def _termDictsFromContext(self, context, symbol):
vecs = NameToDictMap()
allNgrams = OccurrenceCounter()
context.neighbours.append(context.origin)
for neighbour in context.neighbours:
nOcc = neighbour.nOccurrences
location = neighbour.location
expressions = self.treeToExprConverter.getExpressionsForSymbol(
location, symbol)
# expressions.append('@+$_+@')
# expressions.append('@+EXPR@+$_+@+@')
# print 'FOO %s: %s: %s' % (symbol, location, expressions)
neighbour.setExpressions(expressions)
# add null-vector for function if it does not contain expressions
if len(expressions) == 0:
vecs.add(None, location)
for expr in expressions:
# vecs.add(expr, location, 1.0/nOcc)
# vecs.add(expr, location, 1.0)
vecs.setItem(expr, location, 1.0)
allNgrams.add(expr)
context.neighbours.pop()
if len(vecs.d) == 0 or len(allNgrams.d) == 0:
return None
return (vecs, allNgrams)
def createMatrixForFunctionNames(self, functionNames):
self._loadFunc2SubtreesMap()
self.nameToDictMap = NameToDictMap()
self.allSymbolsDict = OccurrenceCounter()
nameDictMapToMatrix = NameDictMapToMatrix()
functions = [(doc, self.func2SubtreesMap.d[doc])
for doc in functionNames]
for (doc, func) in functions:
for (ngram, nOccurrences) in func.iteritems():
for unused in xrange(nOccurrences):
self.nameToDictMap.add(ngram, doc)
self.allSymbolsDict.add(ngram)
nameDictMapToMatrix.convertFromDicts(self.nameToDictMap,
self.allSymbolsDict)
newTermDocMatrix = nameDictMapToMatrix.termDocumentMatrix
return newTermDocMatrix
def __init__(self):
self.vecs = NameToDictMap()
self.allSymbols = OccurrenceCounter()
