public static void main(String[] args) {
String s = "5*Toke*10";
Hashtable<String, Double> Ov = new Hashtable();
Ov.put("Toke", 10.0);
Tokenizer myToken = new Tokenizer(s);
IntoPost ITP = new IntoPost(myToken);
TheQueue<String> The = ITP.translate();
Evaluate Eval = new Evaluate(The);
double x = Eval.evaluate(Ov);
System.out.println(x);
}
/*
* (non-Javadoc)
*
* @see java.lang.Runnable#run()
*/
public void run() {
// Do not run something terminated
if (isTerminated()) return;
logger.info("Run optimizer");
generation = 0;
while (!isTerminated()) {
pauseLock.lock();
try {
while (isPaused) unpaused.await();
if (isTerminated) return;
} catch (InterruptedException ie) {
// Finish
return;
} finally {
pauseLock.unlock();
}
//
// Initialise the population with a dummy individual
//
if (population.isEmpty()) {
Solution solution = new Solution();
for (Node_Variable variable : request.variables())
solution.add(new Binding(variable, Node.NULL));
population.add(solution);
}
// Increment the generation counter
++generation;
//
// Generate a new set of offspring and copy the parents into it
// first
//
// logger.info("Generate");
Set<Solution> newPopulation = new HashSet<Solution>();
newPopulation.addAll(population); // Add the parents
generateOp.createPopulation(population, newPopulation);
//
// Evaluate all of them
//
// logger.info("Evaluate " + newPopulation.size());
// Counts the number of different solutions
evaluationsCounter += newPopulation.size() - population.size();
evaluateOp.evaluatePopulation(newPopulation);
/*
* String buffer = "Fitnesses "; for (Solution s : newPopulation)
* buffer += s.getFitness() + " "; logger.info(buffer);
*/
// Provide feed back to the generation operator
generateOp.updateProviderRewards(newPopulation);
//
// Get rid of the previous population and insert the kids
//
// logger.info("Cut");
population.clear();
population.addAll(newPopulation);
while (population.size() > POPULATION_SIZE) population.remove(population.first());
//
// Track for optimality
//
double topFitness = population.last().getFitness();
for (Solution s : population) {
// Increment age
if (s.getFitness() != topFitness) s.resetAge();
s.incrementAge();
// Check optimality
s.setOptimal(false);
if (s.getAge() >= MAXIMUM_GENERATION && s.getFitness() > 0) s.setOptimal(true);
if (s.getFitness() == 1.0d) s.setOptimal(true);
// If the solution is optimal add its (valid!) triples to the
// black
// list
if (s.isOptimal()) {
synchronized (blackListedTriples) {
blackListedTriples.addAll(request.getTripleSet(s));
}
}
// Print solution
// logger.info(s.toString());
}
logger.info("Generation " + generation + ", best fitness=" + topFitness);
for (Solution s : population) logger.info(s.toString());
//
// Notify observers that a loop has been done
//
setChanged();
notifyObservers(population);
// for (Solution s : population)
// if (s.isOptimal())
// this.terminate();
//
// Wait a bit for the data layer
//
datalayer.waitForLatencyBuffer();
//
// Remove all optimum individuals from the population
//
List<Solution> toRemove = new ArrayList<Solution>();
for (Solution s : population) if (s.isOptimal()) toRemove.add(s);
population.removeAll(toRemove);
}
}
