def test_lorenz() -> None:
error = math.inf
window = 0
prior_mu: Union[Tuple[float, float, float],
Tuple[numpy_types, numpy_types,
numpy_types]] = (3., 3., 3.)
model = LorenzModel(sigma, beta, rho, time_step)
observed = list(model.run_model(window_size * max_windows))
while error > converged_error and window < max_windows:
xt0 = Gaussian(prior_mu[0], 1.0)
yt0 = Gaussian(prior_mu[1], 1.0)
zt0 = Gaussian(prior_mu[2], 1.0)
graph_time_steps = list(build_graph((xt0, yt0, zt0)))
xt0.set_and_cascade(prior_mu[0])
yt0.set_and_cascade(prior_mu[1])
zt0.set_and_cascade(prior_mu[2])
apply_observations(graph_time_steps, window, observed)
optimizer = GradientOptimizer(xt0)
optimizer.max_a_posteriori()
posterior = get_time_slice_values(graph_time_steps, window_size - 1)
post_t = (window + 1) * (window_size - 1)
actual_at_post_t = observed[post_t]
error = math.sqrt((actual_at_post_t.x - posterior[0])**2 +
(actual_at_post_t.y - posterior[1])**2 +
(actual_at_post_t.z - posterior[2])**2)
prior_mu = (posterior[0], posterior[1], posterior[2])
window += 1
assert error <= converged_error
return list(map(lambda v: v.get_value(), time_slice))
error = math.inf
window = 0
prior_mu = (3., 3., 3.)
model = LorenzModel(sigma, beta, rho, time_step)
observed = list(model.run_model(window_size * max_windows))
while error > converged_error and window < max_windows:
xt0 = Gaussian(prior_mu[0], 1.0)
yt0 = Gaussian(prior_mu[1], 1.0)
zt0 = Gaussian(prior_mu[2], 1.0)
graph_time_steps = list(build_graph((xt0, yt0, zt0)))
xt0.set_and_cascade(prior_mu[0])
yt0.set_and_cascade(prior_mu[1])
zt0.set_and_cascade(prior_mu[2])
apply_observations(graph_time_steps, window, observed)
optimizer = GradientOptimizer(xt0)
optimizer.max_a_posteriori()
posterior = get_time_slice_values(graph_time_steps, window_size - 1)
post_t = (window + 1) * (window_size - 1)
actual_at_post_t = observed[post_t]
error = math.sqrt((actual_at_post_t.x - posterior[0])**2 +
(actual_at_post_t.y - posterior[1])**2 +
(actual_at_post_t.z - posterior[2])**2)
prior_mu = (posterior[0], posterior[1], posterior[2])