def __search(self, notes: List[str], cube: Cube, depth: int,
estimate: int) -> Tuple[int, str]:
"""A function to actually perform the search to the current search depth
by using recursion. Also contains the pruning if the minimum distance
increases"""
if self.__checked % 10000 == 0:
print(f"Depth: {depth:2d}, checked: {self.__checked:,}+, " +
f"(pruned: {self.__skipped:,}+)",
end="\r")
if len(notes) >= depth or estimate <= 0:
if cube.is_solved:
return (len(notes), " ".join(notes))
return (-1, "")
for move in cube.moves:
if Cube.skip_move(notes, move):
self.__skipped += 1
continue
new_cube = copy.deepcopy(cube)
new_cube.twist_by_notation(move)
new_estimate = self.__tables.get_distance(self.coordinate(cube))
if new_estimate != -1:
if estimate < new_estimate:
self.__skipped += 1
continue
estimate = new_estimate + 1
self.__checked += 1
result = self.__search(notes + [move], new_cube, depth,
estimate - 1)
if result[0] > 0:
return result
return (-1, "")
def __search(self, phase, notes: List[str], cube: Cube,
depth: int) -> Tuple[int, str]:
"""A function to actually perform the search to the current search depth
by using recursion.
TODO: Also contains the pruning if the minimum distance increases.
Return a tuple with the amount of moves as the first item and the
solution as a notation str as the second item.
In case a valid solution was not found the amount of moves is negative.
(currently always -1)"""
if self.__checked % 10000 == 0:
print(f"Depth: {depth:2d}, checked: {self.__checked:,}+ " +
f"(pruned: {self.__skipped:,}+)",
end="\r")
if depth <= len(notes):
if phase == 1 and cube.is_domino or cube.is_solved:
return (len(notes), " ".join(notes))
return (-1, "")
for move in Cube.moves if phase == 1 else self.phase2_moves:
if Cube.skip_move(notes, move):
self.__skipped += 1
continue
new_cube = copy.deepcopy(cube)
new_cube.twist_by_notation(move)
self.__checked += 1
result = self.__search(phase, notes + [move], new_cube, depth)
if result[0] > 0:
return result
return (-1, "")
def generation_search(self, notes: List[str], cube: Cube, depth: int,
distance: int) -> None:
"""A function to search all of the patterns indexes to generate the
pruning tables required by the Korf's algorithm to solve any cube in
less than about 10^12 years.
Unfortunately this function currently takes about 10^13 years with depth
20 to finish on a modern desktop computer."""
# FIXME: Optimize to run in less than a week.
# FIXME: Pruning while generating doesn't seem to work correctly.
if distance >= depth:
self.__tables.set_distance(self.coordinate(cube), distance)
return
for move in cube.moves:
if Cube.skip_move(notes, move):
continue
new_cube = copy.deepcopy(cube)
new_cube.twist_by_notation(move)
estimate = self.__tables.get_distance(self.coordinate(new_cube))
if estimate != -1 and estimate - depth < distance:
# print(f"Depth: {depth}, distance: {distance}, move: {move}")
continue
self.generation_search(notes + [move], new_cube, depth,
distance + 1)
