/**
* Returns an instance of this class which is the simplified representation of the formula
* represented by this object. May return this object if the formula is already simplified.
*
* <p>This method uses the Quine-McCluskey algorithm and code described at
* http://en.literateprograms.org/Quine-McCluskey_algorithm_%28Java%29
*
* @return The simplified representation of this formula, or this object, if already simplified.
*/
public BooleanFormula simplify() {
if (isSimplified) {
return this;
}
if (booleanTerms == null || booleanTerms.size() < 2) {
isSimplified = true;
return this;
}
// Remove true and false terms. False terms are discarded. True terms
// make the entire formula evaluate to true.
Set<BooleanTerm> trimmed = new HashSet<BooleanTerm>();
for (BooleanTerm term : booleanTerms) {
if (term.isTrue()) {
booleanTerms = null;
isSimplified = true;
return this;
} else if (term.isFalse()) {
continue; // omitting a false term
} else {
trimmed.add(term);
}
}
if (trimmed.size() != booleanTerms.size()) {
booleanTerms = trimmed;
}
// Determine number of unique variable names in the formula
Map<String, Integer> names = new TreeMap<String, Integer>();
for (Set<BooleanVar> term : booleanTerms) {
for (BooleanVar state : term) {
names.put(state.name, -1);
}
}
int i = 0;
for (Map.Entry<String, Integer> entry : names.entrySet()) {
entry.setValue(i++);
}
int count = names.size();
if (count >= Integer.SIZE) {
throw new IllegalArgumentException(Integer.toString(count));
}
List<Term> terms = new ArrayList<Term>(booleanTerms.size());
for (Set<BooleanVar> term : booleanTerms) {
byte[] bytes = new byte[count];
for (i = 0; i < count; i++) bytes[i] = Term.DontCare;
boolean ignoreTerm = false;
for (BooleanVar state : term) {
int index = names.get(state.name);
byte val = state.state ? (byte) 1 : (byte) 0;
if (bytes[index] == Term.DontCare) bytes[index] = val;
else if (bytes[index] != val) {
// Mutually exclusive states. Don't consider this term
ignoreTerm = true;
break;
}
}
if (!ignoreTerm) {
terms.add(new Term(bytes));
}
}
Set<BooleanTerm> result = new HashSet<BooleanTerm>();
if (terms.size() == 0) {
return new BooleanFormula(false);
}
terms = expandDontCares(count, terms);
Formula formula = new Formula(terms);
formula.reduceToPrimeImplicants();
formula.reducePrimeImplicantsToSubset();
// now convert back to featureExpression form
List<Term> termList = formula.getTermList();
if (termList.size() == 0) {
return new BooleanFormula(false);
}
for (Term term : termList) {
byte[] varValues = term.getVarValues();
Set<BooleanVar> states = new HashSet<BooleanVar>();
for (Map.Entry<String, Integer> entry : names.entrySet()) {
if (varValues[entry.getValue()] == (byte) 1) {
states.add(new BooleanVar(entry.getKey(), true));
} else if (varValues[entry.getValue()] == (byte) 0) {
states.add(new BooleanVar(entry.getKey(), false));
}
}
if (states.size() > 0) {
result.add(new BooleanTerm(states));
}
}
if (result.size() == 0) {
return new BooleanFormula(true);
}
BooleanFormula newFormula = new BooleanFormula();
newFormula.booleanTerms = result;
newFormula.isSimplified = true;
return newFormula;
}
