word = General.assignWrodClass(word)
else:
tags_curr = list(e[word].keys())
V[i + 1] = {}
bp[i + 1] = {}
for t in tags_p:
V[i + 1][t] = {}
bp[i + 1][t] = {}
for r in tags_curr:
l = {}
for tT in tags_p2:
l[tT] = (V[i][tT][t]) * getScore(word, r, tT, t)
V[i + 1][t][r] = max(list(l.values()))
bp[i + 1][t][r] = General.argmax(l)
tags_p2 = tags_p
tags_p = tags_curr
V.pop(0)
bp.pop(0)
endMatrix = map(lambda x: x.values(), V[n].values())
maxEnd = list(map(max, endMatrix))
maxV = max(maxEnd)
maxTIndex = maxEnd.index(maxV)
maxT = list(V[n].keys())[maxTIndex]
maxR = General.argmax(V[n][maxT])
y = [0 for i in range(0, n + 1)]
y[n] = maxR
y[n - 1] = maxT
a = row.split("\t")
b = a[0].split(" ")
if b[0] not in e:
e[b[0]] = {}
e[b[0]][b[1]] = float(a[1])
total_count = 0
total_good = 0
taged_to_file = []
for row in input_f:
tag_seq = ["SS","SS"]
output = []
for i,word in enumerate(row):
predict_tag = General.argmax(getScore(word[0], tag_seq[-1], tag_seq[-2]))
tag_seq.append(predict_tag)
output.append(word[0] + "/" + predict_tag)
if toCompare:
total_count += 1
if predict_tag == word[1]:
total_good += 1
if toCompare:
print("Rate is: " + str(total_good) + "/" + str(total_count) + " = " + str(float(total_good) / total_count))
taged_to_file.append(" ".join(output))
out_file = open(greedy_hmm_output, 'w')
out_file.write('\n'.join(taged_to_file))
out_file.close()
bp[i + 1] = {}
for t in tags_p:
w2_t = t
V[i + 1][t] = {}
bp[i + 1][t] = {}
l = {}
for r in tags_curr:
l[r] = {}
for tT in tags_p2:
w1_t = tT
score = (V[i][tT][t]) + getScore(w1,w2,word,w4,w5,w1_t,w2_t)
for r in tags_curr:
l[r][tT] = score[features_m[r]]
for r in tags_curr:
V[i + 1][t][r] = max(list(l[r].values()))
bp[i + 1][t][r] = General.argmax(l[r])
tags_p2 = tags_p
tags_p = tags_curr
V.pop(0)
bp.pop(0)
endMatrix = map(lambda x: x.values(), V[row_l-1].values())
maxEnd = list(map(max, endMatrix))
maxV = max(maxEnd)
maxTIndex = maxEnd.index(maxV)
maxT = list(V[row_l-1].keys())[maxTIndex]
maxR = General.argmax(V[row_l-1][maxT])
y = [0 for i in range(0, row_l)]
y[row_l-1] = maxR
y[row_l-2] = maxT
for i in reversed(range(0, row_l-2)):
y[i] = bp[i + 2][y[i + 1]][y[i + 2]]
