/** Prints the classifier. */
public void print() {
System.out.print(" Act: " + action + " Conf: ");
for (int i = 0; i < rep.length; i++) {
rep[i].print();
}
if (parameters != null) parameters.print();
} // end print
/**
* Prints the classifier to the specified file.
*
* @param fout is the file output where the classifier has to be printed.
*/
public void print(PrintWriter fout) {
fout.print(" ");
for (int i = 0; i < rep.length; i++) {
rep[i].print(fout);
}
fout.print(" " + action + " ");
if (parameters != null) parameters.print(fout);
}
/**
* Returns if the classifier of the class subsumes the classifier passed as a parameter.
*
* @param cl is the subsumed classifier.
* @return a boolean indicating if it subsumes
*/
public boolean doesSubsume(Classifier cl) {
if (rep.getAction() != cl.rep.getAction()) return false;
if (parameters.couldSubsume()) {
if (rep.isMoreGeneral(cl.rep)) {
return true;
}
}
return false;
} // end doesSubsume
/**
* Returns if the classifier of the class subsumes the classifier passed as a parameter.
*
* @param cl is the subsumed classifier.
* @return a boolean indicating if it subsumes
*/
public boolean doesSubsume(Classifier cl) {
int i;
// First, check if the condition is the same
if (action != cl.getAction()) return false;
// Then, check that is more general
if (parameters.couldSubsume()) {
for (i = 0; i < rep.length; i++) {
if (!rep[i].isMoreGeneral(cl.rep[i])) return false;
}
return true;
}
return false;
} // end doesSubsume
/**
* Prints the desnormalized classifier to the specified file.
*
* @param fout is the file output where the classifier has to be printed.
*/
public void printNotNorm(PrintWriter fout) {
int i = 0;
fout.print(" ");
try {
for (i = 0; i < rep.length; i++) {
if (Config.typeOfAttributes[i].equals("ternary")) {
rep[i].print(fout);
} else if (Config.typeOfAttributes[i].equals("integer")) {
rep[i].printNotNorm(fout, Config.attBounds[i][0]);
} else if (Config.typeOfAttributes[i].equals("real")) {
rep[i].printNotNorm(fout, Config.attBounds[i][0], Config.attBounds[i][1]);
}
}
} catch (Exception e) {
System.out.println("Exception when printing the attribute: " + i);
e.printStackTrace();
}
fout.print("\t " + (String) Config.classConv.elementAt(action));
if (parameters != null) parameters.print(fout);
}
/**
* Returns the numerosity of the classifier.
*
* @return an integer with the numerosity
*/
public int getNumerosity() {
return parameters.getNumerosity();
} // end getNumerosity
/**
* Returns the time stamp of the classifier.
*
* @return an integer with the time stamp of the classifier.
*/
public int getTime() {
return parameters.getTime();
} // end getTime
/**
* Sets the prediction error
*
* @param predErr is the prediction error value.
*/
public void setPredError(double predErr) {
parameters.setPredError(predErr);
} // end setPredError
/**
* Increases the number of matches
*
* @param num is the number of matches to be increased.
*/
public void increaseNumberMatches(int num) {
parameters.increaseNumberMatches(num);
} // end increaseNumberMatches
/**
* Computes the generality of the classifier and stores it in it corresponding parameter in the
* class Parameters.
*/
public void calculateGenerality() {
parameters.setGenerality(getGenerality());
} // end calculateGenerality
/**
* Returns if the classifier can subsume. The classifier has to be sufficiently accurate and
* sufficiently experienced to subsume another classifier.
*
* @return a boolean indicating if the classifier can subsume.
*/
public boolean couldSubsume() {
return parameters.couldSubsume();
} // end couldSubsume
/**
* Updates the parameters of a classifier.
*
* @param microClSum is the numerosity of the action set (all the action set.).
* @param classOfExample is the class of the example.
*/
public void updateParameters(double microClInC, int classOfExample) {
parameters.updateParameters(microClInC, action, classOfExample);
} // end updateParameters
/**
* Sets the time stamp for this classifier.
*
* @param tStamp is the time stamp
*/
public void setTime(int tStamp) {
parameters.setTime(tStamp);
} // end setTime
/** Prints the classifier. */
public void print() {
rep.print();
if (parameters != null) parameters.print();
}
/**
* Prints the desnormalized classifier to the specified file.
*
* @param fout is the file output where the classifier has to be printed.
*/
public void printNotNorm(PrintWriter fout) {
rep.printNotNorm(fout);
if (parameters != null) parameters.print(fout);
}
public boolean couldComp() {
return parameters.couldComp();
}
/**
* Adds the value passed to the usefulTimes parameter of the classifier
*
* @param num is the value that has to be added
*/
public void addUsefulTimes(int num) {
parameters.addUsefulTimes(num);
}
/**
* Returns if the classifier is experienced, and accurate enough to be in the reduction set.
* Otherwise, the classifier will not be included in the reduction
*
* @param maxReward is the maximum reward of the environment.
* @return a boolean indicating if the classifier can be in the reduction set.
*/
public boolean couldReduce(double maxReward) {
return parameters.couldReduce(maxReward);
}
/**
* Returns the estimated action set size.
*
* @return an integer with the estimated action set size.
*/
public double getCSize() {
return parameters.getCSize();
} // end getASize
/**
* Updates the fitness of a classifier
*
* @param kSum is the sum of all accuracies.
* @param k is the accuracy of the classifier.
*/
public void updateFitness(double kSum, double k) {
parameters.updateFitness(kSum, k);
} // end updateFitness
/**
* Returns the experience of the classifier.
*
* @return an integer with the experience of the classifier.
*/
public int getExperience() {
return parameters.getExperience();
} // end getExperience
/**
* Sets the useful parameter
*
* @param value is the value that the useful param has to be set.
*/
public void setUseful(boolean value) {
parameters.setUseful(value);
} // end setUseful
/** Sets the numerosity of the classifier. */
public void setNumerosity(int num) {
parameters.setNumerosity(num);
} // end setNumerosity
/**
* Sets the prediction value.
*
* @param pred is the prediction to be set
*/
public void setPrediction(double pred) {
parameters.setPrediction(pred);
} // end setPrediction
/**
* Increases the numerosity of the classifier.
*
* @param num is the number to increase the numerosity of the classifier.
*/
public void increaseNumerosity(int num) {
parameters.increaseNumerosity(num);
} // end increaseNumerosity
/**
* Returns the accuracy of the classifier.
*
* @return a double with the accuracy
*/
public double getAccuracy() {
return parameters.getAccuracy();
} // end getAccuracy
/**
* Sets the number of matches
*
* @param num is the number of matches to be set.
*/
public void setNumberMatches(int num) {
parameters.setNumberMatches(num);
} // end setNumberMatches
/**
* Returns the fitness of the "micro-classifier"
*
* @return a double with the micro-classifier fitness
*/
public double getMicroClFitness() {
return parameters.getMicroClFitness();
} // end getMicroClFitness
/**
* Returns the probability of a classifier to be deleted.
*
* @return a double with the probability of being deleted.
* @param avFitness is the average fitness of the set.
*/
public double deletionVote(double avFitness) {
return parameters.deletionVote(avFitness);
} // end deletionVote
/**
* Sets the usefulTimes parameter
*
* @param value is the value that the useful param has to be set.
*/
public void setUseful(int value) {
parameters.setUsefulTimes(value);
} // end setUseful