Algoritmo para resolver o Sudoku
quero escrever um código em python para resolver um puzzle de sudoku. Vocês têm alguma ideia sobre um bom algoritmo para este propósito? Li algures na net sobre um algoritmo que o resolve preenchendo a caixa inteira com todos os números possíveis e inserindo valores conhecidos nas caixas correspondentes.A partir da linha e coloumn de valores conhecidos o valor conhecido é removido.Se conhecem melhor algoritmo do que este, por favor ajudem-me a escrever um. Também estou confuso como devo ler o valores conhecidos do utilizador. É realmente difícil digitar os valores um a um através do console. Alguma maneira fácil para isto além de usar gui?
5 answers
Algoritmo
- encontrar todos os valores legais de uma dada célula
- para cada valor legal, vá recursivamente e tente resolver a grade
Solução
É preciso 9x9 grelha parcialmente cheia de números. Uma célula com o valor 0 indica que é não cheio.
Código
def findNextCellToFill(grid, i, j):
for x in range(i,9):
for y in range(j,9):
if grid[x][y] == 0:
return x,y
for x in range(0,9):
for y in range(0,9):
if grid[x][y] == 0:
return x,y
return -1,-1
def isValid(grid, i, j, e):
rowOk = all([e != grid[i][x] for x in range(9)])
if rowOk:
columnOk = all([e != grid[x][j] for x in range(9)])
if columnOk:
# finding the top left x,y co-ordinates of the section containing the i,j cell
secTopX, secTopY = 3 *(i//3), 3 *(j//3) #floored quotient should be used here.
for x in range(secTopX, secTopX+3):
for y in range(secTopY, secTopY+3):
if grid[x][y] == e:
return False
return True
return False
def solveSudoku(grid, i=0, j=0):
i,j = findNextCellToFill(grid, i, j)
if i == -1:
return True
for e in range(1,10):
if isValid(grid,i,j,e):
grid[i][j] = e
if solveSudoku(grid, i, j):
return True
# Undo the current cell for backtracking
grid[i][j] = 0
return False
Testar o código
>>> input = [[5,1,7,6,0,0,0,3,4],[2,8,9,0,0,4,0,0,0],[3,4,6,2,0,5,0,9,0],[6,0,2,0,0,0,0,1,0],[0,3,8,0,0,6,0,4,7],[0,0,0,0,0,0,0,0,0],[0,9,0,0,0,0,0,7,8],[7,0,3,4,0,0,5,6,0],[0,0,0,0,0,0,0,0,0]]
>>> solveSudoku(input)
True
>>> input
[[5, 1, 7, 6, 9, 8, 2, 3, 4], [2, 8, 9, 1, 3, 4, 7, 5, 6], [3, 4, 6, 2, 7, 5, 8, 9, 1], [6, 7, 2, 8, 4, 9, 3, 1, 5], [1, 3, 8, 5, 2, 6, 9, 4, 7], [9, 5, 4, 7, 1, 3, 6, 8, 2], [4, 9, 5, 3, 6, 2, 1, 7, 8], [7, 2, 3, 4, 8, 1, 5, 6, 9], [8, 6, 1, 9, 5, 7, 4, 2, 3]]
O algoritmo acima é muito básico de backtracking que é explicado em muitos lugares. Mas a mais interessante e natural das estratégias de resolução de sudoku que encontrei é esta aqui.
Este método é conhecido como verificação para a frente e olhar para a frente ( http://ktiml.mff.cuni.cz / ~bartak/constraints/propagation.html).
A implementação abaixo precisa de uma iteração (chamadas de solução) enquanto a implementação de hari precisa de 487. Claro que o meu código é um pouco mais longo. O método de propagação também não é ideal.import sys
from copy import deepcopy
def output(a):
sys.stdout.write(str(a))
N = 9
field = [[5,1,7,6,0,0,0,3,4],
[2,8,9,0,0,4,0,0,0],
[3,4,6,2,0,5,0,9,0],
[6,0,2,0,0,0,0,1,0],
[0,3,8,0,0,6,0,4,7],
[0,0,0,0,0,0,0,0,0],
[0,9,0,0,0,0,0,7,8],
[7,0,3,4,0,0,5,6,0],
[0,0,0,0,0,0,0,0,0]]
def print_field(field):
if not field:
output("No solution")
return
for i in range(N):
for j in range(N):
cell = field[i][j]
if cell == 0 or isinstance(cell, set):
output('.')
else:
output(cell)
if (j + 1) % 3 == 0 and j < 8:
output(' |')
if j != 8:
output(' ')
output('\n')
if (i + 1) % 3 == 0 and i < 8:
output("- - - + - - - + - - -\n")
def read(field):
""" Read field into state (replace 0 with set of possible values) """
state = deepcopy(field)
for i in range(N):
for j in range(N):
cell = state[i][j]
if cell == 0:
state[i][j] = set(range(1,10))
return state
state = read(field)
def done(state):
""" Are we done? """
for row in state:
for cell in row:
if isinstance(cell, set):
return False
return True
def propagate_step(state):
""" Propagate one step """
new_units = False
for i in range(N):
row = state[i]
values = set([x for x in row if not isinstance(x, set)])
for j in range(N):
if isinstance(state[i][j], set):
state[i][j] -= values
if len(state[i][j]) == 1:
state[i][j] = state[i][j].pop()
new_units = True
elif len(state[i][j]) == 0:
return False, None
for j in range(N):
column = [state[x][j] for x in range(N)]
values = set([x for x in column if not isinstance(x, set)])
for i in range(N):
if isinstance(state[i][j], set):
state[i][j] -= values
if len(state[i][j]) == 1:
state[i][j] = state[i][j].pop()
new_units = True
elif len(state[i][j]) == 0:
return False, None
for x in range(3):
for y in range(3):
values = set()
for i in range(3*x, 3*x+3):
for j in range(3*y, 3*y+3):
cell = state[i][j]
if not isinstance(cell, set):
values.add(cell)
for i in range(3*x, 3*x+3):
for j in range(3*y, 3*y+3):
if isinstance(state[i][j], set):
state[i][j] -= values
if len(state[i][j]) == 1:
state[i][j] = state[i][j].pop()
new_units = True
elif len(state[i][j]) == 0:
return False, None
return True, new_units
def propagate(state):
""" Propagate until we reach a fixpoint """
while True:
solvable, new_unit = propagate_step(state)
if not solvable:
return False
if not new_unit:
return True
def solve(state):
""" Solve sudoku """
solvable = propagate(state)
if not solvable:
return None
if done(state):
return state
for i in range(N):
for j in range(N):
cell = state[i][j]
if isinstance(cell, set):
for value in cell:
new_state = deepcopy(state)
new_state[i][j] = value
solved = solve(new_state)
if solved is not None:
return solved
return None
print_field(solve(state))
Recuar pode ser rápido o suficiente dado que está a mover-se dentro das restrições e está a escolher as células sabiamente. Você também pode querer verificar a minha resposta neste tópico sobre a otimização do algoritmo . Mas aqui vou concentrar-me no algoritmo e no próprio código.
A essência do algoritmo é começar a iterar a grelha e fazer decide o que fazer-preencher uma célula, ou tentar outro dígito para a mesma célula, ou esvaziar uma célula e voltar para a célula anterior, etc. É importante notar que não há nenhuma maneira determinística de saber quantos passos ou iterações você vai precisar para resolver o quebra-cabeça. Portanto, você realmente tem duas opções-para usar um laço while ou para usar recursão. Ambos podem continuar iterando até que uma solução seja encontrada ou até que uma falta de solução seja provada. A vantagem da recursão é que é capaz de ramificar e geralmente suporta lógicas e algoritmos mais complexos, mas a desvantagem é que é mais difícil de implementar e muitas vezes difícil de depurar. Para a minha implementação do backtracking eu usei um laço while porque nenhum Raming é necessário, o algoritmo procura de uma forma linear única-threaded.
A lógica é assim:Embora verdadeiro: (iterações principais)
- se todas as células em branco foram iteradas e a última célula em branco iterated não tem quaisquer dígitos restantes para ser tentado-pare aqui porque não há solução.
- Se não existirem células em branco validam a grelha. Se a grelha for válida, pare aqui e devolva a solução.
- Se houver células em branco, Escolha a célula seguinte. Se essa célula tiver pelo menos um dígito possível, atribua-o e continue para a próxima iteração principal.
- se restar pelo menos uma opção para a célula actual e não existirem células em branco ou todas as células em branco tiverem iterated, assign the remaining choice and continue to the next main iteration.
- Se nenhuma das coisas acima é verdade, então é hora de voltar atrás. Apagar a célula actual e introduzir o ciclo abaixo.
Embora seja verdade: (iterações de backtrack)
- Se não houver mais células para recuar até - parar aqui porque lá não é solução.
- seleccione a célula anterior de acordo com o histórico de retrocesso.
- se a célula não tiver mais opções, esvazia a cela e continue para a próxima iteração backtrack.
- atribuir o próximo dígito disponível à célula actual, separando-se de retroceder e voltar às iterações principais.
Algumas características do algoritmo:
Ele mantém um registro das células visitadas na mesma ordem para que ele possa retroceder a qualquer momento
Ele mantém um registro de escolhas para cada célula para que ele não tente o mesmo dígito para a mesma célula duas vezes
As opções disponíveis para uma célula estão sempre dentro das restrições do Sudoku (linha, coluna e quadrante 3x3))
Esta implementação em particular tem alguns métodos diferentes de escolher a próxima célula e o próximo dígito, dependendo dos parâmetros de entrada ( Mais informação no tópico de optimização)
Se for dada uma grelha em branco, então irá gerar um 'puzzle' Sudoku válido (usar com o parâmetro de optimização "C", a fim de gerar uma grelha aleatória sempre)
Se for dada uma grelha resolvida irá reconhecê - la e imprimir uma mensagem
O código completo é:
import random, math, time
class Sudoku:
def __init__( self, _g=[] ):
self._input_grid = [] # store a copy of the original input grid for later use
self.grid = [] # this is the main grid that will be iterated
for i in _g: # copy the nested lists by value, otherwise Python keeps the reference for the nested lists
self._input_grid.append( i[:] )
self.grid.append( i[:] )
self.empty_cells = set() # set of all currently empty cells (by index number from left to right, top to bottom)
self.empty_cells_initial = set() # this will be used to compare against the current set of empty cells in order to determine if all cells have been iterated
self.current_cell = None # used for iterating
self.current_choice = 0 # used for iterating
self.history = [] # list of visited cells for backtracking
self.choices = {} # dictionary of sets of currently available digits for each cell
self.nextCellWeights = {} # a dictionary that contains weights for all cells, used when making a choice of next cell
self.nextCellWeights_1 = lambda x: None # the first function that will be called to assign weights
self.nextCellWeights_2 = lambda x: None # the second function that will be called to assign weights
self.nextChoiceWeights = {} # a dictionary that contains weights for all choices, used when selecting the next choice
self.nextChoiceWeights_1 = lambda x: None # the first function that will be called to assign weights
self.nextChoiceWeights_2 = lambda x: None # the second function that will be called to assign weights
self.search_space = 1 # the number of possible combinations among the empty cells only, for information purpose only
self.iterations = 0 # number of main iterations, for information purpose only
self.iterations_backtrack = 0 # number of backtrack iterations, for information purpose only
self.digit_heuristic = { 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 } # store the number of times each digit is used in order to choose the ones that are least/most used, parameter "3" and "4"
self.centerWeights = {} # a dictionary of the distances for each cell from the center of the grid, calculated only once at the beginning
# populate centerWeights by using Pythagorean theorem
for id in range( 81 ):
row = id // 9
col = id % 9
self.centerWeights[ id ] = int( round( 100 * math.sqrt( (row-4)**2 + (col-4)**2 ) ) )
# for debugging purposes
def dump( self, _custom_text, _file_object ):
_custom_text += ", cell: {}, choice: {}, choices: {}, empty: {}, history: {}, grid: {}\n".format(
self.current_cell, self.current_choice, self.choices, self.empty_cells, self.history, self.grid )
_file_object.write( _custom_text )
# to be called before each solve of the grid
def reset( self ):
self.grid = []
for i in self._input_grid:
self.grid.append( i[:] )
self.empty_cells = set()
self.empty_cells_initial = set()
self.current_cell = None
self.current_choice = 0
self.history = []
self.choices = {}
self.nextCellWeights = {}
self.nextCellWeights_1 = lambda x: None
self.nextCellWeights_2 = lambda x: None
self.nextChoiceWeights = {}
self.nextChoiceWeights_1 = lambda x: None
self.nextChoiceWeights_2 = lambda x: None
self.search_space = 1
self.iterations = 0
self.iterations_backtrack = 0
self.digit_heuristic = { 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 }
def validate( self ):
# validate all rows
for x in range(9):
digit_count = { 0:1, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 }
for y in range(9):
digit_count[ self.grid[ x ][ y ] ] += 1
for i in digit_count:
if digit_count[ i ] != 1:
return False
# validate all columns
for x in range(9):
digit_count = { 0:1, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 }
for y in range(9):
digit_count[ self.grid[ y ][ x ] ] += 1
for i in digit_count:
if digit_count[ i ] != 1:
return False
# validate all 3x3 quadrants
def validate_quadrant( _grid, from_row, to_row, from_col, to_col ):
digit_count = { 0:1, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 }
for x in range( from_row, to_row + 1 ):
for y in range( from_col, to_col + 1 ):
digit_count[ _grid[ x ][ y ] ] += 1
for i in digit_count:
if digit_count[ i ] != 1:
return False
return True
for x in range( 0, 7, 3 ):
for y in range( 0, 7, 3 ):
if not validate_quadrant( self.grid, x, x+2, y, y+2 ):
return False
return True
def setCell( self, _id, _value ):
row = _id // 9
col = _id % 9
self.grid[ row ][ col ] = _value
def getCell( self, _id ):
row = _id // 9
col = _id % 9
return self.grid[ row ][ col ]
# returns a set of IDs of all blank cells that are related to the given one, related means from the same row, column or quadrant
def getRelatedBlankCells( self, _id ):
result = set()
row = _id // 9
col = _id % 9
for i in range( 9 ):
if self.grid[ row ][ i ] == 0: result.add( row * 9 + i )
for i in range( 9 ):
if self.grid[ i ][ col ] == 0: result.add( i * 9 + col )
for x in range( (row//3)*3, (row//3)*3 + 3 ):
for y in range( (col//3)*3, (col//3)*3 + 3 ):
if self.grid[ x ][ y ] == 0: result.add( x * 9 + y )
return set( result ) # return by value
# get the next cell to iterate
def getNextCell( self ):
self.nextCellWeights = {}
for id in self.empty_cells:
self.nextCellWeights[ id ] = 0
self.nextCellWeights_1( 1000 ) # these two functions will always be called, but behind them will be a different weight function depending on the optimization parameters provided
self.nextCellWeights_2( 1 )
return min( self.nextCellWeights, key = self.nextCellWeights.get )
def nextCellWeights_A( self, _factor ): # the first cell from left to right, from top to bottom
for id in self.nextCellWeights:
self.nextCellWeights[ id ] += id * _factor
def nextCellWeights_B( self, _factor ): # the first cell from right to left, from bottom to top
self.nextCellWeights_A( _factor * -1 )
def nextCellWeights_C( self, _factor ): # a randomly chosen cell
for id in self.nextCellWeights:
self.nextCellWeights[ id ] += random.randint( 0, 999 ) * _factor
def nextCellWeights_D( self, _factor ): # the closest cell to the center of the grid
for id in self.nextCellWeights:
self.nextCellWeights[ id ] += self.centerWeights[ id ] * _factor
def nextCellWeights_E( self, _factor ): # the cell that currently has the fewest choices available
for id in self.nextCellWeights:
self.nextCellWeights[ id ] += len( self.getChoices( id ) ) * _factor
def nextCellWeights_F( self, _factor ): # the cell that currently has the most choices available
self.nextCellWeights_E( _factor * -1 )
def nextCellWeights_G( self, _factor ): # the cell that has the fewest blank related cells
for id in self.nextCellWeights:
self.nextCellWeights[ id ] += len( self.getRelatedBlankCells( id ) ) * _factor
def nextCellWeights_H( self, _factor ): # the cell that has the most blank related cells
self.nextCellWeights_G( _factor * -1 )
def nextCellWeights_I( self, _factor ): # the cell that is closest to all filled cells
for id in self.nextCellWeights:
weight = 0
for check in range( 81 ):
if self.getCell( check ) != 0:
weight += math.sqrt( ( id//9 - check//9 )**2 + ( id%9 - check%9 )**2 )
def nextCellWeights_J( self, _factor ): # the cell that is furthest from all filled cells
self.nextCellWeights_I( _factor * -1 )
def nextCellWeights_K( self, _factor ): # the cell whose related blank cells have the fewest available choices
for id in self.nextCellWeights:
weight = 0
for id_blank in self.getRelatedBlankCells( id ):
weight += len( self.getChoices( id_blank ) )
self.nextCellWeights[ id ] += weight * _factor
def nextCellWeights_L( self, _factor ): # the cell whose related blank cells have the most available choices
self.nextCellWeights_K( _factor * -1 )
# for a given cell return a set of possible digits within the Sudoku restrictions
def getChoices( self, _id ):
available_choices = {1,2,3,4,5,6,7,8,9}
row = _id // 9
col = _id % 9
# exclude digits from the same row
for y in range( 0, 9 ):
if self.grid[ row ][ y ] in available_choices:
available_choices.remove( self.grid[ row ][ y ] )
# exclude digits from the same column
for x in range( 0, 9 ):
if self.grid[ x ][ col ] in available_choices:
available_choices.remove( self.grid[ x ][ col ] )
# exclude digits from the same quadrant
for x in range( (row//3)*3, (row//3)*3 + 3 ):
for y in range( (col//3)*3, (col//3)*3 + 3 ):
if self.grid[ x ][ y ] in available_choices:
available_choices.remove( self.grid[ x ][ y ] )
if len( available_choices ) == 0: return set()
else: return set( available_choices ) # return by value
def nextChoice( self ):
self.nextChoiceWeights = {}
for i in self.choices[ self.current_cell ]:
self.nextChoiceWeights[ i ] = 0
self.nextChoiceWeights_1( 1000 )
self.nextChoiceWeights_2( 1 )
self.current_choice = min( self.nextChoiceWeights, key = self.nextChoiceWeights.get )
self.setCell( self.current_cell, self.current_choice )
self.choices[ self.current_cell ].remove( self.current_choice )
def nextChoiceWeights_0( self, _factor ): # the lowest digit
for i in self.nextChoiceWeights:
self.nextChoiceWeights[ i ] += i * _factor
def nextChoiceWeights_1( self, _factor ): # the highest digit
self.nextChoiceWeights_0( _factor * -1 )
def nextChoiceWeights_2( self, _factor ): # a randomly chosen digit
for i in self.nextChoiceWeights:
self.nextChoiceWeights[ i ] += random.randint( 0, 999 ) * _factor
def nextChoiceWeights_3( self, _factor ): # heuristically, the least used digit across the board
self.digit_heuristic = { 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0 }
for id in range( 81 ):
if self.getCell( id ) != 0: self.digit_heuristic[ self.getCell( id ) ] += 1
for i in self.nextChoiceWeights:
self.nextChoiceWeights[ i ] += self.digit_heuristic[ i ] * _factor
def nextChoiceWeights_4( self, _factor ): # heuristically, the most used digit across the board
self.nextChoiceWeights_3( _factor * -1 )
def nextChoiceWeights_5( self, _factor ): # the digit that will cause related blank cells to have the least number of choices available
cell_choices = {}
for id in self.getRelatedBlankCells( self.current_cell ):
cell_choices[ id ] = self.getChoices( id )
for c in self.nextChoiceWeights:
weight = 0
for id in cell_choices:
weight += len( cell_choices[ id ] )
if c in cell_choices[ id ]: weight -= 1
self.nextChoiceWeights[ c ] += weight * _factor
def nextChoiceWeights_6( self, _factor ): # the digit that will cause related blank cells to have the most number of choices available
self.nextChoiceWeights_5( _factor * -1 )
def nextChoiceWeights_7( self, _factor ): # the digit that is the least common available choice among related blank cells
cell_choices = {}
for id in self.getRelatedBlankCells( self.current_cell ):
cell_choices[ id ] = self.getChoices( id )
for c in self.nextChoiceWeights:
weight = 0
for id in cell_choices:
if c in cell_choices[ id ]: weight += 1
self.nextChoiceWeights[ c ] += weight * _factor
def nextChoiceWeights_8( self, _factor ): # the digit that is the most common available choice among related blank cells
self.nextChoiceWeights_7( _factor * -1 )
def nextChoiceWeights_9( self, _factor ): # the digit that is the least common available choice across the board
cell_choices = {}
for id in range( 81 ):
if self.getCell( id ) == 0:
cell_choices[ id ] = self.getChoices( id )
for c in self.nextChoiceWeights:
weight = 0
for id in cell_choices:
if c in cell_choices[ id ]: weight += 1
self.nextChoiceWeights[ c ] += weight * _factor
def nextChoiceWeights_a( self, _factor ): # the digit that is the most common available choice across the board
self.nextChoiceWeights_9( _factor * -1 )
# the main function to be called
def solve( self, _nextCellMethod, _nextChoiceMethod, _start_time, _prefillSingleChoiceCells = False ):
s = self
s.reset()
# initialize optimization functions based on the optimization parameters provided
"""
A - the first cell from left to right, from top to bottom
B - the first cell from right to left, from bottom to top
C - a randomly chosen cell
D - the closest cell to the center of the grid
E - the cell that currently has the fewest choices available
F - the cell that currently has the most choices available
G - the cell that has the fewest blank related cells
H - the cell that has the most blank related cells
I - the cell that is closest to all filled cells
J - the cell that is furthest from all filled cells
K - the cell whose related blank cells have the fewest available choices
L - the cell whose related blank cells have the most available choices
"""
if _nextCellMethod[ 0 ] in "ABCDEFGHIJKLMN":
s.nextCellWeights_1 = getattr( s, "nextCellWeights_" + _nextCellMethod[0] )
elif _nextCellMethod[ 0 ] == " ":
s.nextCellWeights_1 = lambda x: None
else:
print( "(A) Incorrect optimization parameters provided" )
return False
if len( _nextCellMethod ) > 1:
if _nextCellMethod[ 1 ] in "ABCDEFGHIJKLMN":
s.nextCellWeights_2 = getattr( s, "nextCellWeights_" + _nextCellMethod[1] )
elif _nextCellMethod[ 1 ] == " ":
s.nextCellWeights_2 = lambda x: None
else:
print( "(B) Incorrect optimization parameters provided" )
return False
else:
s.nextCellWeights_2 = lambda x: None
# initialize optimization functions based on the optimization parameters provided
"""
0 - the lowest digit
1 - the highest digit
2 - a randomly chosen digit
3 - heuristically, the least used digit across the board
4 - heuristically, the most used digit across the board
5 - the digit that will cause related blank cells to have the least number of choices available
6 - the digit that will cause related blank cells to have the most number of choices available
7 - the digit that is the least common available choice among related blank cells
8 - the digit that is the most common available choice among related blank cells
9 - the digit that is the least common available choice across the board
a - the digit that is the most common available choice across the board
"""
if _nextChoiceMethod[ 0 ] in "0123456789a":
s.nextChoiceWeights_1 = getattr( s, "nextChoiceWeights_" + _nextChoiceMethod[0] )
elif _nextChoiceMethod[ 0 ] == " ":
s.nextChoiceWeights_1 = lambda x: None
else:
print( "(C) Incorrect optimization parameters provided" )
return False
if len( _nextChoiceMethod ) > 1:
if _nextChoiceMethod[ 1 ] in "0123456789a":
s.nextChoiceWeights_2 = getattr( s, "nextChoiceWeights_" + _nextChoiceMethod[1] )
elif _nextChoiceMethod[ 1 ] == " ":
s.nextChoiceWeights_2 = lambda x: None
else:
print( "(D) Incorrect optimization parameters provided" )
return False
else:
s.nextChoiceWeights_2 = lambda x: None
# fill in all cells that have single choices only, and keep doing it until there are no left, because as soon as one cell is filled this might bring the choices down to 1 for another cell
if _prefillSingleChoiceCells == True:
while True:
next = False
for id in range( 81 ):
if s.getCell( id ) == 0:
cell_choices = s.getChoices( id )
if len( cell_choices ) == 1:
c = cell_choices.pop()
s.setCell( id, c )
next = True
if not next: break
# initialize set of empty cells
for x in range( 0, 9, 1 ):
for y in range( 0, 9, 1 ):
if s.grid[ x ][ y ] == 0:
s.empty_cells.add( 9*x + y )
s.empty_cells_initial = set( s.empty_cells ) # copy by value
# calculate search space
for id in s.empty_cells:
s.search_space *= len( s.getChoices( id ) )
# initialize the iteration by choosing a first cell
if len( s.empty_cells ) < 1:
if s.validate():
print( "Sudoku provided is valid!" )
return True
else:
print( "Sudoku provided is not valid!" )
return False
else: s.current_cell = s.getNextCell()
s.choices[ s.current_cell ] = s.getChoices( s.current_cell )
if len( s.choices[ s.current_cell ] ) < 1:
print( "(C) Sudoku cannot be solved!" )
return False
# start iterating the grid
while True:
#if time.time() - _start_time > 2.5: return False # used when doing mass tests and don't want to wait hours for an inefficient optimization to complete
s.iterations += 1
# if all empty cells and all possible digits have been exhausted, then the Sudoku cannot be solved
if s.empty_cells == s.empty_cells_initial and len( s.choices[ s.current_cell ] ) < 1:
print( "(A) Sudoku cannot be solved!" )
return False
# if there are no empty cells, it's time to validate the Sudoku
if len( s.empty_cells ) < 1:
if s.validate():
print( "Sudoku has been solved! " )
print( "search space is {}".format( self.search_space ) )
print( "empty cells: {}, iterations: {}, backtrack iterations: {}".format( len( self.empty_cells_initial ), self.iterations, self.iterations_backtrack ) )
for i in range(9):
print( self.grid[i] )
return True
# if there are empty cells, then move to the next one
if len( s.empty_cells ) > 0:
s.current_cell = s.getNextCell() # get the next cell
s.history.append( s.current_cell ) # add the cell to history
s.empty_cells.remove( s.current_cell ) # remove the cell from the empty queue
s.choices[ s.current_cell ] = s.getChoices( s.current_cell ) # get possible choices for the chosen cell
if len( s.choices[ s.current_cell ] ) > 0: # if there is at least one available digit, then choose it and move to the next iteration, otherwise the iteration continues below with a backtrack
s.nextChoice()
continue
# if all empty cells have been iterated or there are no empty cells, and there are still some remaining choices, then try another choice
if len( s.choices[ s.current_cell ] ) > 0 and ( s.empty_cells == s.empty_cells_initial or len( s.empty_cells ) < 1 ):
s.nextChoice()
continue
# if none of the above, then we need to backtrack to a cell that was previously iterated
# first, restore the current cell...
s.history.remove( s.current_cell ) # ...by removing it from history
s.empty_cells.add( s.current_cell ) # ...adding back to the empty queue
del s.choices[ s.current_cell ] # ...scrapping all choices
s.current_choice = 0
s.setCell( s.current_cell, s.current_choice ) # ...and blanking out the cell
# ...and then, backtrack to a previous cell
while True:
s.iterations_backtrack += 1
if len( s.history ) < 1:
print( "(B) Sudoku cannot be solved!" )
return False
s.current_cell = s.history[ -1 ] # after getting the previous cell, do not recalculate all possible choices because we will lose the information about has been tried so far
if len( s.choices[ s.current_cell ] ) < 1: # backtrack until a cell is found that still has at least one unexplored choice...
s.history.remove( s.current_cell )
s.empty_cells.add( s.current_cell )
s.current_choice = 0
del s.choices[ s.current_cell ]
s.setCell( s.current_cell, s.current_choice )
continue
# ...and when such cell is found, iterate it
s.nextChoice()
break # and break out from the backtrack iteration but will return to the main iteration
Exemplo chamado usando o Sudoku mais difícil do mundo de acordo com este artigo http://www.telegraph.co.uk/news/science/science-news/9359579/Worlds-hardest-sudoku-can-you-crack-it.html
hardest_sudoku = [
[8,0,0,0,0,0,0,0,0],
[0,0,3,6,0,0,0,0,0],
[0,7,0,0,9,0,2,0,0],
[0,5,0,0,0,7,0,0,0],
[0,0,0,0,4,5,7,0,0],
[0,0,0,1,0,0,0,3,0],
[0,0,1,0,0,0,0,6,8],
[0,0,8,5,0,0,0,1,0],
[0,9,0,0,0,0,4,0,0]]
mySudoku = Sudoku( hardest_sudoku )
start = time.time()
mySudoku.solve( "A", "0", time.time(), False )
print( "solved in {} seconds".format( time.time() - start ) )
E o exemplo de saída é:
Sudoku has been solved!
search space is 9586591201964851200000000000000000000
empty cells: 60, iterations: 49559, backtrack iterations: 49498
[8, 1, 2, 7, 5, 3, 6, 4, 9]
[9, 4, 3, 6, 8, 2, 1, 7, 5]
[6, 7, 5, 4, 9, 1, 2, 8, 3]
[1, 5, 4, 2, 3, 7, 8, 9, 6]
[3, 6, 9, 8, 4, 5, 7, 2, 1]
[2, 8, 7, 1, 6, 9, 5, 3, 4]
[5, 2, 1, 9, 7, 4, 3, 6, 8]
[4, 3, 8, 5, 2, 6, 9, 1, 7]
[7, 9, 6, 3, 1, 8, 4, 5, 2]
solved in 1.1600663661956787 seconds
- Configuração: para cada quadrado, tem um conjunto de opções para cada número que mostra os números permitidos. Riscando: assim como quando as pessoas no trem estão resolvendo no papel, você pode interativamente riscar números conhecidos. Qualquer quadrado com apenas um número irá acciona outro cruzamento. Isto resultará na resolução de todo o quebra-cabeça, ou ficará sem gatilhos. Foi aqui que fiquei parado da última vez. Só há 9! = 362880 maneiras de organizar 9 números, facilmente pré-complicado em um sistema moderno. Todas as linhas, colunas e quadrados 3x3 devem ser uma dessas permutações. Uma vez que você tem um monte de números lá, você pode fazer o que você fez com o riscamento. Para cada linha / coluna / 3x3, você pode riscar 1/9 dos 9! permutações se você tem um número, 1 / (8*9) Se você tem 2, e assim por diante.
- permutações cruzadas: agora você tem um monte de linhas e colunas com conjuntos de permutações potenciais. Mas há outra restrição: uma vez que você define uma linha, as colunas e 3x3s são imensamente reduzidos no que eles podem ser. Você pode fazer uma busca em árvore daqui para encontrar uma solução.