# This file contains various constants required for the definition of the model
# or to set up the optimization
import autograd.numpy as anp
from dataclasses import dataclass
DEFAULT_ENCODING = "logarithm" # the other choices is positive
NUMERICAL_JITTER = 1e-9
INITIAL_NOISE_VARIANCE = 1e-3
INITIAL_MEAN_VALUE = 0.0
INITIAL_COVARIANCE_SCALE = 1.0
INITIAL_INVERSE_BANDWIDTHS = 1.0
INITIAL_WARPING = 1.0
INVERSE_BANDWIDTHS_LOWER_BOUND = 1e-4
INVERSE_BANDWIDTHS_UPPER_BOUND = 100
COVARIANCE_SCALE_LOWER_BOUND = 1e-3
COVARIANCE_SCALE_UPPER_BOUND = 1e3
NOISE_VARIANCE_LOWER_BOUND = 1e-9
NOISE_VARIANCE_UPPER_BOUND = 1e6
WARPING_LOWER_BOUND = 0.25
WARPING_UPPER_BOUND = 4.0
MIN_POSTERIOR_VARIANCE = 1e-12
MIN_CHOLESKY_DIAGONAL_VALUE = 1e-10
DATA_TYPE = anp.float64
[docs]
@dataclass
class OptimizationConfig:
lbfgs_tol: float
lbfgs_maxiter: int
verbose: bool
n_starts: int
[docs]
@dataclass
class MCMCConfig:
"""
``n_samples`` is the total number of samples drawn. The first ``n_burnin`` of
these are dropped (burn-in), and every ``n_thinning`` of the rest is
returned. This means we return
``(n_samples - n_burnin) // n_thinning`` samples.
"""
n_samples: int
n_burnin: int
n_thinning: int
DEFAULT_OPTIMIZATION_CONFIG = OptimizationConfig(
lbfgs_tol=1e-6, lbfgs_maxiter=500, verbose=False, n_starts=5
)
DEFAULT_MCMC_CONFIG = MCMCConfig(n_samples=300, n_burnin=250, n_thinning=5)