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Copy pathCS3243_P2_Sudoku_version8_revised.py
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CS3243_P2_Sudoku_version8_revised.py
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import sys
import copy
import time
# Running script: given code can be run with the command:
# python file.py, ./path/to/init_state.txt ./output/output.txt
def puzzleCopy(puzzle):
puzzle_copy = [[puzzle[i][j] for j in xrange(9)]for i in xrange(9)]
return puzzle_copy
class Cell:
def __init__(self, value):
self.value = value
self.domain = set()
self.neighbors = set()
def __str__(self):
return str(self.value)
class SudokuPuzzle:
def __init__(self, matrix, row_constraints, col_constraints, box_constraints, depth):
self.matrix = matrix
self.row_constraints = row_constraints
self.col_constraints = col_constraints
self.box_constraints = box_constraints
self.initialize_domains()
self.initialize_neighbors()
self.AC_3(dict())
self.count = 0
self.no_of_assignment = 0
self.depth = depth
def __hash__(self):
return hash(str(self.matrix))
def __str__(self):
out = ""
for row in range(9):
for col in range(9):
out = out + " " + str(self.matrix[row][col])
out = out + "\n"
return out
#initialize the domain of each cell inside the Sudoku puzzle
def initialize_domains(self):
for row in range(9):
for col in range(9):
domain = self.row_constraints[row].intersection(self.col_constraints[col],
self.box_constraints[row//3][col//3])
self.matrix[row][col].domain = domain
# initialize the neighbors of each cell inside the Sudoku puzzle
def initialize_neighbors(self):
for row in range(9):
for col in range(9):
self.matrix[row][col].neighbors = self.find_neighbors(row, col)
# find all unassigned neighbor cells of the cell at coordinate (row, col)
def find_neighbors(self, row, col):
neighbors = set()
for i in range(9):
if i != row and self.matrix[i][col].value == 0:
neighbors.add((i, col))
if i != col and self.matrix[row][i].value == 0:
neighbors.add((row, i))
box_row = row // 3 * 3
box_col = col // 3 * 3
for i in range(box_row, box_row + 3):
for j in range(box_col, box_col + 3):
if i != row and j != col and self.matrix[i][j].value == 0:
neighbors.add((i, j))
return neighbors
# choose the coordinate of the next cell to be assigned
# heuristics: Most Constrained Variable and Most Constraining Variable
def choose_cell_to_assign(self):
min_domain = 100
max_degree = -1
chosen_row = None
chosen_col = None
for row in range(9):
for col in range(9):
if self.matrix[row][col].value == 0:
domain_size = len(self.matrix[row][col].domain)
if domain_size < min_domain:
min_domain = domain_size
chosen_row = row
chosen_col = col
elif domain_size == min_domain:
degree = len(self.matrix[row][col].neighbors)
if degree > max_degree:
max_degree = degree
chosen_row = row
chosen_col = col
return (chosen_row, chosen_col)
# assign a value to a cell, update domains and neighbors set, and record domain changes
def assign(self, row, col, new_value, domain_changes):
self.no_of_assignment += 1
self.depth += 1
self.matrix[row][col].value = new_value
# update domains and neighbor set for the neighbor cells of (row, col)
for i, j in self.matrix[row][col].neighbors:
self.matrix[i][j].neighbors.remove((row, col))
if new_value in self.matrix[i][j].domain and self.matrix[i][j].value == 0:
self.matrix[i][j].domain.remove(new_value)
if domain_changes.has_key((i, j)):
domain_changes[(i, j)].add(new_value)
else:
domain_changes[(i, j)] = set([new_value])
# only runs AC_3 at after every 20 assignments
if self.no_of_assignment % 20 == 0 or self.depth = 80:
self.AC_3(domain_changes)
# unassign a value from a cell and revert changes to domains and neighbors set
def undo_assign(self, row, col, domain_changes):
self.no_of_assignment -= 1
self.depth -= 1
self.matrix[row][col].value = 0
for i, j in self.matrix[row][col].neighbors:
self.matrix[i][j].neighbors.add((row, col))
self.undo_domain_changes(domain_changes)
# check if the current sudoku state is solvable
def is_valid(self):
for i in range(9):
for j in range(9):
if self.matrix[i][j].value == 0 and len(self.matrix[i][j].domain) == 0:
return False
return True
# Initialize every arc among unassigned cells
def intitializeAC3_queue(self):
queue = list()
for row in range(9):
for col in range(9):
if self.matrix[row][col].value != 0:
continue
for neighbor_row, neighbor_col in self.matrix[row][col].neighbors:
if len(self.matrix[neighbor_row][neighbor_col].domain) == 1 and self.matrix[row][col].value == 0:
queue.append(((row, col), (neighbor_row, neighbor_col)))
return queue
# Revise the domains of two cells with the arc between (row, col) and (neighbor_row, neighbor_col)
# Pre-condition: domain of (neighbor_row, neighbor_col) has only 1 value
def revise(self, row, col, neighbor_row, neighbor_col, domain_changes):
domain1 = self.matrix[row][col].domain
domain2 = self.matrix[neighbor_row][neighbor_col].domain
revise = False
if len(domain2) != 1:
return False
for value in domain2:
if value in domain1:
domain1.remove(value)
if domain_changes.has_key((row, col)):
domain_changes[(row, col)].add(value)
else:
domain_changes[(row, col)] = set([value])
revise = True
return revise
# Update the queue with more arcs
def update_queue(self, queue, row, col, neighbor_row, neighbor_col):
for (i, j) in self.matrix[row][col].neighbors:
if (i, j) != (row, col) \
and (i, j) != (neighbor_row, neighbor_col) \
and self.matrix[i][j].value == 0:
queue.append(((i, j), (row, col)))
def AC_3(self, domain_changes):
queue = self.intitializeAC3_queue()
while queue:
(row, col), (neighbor_row, neighbor_col) = queue.pop(0)
if self.revise(row, col, neighbor_row, neighbor_col, domain_changes):
if len(self.matrix[row][col].domain) == 0:
return False
if len(self.matrix[row][col].domain) == 1:
self.update_queue(queue, row, col, neighbor_row, neighbor_col)
# print(str(row) + " " + str(col) + " " + str(neighbor_row) + " " + str(neighbor_col))
return True
def undo_domain_changes(self, domain_changes):
for (row, col), changes in domain_changes.items():
while changes:
self.matrix[row][col].domain.add(changes.pop())
def backtrack_search(self):
self.count += 1
if self.is_answer():
return True
if not self.is_valid():
return False
(row, col) = self.choose_cell_to_assign()
domain_copy = self.matrix[row][col].domain.copy()
for new_value in domain_copy:
domain_changes = dict()
self.assign(row, col, new_value, domain_changes)
result = self.backtrack_search()
if result is True:
return True
else:
self.undo_assign(row, col, domain_changes)
def is_answer(self):
for row in range(9):
for col in range(9):
if self.matrix[row][col].value == 0:
return False
return True
class Sudoku(object):
def __init__(self, puzzle):
# you may add more attributes if you need
self.puzzle = puzzle # self.puzzle is a list of lists
self.ans = puzzleCopy(puzzle) # self.ans is a list of lists
self.depth = 0 # depth represent the number of cells that have been assigned value
self.matrix = self.initialize_cells(self.puzzle)
self.row_constraints = [set([1, 2, 3, 4, 5, 6, 7, 8, 9]) for i in
range(9)] # set of values that haven't appeared in each row
self.col_constraints = [set([1, 2, 3, 4, 5, 6, 7, 8, 9]) for i in
range(9)] # set of values that haven't appeared in each collumn
self.box_constraints = [[set([1, 2, 3, 4, 5, 6, 7, 8, 9]) for i in range(3)] for j in
range(3)] # set of values that haven't appeared in each 3x3 box
self.initialize_constraints()
# initialize the value inside each cell with given input
def initialize_cells(self, puzzle):
matrix = [[Cell(0) for i in range(9)] for j in range(9)]
for row in range(9):
for col in range(9):
matrix[row][col].value = puzzle[row][col]
return matrix
# initialize the row, collumn, and 3x3 box constraints of the Sudoku puzzle
def initialize_constraints(self):
for row in range(9):
for col in range(9):
value = self.matrix[row][col].value
if value != 0:
self.depth += 1
self.row_constraints[row].remove(value)
self.col_constraints[col].remove(value)
self.box_constraints[row // 3][col // 3].remove(value)
def solve(self):
# TODO: Write your code here
start_time = time.time()
sudokuPuzzle = SudokuPuzzle(self.matrix, self.row_constraints, self.col_constraints, self.box_constraints, self.depth)
sudokuPuzzle.backtrack_search()
end_time = time.time()
print("Version: BackTracking Search + Reduced AC3 + Most Constrained + Most Constraining Variable")
print("Time elapsed " + str(end_time - start_time))
print("Number of states traversed: " + str(sudokuPuzzle.count))
return sudokuPuzzle.matrix
# you may add more classes/functions if you think is useful
# However, ensure all the classes/functions are in this file ONLY
# Note that our evaluation scripts only call the solve method.
# Any other methods that you write should be used within the solve() method.
if __name__ == "__main__":
# STRICTLY do NOT modify the code in the main function here
if len(sys.argv) != 3:
print ("\nUsage: python CS3243_P2_Sudoku_XX.py input.txt output.txt\n")
raise ValueError("Wrong number of arguments!")
try:
f = open(sys.argv[1], 'r')
except IOError:
print ("\nUsage: python CS3243_P2_Sudoku_XX.py input.txt output.txt\n")
raise IOError("Input file not found!")
puzzle = [[0 for i in range(9)] for j in range(9)]
lines = f.readlines()
i, j = 0, 0
for line in lines:
for number in line:
if '0' <= number <= '9':
puzzle[i][j] = int(number)
j += 1
if j == 9:
i += 1
j = 0
sudoku = Sudoku(puzzle)
ans = sudoku.solve()
with open(sys.argv[2], 'a') as f:
for i in range(9):
for j in range(9):
f.write(str(ans[i][j]) + " ")
f.write("\n")