def Score(self, lenText):
score = np.zeros(lenText)
smooth_score = np.zeros(lenText)
for i in range(1, lenText-1): # iterate over all wiindows (as many as num of sentences),
# each can be potential segment boundary but first and last sentences
maxim_i = np.maximum(0, i-self.winLength)
maxim_f = np.maximum(1, i)
minim = np.maximum(np.minimum(lenText, i + self.winLength), maxim_f)
win_left_idx = []
win_right_idx = []
for x in range(maxim_i, maxim_f): # min and max to avoid index problems
win_left_idx.append(x)
for x in range(i, minim): # min and max to avoid index problems
win_right_idx.append(x)
if i > 1:
win_l = [self.prep.speakers[win_left_idx[0] : win_left_idx[-1]], self.prep.sentLemma[win_left_idx[0] : win_left_idx[-1]]] #create win left_i
else: #when i = 1 --> win_l is only the first el
win_l = [[self.prep.speakers[0]], [self.prep.sentLemma[0]]]
if i < lenText - 2:
win_r = [self.prep.speakers[win_right_idx[0] : win_right_idx[-1] +1], self.prep.sentLemma[win_right_idx[0] : win_right_idx[-1] +1]] #create win left_i
else:
win_r = [[self.prep.speakers[-1]], [self.prep.sentLemma[-1]]]
WC_l = self.WC(win_l)
WC_r = self.WC(win_r)
dist_wc = Help.Dist(WC_l, WC_r)
WI_l = self.WI(win_l[1], win_l[0])
WI_r = self.WI(win_r[1], win_r[0])
dist_wi = Help.Dist(WI_l, WI_r)
score[i] = dist_wc + dist_wi
for i in range(1, lenText-1):
temp_score = 0 #loc score for smoothing
bound = self.SafeSmooth(i, len(score)) #check not to go out of size when smoothing
low = bound[0]
up = bound[1]
for j in range(low, up):
temp_score += score[j]
smooth_score[i] = temp_score / (1 + self.smoothParam)
return score, smooth_score
def ScoreSegment(self, segm):
sum_c = []
c_idx = []
cat = Help.GenCat(self.Ns) #generate categories vector
cat = cat[1:] #[0,0,0,0,0] not allowed, there's always at least one speaker
#sum_c score per each cat, find min
#c_idx idx of each cat, to find the cat corresp to min score
for c in cat:
sum_dist = Help.Dist(Help.Dstr(segm, self.Ns), Help.DstrId(self.Ns,c)) #score segments
sum_c.append(sum_dist)
c_idx.append(c)
#c single category boolean vector used for calling dstr_id
min_score = np.min(sum_c)
min_cat = c_idx[np.argmin(sum_c)] # in the list of categories, take el of idx that minimizes the score
return min_score, min_cat