moved icpm code (back) to seperate file
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kwikrick
parent
d0381e7e9b
commit
4ca358c1ef
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@ -976,249 +976,3 @@ def rootname(cluster):
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"""returns the name of the root variable associated with the name of a cluster variable"""
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return "root#"+str(id(cluster))
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class KindFilter(Filter):
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def __init__(self, kind, incrset):
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kind2class["rigid"]=Rigid
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kind2class["hog"]=Hedgehog
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kind2class["balloon"]=Balloon
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kind2class["distance"]=Rigid
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kind2class["point"]=Rigid
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kind2maxpoints["rigid"]=0
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kind2maxpoints["hog"]=0
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kind2maxpoints["balloon"]=0
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kind2maxpoints["distance"]=2
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kind2maxpoints["point"]=1
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self._kind = kind
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self._classobj = kind2class(kind)
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self._maxpoints = kind2maxpoints(kind)
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IncrementalSet.__init__(self,[incrset])
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def _receive_add(self, source, object):
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if isinstance(object,self._classobj) and len(object.vars) == self.maxpoints:
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self._add(object)
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def _receive_remove(self, source, object):
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self._remove(object)
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def __eq__(self, other):
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if isinstance(other, KindFilter):
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return (self._incrset, self._kind)==(other._incrset,other._kind)
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else:
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return False
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def __hash__(self):
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return hash((self._incrset, self._kind))
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def __repr__(self):
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return "KindFilter(%s,%s)"%(str(self._kind),str(self._incrset))
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# ---------------------------------------------------------
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# ---------- incremental cluster pattern matching ---------
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# ---------------------------------------------------------
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class KindFilter(Filter):
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def __init__(self, kind, minpoints, incrset):
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kind2class = {}
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kind2class["rigid"]=Rigid
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kind2class["hog"]=Hedgehog
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kind2class["balloon"]=Balloon
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kind2class["distance"]=Rigid
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kind2class["point"]=Rigid
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kind2maxpoints = {}
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kind2maxpoints["rigid"]=0
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kind2maxpoints["hog"]=0
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kind2maxpoints["balloon"]=0
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kind2maxpoints["distance"]=2
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kind2maxpoints["point"]=1
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self._kind = kind
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self._classobj = kind2class[kind]
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self._maxpoints = kind2maxpoints[kind]
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self._input = incrset
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self._minpoints = minpoints
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IncrementalSet.__init__(self,[incrset])
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def _receive_add(self, source, object):
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if (isinstance(object,self._classobj) and
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(len(object.vars) <= self._maxpoints or self._maxpoints == 0) and
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len(object.vars) >= self._minpoints):
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self._add(object)
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def _receive_remove(self, source, object):
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self._remove(object)
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def __eq__(self, other):
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if isinstance(other, KindFilter):
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return (self._input, self._kind, self._minpoints)==(other._input,other._kind, self._minpoints)
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else:
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return False
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def __hash__(self):
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return hash((self._input, self._kind, self._minpoints))
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def __repr__(self):
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return "KindFilter(%s,%s,%s)"%(str(self._kind),str(self._minpoints),str(self._input))
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class NConnectedPairs(IncrementalSet):
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"""Incremental set of all unordered pairs of N-connected clusters in 1 incremental sets."""
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def __init__(self, solver, n, incrset1, incrset2):
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"""Creates an incremental set of all pairs frozetset([c1, c2]) from incrset1 and incrset2 respectively,
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that are connected with N variables, according to solver"""
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# defining variables
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self._solver = solver
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self._incrset1 = incrset1
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self._incrset2 = incrset2
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self._n = n
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# map from objects to sets of pairs
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self._map = {}
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# super init
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IncrementalSet.__init__(self, [incrset1, incrset2])
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def _receive_add(self,source, obj):
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# determine 1-connected objects
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connected = set()
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for var in obj.vars:
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dependend = self._solver.find_dependend(var)
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# check that connected objects in both sets
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if source == self._incrset1:
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dependend = filter(lambda x: x in self._incrset2, dependend)
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elif source == self._incrset2:
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dependend = filter(lambda x: x in self._incrset1, dependend)
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connected.update(dependend)
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# dont pair (obj,obj)
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if obj in connected:
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connected.remove(obj)
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# for each connected object, check that #shared vars == n
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for obj2 in connected:
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shared = obj.vars.intersection(obj2.vars)
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if len(shared) == self._n:
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# add new pair
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pair = frozenset([obj, obj2])
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self._add(pair)
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# add to mapping
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if obj not in self._map:
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self._map[obj] = set()
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if obj2 not in self._map:
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self._map[obj2] = set()
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self._map[obj].add(pair)
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self._map[obj2].add(pair)
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def _receive_remove(self,source, obj):
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# remove all pairs that contain obj
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for pair in self._map[obj]:
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(obj1,obj2) = pair
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self._remove(pair)
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# remove pair from mapping
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self._map[obj1].remove(par)
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self._map[obj2].remove(pair)
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# clean up mapping
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if len(self._map[obj1]) == 0:
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del self._map[obj1]
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if len(self._map[obj2]) == 0:
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del self._map[obj2]
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def __eq__(self, other):
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if isinstance(other, NConnectedPairs):
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return (self._solver == other._solver and
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self._incrset1 == other._incrset1 and
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self._incrset2 == other._incrset2 and
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self._n == other._n)
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else:
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return False
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def __hash__(self):
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return hash((self._solver, self._incrset1, self._incrset2, self._n))
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class PatternMatches(IncrementalSet):
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"""Incrementally matches patterns of clusters"""
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def __init__(self, pattern, solver):
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self._solver = solver
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# convert pattern to a set of tuples
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listoftuples = []
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for clusterpattern in pattern:
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(kind, clustername, pointnames) = clusterpattern
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listoftuples.append(tuple([kind, clustername, tuple(pointnames)]))
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setoftuples = frozenset(listoftuples)
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self._pattern = setoftuples
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# create sub-sets for correct type of input clusters
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self._subs = []
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# NOTE: we cannot use source like this to link it to a pattern, because sources may be equal!
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self._source2pattern = {}
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for clusterpattern in self._pattern:
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(kind, clustername, pointnames) = clusterpattern
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sub = KindFilter(kind, len(pointnames),self._solver.top_level())
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self._subs.append(sub)
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# NOTE: we cannot use source like this to link it to a pattern, because sources may be equal!
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self._source2pattern[sub]=[clusterpattern]
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print "creating",sub,"for",self._source2pattern[sub]
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#rof
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# create sub-sets for all pairs of clusters
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listoftuples = list(setoftuples)
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l = len(listoftuples)
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for i in range(l):
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for j in range(i+1,l):
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cp1 = listoftuples[i]
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cp2 = listoftuples[j]
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(kind1, clustername1, pointnames1) = cp1
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(kind2, clustername2, pointnames2) = cp2
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shared = set(pointnames1).intersection(set(pointnames2))
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n = len(shared)
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if n > 0:
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kindfilter1 = KindFilter(kind1, len(pointnames1),self._solver.top_level())
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kindfilter2 = KindFilter(kind2, len(pointnames2),self._solver.top_level())
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sub = NConnectedPairs(solver, n, kindfilter1,kindfilter2)
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self._subs.append(sub)
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# NOTE: we cannot use source like this to link it to a pattern, because sources may be equal!
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self._source2pattern[sub]=[cp1,cp2]
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print "creating",sub,"for",self._source2pattern[sub]
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IncrementalSet.__init__(self, self._subs)
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def _receive_add(self, source, object):
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# NOTE: we cannot use source like this to link it to a pattern, because sources may be equal!
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print "receive",object
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pattern = self._source2pattern[source]
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print "matching sub-pattern", pattern
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#raise NotImplementedError
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def _receive_remove(self, source, object):
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raise NotImplementedError
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def __eq__(self, other):
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if isinstance(other, PatternMatches):
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return (self._solver, self._pattern)==(other._solver,other._pattern)
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else:
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return False
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def __hash__(self):
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return hash((self._solver, self._pattern))
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def __repr__(self):
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return "PatternMatches(%s,%s)"%(str(self._pattern),str(self._solver))
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# -------------------------------------------------------
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# -------------------------- test code ------------------
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# -------------------------------------------------------
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def test_icpm():
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solver = ClusterSolver([])
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solver.add(Rigid(["p5"]))
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solver.add(Rigid(["p1","p2"]))
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solver.add(Rigid(["p2","p3"]))
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solver.add(Rigid(["p1","p3","p4","p5"]))
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pattern = [["rigid","$d_ab",["$a", "$b"]],
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["rigid", "$d_ac",["$a", "$c"]],
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["rigid", "$d_bc",["$b","$c"]]]
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matches = PatternMatches(pattern, solver)
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print list(matches)
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if __name__ == "__main__":
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test_icpm()
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