def CreateLus(self):
Ns = self.prep.numSpeakers
Lus = np.zeros((len(self.segm.cleanSentences[self.íter]), Ns))
for i in range(0, len(self.segm.cleanSentences[self.íter])):
v1 = Help.CreateSentenceVector(
self.segm.cleanSentences[self.íter][i], self.freqVec,
self.prep.singleWords)
for j in range(0, Ns):
if j + 1 in self.segm.cleanSpeakers[self.íter]:
v2 = Help.CreateSpeakerVector(
j, self.segm.cleanSentences[self.íter],
self.segm.cleanSpeakers[self.íter], self.speakVec)
if Help.NotValidCos(v1, v2):
v1, v2 = Help.ReshapeVec(v1, v2)
cos_dist = 1 - sp.distance.cosine(v1, v2)
if math.isnan(cos_dist):
Lus[i][j] = 0.
else:
Lus[i][j] = cos_dist
return Lus
def WI(self, w, s):
WI_vec = np.zeros(self.Ns)
if w:
suidf_win = Help.CreateSentenceVector(w, self.freq, self.prep.singleWords)
den = 0 #doesn't have to be reset
num = [] #append num per each speaker
for j in range(self.Ns):
num_t = 0 #numerator for given speaker
for k in range(len(s)):
if s[k] == j+1:
num_t += suidf_win[k]
den += suidf_win[k]
num.append(num_t)
for j in range(0, self.Ns):
WI_vec[j] = Help.SafeDiv(num[j] , den)
return WI_vec
def CreateLuu(
self,
top=False,
lex=True
): # top=True means the function computes topical similarity, else lex=True computes lexical similarity
Luu = np.zeros((len(self.segm.cleanSentences[self.íter]),
len(self.segm.cleanSentences[self.íter])
)) # matrix [num_utterances X num_utterances]
if (top and lex) or (
(not top) and (not lex)
): # if error in passing parameters (Luu can be based only on one kind of similarity)
top = False # reset default parameters
lex = True # reset default parameters
if top: #topic similarity
prob_top_sent = np.zeros(
(len(self.topicModel['Terms']),
len(self.segm.cleanSentences[self.íter])))
for x in range(len(self.topicModel['Terms'])):
for y in range(len(self.segm.cleanSentences[self.íter])):
num = 0
den = 0
for w in self.segm.cleanSentences[self.íter][y]:
# idx_w = find_index_word(w, corpus, tokens_topic_model) #ret -1 if w not in corpus
try:
tk_id = self.topicModel['Dictionary'].token2id[w]
num += (Help.FreqWordInSentence(
w, self.segm.cleanSentences[self.íter][y]) *
self.topicModel['Terms'][x][tk_id])
except:
num += (Help.FreqWordInSentence(
w, self.segm.cleanSentences[self.íter][y]) *
self.small)
den += Help.FreqWordInSentence(
w, self.segm.cleanSentences[self.íter][y])
prob_top_sent[x][y] = Help.SafeDiv(num, den)
for x in range(len(self.segm.cleanSentences[self.íter])):
for y in range(len(self.segm.cleanSentences[self.íter])):
LTS_sum = 0
prob = 0
for w in self.segm.cleanSentences[self.íter][y]:
wFreq = self.ComputeTermFrequency(
w
) #creates a vector with the frequency of the word per each doc
if np.sum(
wFreq
): #if w doesn't appear in the dictionary, don't waste time
LTS_sum += self.CopmputeLTS(
wFreq
) #return sum over all topics (LTS of a single word with frequency term_freq)
prob = Help.SumTopics(prob_top_sent,
x) #should I pass x or y?
Luu[x][y] = LTS_sum * prob
else: #lexical similarity
for i in range(len(self.segm.cleanSentences[self.íter])):
v1 = Help.CreateSentenceVector(
self.segm.cleanSentences[self.íter][i], self.freqVec,
self.prep.singleWords)
for j in range(len(self.segm.cleanSentences[self.íter])):
v2 = Help.CreateSentenceVector(
self.segm.cleanSentences[self.íter][j], self.freqVec,
self.prep.singleWords)
if Help.NotValidCos(v1, v2):
v1, v2 = Help.ReshapeVec(v1, v2)
#if complains about v1, v2 dimensions, add zeros (or ones, idk yet, is a cosine distance) to match those dimensions
cos_sim = 1 - sp.distance.cosine(v1, v2)
if math.isnan(cos_sim):
Luu[i][j] = 0.
else:
Luu[i][j] = cos_sim
# cosine similarity only if vectors of same size
# return norm(Luu, norm='l1') #matrix representing lexical (topic) similarity via word overlap (via LDA)
return Luu