from kernel_hmc.mini_mcmc.mini_mcmc import mini_mcmc
from kernel_hmc.proposals.metropolis import StandardMetropolis,\
AdaptiveMetropolis, KernelAdaptiveMetropolis
from kernel_hmc.tools.log import Log
import matplotlib.pyplot as plt
import numpy as np
# banana gradient depends on theano, which is an optional dependency
try:
from kernel_hmc.densities.banana import Banana
banana_available = True
except ImportError:
banana_available = False
Log.set_loglevel(20)
if __name__ == '__main__':
"""
This example shows how to run all Metropolis-Hastings sampler (including
the Kernel Adaptive Metropolis-Hastings) from the paper on a simple target.
"""
# possible to change
D = 2
N = 1000
# target is banana density, fallback to Gaussian if theano is not present
if banana_available:
target = Banana(D=D)
else:
target = IsotropicZeroMeanGaussian(D=D)
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.
"""
from kernel_hmc.examples.plotting import visualise_trace
from kernel_hmc.mini_mcmc.mini_mcmc import mini_mcmc
from kernel_hmc.proposals.metropolis import StandardMetropolis,\
AdaptiveMetropolis, KernelAdaptiveMetropolis
from kernel_hmc.tools.log import Log
import matplotlib.pyplot as plt
import numpy as np
# banana gradient depends on theano, which is an optional dependency
try:
from kernel_hmc.densities.banana import Banana
banana_available = True
except ImportError:
banana_available = False
Log.set_loglevel(20)
if __name__ == '__main__':
"""
This example shows how to run all Metropolis-Hastings sampler (including
the Kernel Adaptive Metropolis-Hastings) from the paper on a simple target.
"""
# possible to change
D = 2
N = 1000
# target is banana density, fallback to Gaussian if theano is not present
if banana_available:
target = Banana(D=D)
else:
target = IsotropicZeroMeanGaussian(D=D)
