string Entity::checkVicinity_two( map< string , Payoff > & d, map< string , Payoff > & d_two, unsigned short int maxN)
/* this version keeps landscape 2 choices constant */
{
Payoff Move;
string tempConfig = itsPayoff.GetConfig(), tempBinary, tempConfig_two;
double currentAverage = (d[ itsPayoff.GetConfig() ].GetFitness() + d_two[ flipConfigString(itsPayoff.GetConfig()) ].GetFitness()) / (double) 2;
unsigned short int i, j;
unsigned short int strLength;
vector <Payoff> averages;
double tempAverage;
strLength = tempConfig.length();
for ( i = maxN ; i < 2*maxN ; i++)
{
tempConfig = itsPayoff.GetConfig();
if ( tempConfig[i] == '0' ) { tempConfig[i] = '1'; }
else { tempConfig[i] = '0'; };
tempConfig_two = flipConfigString( tempConfig );
tempAverage = (d[tempConfig].GetFitness() + d_two[tempConfig_two].GetFitness()) / (double) 2;
averages.push_back ( Payoff(tempConfig,tempAverage) );
}
Move.SetFitness(0);
vector <Payoff>::iterator IT;
for ( IT = averages.begin() ; IT != averages.end() ; IT++ )
{
if ( Move.GetFitness() < (*IT).GetFitness() )
{
Move.SetConfig( (*IT).GetConfig() );
Move.SetFitness( (*IT).GetFitness() );
}
}
if ( Move.GetFitness() <= itsPayoff.GetFitness() ) { return "EMPTY"; }
return Move.GetConfig();
}
string Entity::checkVicinity_all( map< string , Payoff > & d, map< string , Payoff > & d_two)
/* this version, once complete, will consider both landscapes and make the move that provides the biggest average */
{
Payoff Move;
string tempConfig = itsPayoff.GetConfig();
string tempConfig_two;
unsigned short int i;
unsigned short int strLength;
vector <Payoff> averages;
double tempAverage;
strLength = tempConfig.length();
for ( i = 0 ; i < strLength ; i++)
{
tempConfig = itsPayoff.GetConfig();
if ( tempConfig[i] == '0' ) { tempConfig[i] = '1'; }
else { tempConfig[i] = '0'; }
tempConfig_two = flipConfigString( tempConfig );
tempAverage = (d[tempConfig].GetFitness() + d_two[tempConfig_two].GetFitness()) / (double) 2;
averages.push_back ( Payoff(tempConfig,tempAverage) );
}
Move.SetFitness(0);
vector <Payoff>::iterator IT;
for ( IT = averages.begin() ; IT != averages.end() ; IT++)
{
if (Move.GetFitness() < (*IT).GetFitness() )
{
Move.SetConfig( (*IT).GetConfig() );
Move.SetFitness( (*IT).GetFitness() );
}
}
if ( Move.GetFitness() <= itsPayoff.GetFitness() ) { return "EMPTY"; }
return Move.GetConfig();
}
void Entity::Reset( Landscape & LJoined )
{
map <string, Payoff >::iterator IT;
IT = LJoined.d.begin();
itsPayoff.SetConfig( (*IT).second.GetConfig() );
itsPayoff.SetFitness( (*IT).second.GetFitness() );
}
bool Entity::Move( string Move, map< string , Payoff > & d, map< string , Payoff > & d_two )
{
if ( Move == "EMPTY" ) { return 0; }
cout << "Move from " << itsPayoff.GetConfig() << ":" << itsPayoff.GetFitness() << " to ";
itsPayoff.SetConfig( Move );
itsPayoff.SetFitness( (d[Move].GetFitness() + d_two[ flipConfigString(Move) ].GetFitness()) / (double) 2 );
cout << itsPayoff.GetConfig() << ":" << itsPayoff.GetFitness() << " is successful." << endl;
return 1;
}
void Entity::Reset( map< string , Payoff > & d, map< string , Payoff > & d_two )
{
map <string, Payoff >::iterator IT;
IT = d.begin();
map <string, Payoff >::iterator IT_TWO;
IT_TWO = d_two.begin();
itsPayoff.SetConfig( (*IT).second.GetConfig() );
itsPayoff.SetFitness( ((*IT).second.GetFitness() + (*IT_TWO).second.GetFitness()) / (double) 2 );
}
string Search::checkVicinityDist( JoinedLandscape & thisLandscape, Entity & thisEntity, bool Sequential, int WhichLandscape )
/* randomly selects one move weighted by distribution */
{
tempPayoff.SetConfig ( thisEntity.GetConfig() );
if ( !thisEntity.IsAlive() ) { return "EMPTY"; }
string tempConfig = tempPayoff.GetConfig();
int i, j;
unsigned short int strLength;
double tempFitness;
map <string,int> chance;
vector <string> Configs;
strLength = tempConfig.length();
Configs.push_back( tempConfig );
for ( i = 0 ; i < strLength; i++ )
{
tempConfig = tempPayoff.GetConfig();
if ( tempConfig[i] == '0' ) { tempConfig[i] = '1'; }
else { tempConfig[i] = '0'; }
Configs.push_back( tempConfig );
}
unsigned int total = 0;
switch (WhichLandscape)
{
case 0: pLandscape = &thisLandscape;
break;
case 1: pLandscape = &thisLandscape.LOne;
break;
case 2: pLandscape = &thisLandscape.LTwo;
break;
}
vector <string>::iterator IT;
for ( IT = Configs.begin() ; IT != Configs.end() ; IT++ )
{
chance.insert( pair<string,int> ( (*IT), thisLandscape.d[(*IT)].GiveDistribution() ) );
total += thisLandscape.d[(*IT)].GiveDistribution();
}
i = rand() % total;
map <string,int>::reverse_iterator IT_TWO;
for ( IT_TWO = chance.rbegin() ; IT_TWO != chance.rend() ; IT_TWO++ ) // this random number might be 1 off
{
i = i-(*IT_TWO).second;
tempConfig = (*IT_TWO).first;
if (i < 0) { break; }
}
tempFitness = thisLandscape.d[tempConfig].GetFitness();
tempPayoff.SetConfig( tempConfig );
tempPayoff.SetFitness( tempFitness );
// if ( Move.GetFitness() <= d[ itsPayoff.GetConfig() ].GetFitness() ) { return "EMPTY"; }
return tempPayoff.GetConfig();
}
string Search::checkVicinityNonrandom( JoinedLandscape & thisLandscape, Entity & thisEntity, bool Sequential, int WhichLandscape )
/* this version finds the move with the greatest improvement choices constant */
{
tempPayoff.SetConfig ( thisEntity.GetConfig() ); // tempPayoff is current value for thisEntity
if ( !thisEntity.IsAlive() ) { return "EMPTY"; }
string configuration = tempPayoff.GetConfig(); // configuration is a temporary string to be returned as the potential move
unsigned short int i, j, strLength, searchLength;
double tempAverage = 0;
vector <Payoff> averages;
strLength = configuration.length();
switch (WhichLandscape)
{
case 0: pLandscape = &thisLandscape;
searchLength = strLength;
break;
case 1: pLandscape = &thisLandscape.LOne;
searchLength = strLength / 2;
break;
case 2: pLandscape = &thisLandscape.LTwo;
configuration = flipConfigString( configuration );
tempPayoff.SetConfig ( flipConfigString ( tempPayoff.GetConfig() ) );
searchLength = strLength / 2;
break;
}
tempPayoff.SetFitness ( (*pLandscape).d[configuration].GetFitness() );
for ( i = 0 ; i < searchLength ; i++)
{
configuration = tempPayoff.GetConfig(); // reset configuration
if ( configuration[i] == '0' ) { configuration[i] = '1'; } // flip one decision
else { configuration[i] = '0'; };
tempAverage = (*pLandscape).d[configuration].GetFitness(); // get the new value for the flipped string
averages.push_back ( Payoff(configuration, tempAverage) ); // add to the list of possibilties
}
configuration = tempPayoff.GetConfig(); // resetting configuration to the Entity's current value
if (WhichLandscape == 2) { configuration = flipConfigString (configuration); } // ugly hack to make the second landscape work
tempAverage = tempPayoff.GetFitness(); // resetting fitness to the Entity's current value
vector <Payoff>::iterator IT;
for ( IT = averages.begin() ; IT != averages.end() ; IT++ )
{
if ( tempAverage < (*IT).GetFitness() ) // if one of the averages is greater
{
configuration = (*IT).GetConfig(); // set the move information
tempAverage = (*IT).GetFitness(); // similar
}
} // the move information should be the greatest possible
if ( tempAverage <= tempPayoff.GetFitness() ) { return "EMPTY"; } // if less, don't recommend a move
if ( isEqual (tempAverage, tempPayoff.GetFitness() ) ) { return "EMPTY"; } // if equal, no reason to move
if ( WhichLandscape == 2 ) { return flipConfigString ( configuration ); } // flip the string back for the potential move
return configuration;
}
bool Entity::Move( string destination, Landscape & LJoined )
{
if ( destination == "EMPTY" ) { return 0; }
string previous = itsPayoff.GetConfig();
LJoined.SubFromDistribution( previous );
// cout << "Move from " << itsPayoff.GetConfig() << ":" << itsPayoff.GetFitness() << " to ";
itsPayoff.SetConfig( destination );
itsPayoff.SetFitness( LJoined.d[destination].GetFitness() ) ;
// cout << "to " << itsPayoff.GetConfig() << ":" << itsPayoff.GetFitness() << endl;
LJoined.AddToDistribution( destination );
movesTaken++;
return 1;
}
/* ********************************************* BLIND ************************ */
string Entity::checkVicinity_one_blind( map< string , Payoff > & d, map< string , Payoff > & d_two, unsigned short int maxN )
/* this version keeps landscape 2 choices constant */
{
Payoff Move;
string tempConfig = itsPayoff.GetConfig();
unsigned short int i, j;
unsigned short int strLength;
double tempFitness;
strLength = tempConfig.length();
tempConfig = itsPayoff.GetConfig();
i = rand() % (maxN);
if ( tempConfig[i] == '0' ) { tempConfig[i] = '1'; }
else { tempConfig[i] = '0'; };
tempFitness = d[tempConfig].GetFitness();
Move.SetConfig( tempConfig );
Move.SetFitness( tempFitness );
if ( Move.GetFitness() <= d[ itsPayoff.GetConfig() ].GetFitness() ) { return "EMPTY"; }
return Move.GetConfig();
}