这段日子实现了十几种数独的解题技巧,说实话,花费的时间比我想象的要长得多。本来说了要看论文的,结果心里痒痒,看着论文,心里想着实现这些解法的基础数据结构等等,于是忍不住小试了一下,一发不可收拾,就这样做了两个星期。中间生了一场病,在宿舍里躺了几天,顺便看了几本书,从《万寿寺》到《红拂夜奔》到《寻找无双》,也不知道是感冒药吃多了比较敏感,还是真的感触太大,有一天晚上看完《红拂夜奔》后,竟失声痛哭起来。我体会着那种无尽的绝望,甚至已经无力悲愤了。昨夜又看了《三十而立》,逻辑上的“人人都要死,皇帝是人,所以皇帝必死”与为生存目的而喊出的“皇上万岁万岁万万岁”相对立,可是人都能接受,人是多么复杂的生物啊。嗯哼,我跑题了。
总共有十几种解题技巧,其中最直接的是显式唯一数法和隐式唯一数法。所谓显式唯一数法,是指某个格只有一个候选数可选,这个格自然就只能填这个候选数了。而隐式唯一数法的意思则是,某一行、列或宫只有一个位置可以填某个候选数,当然,这个位置肯定就填这个候选数了。这两个技巧其实根本也算不上技巧了,除了这两个条件,我们还能怎样确定某个格该填哪个数,或者哪个数该填在哪个格呢?剩下的解题技巧,都是在努力删除格子里的候选数,从而使局面浮现出一个显式唯一数,或一个隐式唯一数。除了显/隐式唯一数法,还有显/隐式数对法,显/隐式三数集法以及显/隐式四数集法。在我看来,显式与隐式,一个以位置为中心,一个以候选数为中心,前面提到,显示唯一数法是指某个位置只有一个候选数可选,而显式数对法则是,在一个行/列/宫(以后统称为房)里,有两个位置只有两个相同的候选数可选,那么在这个房的其他位置,可以删除这两个候选数;同理,显式三数集、显式四数集法是指在一个房里,有三个/四个位置只有三/四个相同的候选数可选,那么在这个房的其他位置,可以删除这几个候选数。面隐式数对法则是说,有两个候选数只有两个相同的可选位置,那么这两个位置中的其他候选数均可以删除;同理,隐式三/四数集法是说,有三/四个候选数只有三/四个相同的可选位置,那么在这些位置中,其他的候选数都可以删除。除了以上8种技巧,还有区块删除法,XY形态匹配法(XY-Wing),XYZ形态匹配法(XYZ-Wing),矩形对角法(X-Wing),三链数删减法(SwordFish),四链数删减法(JellyFish)以及唯一矩形法(Unique Rectangle)。除此之外,还有X-Chain单数链法,XY-Chain双数链法以及Forcing Chain,不过我至今没有找到比较好的实现方法,所以算未完成。关于这些技巧的详细介绍,推荐大家看附件的那份手册,其实我所做的事情他全都做了,而且做得非常完整仔细,真是相当的佩服。
既然我已经认定了技巧分两种,一种以位置为中心,一种以候选数为中心,所以我就使用了两个dict来辅助这两种思路,一种就是以位置(r,c)为键,以候选数集为值,即valid_set[(r,c)]=set([a,b,c...]);另一个则以候选数为键,以其可选位置为值,即candi_pos[i]=set([(r1,c1),...,(rn,cn)])。这样,显式唯一数法和隐式唯一数法分别利用这两个dict就可以很容易地完成了:
def _nakedSingleNumber( self ):
self._changed = False
for pos, validset in self._valid_set.items():
if len(validset)<=0:
self._invalid = False
elif len(validset) == 1:
num = validset.pop()
validset.add(num)
print 'pos', pos, 'has only one candidate: %d' %num
self._changed = True
return True
return False
def _hiddenSingleNumber( self ):
self._changed = False
for num, posset in self._candi_pos.items():
#_groupByRow,_groupByCol,_groupByBlock is helper function
rows = self._groupByRow(posset)
for r, row in rows.items():
if len(row)==1:
print 'row%d has only one position for num %d' %(r, num), row[0]
self._changed = True
return True
cols = self._groupByCol(posset)
for c, col in cols.items():
if len(col)==1:
print 'col%d has only one position for num %d' %(c, num), col[0]
self._changed = True
return True
blks = self._groupByBlock(posset)
for b, blk in blks.items():
if len(blk)==1:
print 'blk%d has only one position for num %d' %(b, num), blk[0]
self._changed = True
return True
return False
从我们对显式数对/三数集/四数集法的描述,可以看出,它们其实很相似,只不过涉及到的候选数的个数不相同罢了。显式数对法中,要求两个位置有且只有两个相同的候选数,显式三数集法中,要求有三个位置,他们可以有两个或三个候选数,但是他们涉及到的候选数合起来正好是三个;同理,显式四数集法中要求有四个位置,他们可以有两个、三个或四个候选数,但是他们涉及到的候选数合起来正好是四个。注意到的是这些个位置指的都是同一个房内的。因此我的思路是这样的:分别对行/列/宫进行分析,num表示数集的个数(num=2:数对,num=3:三数集,num=4:四数集),如果该位置的候选数个数小于num,说明这个位置是可选的,将它加入candidate list中。从candidate list中任选num个(当然前提是candidate list中至少有num个元素),求这些位置的候选数的并集,如果正好等于num,则说明他们形成了显式数集。这其中涉及到一个辅助函数,_get_n_from_m(n,m),即从m中任选n个元素而不导致重复,也就是我们的Cm取n,实现如下:
def _get_n_from_m( self, n, m ):
index = range(n)
while index[0]<=m-n:
yield index
i = n-1
if index[i]<m-n+i:
index[i]+=1
else:
index[i-1]+=1
while i>0 and index[i-1]>=m-n+i:
i -= 1
index[i-1]+=1
for j in range(i,n):
index[j]=index[j-1]+1
这个函数很有用,在后面将多次使用到。有了这个函数,接下来实现我们的思路就不难了:
#nakedNumberSet occurs in a [house], [name] it 'row/column/block'
#num=2: pair
#num=3: tripple set
#num=4: quad set
def _helper_nakedNumberSet( self, house, name, num ):
for i, item in house.items():
candidate = []
#if a position has less than or equal to [num] candidates
#add it to the candidate position list
for pos in item:
if len(self._valid_set[pos])<=num:
candidate.append(pos)
#if candidate list has at least [num] elements
#get any [num] of positions
#and have union of candidate numbers of these position
#if this union has exactly [num] candidate numbers
#we can delete these candidate numbers from cells other than these position
if len(candidate)>=num:
for indices in self._get_n_from_m( num, len(candidate)):
uSet = set()
subset = []
toRemove = []
for index in indices:
uSet = uSet.union(self._valid_set[candidate[index]])
if len(uSet)==num:
subset = [ candidate[j] for j in indices ]
for j in subset:
item.remove(j)
for pos in item:
for n in uSet:
if n in self._valid_set[pos]:
toRemove.append( ( pos[0], pos[1], n ))
if toRemove:
print 'in %s%d,' %(name,i),
for (r,c) in subset:
print '(%d, %d)' %(r,c),
print 'have only candidates',
for n in uSet:
print '%d' %n,
print
self._removeCandidate(toRemove)
self._changed = True
return True
return False
同样的,隐式数对/三数集/四数集法也是相通的,只由一个num来区别。而且它的实现思路与显式数集法相仿,只不过由以位置由中心变成了以候选数为中心而已。首先是以房为单位,把那些只有少于num个候选位置的候选数给选出来,然后利用_get_n_from_m从中任选num个,看他们的并集是否正好是num个位置。
def _helper_hiddenNumberSet( self, housename, num ):
con = list()
for i in range(9):
con.append(dict())
groupBy = getattr( self, '_groupBy%s' %housename )
for n, posset in self._candi_pos.items():
house = groupBy( posset )
for i, items in house.items():
if len(items)<=num:
con[i][n]=set(items)
for i in range(9):
if len(con[i])>=num:
for indices in self._get_n_from_m( num, len(con[i])):
ks = con[i].keys()
nSet = []
uSet = set()
toRemove = []
for index in indices:
nSet.append(ks[index])
uSet = uSet.union(con[i][ks[index]])
if len(uSet)==num:
for pos in uSet:
for j in self._valid_set[pos]:
if j not in nSet:
toRemove.append( (pos[0], pos[1], j) )
if toRemove:
self._changed = True
print 'in %s%d,' %(housename, i),
for n in nSet:
print '%d,' %n,
print 'have only candidate postion',
for n in uSet:
print n,
print
self._removeCandidate(toRemove)
return True
return False
区块删减法基本可以用下面两点来概括:
1. 在某一区块即宫中,如果某个候选数只出现在一行(或者一列中),那么可以将该候选数从该行(或者该列)的其他单元格中删除,因为我们知道,每一个行/列/宫都有且只有一个候选数i,如果该候选数i在某个块中只出现在同一行,那么该行便已经确定在这个块中会有这个候选数了,所以该行的其他单元格就不能再有这个候选数了。
2. 在某一行/列中,如果某个候选数只出现在同个块中,那么可以将该候选数从该块中的其他单元格中删除。
def _regionDeletion( self ):
self._changed = False
for num, posset in self._candi_pos.items():
blks = self._groupByBlock(posset)
#1. if in a blk, a candidate number occurs at the same row or same column
#delete the candidate number from other cells of this row/column
for b, blk in blks.items():
(r,c) = blk[1]
sameRow = True
sameCol = True
for (ri,ci) in blk:
if ri!=r:
sameRow = False
if ci!=c:
sameCol = False
if not (sameRow or sameCol):
break
if sameRow:
toRemove = []
for i in range(0,c//3*3)+range((c//3+1)*3, 9):
if (r,i) in self._valid_set and num in self._valid_set[(r,i)]:
toRemove.append((r,i,num))
if toRemove:
self._changed = True
print 'in block%d, num %d occurs at the same row %d' %(b, num, r)
self._removeCandidate(toRemove)
return True
if sameCol:
toRemove = []
for i in range(0, r//3*3)+range((r//3+1)*3, 9):
if (i, c) in self._valid_set and num in self._valid_set[(i,c)]:
toRemove.append((i,c,num))
if toRemove:
self._changed = True
print 'in block%d, num %d occurs at the same col %d' %(b, num, c)
self._removeCandidate(toRemove)
return True
#2.1 if in a row, a candidate number occurs at the same block,
#delete the candidate number from other cells of this block
rows = self._groupByRow( posset )
for r, row in rows.items():
(r,c) = row[1]
b = (r//3)*3+c//3
sameBlock = True
for (ri, ci) in row:
bi = (ri//3)*3+ci//3
if b != bi:
sameBlock = False
break
if sameBlock:
toRemove = []
for i in range(r//3*3,r)+range(r+1, (r//3+1)*3):
for j in range(c//3*3, (c//3+1)*3):
if (i, j) in self._valid_set and num in self._valid_set[(i,j)]:
toRemove.append((i,j,num))
if toRemove:
print 'in row%d, num %d occurs at the same block %d' %(r, num, b)
self._changed = True
self._removeCandidate(toRemove)
return True
#2.2 if in a column, a candidate number occurs at the same block,
#delete the candidate number from other cells of this block
cols = self._groupByCol( posset )
for c, col in cols.items():
(r,c) = col[1]
b = (r//3)*3+c//3
sameBlock = True
for (ri, ci) in col:
bi = (ri//3)*3 + ci//3
if b!=bi:
sameBlock = False
break
if sameBlock:
toRemove = []
for i in range(r//3*3, (r//3+1)*3):
for j in range(c//3*3, c)+range(c+1, (c//3+1)*3):
if (i,j) in self._valid_set and num in self._valid_set[(i,j)]:
toRemove.append((i,j,num))
if toRemove:
print 'in col%d, num %d occurs at the same block %d' %(c, num, b)
self._changed=True
self._removeCandidate(toRemove)
return True
return False
XY-Wing描述起来似乎有些麻烦,事实上它应该是三数集的一种变种。假设有一个数格,该格(我们且称它为A格)有且只有候选数X,Y,在该格所在的house里,也就是它能影响到的行/列/宫里,存在另外两个位置(我们且称它为B格和C格),它们分别有且只有候选数XZ和YZ(注意XZ和YZ如果在同一个房里,就变成显式三数集了,因此该技巧在三数集后用较有成效),如果A格为X,那么B格必然为Z;如果A格为X,那么C格必然为Z,也就是说,在BC两格能共同看到的格子不应该有候选数Z,也就是说可以将Z从BC两格可以看到共同看到的格子中删去。因此,实现的时候,首先是找到XY,即从一个只有两个候选数的格子出发,找出该格子所在的行/列/宫所有只有两个候选数,且这两个候选数中有且只有一个与该格相同的候选数的格,并从这些格中任取两个,如果这两个格的候选数能构成XZ和YZ,即,两个格的候选数的并集减去XY得到一个Z,我们即可将Z从这两个格共同的影响的格中删去。
def _XYWing( self ):
self._changed = False
for pos, vset in self._valid_set.items():
if len(vset)==2:
candidate = dict()
for i in range(1,10):
candidate[i]=[]
row = get_conflict_list( pos[0]*9+pos[1] )
plist = [divmod(i,9) for i in row]
for i in plist:
if i in self._valid_set:
if len(self._valid_set[i])==2 and \
len(vset.intersection(self._valid_set[i]))==1:
z = self._valid_set[i].difference(vset).pop()
candidate[z].append(i)
toRemove = []
XZ=None
YZ=None
for i in range(1,10):
#XZ and YZ should not be at the same row or column
if len(candidate[i])>=2 :
for indices in self._get_n_from_m( 2, len(candidate[i]) ):
pos1 = candidate[i][indices[0]]
pos2 = candidate[i][indices[1]]
uset = self._valid_set[pos1].union(self._valid_set[pos2])
uset.remove(i)
if uset==vset:
XZ = pos1
YZ = pos2
seeA = get_conflict_list(pos1[0]*9+pos1[1])
seeB = get_conflict_list(pos2[0]*9+pos2[1])
seeBoth = set(seeA).intersection(set(seeB))
seelist = [divmod(j,9) for j in seeBoth]
seelist.remove(pos)
for item in seelist:
if item in self._valid_set and i in self._valid_set[item]:
toRemove.append((item[0], item[1], i))
if toRemove:
self._changed = True
print 'XYWing,', pos, 'as XY', 'and', XZ, 'and', YZ, 'as XZ and YZ respectively'
self._removeCandidate(toRemove)
return True
return False
XYZ-Wing是XY-Wing的一种扩展,A格由XY扩展到XYZ,B格和C格分别为XZ跟YZ,同时,B格和C格中有一个跟A格在同一个宫里。假设B格跟A格在同一个宫里,C或者跟A同行,或者跟它同列(如果同宫的话就是显式三数集了),如果C跟A同行,则在AB所在的宫AC所在的行的交集,除A以外的另外两个格不应该有候选数Z,因为(1)C如果为Z,这一行自然不会再有其他为Z;(2)如果C不为Z,那么C为Y,A跟B的候选数为XZ,即显式数对,AB所在的宫的其他格都应该删除候选数Z。对于C与A同列也是同样的情况。实现的时候,先找出一个有三个候选数的格,并找出其所在的宫的所有可能的XZ,然后再找出其所在的行/列可能的YZ,综合得ABC格。
def _XYZWing( self ):
for pos, vset in self._valid_set.items():
if len(vset)==3:
index = pos[0]*9+pos[1]
blk = get_block(index)
plist = [divmod(i,9) for i in blk]
#vset is XYZ
cand_XY = []
for i in plist:
if i in self._valid_set and len(self._valid_set[i])==2 and \
self._valid_set[i].issubset(vset):
cand_XY.append(i)
if not cand_XY:
continue
#looking for YZ
#first in row
row = get_row(index)
for i in set(row).intersection(set(blk)):
row.remove(i)
plist = [divmod(i,9) for i in row]
cand_YZ = []
for i in plist:
if i in self._valid_set and len(self._valid_set[i])==2 and \
self._valid_set[i].issubset(vset):
cand_YZ.append(i)
toRemove=[]
for i in cand_XY:
for j in cand_YZ:
if self._valid_set[i].union(self._valid_set[j]) == vset:
Y = self._valid_set[i].intersection(self._valid_set[j]).pop()
for k in [ (pos[0], (pos[1]//3)*3+(pos[1]+3-1)%3), (pos[0], (pos[1]//3)*3+(pos[1]+1)%3) ]:
if k!=i and k!=j and k in self._valid_set:
if Y in self._valid_set[k]:
toRemove.append((k[0],k[1],Y))
if toRemove:
self._changed = True
print 'XYZWing,', pos, 'as XYZ', 'and', i, 'and', j, 'as XY and YZ respectively'
self._removeCandidate(toRemove)
return True
#found not YZ in row, turn to col
col = get_column(index)
for i in set(col).intersection(set(blk)):
col.remove(i)
plist = [divmod(i,9) for i in col]
cand_YZ=[]
for i in plist:
if i in self._valid_set and len(self._valid_set[i])==2 and \
self._valid_set[i].issubset(vset):
cand_YZ.append(i)
toRemove = []
for i in cand_XY:
for j in cand_YZ:
if self._valid_set[i].union(self._valid_set[j]) == vset:
Y = self._valid_set[i].intersection(self._valid_set[j]).pop()
for k in [( (pos[0]//3)*3+(pos[0]+3-1)%3, pos[1]), ((pos[0]//3)*3+(pos[0]+1)%3, pos[1]) ]:
if k!=i and k!=j and k in self._valid_set:
if Y in self._valid_set[k]:
toRemove.append((k[0],k[1],Y))
if toRemove:
self._changed = True
print 'XYZWing,', pos, 'as XYZ and', i, 'and', j, 'as XY and YZ respectively'
self._removeCandidate(toRemove)
return True
return False
接下来的X-Wing,SwordFish和JellyFish跟显/隐式数对、三数集、四数集之间的关系是相似的。X-Wing说的是如果一个数字正好出现且只出现在某两行(列)的相同的两列(行)上,则这个数字就可以从这两列(行)上其他的单元格的候选数中删除。而SwordFish将这种考虑扩展到三行三列上, 而JellyFish则将其扩展到四行四列上。首先它是以候选数为中心的,并且只考虑行、列。我们可以将候选数一个一个_groupByRow,然后挑出所有在行上只有等于或少于[num]个候选位置的行,任取[num]行并对其所在列做并集,如果正好为[num]列,那么根据这个[num]的不同可以找到X-Wing,SwordFish,JellyFish。同样的情况可以发生在列上。
def _XWing( self, n ):
for num, posset in self._candi_pos.items():
rows = self._groupByRow(posset)
pos = dict()
for r, row in rows.items():
if len(row)<=n:
pos[r]=set()
for (r,c) in row:
pos[r].add(c)
if len(pos)<n:
continue
#keys stores the rows that have less that n position for num
#while pos.items() contains what column are they
keys = pos.keys()
for indices in self._get_n_from_m( n, len(pos) ):
uset = set()
for index in indices:
uset = uset.union(pos[keys[index]])
#if their union have exactly n len
#that mean they are candidates for X-wing(n=2), swordfish(n=3) or JellyFish(n=4)
toRemove = []
if len(uset)==n:
removeR = range(9)
for index in indices:
removeR.remove(keys[index])
removels = [(r,c) for (r,c) in product(removeR, list(uset)) ]
for i in removels:
if i in self._valid_set and num in self._valid_set[i]:
toRemove.append( (i[0], i[1], num) )
if toRemove:
self._changed = True
if n==2:
print 'X-Wing:',
elif n==3:
print 'SwordFish:',
elif n==4:
print 'JellyFish:',
print 'in row',
for index in indices:
print keys[index],
print 'num %d occurs only on column' %num,
for index in uset:
print index,
print
self._removeCandidate(toRemove)
return True
cols = self._groupByCol(posset)
pos = dict()
for c, col in cols.items():
if len(col)<=n:
pos[c]=set()
for (r,c) in col:
pos[c].add(r)
if len(pos)<n:
continue
keys = pos.keys()
for indices in self._get_n_from_m( n, len(pos) ):
uset = set()
for index in indices:
uset = uset.union(pos[keys[index]])
if len(uset) == n:
toRemove = []
removeC = range(9)
for index in indices:
removeC.remove(keys[index])
removels = [(r,c) for (r,c) in product(list(uset), removeC)]
for i in removels:
if i in self._valid_set and num in self._valid_set[i]:
toRemove.append( (i[0], i[1], num) )
if toRemove:
self._changed = True
if n==2:
print 'X-Wing:',
elif n==3:
print 'SwordFish:',
elif n==4:
print 'JellyFish:',
print 'in col',
for index in indices:
print keys[index],
print 'num %d occurs only on row' %num,
for index in uset:
print index,
print
self._removeCandidate(toRemove)
return True
return False
唯一矩形法利用数独的唯一性质。唯一矩形由两个候选数,四个数格组成。四个格中至少有一个有一个额外候选数,否则该数独将不唯一。唯一矩形有7种情况可供分析,通过对手册里七种情况的描述,我通过额外候选数的情况来实现这七种情况的分流。
首先是如何到一个矩形,首先我们要分析一个可能的矩形的特征:
(1)四个格分布在两个九宫格里。
(2)组成矩形的四个格中,至少有一个只有两个候选数
(3)另外三个格均包含这两个候选数。
利用这三个特征,我们可以这样找一个矩形:首先找到一个只有两个候选数的数格,在它所在的宫与行(或列)的交集中找另一个顶点,该顶点的特征是其候选数包含这两个候选数;沿着这两个顶点平行过去找另外两个顶点。
def _helper_get_rectangle( self ):
for pos, vset in self._valid_set.items():
if len(vset) == 2:
r1 = (pos[0]//3)*3+(pos[0]+3-1)%3
r2 = (pos[0]//3)*3+(pos[0]+1)%3
c1 = (pos[1]//3)*3+(pos[1]+3-1)%3
c2 = (pos[1]//3)*3+(pos[1]+1)%3
candi = [ (r1, pos[1]), (r2, pos[1]), (pos[0], c1), (pos[0], c2) ]
candidate = []
for i in candi:
if i in self._valid_set and vset.issubset(self._valid_set[i]):
candidate.append(i)
for i in candidate:
if i[0]==pos[0]:
col1 = [divmod(j,9) for j in get_column(pos[0]*9+pos[1])]
col = [ j for j in col1 if j in self._valid_set and vset.issubset(self._valid_set[j]) ]
for j in col:
r,c = j[0],i[1]
if (r,c) in self._valid_set and vset.issubset(self._valid_set[(r,c)]):
yield (pos, i, j, (r,c))
if i[1]==pos[1]:
row1 = [divmod(j,9) for j in get_row(pos[0]*9+pos[1])]
row = [ j for j in row1 if j in self._valid_set and vset.issubset(self._valid_set[j]) ]
for j in row:
r,c = i[0],j[1]
if (r,c) in self._valid_set and vset.issubset(self._valid_set[(r,c)]):
yield (pos, i, j, (r,c))
yield None
找到矩形后,将是对矩形进行分析,以额外候选数及其有多少个格有额外候选数,以及这些格的位置,来决定它是哪一种唯一矩形从而采取相应的战略。
1. 如果只有一个额外候选数:
1.1 矩形中只有一个格有该额外候选数,uniqueRectangle1,该格只能填这个额外候选数
1.2 矩形中有两个格有该额外候选数,uniqueRectangle2,这两个格中必然有一个要填这个候选数,因此将该候选数从这两个格共同能看到的格中删去
1.3 矩形中有三个格有该额外候选数,uniqueRectangle5,如1.2相似,只不过现在将该候选数从这三个格能共同看到的格中删去
2. 如果有两个额外候选数
2.1 如果两个额外候选数在同一个格中,该格只能填这两个额外候选数中的一个(这一点是我现在总结的时候突然想到的)
2.2 如果这两个额外候选数在两个格中,且这两个格在同一行或列或宫中(注意,也就是它们同边),可以考虑使用三数集法;如果这两个矩形候选数中有某个在所在的行/列只有这两个候选位置,那么另一个矩形候选数一定被删除。
2.3 如果这两个额外候选数在两个格中,且这两个格是对角,可以利用强链关系看是否可以删除某个矩形候选数(具体看手册)
3. 有多个额外候选数。由手册中的描述,A肯定是那个只有两个候选数的格,而D则是其对角,只需要检查是否满足手册所说的强链关系。
def _uniqueRectangle( self ):
self._changed = False
rec_gen = self._helper_get_rectangle()
rec = rec_gen.next()
rec_type = 0
while rec:
rec_can = self._valid_set[rec[0]]
canlist = list(rec_can)
extra = dict()
for i in rec:
dset = self._valid_set[i].difference(rec_can)
while dset:
d = dset.pop()
if d not in extra:
extra[d] = []
extra[d].append(i)
toRemove = []
if len(extra)==1:
exnum = extra.keys()[0]
#uniqueRectangle1
if len(extra[exnum]) == 1:
rec_type = 1
toRemove.append( (extra[exnum][0][0], extra[exnum][0][1], canlist[0]) )
toRemove.append( (extra[exnum][0][0], extra[exnum][0][1], canlist[1]) )
#uniqueRectangle2 if extra[exnum]==2 and uniqueRectangle5 if extra[exnum]==3
else:
commonset = set()
for cell in extra[exnum]:
conflict = get_conflict_list(cell[0]*9+cell[1])
if commonset:
commonset = commonset.intersection(set(conflict))
else:
commonset = set(conflict)
common = [divmod(i, 9) for i in commonset]
for i in common:
if i in self._valid_set and exnum in self._valid_set[i]:
toRemove.append( (i[0], i[1], exnum) )
if toRemove:
t = len(extra[exnum])
if t == 2:
rec_type = 2
elif t == 3:
rec_type = 5
elif len(extra) == 2:
exnum = extra.keys()
if len(extra[exnum[0]])==1 and len(extra[exnum[1]])==1:
exset = set()
exset.add(exnum[0])
exset.add(exnum[1])
pos1 = extra[exnum[0]][0]
pos2 = extra[exnum[1]][0]
index1 = pos1[0]*9+pos1[1]
index2 = pos2[0]*9+pos2[1]
if index2 == index1:
self._changed = True
toRemove.append( (pos1[0],pos1[1], canlist[0]) )
toRemove.append( (pos1[0], pos1[1], canlist[2]) )
self._removeCandidate(toRemove)
return True
def _helper_two_extra( scan ):
for i in scan:
if len(self._valid_set[i])==3 and exset.issubset(self._valid_set[i]):
for j in scan:
#uniqueRectangle3
if len(self._valid_set[j])==2 and \
self._valid_set[j].issubset(self._valid_set[i]):
for k in scan:
for t in self._valid_set[i]:
if k!=i and k!=j and t in self._valid_set[k]:
toRemove.append( (k[0], k[1], t) )
if toRemove:
rec_type = 3
print 'in rectangle',
#for rect in rec:
# print rect,
print 'in', pos1, pos2, "'s common house'",
print 'naked tripple set is used'
return rec_type
count1 = 0
count2 = 0
for i in scan:
if canlist[0] in self._valid_set[i]:
count1 += 1
if canlist[1] in self._valid_set[i]:
count2 += 1
#uniqueRectangle4
if count1 == 0:
toRemove.append( (pos1[0], pos1[1], canlist[1]) )
toRemove.append( (pos2[0], pos2[1], canlist[1]) )
elif count2 == 0:
toRemove.append( (pos1[0], pos1[1], canlist[0]) )
toRemove.append( (pos2[0], pos2[1], canlist[0]) )
if toRemove:
rec_type = 4
#print 'in rectangle',
#for rect in rec:
# print rect,
#print
return rec_type
#at the same row
if pos1[0] == pos2[0]:
row = get_row(index1)
row.remove(index2)
scanRow = [(r,c) for (r,c) in (divmod(i, 9) for i in row) if (r,c) in self._valid_set]
rec_type = _helper_two_extra( scanRow )
#at the same column
if pos1[1] == pos2[1]:
col = get_column(index1)
col.remove(index2)
scanCol = [(r,c) for (r,c) in (divmod(i,9) for i in col) if (r,c) in self._valid_set]
rec_type = _helper_two_extra( scanCol )
#at the same block
if pos1[0]//3==pos2[0]//3 and pos1[1]//3==pos2[1]//3:
blk = get_block( index1 )
blk.remove(index2)
scanBlk = [(r,c) for (r,c) in (divmod(i,9) for i in blk) if (r,c) in self._valid_set]
rec_type = _helper_two_extra( scanBlk )
#uniqueRectangle6
if pos1[0]!=pos2[0] and pos1[1]!=pos2[1]:
posset1 = self._candi_pos[canlist[0]]
posset2 = self._candi_pos[canlist[1]]
row1 = self._groupByRow(posset1)
row2 = self._groupByRow(posset2)
col1 = self._groupByCol(posset1)
col2 = self._groupByCol(posset2)
if (len(row1[pos1[0]])==2 or len(col1[pos1[1]])==2) and \
(len(row1[pos2[0]])==2 or len(col1[pos2[1]])==2):
toRemove.append( (pos1[0], pos1[1], canlist[0]) )
toRemove.append( (pos2[0], pos2[1], canlist[0]) )
elif (len(row2[pos1[0]])==2 or len(col2[pos1[1]])==2) and \
(len(row2[pos2[0]])==2 or len(col2[pos2[1]])==2):
toRemove.append( (pos1[0], pos1[1], canlist[1]) )
toRemove.append( (pos2[0], pos2[1], canlist[1]) )
if toRemove:
rec_type = 6
print 'uniqueRectangle6'
if not toRemove:
#see if it satisfies requirements of uniqueRectangle7
#A is definitely rec[0] while D is definitely rec[3]
#test whether a candidate occures both only twice on D's row and column
D = rec[3]
Dindex = D[0]*9+D[1]
Drow = [ divmod(i,9) for i in get_row(Dindex) ]
Dcol = [ divmod(i,9) for i in get_column(Dindex) ]
row = [ (r,c) for (r,c) in Drow if (r,c) in self._valid_set ]
col = [ (r,c) for (r,c) in Dcol if (r,c) in self._valid_set ]
countAr = 0
countBr = 0
countAc = 0
countBc = 0
for i in row:
if canlist[0] in self._valid_set[i]:
countAr += 1
if canlist[1] in self._valid_set[i]:
countBr += 1
for i in col:
if canlist[0] in self._valid_set[i]:
countAc += 1
if canlist[1] in self._valid_set[i]:
countBc += 1
if (countAr==1 and countAc==1):
toRemove.append( (D[0],D[1], canlist[1]) )
elif (countBr==1 and countBc==1):
toRemove.append( (D[0], D[1], canlist[0]) )
rec_type= 7
if toRemove:
self._changed = True
print 'uniqueRectangle%d' %rec_type,
for i in rec:
print i,
print
self._removeCandidate( toRemove )
return True
rec = rec_gen.next()
return False
至于X-Chain,XY-Chain和ForcingChain,我只能感觉到他们应该也是相通的,可是具体要实现起来,我还是觉得麻烦,不知道从何下手。这两天被这个东西搞到有些沮丧,为了换一下心境,我便决定先总结一下吧。好久没有写博客了,当是对我这段时间的一点总结。说实话,热情已经下跌了很多,这段时间很无聊,很无聊,还是换点别的事情做吧~!