class Solution(SudokuDescr):
def __init__(self, descr=None):
SudokuDescr.__init__(self, matrix=descr.matrix)
self.steps = [] # ((x, y), value)
x, y = self.matrix.shape
self.probability = Matrix(x=x, y=y, default_func=lambda ix, iy: Probability(self.values()))
self.pending = [] # (x, y), value
self.random = []
indexes = set()
for init in self.matrix:
if init.v != 0:
indexes.add(init.index())
self.add_step(init.index(), init.v)
new_pending = []
for i in self.pending:
if not i[0] in indexes:
new_pending.append(i)
self.pending = new_pending
def probability_line(self, index):
return [
SolverMethod.belonged_box(index, self.probability, self.box_shape()),
self.probability.row(index[1]),
self.probability.column(index[0])
]
def add_step(self, index, value):
lines = self.probability_line(index)
for l in lines:
for i in l:
if i.index() != index and i.v.erase(value, len(self.steps)):
self.add_pending(i.index(), i.v.value())
self.probability.item_i(index).v.set(value, len(self.steps))
self.steps.append((index, value))
self.matrix.item_i(index).v = value
def add_pending(self, index, value):
self.pending.append((index, value))
def add_random_choice(self, index, values):
vs = copy(values)
v = vs.pop()
self.random.append((index, vs, len(self.steps)))
self.add_step(index, v)
def rollback_random(self):
# revert to 0 in matrix
r = self.random.pop()
while not r[1]:
r = self.random.pop()
rev = r[2]
while rev != len(self.steps):
s = self.steps.pop()
self.matrix.item_i(s[0]).v = 0
for item in self.probability:
item.v.rollback(rev)
self.pending = []
self.add_random_choice(r[0], r[1])
def process_pending(self):
while self.pending:
item = self.pending.pop()
self.add_step(item[0], item[1])
def is_done(self):
x, y = self.matrix.shape
return x*y == len(self.steps)