<s> """<STR_LIT>""" <EOL> from cPickle import dump , load <EOL> from matplotlib . pyplot import plot , show , legend <EOL> from numpy import zeros , fill_diagonal , asarray , arange , sqrt , linspace <EOL> import numpy <EOL> from numpy . random import rand , randn , randint <EOL> import time <EOL> from kameleon_mcmc . distribution . Hopfield import Hopfield <EOL> from kameleon_mcmc . distribution . full_conditionals . HopfieldFullConditionals import HopfieldFullConditionals <EOL> from kameleon_mcmc . kernel . HypercubeKernel import HypercubeKernel <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 . DiscreteKameleon import DiscreteKameleon <EOL> from kameleon_mcmc . mcmc . samplers . Gibbs import Gibbs <EOL> from kameleon_mcmc . mcmc . samplers . StandardMetropolisDiscrete import StandardMetropolisDiscrete <EOL> def create_ground_truth ( ) : <EOL> filename_chain = "<STR_LIT>" <EOL> filename_Z = "<STR_LIT>" <EOL> filename_hopfield = "<STR_LIT>" <EOL> try : <EOL> f = open ( filename_Z , "<STR_LIT:r>" ) <EOL> Z = load ( f ) <EOL> f . close ( ) <EOL> f = open ( filename_hopfield , "<STR_LIT:r>" ) <EOL> hopfield = load ( f ) <EOL> f . close ( ) <EOL> print ( "<STR_LIT>" ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> try : <EOL> f = open ( filename_hopfield , "<STR_LIT:r>" ) <EOL> hopfield = load ( f ) <EOL> f . close ( ) <EOL> d = hopfield . dimension <EOL> print ( "<STR_LIT>" ) <EOL> except IOError : <EOL> d = <NUM_LIT:50> <EOL> b = randn ( d ) <EOL> V = randn ( d , d ) <EOL> W = V + V . T <EOL> fill_diagonal ( W , zeros ( d ) ) <EOL> hopfield = Hopfield ( W , b ) <EOL> f = open ( filename_hopfield , "<STR_LIT:w>" ) <EOL> dump ( hopfield , f ) <EOL> f . close ( ) <EOL> num_iterations = <NUM_LIT> <EOL> warm_up = <NUM_LIT> <EOL> thin = <NUM_LIT> <EOL> current_state = [ rand ( ) < <NUM_LIT:0.5> for _ in range ( d ) ] <EOL> distribution = HopfieldFullConditionals ( full_target = hopfield , <EOL> current_state = current_state , <EOL> schedule = "<STR_LIT>" ) <EOL> mcmc_sampler = Gibbs ( distribution ) <EOL> mcmc_params = MCMCParams ( start = asarray ( current_state , dtype = numpy . bool8 ) , num_iterations = num_iterations ) <EOL> chain = MCMCChain ( mcmc_sampler , mcmc_params ) <EOL> chain . append_mcmc_output ( StatisticsOutput ( plot_times = True , lag = <NUM_LIT:1000> ) ) <EOL> chain . run ( ) <EOL> try : <EOL> f = open ( filename_chain , "<STR_LIT:w>" ) <EOL> dump ( chain , f ) <EOL> f . close ( ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> Z = chain . samples [ ( warm_up ) : ] <EOL> Z = Z [ arange ( len ( Z ) , step = thin ) ] <EOL> Z = Z . astype ( numpy . bool8 ) <EOL> try : <EOL> f = open ( filename_Z , "<STR_LIT:w>" ) <EOL> dump ( Z , f ) <EOL> f . close ( ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> return Z , hopfield <EOL> def run_kameleon_chain ( Z , hopfield , start , num_iterations ) : <EOL> threshold = <NUM_LIT> <EOL> spread = <NUM_LIT> <EOL> gamma = <NUM_LIT> <EOL> kernel = HypercubeKernel ( gamma ) <EOL> sampler = DiscreteKameleon ( hopfield , kernel , Z , threshold , spread ) <EOL> params = MCMCParams ( start = start , num_iterations = num_iterations ) <EOL> chain = MCMCChain ( sampler , params ) <EOL> chain . run ( ) <EOL> return chain <EOL> def run_gibbs_chain ( hopfield , start , num_iterations ) : <EOL> d = hopfield . dimension <EOL> current_state = [ x for x in start ] <EOL> distribution = HopfieldFullConditionals ( full_target = hopfield , <EOL> current_state = current_state ) <EOL> sampler = Gibbs ( distribution ) <EOL> params = MCMCParams ( start = asarray ( current_state , dtype = numpy . bool8 ) , num_iterations = num_iterations * d ) <EOL> chain = MCMCChain ( sampler , params ) <EOL> chain . append_mcmc_output ( StatisticsOutput ( plot_times = True , lag = <NUM_LIT:1000> ) ) <EOL> chain . run ( ) <EOL> return chain <EOL> def run_sm_chain ( hopfield , start , num_iterations ) : <EOL> current_state = [ x for x in start ] <EOL> spread = <NUM_LIT> <EOL> sampler = StandardMetropolisDiscrete ( hopfield , spread ) <EOL> params = MCMCParams ( start = asarray ( current_state , dtype = numpy . bool8 ) , num_iterations = num_iterations ) <EOL> chain = MCMCChain ( sampler , params ) <EOL> chain . append_mcmc_output ( StatisticsOutput ( plot_times = True , lag = <NUM_LIT:1000> ) ) <EOL> chain . run ( ) <EOL> return chain <EOL> def main ( ) : <EOL> Z , hopfield = create_ground_truth ( ) <EOL> d = hopfield . dimension <EOL> print ( "<STR_LIT>" % len ( Z ) ) <EOL> num_iterations = <NUM_LIT> <EOL> warm_up = <NUM_LIT:1000> <EOL> thin = <NUM_LIT:100> <EOL> start = randint ( <NUM_LIT:0> , <NUM_LIT:2> , d ) . astype ( numpy . bool8 ) <EOL> timestring = time . strftime ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" % num_iterations ) <EOL> sm_chain = run_sm_chain ( hopfield , start , num_iterations ) <EOL> try : <EOL> fname = "<STR_LIT>" + timestring + "<STR_LIT>" <EOL> f = open ( fname , "<STR_LIT:w>" ) <EOL> dump ( sm_chain , f ) <EOL> f . close ( ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" % ( num_iterations * d ) ) <EOL> gibbs_chain = run_gibbs_chain ( hopfield , start , num_iterations ) <EOL> try : <EOL> fname = "<STR_LIT>" + timestring + "<STR_LIT>" <EOL> f = open ( fname , "<STR_LIT:w>" ) <EOL> dump ( gibbs_chain , f ) <EOL> f . close ( ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" % num_iterations ) <EOL> kameleon_chain = run_kameleon_chain ( Z , hopfield , start , num_iterations ) <EOL> try : <EOL> fname = "<STR_LIT>" + timestring + "<STR_LIT>" <EOL> f = open ( fname , "<STR_LIT:w>" ) <EOL> dump ( kameleon_chain , f ) <EOL> f . close ( ) <EOL> except IOError : <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" ) <EOL> S_g = gibbs_chain . samples [ warm_up : ] <EOL> S_g = S_g [ arange ( len ( S_g ) , step = thin * d ) ] . astype ( numpy . bool8 ) <EOL> S_k = kameleon_chain . samples [ warm_up : ] <EOL> S_k = S_k [ arange ( len ( S_k ) , step = thin ) ] . astype ( numpy . bool8 ) <EOL> S_sm = sm_chain . samples [ warm_up : ] <EOL> S_sm = S_sm [ arange ( len ( S_sm ) , step = thin ) ] . astype ( numpy . bool8 ) <EOL> print ( "<STR_LIT>" % len ( S_g ) ) <EOL> print ( "<STR_LIT>" % len ( S_k ) ) <EOL> print ( "<STR_LIT>" % len ( S_sm ) ) <EOL> print ( "<STR_LIT>" ) <EOL> kernel = HypercubeKernel ( <NUM_LIT> ) <EOL> num_evaluations = <NUM_LIT:10> <EOL> inds_g = linspace ( <NUM_LIT:0> , len ( S_g ) , num_evaluations ) . astype ( numpy . int ) <EOL> inds_k = linspace ( <NUM_LIT:0> , len ( S_k ) , num_evaluations ) . astype ( numpy . int ) <EOL> inds_sm = linspace ( <NUM_LIT:0> , len ( S_sm ) , num_evaluations ) . astype ( numpy . int ) <EOL> mmds = zeros ( ( <NUM_LIT:3> , num_evaluations - <NUM_LIT:1> ) ) <EOL> for i in arange ( num_evaluations - <NUM_LIT:1> ) : <EOL> mmds [ <NUM_LIT:0> , i - <NUM_LIT:1> ] = sqrt ( kernel . estimateMMD ( S_g [ : inds_g [ i + <NUM_LIT:1> ] ] , Z ) ) <EOL> mmds [ <NUM_LIT:1> , i - <NUM_LIT:1> ] = sqrt ( kernel . estimateMMD ( S_k [ : inds_k [ i + <NUM_LIT:1> ] ] , Z ) ) <EOL> mmds [ <NUM_LIT:2> , i - <NUM_LIT:1> ] = sqrt ( kernel . estimateMMD ( S_sm [ : inds_sm [ i + <NUM_LIT:1> ] ] , Z ) ) <EOL> print ( mmds ) <EOL> plot ( inds_g [ <NUM_LIT:1> : ] , mmds [ <NUM_LIT:0> , : ] ) <EOL> plot ( inds_k [ <NUM_LIT:1> : ] , mmds [ <NUM_LIT:1> , : ] ) <EOL> plot ( inds_sm [ <NUM_LIT:1> : ] , mmds [ <NUM_LIT:2> , : ] ) <EOL> legend ( [ "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" ] ) <EOL> show ( ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> main ( ) 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