def build_evolution_pCN_sampler(observation_operator, u, data, noise, prior,
posterior, rng):
potential = EvolutionPotential(observation_operator, data, noise)
proposer = pCNProposer(beta=0.25, prior=prior)
accepter = CountedAccepter(pCNAccepter(potential=potential))
return MCMCSampler(proposer, accepter, rng)
def create_pCNSampler(density):
mean = np.array([0])
covariance = np.array([1], ndmin=2)
prior = GaussianDistribution(mean, covariance)
potential = AnalyticPotential(posterior=density, prior=prior)
# beta != delta of other proposers, but
# it could easily be translated if someone took the 30s to do it
proposer = pCNProposer(beta=0.25, prior=prior)
accepter = pCNAccepter(potential=potential)
return MCMCSampler(proposer, accepter, np.random.default_rng(1))
def create_StandardRWSampler(density):
mean = np.array([0])
covariance = np.array([1], ndmin=2)
sqrt_covariance = np.array([1], ndmin=2)
prior = GaussianDistribution(mean, covariance)
potential = AnalyticPotential(posterior=density, prior=prior)
proposer = StandardRWProposer(delta=0.25,
dims=1,
sqrt_covariance=sqrt_covariance)
accepter = StandardRWAccepter(potential=potential, prior=prior)
return MCMCSampler(proposer, accepter, np.random.default_rng(1))
def autocorrelation(samples, tau_max):
avg_over = int(len(samples[0, :]) / tau_max)
assert avg_over > 0, ("Not enough samples to compute autocorrelation"
"with specified length")
n_vars = len(samples[:, 0])
ac = np.zeros((n_vars, tau_max))
for i in range(avg_over):
for var in range(n_vars):
vals = samples[var, i * tau_max:(i + 1) * tau_max]
ac[var, :] += MCMCSampler.autocorr(vals)
ac /= avg_over
return ac
def create_mcmc_sampler():
rng = np.random.default_rng(2)
# Proposer
prior = Settings.Prior.get_distribution()
proposer = pCNProposer(Settings.Sampling.beta, prior)
# Accepter
integrator = create_integrator()
measurer = create_measurer()
IC_true = PerturbedRiemannIC(Settings.Simulation.IC.ground_truth)
observation_operator = FVMObservationOperator(PerturbedRiemannIC,
Settings.Prior.mean,
integrator, measurer)
ground_truth = measurer(integrator(IC_true))
noise = Settings.Noise.get_distribution()
potential = EvolutionPotential(observation_operator, ground_truth, noise)
accepter = CountedAccepter(pCNAccepter(potential))
return MCMCSampler(proposer, accepter, rng)
def create_mcmc_sampler():
# Proposer
prior = Settings.Prior.get_distribution()
# proposer = ConstSteppCNProposer(Settings.Sampling.step, prior)
# proposer = ConstStepStandardRWProposer(Settings.Sampling.step, prior)
proposer = VarStepStandardRWProposer(Settings.Sampling.step, prior)
# Accepter
integrator = create_integrator()
measurer = create_measurer()
IC_true = PerturbedRiemannIC(Settings.Simulation.IC.ground_truth)
observation_operator = FVMObservationOperator(PerturbedRiemannIC,
Settings.Prior.mean,
integrator, measurer)
ground_truth = measurer(integrator(IC_true))
noise = Settings.Noise.get_distribution()
potential = EvolutionPotential(observation_operator, ground_truth, noise)
# accepter = CountedAccepter(pCNAccepter(potential))
accepter = CountedAccepter(StandardRWAccepter(potential, prior))
return MCMCSampler(proposer, accepter, Settings.Sampling.rng)
def create_AnalyticSampler(density):
proposer = StandardRWProposer(0.25, 1)
accepter = AnalyticAccepter(density)
return MCMCSampler(proposer, accepter, np.random.default_rng(1))
def main():
rng = np.random.default_rng(1)
data_dir = "/home/david/fs20/thesis/code/report/data/"
# Parameters of simulation
K, J = 6, 4
sim_length = 20
# True Theta
theta = np.array([10, 10, 1, 10]) # F, h, c, b
r = 0.5 # noise level
# Characteristics of system
T = 500
try:
Y = np.load(data_dir + f"Y_{K=}_{J=}_{T=}.npy")
print("Loaded existing simulation results")
except FileNotFoundError:
print("Running simulation to generate fake measurements")
Y = run_lorenz96(K, J, theta, T)
np.save(data_dir + f"Y_{K=}_{J=}_{T=}", Y)
print(f"{Y.shape=}")
moment_function_values = moment_function(Y, K, J)
moment_function_means = np.mean(moment_function_values, axis=1)
moment_function_variances = np.var(moment_function_values, axis=1)
print(f"{moment_function_variances.shape=}")
noise = GaussianDistribution(mean=np.zeros_like(moment_function_variances),
covariance=r**2 *
np.diag(moment_function_variances))
prior_means = np.array([12, 8, 9]) # F, h, b
prior_covariance = np.diag([10, 1, 10])
# From theory: prior is always assumed to be centered,
# so I do MCMC over pertubations from given prior means
prior = GaussianDistribution(np.zeros_like(prior_means), prior_covariance)
observation_operator = LorenzObservationOperator(K, J, sim_length,
theta[2], prior_means,
Y[:, -1])
# don't need huge array anymore
del Y
potential = EvolutionPotential(observation_operator, moment_function_means,
noise)
proposer = pCNProposer(beta=0.5, prior=prior)
accepter = CountedAccepter(pCNAccepter(potential=potential))
sampler = MCMCSampler(proposer, accepter, rng)
u_0 = np.array([-1.9, 1.9, 0.9]) # start close to true theta
n_samples = 2000
try:
samples = np.load(data_dir +
f"S_{K=}_{J=}_T={sim_length}_{r=}_{n_samples=}.npy")
print("Loaded existing sampling results")
except FileNotFoundError:
print("Generating samples")
samples = sampler.run(u_0=u_0,
n_samples=n_samples,
burn_in=100,
sample_interval=1)
np.save(data_dir + f"S_{K=}_{J=}_T={sim_length}_{r=}_{n_samples=}",
samples)
print(f"{samples.shape=}")
# to conform with the output-shape of
# solve_ivp. Might be worthwile to change it in the sampler,
# but then I break older scripts
samples = samples.T
# Add pertubations to means
for i in range(len(samples[0, :])):
samples[:, i] += prior_means
# Plot densities
priors = [
GaussianDistribution(mu, np.sqrt(sigma_sq))
for mu, sigma_sq in zip(prior_means, np.diag(prior_covariance))
]
intervals = [(-5, 25)] * 3
names = ["F", "h", "b"]
fig, plts = plt.subplots(1, 3, figsize=(20, 10))
plot_info = zip(priors, intervals, [theta[0], theta[1], theta[3]], names,
plts)
for i, (prior, interval, true_val, name, ax) in enumerate(plot_info):
ax.hist(samples[i, :], density=True)
x_range = np.linspace(*interval)
ax.plot(x_range, [prior(x) for x in x_range])
ax.axvline(true_val, c='r')
ax.set_title(f"Prior and posterior for {name}")
ax.set(xlabel=name, ylabel="Probability")
fig.suptitle("Posteriors and priors")
store_figure(f"combined_{K=}_{J=}_T={sim_length}_{r=}")
# Average the autocorrelation
ac = np.zeros((3, 100))
n = 10
for i in range(1, 1 + n):
for var in range(3):
ac[var, :] += MCMCSampler.autocorr(samples[var,
i * 100:(i + 1) * 100])
ac /= n
plt.plot(ac[0, :], label="F")
plt.plot(ac[1, :], label="h")
plt.plot(ac[2, :], label="b")
plt.title("Autocorrelation")
plt.xlabel("Lag")
plt.legend()
store_figure(f"lorenz_ac_avg_{K=}_{J=}_T={sim_length}_{r=}")