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package org.opt4j.tutorial.minones;

import java.util.HashSet;
import java.util.Random;
import java.util.Set;

import org.opt4j.core.Genotype;
import org.opt4j.core.common.random.Rand;
import org.opt4j.satdecoding.AbstractSATDecoder;
import org.opt4j.satdecoding.Constraint;
import org.opt4j.satdecoding.Literal;
import org.opt4j.satdecoding.Model;
import org.opt4j.satdecoding.SATManager;

import com.google.inject.Inject;

public class MinOnesDecoder extends AbstractSATDecoder {

	@Inject
	public MinOnesDecoder(SATManager satManager, Rand random) {
		super(satManager, random);
	}

	// Here you can set the constraints of your problem. In our case, we will
	// randomly generate a problem as a 3SAT problem (3 literals per clause)
	// with 1000 variables and 1000 clauses. This problem is known to be
	// NP-complete. However, we hope that there exists at least one feasible
	// solution (and with the seed 0 of random it does ;) ).
	@Override
	public Set createConstraints() {
		Set constraints = new HashSet();
		Random random = new Random(0);

		for (int i = 0; i < 1000; i++) {
			Constraint clause = new Constraint(">=", 1);
			HashSet vars = new HashSet();
			do {
				vars.add(random.nextInt(1000));
			} while (vars.size() < 3);

			for (int n : vars) {
				clause.add(new Literal(n, random.nextBoolean()));
			}

			constraints.add(clause);
		}

		return constraints;
	}

	@Override
	public MinOnesResult convertModel(Model model) {

		MinOnesResult minOnesResult = new MinOnesResult();

		for (int i = 0; i < 1000; i++) {
			minOnesResult.add(model.get(i));
		}

		return minOnesResult;
	}

}