def getCondensationSet(cls, rule, model, checker, sentenceNameSource):
""" generated source for method getCondensationSet """
varsInRule = GdlUtils.getVariables(rule)
varsInHead = GdlUtils.getVariables(rule.getHead())
varsNotInHead = ArrayList(varsInRule)
varsNotInHead.removeAll(varsInHead)
for var in varsNotInHead:
ConcurrencyUtils.checkForInterruption()
for literal in rule.getBody():
if GdlUtils.getVariables(literal).contains(var):
minSet.add(literal)
for literal in minSet:
if isinstance(literal, (GdlRelation, )):
varsSupplied.addAll(GdlUtils.getVariables(literal))
elif isinstance(literal, (GdlDistinct, )) or isinstance(literal, (GdlNot, )):
varsNeeded.addAll(GdlUtils.getVariables(literal))
varsNeeded.removeAll(varsSupplied)
if not varsNeeded.isEmpty():
continue
for varNeeded in varsNeeded:
for literal in rule.getBody():
if isinstance(literal, (GdlRelation, )):
if GdlUtils.getVariables(literal).contains(varNeeded):
suppliers.add(literal)
candidateSuppliersList.add(suppliers)
for suppliers in candidateSuppliersList:
if Collections.disjoint(suppliers, literalsToAdd):
literalsToAdd.add(suppliers.iterator().next())
minSet.addAll(literalsToAdd)
if goodCondensationSetByHeuristic(minSet, rule, model, checker, sentenceNameSource):
return minSet
return None
def getFunctionAddedChildren(self, analyticFunctionOrdering):
""" generated source for method getFunctionAddedChildren """
# We can't just add those functions that
# are "ready" to be added. We should be adding all those variables
# "leading up to" the functions and then applying the functions.
# We can even take this one step further by only adding one child
# per remaining constant function; we choose as our function output the
# variable that is a candidate for functionhood that has the
# largest domain, or one that is tied for largest.
# New criterion: Must also NOT be in preassignment.
children = ArrayList()
# It would be really nice here to just analytically choose
# the set of functions we're going to use.
# Here's one approach for doing that:
# For each variable, get a list of the functions that could
# potentially produce it.
# For all the variables with no functions, add them.
# Then repeatedly find the function with the fewest
# number of additional variables (hopefully 0!) needed to
# specify it and add it as a function.
# The goal here is not to be optimal, but to be efficient!
# Certain games (e.g. Pentago) break the old complete search method!
# TODO: Eventual possible optimization here:
# If something is dependent on a connected component that it is
# not part of, wait until the connected component is resolved
# (or something like that...)
if analyticFunctionOrdering and len(self.functionalSentencesInfo) > 8:
# For each variable, a list of functions
# (refer to functions by their indices)
# and the set of outstanding vars they depend on...
# We start by adding to the varOrdering the vars not produced by functions
# First, we have to find them
while i < len(self.functionalSentencesInfo):
for producibleVar in producibleVars:
if not functionsProducingVars.containsKey(producibleVar):
functionsProducingVars.put(producibleVar, HashSet())
functionsProducingVars.get(producibleVar).add(i)
i += 1
# Non-producible vars get iterated over before we start
# deciding which functions to add
for var in varsToAssign:
if not self.varOrdering.contains(var):
if not functionsProducingVars.containsKey(var):
# Add var to the ordering
self.varOrdering.add(var)
self.functionalConjunctIndices.add(-1)
self.varSources.add(-1)
# Map is from potential set of dependencies to function indices
# Create this map...
while i < len(self.functionalSentencesInfo):
# Variables already in varOrdering don't go in dependents list
producibleVars.removeAll(self.varOrdering)
allVars.removeAll(self.varOrdering)
for producibleVar in producibleVars:
dependencies.addAll(allVars)
dependencies.remove(producibleVar)
if not functionsHavingDependencies.containsKey(dependencies):
functionsHavingDependencies.put(dependencies, HashSet())
functionsHavingDependencies.get(dependencies).add(i)
i += 1
# Now, we can keep creating functions to generate the remaining variables
while len(self.varOrdering) < len(self.varsToAssign):
if functionsHavingDependencies.isEmpty():
raise RuntimeException("We should not run out of functions we could use")
# Find the smallest set of dependencies
if functionsHavingDependencies.containsKey(Collections.emptySet()):
dependencySetToUse = Collections.emptySet()
else:
for dependencySet in functionsHavingDependencies.keySet():
if len(dependencySet) < smallestSize:
smallestSize = len(dependencySet)
dependencySetToUse = dependencySet
# See if any of the functions are applicable
for function_ in functions:
producibleVars.removeAll(dependencySetToUse)
producibleVars.removeAll(self.varOrdering)
if not producibleVars.isEmpty():
functionToUse = function_
varProduced = producibleVars.iterator().next()
break
if functionToUse == -1:
# None of these functions were actually useful now?
# Dump the dependency set
functionsHavingDependencies.remove(dependencySetToUse)
else:
# Apply the function
# 1) Add the remaining dependencies as iterated variables
for var in dependencySetToUse:
self.varOrdering.add(var)
self.functionalConjunctIndices.add(-1)
self.varSources.add(-1)
# 2) Add the function's produced variable (varProduced)
self.varOrdering.add(varProduced)
self.functionalConjunctIndices.add(functionToUse)
self.varSources.add(-1)
# 3) Remove all vars added this way from all dependency sets
addedVars.addAll(dependencySetToUse)
addedVars.add(varProduced)
# Tricky, because we have to merge sets
# Easier to use a new map
for entry in functionsHavingDependencies.entrySet():
newKey.removeAll(addedVars)
if not newFunctionsHavingDependencies.containsKey(newKey):
newFunctionsHavingDependencies.put(newKey, HashSet())
newFunctionsHavingDependencies.get(newKey).addAll(entry.getValue())
functionsHavingDependencies = newFunctionsHavingDependencies
# 4) Remove this function from the lists?
for functionSet in functionsHavingDependencies.values():
functionSet.remove(functionToUse)
# Now we need to actually return the ordering in a list
# Here's the quick way to do that...
# (since we've added all the new stuff to ourself already)
return Collections.singletonList(IterationOrderCandidate(self))
else:
# Let's try a new technique for restricting the space of possibilities...
# We already have an ordering on the functions
# Let's try to constrain things to that order
# Namely, if i<j and constant form j is already used as a function,
# we cannot use constant form i UNLESS constant form j supplies
# as its variable something used by constant form i.
# We might also try requiring that c.f. i NOT provide a variable
# used by c.f. j, though there may be multiple possibilities as
# to what it could provide.
if not self.functionalConjunctIndices.isEmpty():
lastFunctionUsedIndex = Collections.max(self.functionalConjunctIndices)
while i < len(self.functionalConjunctIndices):
if self.functionalConjunctIndices.get(i) != -1:
varsProducedByFunctions.add(self.varOrdering.get(i))
i += 1
while i < len(self.functionalSentencesInfo):
if i < lastFunctionUsedIndex:
# We need to figure out whether i could use any of the
# vars we're producing with functions
# TODO: Try this with a finer grain
# i.e., see if i needs a var from a function that is after
# it, not one that might be before it
if Collections.disjoint(varsInSentence, varsProducedByFunctions):
continue
# What is the best variable to grab from this form, if there are any?
if bestVariable == None:
continue
children.add(newCandidate)
i += 1
# If there are no more functions to add, add the completed version
if children.isEmpty():
children.add(IterationOrderCandidate(self))
return children
