def bottom_up_correspondence_scout(codelet):
objectFromInitial = chooseUnmodifiedObject('interStringSalience',
workspace.initial.objects)
objectFromTarget = chooseUnmodifiedObject('interStringSalience',
workspace.target.objects)
assert objectFromInitial.spansString() == objectFromTarget.spansString()
# get the posible concept mappings
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
assert conceptMappings and __slippability(conceptMappings)
#find out if any are distinguishing
distinguishingMappings = [m for m in conceptMappings if m.distinguishing()]
assert distinguishingMappings
# if both objects span the strings, check to see if the
# string description needs to be flipped
opposites = [m for m in distinguishingMappings
if m.initialDescriptionType == slipnet.stringPositionCategory
and m.initialDescriptionType != slipnet.bondFacet]
initialDescriptionTypes = [m.initialDescriptionType for m in opposites]
flipTargetObject = False
if (objectFromInitial.spansString() and
objectFromTarget.spansString() and
slipnet.directionCategory in initialDescriptionTypes
and __allOppositeMappings(formulas.oppositeMappings)
and slipnet.opposite.activation != 100.0):
objectFromTarget = objectFromTarget.flippedVersion()
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
flipTargetObject = True
coderack.proposeCorrespondence(objectFromInitial, objectFromTarget,
conceptMappings, flipTargetObject, codelet)
def important_object_correspondence_scout(codelet):
objectFromInitial = chooseUnmodifiedObject('relativeImportance',
workspace.initial.objects)
descriptors = objectFromInitial.relevantDistinguishingDescriptors()
slipnode = formulas.chooseSlipnodeByConceptualDepth(descriptors)
assert slipnode
initialDescriptor = slipnode
for mapping in workspace.slippages():
if mapping.initialDescriptor == slipnode:
initialDescriptor = mapping.targetDescriptor
targetCandidates = []
for objekt in workspace.target.objects:
for description in objekt.relevantDescriptions():
if description.descriptor == initialDescriptor:
targetCandidates += [objekt]
assert targetCandidates
objectFromTarget = chooseUnmodifiedObject('interStringSalience',
targetCandidates)
assert objectFromInitial.spansString() == objectFromTarget.spansString()
# get the posible concept mappings
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
assert conceptMappings
assert __slippability(conceptMappings)
# find out if any are distinguishing
distinguishingMappings = [m for m in conceptMappings if m.distinguishing()]
assert distinguishingMappings
# if both objects span the strings, check to see if the
# string description needs to be flipped
opposites = [
m for m in distinguishingMappings
if m.initialDescriptionType == slipnet.stringPositionCategory and
m.initialDescriptionType != slipnet.bondFacet]
initialDescriptionTypes = [m.initialDescriptionType for m in opposites]
flipTargetObject = False
if (objectFromInitial.spansString() and
objectFromTarget.spansString() and
slipnet.directionCategory in initialDescriptionTypes and
__allOppositeMappings(formulas.oppositeMappings) and
slipnet.opposite.activation != 100.0):
objectFromTarget = objectFromTarget.flippedVersion()
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
flipTargetObject = True
coderack.proposeCorrespondence(objectFromInitial, objectFromTarget,
conceptMappings, flipTargetObject, codelet)
def top_down_description_scout(codelet):
descriptionType = codelet.arguments[0]
chosenObject = chooseUnmodifiedObject('totalSalience', workspace.objects)
assert chosenObject
__showWhichStringObjectIsFrom(chosenObject)
descriptions = chosenObject.getPossibleDescriptions(descriptionType)
assert descriptions
values = [n.activation for n in descriptions]
i = formulas.selectListPosition(values)
chosenProperty = descriptions[i]
coderack.proposeDescription(chosenObject, chosenProperty.category(),
chosenProperty, codelet)
def bottom_up_correspondence_scout(codelet):
objectFromInitial = chooseUnmodifiedObject('interStringSalience',
workspace.initial.objects)
objectFromTarget = chooseUnmodifiedObject('interStringSalience',
workspace.target.objects)
assert objectFromInitial.spansString() == objectFromTarget.spansString()
# get the posible concept mappings
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
assert conceptMappings
assert __slippability(conceptMappings)
# find out if any are distinguishing
distinguishingMappings = [m for m in conceptMappings if m.distinguishing()]
assert distinguishingMappings
# if both objects span the strings, check to see if the
# string description needs to be flipped
opposites = [
m for m in distinguishingMappings
if m.initialDescriptionType == slipnet.stringPositionCategory and
m.initialDescriptionType != slipnet.bondFacet]
initialDescriptionTypes = [m.initialDescriptionType for m in opposites]
flipTargetObject = False
if (objectFromInitial.spansString() and
objectFromTarget.spansString() and
slipnet.directionCategory in initialDescriptionTypes and
__allOppositeMappings(formulas.oppositeMappings) and
slipnet.opposite.activation != 100.0):
objectFromTarget = objectFromTarget.flippedVersion()
conceptMappings = formulas.getMappings(
objectFromInitial, objectFromTarget,
objectFromInitial.relevantDescriptions(),
objectFromTarget.relevantDescriptions())
flipTargetObject = True
coderack.proposeCorrespondence(objectFromInitial, objectFromTarget,
conceptMappings, flipTargetObject, codelet)
def bottom_up_description_scout(codelet):
chosenObject = chooseUnmodifiedObject('totalSalience', workspace.objects)
assert chosenObject
__showWhichStringObjectIsFrom(chosenObject)
description = formulas.chooseRelevantDescriptionByActivation(chosenObject)
assert description
sliplinks = formulas.similarPropertyLinks(description.descriptor)
assert sliplinks
values = [sliplink.degreeOfAssociation() * sliplink.destination.activation
for sliplink in sliplinks]
i = formulas.selectListPosition(values)
chosen = sliplinks[i]
chosenProperty = chosen.destination
coderack.proposeDescription(chosenObject, chosenProperty.category(),
chosenProperty, codelet)
def bottom_up_bond_scout(codelet):
source = chooseUnmodifiedObject('intraStringSalience', workspace.objects)
__showWhichStringObjectIsFrom(source)
destination = chooseNeighbour(source)
assert destination
logging.info('destination: %s', destination)
bondFacet = __getBondFacet(source, destination)
logging.info('chosen bond facet: %s', bondFacet.get_name())
logging.info('Source: %s, destination: %s', source, destination)
bond_descriptors = __getDescriptors(bondFacet, source, destination)
sourceDescriptor, destinationDescriptor = bond_descriptors
logging.info("source descriptor: %s", sourceDescriptor.name.upper())
logging.info("destination descriptor: %s",
destinationDescriptor.name.upper())
category = sourceDescriptor.getBondCategory(destinationDescriptor)
assert category
if category == slipnet.identity:
category = slipnet.sameness
logging.info('proposing %s bond ', category.name)
coderack.proposeBond(source, destination, category, bondFacet,
sourceDescriptor, destinationDescriptor, codelet)
def __getScoutSource(slipnode, relevanceMethod, typeName):
initialRelevance = relevanceMethod(workspace.initial, slipnode)
targetRelevance = relevanceMethod(workspace.target, slipnode)
initialUnhappiness = workspace.initial.intraStringUnhappiness
targetUnhappiness = workspace.target.intraStringUnhappiness
logging.info('initial : relevance = %d, unhappiness=%d',
initialRelevance, int(initialUnhappiness))
logging.info('target : relevance = %d, unhappiness=%d',
targetRelevance, int(targetUnhappiness))
string = workspace.initial
relevances = initialRelevance + targetRelevance
unhappinesses = initialUnhappiness + targetUnhappiness
randomized = random.random() * (relevances + unhappinesses)
initials = initialRelevance + initialUnhappiness
if randomized > initials:
string = workspace.target
logging.info('target string selected: %s for %s',
workspace.target, typeName)
else:
logging.info('initial string selected: %s for %s',
workspace.initial, typeName)
source = chooseUnmodifiedObject('intraStringSalience', string.objects)
return source
