<s> """<STR_LIT>""" <EOL> from kameleon_mcmc . distribution . Banana import Banana <EOL> from kameleon_mcmc . experiments . ClusterTools import ClusterTools <EOL> from kameleon_mcmc . experiments . SingleChainExperiment import SingleChainExperiment <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 . StatisticsOutput import StatisticsOutput <EOL> from kameleon_mcmc . mcmc . samplers . AdaptiveMetropolis import AdaptiveMetropolis <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 . ma . core import zeros <EOL> import os <EOL> import sys <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> if len ( sys . argv ) != <NUM_LIT:3> : <EOL> print "<STR_LIT>" , str ( sys . argv [ <NUM_LIT:0> ] ) . split ( os . sep ) [ - <NUM_LIT:1> ] , "<STR_LIT>" <EOL> print "<STR_LIT>" <EOL> print "<STR_LIT>" + str ( sys . argv [ <NUM_LIT:0> ] ) . split ( os . sep ) [ - <NUM_LIT:1> ] + "<STR_LIT>" <EOL> exit ( ) <EOL> experiment_dir_base = str ( sys . argv [ <NUM_LIT:1> ] ) <EOL> n = int ( str ( sys . argv [ <NUM_LIT:2> ] ) ) <EOL> experiment_dir = experiment_dir_base + str ( os . path . abspath ( sys . argv [ <NUM_LIT:0> ] ) ) . split ( os . sep ) [ - <NUM_LIT:1> ] . split ( "<STR_LIT:.>" ) [ <NUM_LIT:0> ] + os . sep <EOL> print "<STR_LIT>" , n , "<STR_LIT>" , experiment_dir <EOL> distribution = Banana ( dimension = <NUM_LIT:8> , bananicity = <NUM_LIT> , V = <NUM_LIT:100> ) <EOL> sigma = GaussianKernel . get_sigma_median_heuristic ( distribution . sample ( <NUM_LIT:1000> ) . samples ) <EOL> sigma = <NUM_LIT:5> <EOL> print "<STR_LIT>" , sigma <EOL> kernel = GaussianKernel ( sigma = sigma ) <EOL> for i in range ( n ) : <EOL> mcmc_samplers = [ ] <EOL> burnin = <NUM_LIT> <EOL> num_iterations = <NUM_LIT> <EOL> mcmc_samplers . append ( KameleonWindowLearnScale ( distribution , kernel , stop_adapt = burnin ) ) <EOL> mean_est = zeros ( distribution . dimension , dtype = "<STR_LIT>" ) <EOL> cov_est = <NUM_LIT:1.0> * eye ( distribution . dimension ) <EOL> cov_est [ <NUM_LIT:0> , <NUM_LIT:0> ] = distribution . V <EOL> mcmc_samplers . append ( AdaptiveMetropolisLearnScale ( distribution , mean_est = mean_est , cov_est = cov_est ) ) <EOL> mcmc_samplers . append ( AdaptiveMetropolis ( distribution , mean_est = mean_est , cov_est = cov_est ) ) <EOL> mcmc_samplers . append ( StandardMetropolis ( distribution ) ) <EOL> start = zeros ( distribution . dimension , dtype = "<STR_LIT>" ) <EOL> mcmc_params = MCMCParams ( start = start , num_iterations = num_iterations , burnin = burnin ) <EOL> mcmc_chains = [ MCMCChain ( mcmc_sampler , mcmc_params ) for mcmc_sampler in mcmc_samplers ] <EOL> for mcmc_chain in mcmc_chains : <EOL> mcmc_chain . append_mcmc_output ( StatisticsOutput ( ) ) <EOL> experiments = [ SingleChainExperiment ( mcmc_chain , experiment_dir ) for mcmc_chain in mcmc_chains ] <EOL> for experiment in experiments : <EOL> ClusterTools . submit_experiment ( experiment ) </s>
