<s> """<STR_LIT>""" <EOL> from kameleon_mcmc . distribution . Gaussian import Gaussian <EOL> from kameleon_mcmc . experiments . SingleChainExperiment import SingleChainExperiment <EOL> from kameleon_mcmc . gp . GPData import GPData <EOL> from kameleon_mcmc . gp . mcmc . PseudoMarginalHyperparameterDistribution import PseudoMarginalHyperparameterDistribution <EOL> from kameleon_mcmc . kernel . GaussianKernel import GaussianKernel <EOL> from kameleon_mcmc . mcmc . MCMCChain import MCMCChain <EOL> from kameleon_mcmc . mcmc . MCMCParams import MCMCParams <EOL> from kameleon_mcmc . mcmc . output . PlottingOutput import PlottingOutput <EOL> from kameleon_mcmc . mcmc . output . StatisticsOutput import StatisticsOutput <EOL> from kameleon_mcmc . mcmc . samplers . AdaptiveMetropolisLearnScale import AdaptiveMetropolisLearnScale <EOL> from kameleon_mcmc . mcmc . samplers . KameleonWindowLearnScale import KameleonWindowLearnScale <EOL> from kameleon_mcmc . mcmc . samplers . StandardMetropolis import StandardMetropolis <EOL> from numpy . lib . twodim_base import eye <EOL> from numpy . linalg . linalg import cholesky <EOL> from numpy . ma . core import mean , ones , shape , asarray , zeros <EOL> from numpy . ma . extras import cov <EOL> from numpy . random import permutation , seed <EOL> from scipy . linalg . basic import solve_triangular <EOL> import os <EOL> import sys <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> data , labels = GPData . get_glass_data ( ) <EOL> n = <NUM_LIT> <EOL> seed ( <NUM_LIT:1> ) <EOL> idx = permutation ( len ( data ) ) <EOL> idx = idx [ : n ] <EOL> data = data [ idx ] <EOL> labels = labels [ idx ] <EOL> data -= mean ( data , <NUM_LIT:0> ) <EOL> L = cholesky ( cov ( data . T ) ) <EOL> data = solve_triangular ( L , data . T , lower = True ) . T <EOL> dim = shape ( data ) [ <NUM_LIT:1> ] <EOL> theta_prior = Gaussian ( mu = <NUM_LIT:0> * ones ( dim ) , Sigma = eye ( dim ) * <NUM_LIT:5> ) <EOL> target = PseudoMarginalHyperparameterDistribution ( data , labels , n_importance = <NUM_LIT:100> , prior = theta_prior , ridge = <NUM_LIT> ) <EOL> burnin = <NUM_LIT> <EOL> num_iterations = burnin + <NUM_LIT> <EOL> kernel = GaussianKernel ( sigma = <NUM_LIT> ) <EOL> sampler = KameleonWindowLearnScale ( target , kernel , stop_adapt = burnin ) <EOL> start = zeros ( target . dimension ) <EOL> params = MCMCParams ( start = start , num_iterations = num_iterations , burnin = burnin ) <EOL> chain = MCMCChain ( sampler , params ) <EOL> chain . append_mcmc_output ( StatisticsOutput ( print_from = <NUM_LIT:0> , lag = <NUM_LIT:100> ) ) <EOL> experiment_dir = str ( os . path . abspath ( sys . argv [ <NUM_LIT:0> ] ) ) . split ( os . sep ) [ - <NUM_LIT:1> ] . split ( "<STR_LIT:.>" ) [ <NUM_LIT:0> ] + os . sep <EOL> experiment = SingleChainExperiment ( chain , experiment_dir ) <EOL> experiment . run ( ) <EOL> sigma = GaussianKernel . get_sigma_median_heuristic ( experiment . mcmc_chain . samples . T ) <EOL> print "<STR_LIT>" , sigma </s>
