def computeWordKLDivergenceWithByEddie(self, event):
# this method calls the kl divergence computation by eddie's methods
text1 = ""
text2 = ""
for element in self._event[self._element_type]:
p = createElement(self._element_type, element)
text1 += " "
text1 += p.getText()
if type(event) is not types.DictType:
event = event.toDict()
for element in event[self._element_type]:
p = createElement(self._element_type, element)
text2 += " "
text2 += p.getText()
return kldiv(tokenize(text1), tokenize(text2))
def computeWordKLDivergenceWithByEddie(self, event):
# this method calls the kl divergence computation by eddie's methods
text1 = ''
text2 = ''
for photo in self._event['photos']:
p = Photo(photo)
text1 += ' '
text1 += p.getCaption()
if type(event) is types.DictType:
pass
else:
event = event.toDict()
for photo in event['photos']:
p = Photo(photo)
text2 += ' '
text2 += p.getCaption()
return kldiv(tokenize(text1), tokenize(text2))
def computeWordKLDivergenceWithByEddie(self, event): # this method calls the kl divergence computation by eddie's methods text1 = '' text2 = '' for photo in self._event['photos']: p = Photo(photo) text1 += ' ' text1 += p.getCaption() if type(event) is types.DictType: pass else: event = event.toJSON() for photo in event['photos']: p = Photo(photo) text2 += ' ' text2 += p.getCaption() return kldiv(tokenize(text1), tokenize(text2))
def PhotoDistanceByCaption(photo1, photo2):
p1 = Photo(photo1)
p2 = Photo(photo2)
cap1 = p1.getCaption()
cap2 = p2.getCaption()
cp1 = TextParser(True)
cp1.insertCaption(cap1)
cp2 = TextParser(True)
cp2.insertCaption(cap2)
word_list1 = cp1.getTopWords(-1)
word_list2 = cp2.getTopWords(-1)
if len(word_list1) == 0 or len(word_list2) == 0:
# unable to compare
return None
word_dict1 = {}
for word, freq in word_list1:
word_dict1[word] = freq
word_dict2 = {}
for word, freq in word_list2:
word_dict2[word] = freq
return kldiv(word_dict1, word_dict2)
def ElementDistanceByText(element1, element2):
p1 = createElement(self._element_type, element1)
p2 = createElement(self._element_type, element2)
cap1 = p1.getText()
cap2 = p2.getText()
cp1 = TextParser(True)
cp1.insertText(cap1)
cp2 = TextParser(True)
cp2.insertText(cap2)
word_list1 = cp1.getTopWords(-1)
word_list2 = cp2.getTopWords(-1)
if len(word_list1) == 0 or len(word_list2) == 0:
# unable to compare
return None
word_dict1 = {}
for word, freq in word_list1:
word_dict1[word] = freq
word_dict2 = {}
for word, freq in word_list2:
word_dict2[word] = freq
return kldiv(word_dict1, word_dict2)
def PhotoDistanceByCaption(photo1, photo2):
p1 = Photo(photo1)
p2 = Photo(photo2)
cap1 = p1.getCaption()
cap2 = p2.getCaption()
cp1 = CaptionParser(True)
cp1.insertCaption(cap1)
cp2 = CaptionParser(True)
cp2.insertCaption(cap2)
word_list1 = cp1.getTopWords(-1)
word_list2 = cp2.getTopWords(-1)
if len(word_list1) == 0 or len(word_list2) == 0:
# unable to compare
return None
word_dict1 = {}
for word, freq in word_list1:
word_dict1[word] = freq
word_dict2 ={}
for word, freq in word_list2:
word_dict2[word] = freq
return kldiv(word_dict1, word_dict2)
