from scipy.spatial.distance import cdist, squareform, pdist
from kernel_hmc.densities.gaussian import sample_gaussian, log_gaussian_pdf
from kernel_hmc.proposals.base import ProposalBase, standard_sqrt_schedule
from kernel_hmc.tools.log import Log
import numpy as np
logger = Log.get_logger()
# low rank update depends on "cholupdate" optional dependency
try:
from choldate._choldate import cholupdate
cholupdate_available = True
except ImportError:
cholupdate_available = False
logger.warning("Package cholupdate not available. Adaptive Metropolis falls back to (more expensive) re-estimation of covariance.")
if cholupdate_available:
def rank_one_update_mean_covariance_cholesky_lmbda(u, lmbda=.1, mean=None, cov_L=None, nu2=1., gamma2=None):
"""
Returns updated mean and Cholesky of sum of outer products following a
(1-lmbda)*old + lmbda* step_size*uu^T+lmbda*gamm2*I
rule
Optional: If gamma2 is given, an isotropic term gamma2 * I is added to the uu^T part
where old mean and cov_L=Cholesky(old) (lower Cholesky) are given.
Performs efficient rank-one updates of the Cholesky directly.
"""
