diff --git a/xcs/ClassifierSet.cc b/xcs/ClassifierSet.cc
index d437cf16aaa0e2c45e04f488d1a06ccc2ca2c2d9..9e2231d807c3da01f3503c827b92f5a2d14b8967 100644
--- a/xcs/ClassifierSet.cc
+++ b/xcs/ClassifierSet.cc
@@ -103,8 +103,7 @@ void ClassifierSet::createPredictionArray() {
this->fitnessSumArray.clear();
for (unsigned int i = 0; i < this->parameter.possibleActionsPtr->size();
i++) {
- this->predictionArray.push_back(
- std::numeric_limits<double>::quiet_NaN());
+ this->predictionArray.push_back(-std::numeric_limits<float>::infinity());
this->fitnessSumArray.push_back(0);
}
@@ -117,7 +116,7 @@ void ClassifierSet::createPredictionArray() {
j++) {
if (this->matchSet[i]->getAction() ==
this->parameter.possibleActionsPtr->at(j)) {
- if (std::isnan(this->predictionArray[j])) {
+ if (this->predictionArray[j] == -std::numeric_limits<float>::infinity()) {
this->predictionArray[j] =
this->matchSet[i]->getPrediction() *
this->matchSet[i]->getFitness();
@@ -174,7 +173,7 @@ returnAction ClassifierSet::selectAction() {
// Do pure exploration here
double randomNumber = this->RandomNumberGenerator->getRandom();
int ActionPosition = static_cast<int>(
- std::round(randomNumber * (this->differentActions.size() - 1)));
+ std::round(randomNumber * (this->differentActions.size() - 0.5)));
return returnAction{this->differentActions[ActionPosition], 0};
} else {
// Do pure exploitation here
@@ -206,7 +205,7 @@ returnAction ClassifierSet::selectAction() {
// get random action from the actions with highest prediction value
double randomNumber = this->RandomNumberGenerator->getRandom();
int ActionPosition = static_cast<int>(
- std::round(randomNumber * (bestActions.size() - 1)));
+ std::round(randomNumber * (bestActions.size() - 0.5)));
return returnAction{bestActions[ActionPosition], 1};
}
}
@@ -695,49 +694,48 @@ void ClassifierSet::actionSetSubsumption() {
for (unsigned int i = 0; i < this->actionSet.size(); i++) {
if (this->actionSet[i]->couldSubsume(this->parameter.thetaSub,
this->parameter.e0)) {
- if ((clPtr->getCondition() == "\0")) {
- int numberDoNotCares = 0;
- int numberDoNotCaresClPtr = 0;
- for (unsigned int j = 0; j < clPtr->getCondition().size();
- j++) {
- if (this->actionSet[i]->getCondition()[j] == '#') {
- numberDoNotCares++;
- }
- if (clPtr->getCondition()[j] == '#') {
- numberDoNotCaresClPtr++;
- }
- }
- if (numberDoNotCares > numberDoNotCaresClPtr ||
- ((numberDoNotCares == numberDoNotCaresClPtr) &&
- (this->RandomNumberGenerator->getRandom() < 0.5))) {
+ if (clPtr->getCondition().size() == 0 or this->actionSet[i]->isMoreGeneralThan(clPtr)) {
clPtr = this->actionSet[i];
- delete (deletePtr);
- this->numberofDeletion++;
+
}
}
}
- }
- if (clPtr->getCondition() != "\0") {
+ if (clPtr->getCondition().size() != 0) {
+ // the new,empty Classifier can be deleted since we found a Classifier as candidat
+ // to subsume other Classifier in the actionSet
+ delete (deletePtr);
+ this->numberofDeletion++;
+
this->numberofActionSetSubsumption++;
for (unsigned int i = 0; i < this->actionSet.size(); i++) {
+ //find a Classifier, which can be subsumed
if (clPtr->isMoreGeneralThan(this->actionSet[i])) {
+
clPtr->setNumerosity(clPtr->getNumerosity() +
this->actionSet[i]->getNumerosity());
+
+ //delete the less generelized Classifier from populationSet
for (unsigned int j = 0; j < this->populationSet.size(); j++) {
if (this->actionSet[i] == this->populationSet[j]) {
this->numberofDeletion++;
-
this->populationSet.erase(this->populationSet.begin() +
j);
+ break;
+ }
+ }
+ for (unsigned int j = 0; j < this->matchSet.size(); j++) {
+ if (this->actionSet[i] == this->matchSet[j]) {
+ this->numberofDeletion++;
+ this->matchSet.erase(this->matchSet.begin() + j);
+ break;
}
}
delete (this->actionSet[i]);
this->actionSet.erase(this->actionSet.begin() + i);
- return;
}
}
} else {
- delete (clPtr);
+ delete (deletePtr);
}
}
diff --git a/xcs/Headers/config.hh b/xcs/Headers/config.hh
index 1f7d4a8c96023c527e760f9cc39065c6492bc4bb..16925d37896b6099253a34af6da132c09cb3148d 100644
--- a/xcs/Headers/config.hh
+++ b/xcs/Headers/config.hh
@@ -1,9 +1,11 @@
#if !defined(MYLIB_CONFIG_H)
#define MYLIB_CONFIG_H 1
-constexpr unsigned CYCLE = 155;
+
// Inputsize must be defined as a constant in case binary conditions should be used
-constexpr unsigned INPUTSIZE = 16;
+constexpr unsigned INPUTSIZE = 6;
+
+constexpr unsigned CYCLE = 2174;
/// @brief struct for all user configurable parameters; possibleActions_ptr must set; all default values from scikit-XCS
/// @param possibleActions_ptr Pointer to vector for all possible actions given by the user
@@ -31,7 +33,7 @@ constexpr unsigned INPUTSIZE = 16;
/// @param discountFactorPA Float for discount the largest prediction of the current PredictionArray in multi-step problems
struct XCSParameter{
std::vector<std::string>* possibleActionsPtr;
- unsigned int N = 400;
+ unsigned int N = 1000;
float pGeneral = 0.5;
float beta = 0.2;
float alpha = 0.1;
@@ -46,14 +48,14 @@ struct XCSParameter{
float initError = 0;
float initFitness = 0.01;
float pExplore = 0.5;
- int thetaMatching = 8;
+ int thetaMatching = 2;
bool doGASubsumption = true;
bool doActionSetSubsumption = false;
int thetaSub = 20 ;
unsigned int seed = 20;
bool GAInExploitation = false;
- float discountFactorPA = 0.71;
- bool doSpecify = true;
+ float discountFactorPA = 0.7;
+ bool doSpecify = false;
int n_sp = 20;
float p_sp = 0.5;
};
diff --git a/xcs/main.cc b/xcs/main.cc
index 83682479b70f9af0982f9649c72494d18a001895..e10d4f673e856b0a7520069dfe182bafa649d2f3 100644
--- a/xcs/main.cc
+++ b/xcs/main.cc
@@ -13,7 +13,7 @@
#include "Headers/XCS.hh"
//include the Environment
-#include "Environments/Woods1.hh"
+#include "Environments/Multiplexer.hh"
//include for RandomGenerator
#include "Headers/Random.hh"
@@ -22,9 +22,9 @@ using namespace std;
int main(int argc, char* argv[])
{
+ cout << "XCS START!\n";
//learning measurement
- MovingAvgTracker env;
std::ofstream popSetFile;
std::ofstream performanceFile;
bool popset_in_csv = false;
@@ -37,12 +37,10 @@ int main(int argc, char* argv[])
//init for XCS parameter and action
XCSParameter parameter;
-
//init variables for action and condition
returnAction b;
std::string condition;
-
//init RandomNumberGenerator
unsigned seed=13;
Random* RandomNumberGeneratorPtr = new Random(seed);
@@ -54,7 +52,6 @@ int main(int argc, char* argv[])
{ "popsetFile", required_argument, 0, 'p'},
{ "performanceFile", required_argument, 0, 'f'},
{ "timeFile", required_argument, 0, 't'},
- { "popsetSize", required_argument, 0, 'n'},
0
};
while(1){
@@ -79,7 +76,7 @@ int main(int argc, char* argv[])
//cout << dir_path+"/../"+optarg+".csv" << endl;
performanceFile.open(dir_path+"/../"+optarg+".csv",ios::out);
performance_in_csv = true;
- performanceFile << "Iteration, Exploitation, Steps, Last120IterationsAverage, PopulationSetSize, XCSCalls_per_Iteration" << endl;
+ performanceFile << "Iteration, Reward, Last200IterationsAverage, PopulationSetSize" << endl;
break;
}
case 'p':
@@ -91,18 +88,11 @@ int main(int argc, char* argv[])
break;
}
case 't':
- {
std::string file_path = __FILE__;
std::string dir_path = file_path.substr(0, file_path.rfind("/"));
timeFile.open(dir_path+"/../"+optarg+".csv",ios::out | std::ofstream::app);
time_measurement = true;
break;
- }
- case 'n':
- {
- parameter.N = std::stoi(optarg);
- break;
- }
}
}
@@ -112,79 +102,54 @@ int main(int argc, char* argv[])
//wall time
auto wcts = std::chrono::system_clock::now();
- //init of possible Actions for multistep problem
- parameter.possibleActionsPtr = new std::vector<std::string>{"0","1","2","3","4","5","6","7"};
-
- //init of multistep test environment
- std::string file_path = __FILE__;
- std::string dir_path = file_path.substr(0, file_path.rfind("/"));
- Woods1 testEnv = Woods1(dir_path+"/Environments/Woods1_test.txt",RandomNumberGeneratorPtr);
-
- //init reward variable
- RewardWoods1 reward{0,0};
+ //init possible Actions for single step problems
+ parameter.possibleActionsPtr = new std::vector<std::string>{"0","1"};
- XCS xcs = XCS(parameter);
-
- //informationparameter init
-
- // if the ringBuffer parameters are changed, please init this specific ringBuffer
- // into the RingBuffer.cc file (some are given at the end of the file)
- ringBuffer<unsigned int, 120> stepTracker;
- unsigned steps=0;
- unsigned int iterations = 0;
+ //init of single step test environment
+ Multiplexer testEnv = Multiplexer(2,RandomNumberGeneratorPtr);
- unsigned int number_xcs_calls_per_iteration = 0;
-
- try{
- while(iterations<CYCLE){
- steps = 0;
- number_xcs_calls_per_iteration=0;
- // if(iterations % (CYCLE/10) == 0){
- // std::cout << iterations << " full iterations were made. These are " << (static_cast<float>(iterations)/CYCLE)*100 << "%" << std::endl;
- // fflush(stdout);
+ XCS xcs = XCS(parameter);
+ ringBuffer<unsigned int, 200> tracker;
+ unsigned i = 0;
+ try{
+ while(i < CYCLE){
+ // if(i % (CYCLE/10) == 0){
+ // std::cout << i << " full iterations were made. These are " << (static_cast<float>(i)/CYCLE)*100 << "%" << std::endl;
+ // fflush(stdout);
// }
-
- condition = testEnv.getCondition();
- b = xcs.generateAction(condition);
- number_xcs_calls_per_iteration++;
- reward = testEnv.walk(std::stoi(b.action));
- steps++;
- xcs.provideReward(reward.eop, reward.reward);
- while(!reward.eop){
- condition = testEnv.getCondition();
- b = xcs.generateAction(condition);
- number_xcs_calls_per_iteration++;
- reward = testEnv.walk(std::stoi(b.action));
- steps++;
- if(steps >= 30 && !reward.eop){
- xcs.stopMultiStepRun();
- reward = {0,1};
- testEnv.resetMap();
- }
- else{
- xcs.provideReward(reward.eop, reward.reward);
- }
- }
- testEnv.resetMap();
- if(b.isExploitation){
- stepTracker.add(steps);
- }
- if(performance_in_csv){
- performanceFile << iterations << "," << b.isExploitation << "," << steps << "," << stepTracker.average() << "," << xcs.getPopulationSetSize() << "," << number_xcs_calls_per_iteration << std::endl;
- }
- iterations++;
+ condition = testEnv.getCondition();
+ b = xcs.generateAction(condition);
+ if(b.action == testEnv.getAction()){
+ xcs.provideReward(1,1000);
+ if(b.isExploitation){
+ i++;
+ if(performance_in_csv){
+ tracker.add(1000);
+ performanceFile << i << "," << "1000" << "," << tracker.average() << "," << xcs.getPopulationSetSize() << endl;
+ }
+ }
+ }else{
+ xcs.provideReward(1,0);
+ if(b.isExploitation){
+ i++;
+ if(performance_in_csv){
+ tracker.add(0);
+ performanceFile << i << "," << "0" << "," << tracker.average() << "," << xcs.getPopulationSetSize() << endl;
+ }
+ }
+ }
+ }
+ if(time_measurement){
+ double cpu_duration = (std::clock() - startcputime) / (double)CLOCKS_PER_SEC;
+ std::chrono::duration<double> wctduration = (std::chrono::system_clock::now() - wcts);
+ timeFile << seed << "," << cpu_duration << "," << wctduration.count() << endl;
}
- }catch(std::exception& e){
- std::cout << e.what() << " happens at iteration: " << iterations << endl;
+ if(popset_in_csv){
+ xcs.printPopulationToFile(popSetFile);
+ }
+ }catch(std::exception& e){
+ std::cout << e.what() << " happens at iteration: " << i << endl;
return EXIT_FAILURE;
}
- if(time_measurement){
- double cpu_duration = (std::clock() - startcputime) / (double)CLOCKS_PER_SEC;
- std::chrono::duration<double> wctduration = (std::chrono::system_clock::now() - wcts);
- timeFile << seed << "," << cpu_duration << "," << wctduration.count() << "," << endl;
- }
- if(popset_in_csv){
- xcs.printPopulationToFile(popSetFile);
- }
- return 0;
-}
\ No newline at end of file
+}
+