params: CMGFIntegrals,
emissions: Float[Array, "ntime emission_dim"],
num_iter: int = 1,
inputs: Optional[Float[Array, "ntime input_dim"]]=None
) -> PosteriorGSSMFiltered:
"""Run an (iterated) conditional moments Gaussian filter to produce the
marginal likelihood and filtered state estimates.
Args:
model_params: model parameters.
inf_params: inference parameters that specify how to compute moments.
emissions: array of observations.
num_iter: optional number of linearizations around prior/posterior for update step (default 1).
inputs: optopnal array of inputs.
Returns:
filtered_posterior: posterior object.
"""
num_timesteps = len(emissions)
# Process dynamics function and conditional emission moments to take in control inputs
f = model_params.dynamics_function
m_Y, Cov_Y = model_params.emission_mean_function, model_params.emission_cov_function
f, m_Y, Cov_Y = (_process_fn(fn, inputs) for fn in (f, m_Y, Cov_Y))
inputs = _process_input(inputs, num_timesteps)
# Gaussian expectation value function
g_ev = inf_params.gaussian_expectation
g_cov = inf_params.gaussian_cross_covariance
# Emission distribution
emission_dist = model_params.emission_dist
def _step(carry, t):
ll, pred_mean, pred_cov = carry
# Get parameters and inputs for time index t
Q = _get_params(model_params.dynamics_covariance, 2, t)
u = inputs[t]
y = emissions[t]
# Condition on the emission
log_likelihood, filtered_mean, filtered_cov = _condition_on(pred_mean, pred_cov, m_Y, Cov_Y, u, y, g_ev, g_cov, num_iter, emission_dist)
ll += log_likelihood
# Predict the next state
pred_mean, pred_cov, _ = _predict(filtered_mean, filtered_cov, f, Q, u, g_ev, g_cov)
return (ll, pred_mean, pred_cov), (filtered_mean, filtered_cov)
# Run the general linearization filter
carry = (0.0, model_params.initial_mean, model_params.initial_covariance)
(ll, _, _), (filtered_means, filtered_covs) = lax.scan(_step, carry, jnp.arange(num_timesteps))
return PosteriorGSSMFiltered(marginal_loglik=ll, filtered_means=filtered_means, filtered_covariances=filtered_covs)
[docs]
def iterated_conditional_moments_gaussian_filter(
model_params: ParamsGGSSM,
inf_params: CMGFIntegrals,
emissions: Float[Array, "ntime emission_dim"],
num_iter: int = 2,
inputs: Optional[Float[Array, "ntime input_dim"]]=None
) -> PosteriorGSSMFiltered:
"""Run an iterated conditional moments Gaussian filter.
Args:
model_params: model parameters.
inf_params: inference parameters that specify how to compute moments.
emissions: array of observations.
num_iter: optional number of linearizations around prior/posterior for update step (default 1).
inputs: optional array of inputs.
Returns:
filtered_posterior: posterior object.
"""
filtered_posterior = conditional_moments_gaussian_filter(model_params, inf_params, emissions, num_iter, inputs)
return filtered_posterior
[docs]
def conditional_moments_gaussian_smoother(
model_params: ParamsGGSSM,
inf_params: CMGFIntegrals,
emissions: Float[Array, "ntime emission_dim"],
filtered_posterior: Optional[PosteriorGSSMFiltered]=None,
inputs: Optional[Float[Array, "ntime input_dim"]]=None
) -> PosteriorGSSMSmoothed:
"""Run a conditional moments Gaussian smoother.
Args:
model_params: model parameters.
inf_params: inference parameters that specify how to compute moments.
emissions: array of observations.
num_iter: optional number of linearizations around prior/posterior for update step (default 1).
inputs: optopnal array of inputs.
Returns:
post: posterior object.
"""
num_timesteps = len(emissions)
# Get filtered posterior
if filtered_posterior is None:
filtered_posterior = conditional_moments_gaussian_filter(model_params, inf_params, emissions, inputs=inputs)
ll, filtered_means, filtered_covs, *_ = filtered_posterior
# Process dynamics function to take in control inputs
f = _process_fn(model_params.dynamics_function, inputs)
inputs = _process_input(inputs, num_timesteps)
# Gaussian expectation value function
g_ev = inf_params.gaussian_expectation
g_cov = inf_params.gaussian_cross_covariance
def _step(carry, args):
# Unpack the inputs
smoothed_mean_next, smoothed_cov_next = carry
t, filtered_mean, filtered_cov = args
# Get parameters and inputs for time index t
Q = _get_params(model_params.dynamics_covariance, 2, t)
u = inputs[t]
# Prediction step
pred_mean, pred_cov, pred_cross = _predict(filtered_mean, filtered_cov, f, Q, u, g_ev, g_cov)
G = psd_solve(pred_cov, pred_cross.T).T
# Compute smoothed mean and covariance
smoothed_mean = filtered_mean + G @ (smoothed_mean_next - pred_mean)
smoothed_cov = filtered_cov + G @ (smoothed_cov_next - pred_cov) @ G.T
return (smoothed_mean, smoothed_cov), (smoothed_mean, smoothed_cov)
# Run the smoother
_, (smoothed_means, smoothed_covs) = lax.scan(
_step,
(filtered_means[-1], filtered_covs[-1]),
(jnp.arange(num_timesteps - 1), filtered_means[:-1], filtered_covs[:-1]),
reverse=True
)
# Concatenate the last smoothed mean and covariance
smoothed_means = jnp.vstack((smoothed_means, filtered_means[-1][None, ...]))
smoothed_covs = jnp.vstack((smoothed_covs, filtered_covs[-1][None, ...]))
return PosteriorGSSMSmoothed(
marginal_loglik=ll,
filtered_means=filtered_means,
filtered_covariances=filtered_covs,
smoothed_means=smoothed_means,
smoothed_covariances=smoothed_covs,
)
[docs]
def iterated_conditional_moments_gaussian_smoother(
model_params: ParamsGGSSM,
inf_params: CMGFIntegrals,
emissions: Float[Array, "ntime emission_dim"],
num_iter: int = 2,
inputs: Optional[Float[Array, "ntime input_dim"]]=None
) -> PosteriorGSSMSmoothed:
"""Run an iterated conditional moments Gaussian smoother.
Args:
model_params: model parameters.
inf_params: inference parameters that specify how to compute moments.
emissions: array of observations.
num_iter: optional number of linearizations around prior/posterior for update step (default 1).
inputs: optopnal array of inputs.
Returns:
post: posterior object.
"""
def _step(carry, _):
# Relinearize around smoothed posterior from previous iteration
smoothed_prior = carry
smoothed_posterior = conditional_moments_gaussian_smoother(model_params, inf_params, emissions, smoothed_prior, inputs)
return smoothed_posterior, None
smoothed_posterior, _ = lax.scan(_step, None, jnp.arange(num_iter))
return smoothed_posterior
