<s> """<STR_LIT>""" <EOL> from numpy import logical_xor , sum <EOL> import numpy <EOL> from numpy . random import randn <EOL> from kameleon_mcmc . distribution . Distribution import Distribution <EOL> from kameleon_mcmc . distribution . proposals . DiscreteRandomWalkProposal import DiscreteRandomWalkProposal <EOL> from kameleon_mcmc . kernel . Kernel import Kernel <EOL> from kameleon_mcmc . mcmc . samplers . MCMCSampler import MCMCSampler <EOL> class DiscreteKameleon ( MCMCSampler ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , distribution , kernel , Z , threshold , spread ) : <EOL> if not isinstance ( distribution , Distribution ) : <EOL> raise TypeError ( "<STR_LIT>" ) <EOL> if not isinstance ( kernel , Kernel ) : <EOL> raise TypeError ( "<STR_LIT>" ) <EOL> if not type ( Z ) is numpy . ndarray : <EOL> raise TypeError ( "<STR_LIT>" ) <EOL> if not len ( Z . shape ) == <NUM_LIT:2> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> if not Z . shape [ <NUM_LIT:1> ] == distribution . dimension : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> if not Z . shape [ <NUM_LIT:0> ] > <NUM_LIT:0> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> if not type ( threshold ) is float : <EOL> raise TypeError ( "<STR_LIT>" ) <EOL> if not type ( spread ) is float : <EOL> raise TypeError ( "<STR_LIT>" ) <EOL> if not ( spread > <NUM_LIT:0.> and spread < <NUM_LIT:1.> ) : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> MCMCSampler . __init__ ( self , distribution ) <EOL> self . kernel = kernel <EOL> self . Z = Z <EOL> self . threshold = threshold <EOL> self . spread = spread <EOL> def __str__ ( self ) : <EOL> s = self . __class__ . __name__ + "<STR_LIT>" <EOL> s += "<STR_LIT>" + str ( self . kernel ) <EOL> s += "<STR_LIT>" + str ( self . threshold ) <EOL> s += "<STR_LIT>" + str ( self . spread ) <EOL> s += "<STR_LIT:U+002CU+0020>" + MCMCSampler . __str__ ( self ) <EOL> s += "<STR_LIT:]>" <EOL> return s <EOL> def construct_proposal ( self , y ) : <EOL> k = self . kernel . kernel ( y . reshape ( <NUM_LIT:1> , len ( y ) ) , self . Z ) <EOL> diff = y . astype ( numpy . int64 ) <EOL> diff = diff - self . Z <EOL> beta = randn ( len ( self . Z ) ) <EOL> weighted_sum = sum ( ( k * beta ) . T * diff , <NUM_LIT:0> ) <EOL> thresholded_sum = weighted_sum > self . threshold <EOL> xored = logical_xor ( thresholded_sum , y ) <EOL> return DiscreteRandomWalkProposal ( xored , self . spread ) <EOL> def adapt ( self , mcmc_chain , step_output ) : <EOL> """<STR_LIT>""" <EOL> pass </s>
