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package ch.epfl.maze.physical.zoo;
import ch.epfl.maze.physical.Animal;
import ch.epfl.maze.physical.ProbabilisticAnimal;
import ch.epfl.maze.util.Direction;
import ch.epfl.maze.util.Vector2D;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
/**
* Hamster A.I. that remembers the previous choice it has made and the dead ends
* it has already met.
*
* @author Pacien TRAN-GIRARD
*/
public class Hamster extends ProbabilisticAnimal {
private final List<Vector2D> deadPaths;
/**
* Constructs a hamster with a starting position.
*
* @param position Starting position of the hamster in the labyrinth
*/
public Hamster(Vector2D position) {
super(position);
this.deadPaths = new ArrayList<>();
}
/**
* Discard directions known to lead to dead ends.
*
* @param choices An array of choices
* @return An array of smart choices
*/
private Direction[] excludeDeadPaths(Direction[] choices) {
return (new ArrayList<>(Arrays.asList(choices)))
.stream()
.filter(dir -> !this.deadPaths.contains(this.getPosition().addDirectionTo(dir)))
.collect(Collectors.toList())
.toArray(new Direction[0]);
}
/**
* Moves without retracing directly its steps and by avoiding the dead-ends
* it learns during its journey.
*/
@Override
public Direction move(Direction[] choices) {
Direction[] smartChoices = this.excludeDeadPaths(choices);
if (smartChoices.length == 1) this.deadPaths.add(this.getPosition()); // dead end
return super.move(smartChoices);
}
@Override
public Animal copy() {
return new Hamster(this.getPosition());
}
}
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