def recurse(self, heap, constraints):
"""
Dfs and backtrack for solution.
for item in heap: for choice in item constraints: copy constraints, remove choice, recurse
self.board does not reflect current recursion state of board (constraints do)
but will have final solution when constraints are empty
"""
while heap:
item = heapq.heappop(heap)
coords = item.coords
choices = list(constraints.row[coords.row]
& constraints.col[coords.col]
& constraints.square[coords.square])
if not choices:
return False
for num in choices:
self.board[item.coords.row][item.coords.col] = num
cc = Constraints.copy(constraints)
cc.row[coords.row].remove(num)
cc.col[coords.col].remove(num)
cc.square[coords.square].remove(num)
result = self.recurse(deepcopy(heap), cc) # recurse with new constraints
if result:
return True
return False
def recurse(self, coords, constraints):
"""
Dfs and backtrack for solution.
for choice in constraints: copy constraints, remove choice, recurse
self.board does not reflect current recursion state of board (constraints do)
but will have final solution when constraints are empty
"""
if coords.row == self.n and coords.col == 0:
return True
choices = list(
constraints.row[coords.row] &
constraints.col[coords.col] &
constraints.square[coords.square])
if not choices: # backtrack
return False
while choices:
num_assigned = choices.pop()
self.board[coords.row][coords.col] = num_assigned
cc = Constraints.copy(constraints)
cc.row[coords.row].remove(num_assigned)
cc.col[coords.col].remove(num_assigned)
cc.square[coords.square].remove(num_assigned)
next_coords = self.next_cell_coords(coords)
result = self.recurse(next_coords, cc)
if result:
return True
return False
