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add change log 2. change the converting unit from hanji, etc.

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Tan, Kian-ting 2024-06-28 21:55:08 +08:00
parent 9b7d8d4432
commit 9f2836067a
7 changed files with 1030 additions and 266 deletions
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520
corpus/教典例句.csv
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gitignore Normal file
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model.db
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18
pakkau.py
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@ -7,7 +7,7 @@ import os
import sqlite3
from itertools import chain
model_filename = "model.db"
model_filename = os.path.join(os.path.dirname(os.path.realpath(__file__)), "model.db")
def genmod():
corpus_path = "./corpus/"
@ -44,7 +44,11 @@ def genmod():
for i in new_data:
hanji = i[0]
lomaji = i[1]
'''111'''
hanji = list(zip(hanji, lomaji))
hanji = list(map(lambda x : x[0] + x[1], hanji))
for j in range(len(i[0])):
if not hanji[j] in char_to_pronounce:
char_to_pronounce[hanji[j]] = {lomaji[j] : 1}
@ -65,7 +69,7 @@ def genmod():
for i in new_data:
head_hanji = i[0][0]
head_hanji = i[0][0]+i[1][0]
if head_hanji in init_freq:
init_freq[head_hanji] += 1
@ -86,7 +90,8 @@ def genmod():
cur.execute("CREATE TABLE transition(prev_char, next_char, freq)")
for i in new_data:
hanji = i[0]
hanji_tmp = list(zip(i[0],i[1]))
hanji = list(map(lambda x: x[0]+ x[1], hanji_tmp))
for j in range(len(i[0])-1):
this_hanji = hanji[j]
next_hanji = hanji[j+1]
@ -111,7 +116,6 @@ def genmod():
def get_homophones(pron, cur, con):
homophones_raw = cur.execute("select hanji FROM pronounce where lomaji = ?", (pron, )).fetchall()
homophones = list(map(lambda x: x[0], homophones_raw))
return homophones
def convert(sentences):
@ -171,7 +175,7 @@ def convert_one_sentence(sentence):
for i in homophones_sequence[0]:
i_freq = cur.execute('''select initial.freq FROM initial
WHERE initial.char = ?''', (i['char'])).fetchall()[0][0]
WHERE initial.char = ?''', (i['char'],)).fetchall()[0][0]
i['prob'] = i_freq / head_freq_total
@ -268,8 +272,10 @@ on p.hanji = p2.hanji where p2.lomaji = ?''', (small_capized[i],)).fetchall()[0]
current = current_ls[0]["char"]
prev_char = current_ls[0]["prev_char"]
return_result = list(filter(lambda x : x != "", return_result))
return_result = list(map(lambda x : x[0] if re.match(u'[⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎𪜀-\U0002b73f]', x)
else x, return_result))
return return_result

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pakkau.py~ Normal file
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import re
import pandas as pd
import math
from functools import reduce
import argparse
import os
import sqlite3
from itertools import chain
model_filename = "model.db"
def genmod():
corpus_path = "./corpus/"
df_list = []
for file in os.listdir(corpus_path):
if file.endswith(".csv"):
df = pd.read_csv(corpus_path+file, header=0, names=['hanji', 'lomaji'])
df_list.append(df)
df = pd.concat(df_list)
df['lomaji'] = df['lomaji'].str.lower()
new_data = []
for index, row in df.iterrows():
hanji = list(filter(lambda x : re.match("[^、（）；：，。！？「」『』]", x), list(row['hanji'])))
tl = re.split(r'(:?[!?;,.\"\'\(\):]|[-]+|\s+)', row['lomaji'])
tl2 = list(filter(lambda x : re.match(r"([^\(\)^!:?; \'\",.\-\u3000])", x), tl))
new_data.append((hanji, tl2))
if (len(hanji) != len(tl2)):
raise ValueError(f"length of hanji {hanji} is different from romaji {tl2}.")
#model_filename = "model.db"
try:
os.remove(model_filename)
except OSError:
pass
con = sqlite3.connect(model_filename)
cur = con.cursor()
cur.execute("CREATE TABLE pronounce(hanji, lomaji, freq)")
char_to_pronounce = {}
for i in new_data:
hanji = i[0]
lomaji = i[1]
for j in range(len(i[0])):
if not hanji[j] in char_to_pronounce:
char_to_pronounce[hanji[j]] = {lomaji[j] : 1}
elif not lomaji[j] in char_to_pronounce[hanji[j]]:
char_to_pronounce[hanji[j]][lomaji[j]] = 1
else:
char_to_pronounce[hanji[j]][lomaji[j]] += 1
for i in char_to_pronounce.keys():
hanji = char_to_pronounce[i]
for j in hanji.keys():
cur.execute("INSERT INTO pronounce VALUES(?, ?, ?)", (i,j, hanji[j]))
all_chars = char_to_pronounce.keys()
init_freq = {} #詞kap句開始ê字出現次數
cur.execute("CREATE TABLE initial(char, freq)")
for i in new_data:
head_hanji = i[0][0]
if head_hanji in init_freq:
init_freq[head_hanji] += 1
else:
init_freq[head_hanji] = 1
#補字
min_weight = 0.1
for i in all_chars:
if not i in init_freq.keys():
init_freq[i] = 0.1
for i in init_freq.keys():
cur.execute("INSERT INTO initial VALUES(?, ?)", (i, init_freq[i]))
char_transition = {}
cur.execute("CREATE TABLE transition(prev_char, next_char, freq)")
for i in new_data:
hanji = i[0]
for j in range(len(i[0])-1):
this_hanji = hanji[j]
next_hanji = hanji[j+1]
if not this_hanji in char_transition:
char_transition[this_hanji] = {next_hanji : 1}
elif not next_hanji in char_transition[this_hanji]:
char_transition[this_hanji][next_hanji] = 1
else:
char_transition[this_hanji][next_hanji] += 1
for i in char_transition.keys():
next_char = char_transition[i]
for j in next_char.keys():
cur.execute("INSERT INTO transition VALUES(?, ?, ?)", (i, j, next_char[j]))
#get_homophones("lí", cur, con)
con.commit()
con.close()
def get_homophones(pron, cur, con):
homophones_raw = cur.execute("select hanji FROM pronounce where lomaji = ?", (pron, )).fetchall()
homophones = list(map(lambda x: x[0], homophones_raw))
return homophones
def convert(sentences):
splitted = re.split(r'(:?[!?;,.\"\'\(\):])', sentences)
splitted_cleaned = list(filter(lambda x : x != '', splitted))
result = list(map(lambda s : convert_one_sentence(s), splitted_cleaned))
flatten_result = [x for xs in result for xss in xs for x in xss]
result_string = "".join(flatten_result)
print(result_string)
return result_string
def convert_one_sentence(sentence):
full_width = ["！", "？", "；","：","，","。", "（", "）"]
half_width = ["!", "?", ";", ":", ",", ".", "(", ")"]
if len(sentence) == 1:
for i in range(len(half_width)):
if sentence[0] == half_width[i]:
return [[full_width[i]]]
weight = 2/3
splitted = re.split(r'(--?|\s+)', sentence)
filtered = list(filter(lambda x :not re.match(r'(--?|\s+)', x), splitted))
small_capized = list(map(lambda x : x.lower(), filtered))
print("======", small_capized)
con = sqlite3.connect(model_filename)
cur = con.cursor()
homophones_sequence_raw = list(map(lambda x : get_homophones(x, con, cur), small_capized))
homophones_sequence = [list(map (lambda x : {"char": x,
"prev_char": None,
"prob" : 1}, i)) for i in homophones_sequence_raw]
head_freqs = list(map(lambda x : x[0], cur.execute('''select initial.freq FROM initial
INNER JOIN pronounce ON pronounce.hanji = initial.char
WHERE pronounce.lomaji = ?''', (small_capized[0], )).fetchall()))
return_result = [None] * len(small_capized)
if head_freqs == []:
return_result[0] = filtered[0]
homophones_sequence[0] = [{"char": filtered[0],
"prev_char": None,
"prob" : 1}]
else:
head_freq_total = reduce(lambda x , y : x + y, head_freqs)
for i in homophones_sequence[0]:
i_freq = cur.execute('''select initial.freq FROM initial
WHERE initial.char = ?''', (i['char'])).fetchall()[0][0]
i['prob'] = i_freq / head_freq_total
print(i)
#for i in homophones_sequence[0]:
print("+++++", return_result)
if len(small_capized) == 1:
max_prob = -math.inf
max_prob_char = None
for i in homophones_sequence[0]:
if i['prob'] > max_prob:
max_prob_char = i['char']
max_prob = i['prob']
return_result[0] = max_prob_char
else:
for i in range(1,len(small_capized)):
char_freqs = list(map(lambda x : x[0], cur.execute('''select initial.freq FROM initial
INNER JOIN pronounce ON pronounce.hanji = initial.char
WHERE pronounce.lomaji = ?''', (small_capized[i], )).fetchall()))
if char_freqs == []:
return_result[i] = filtered[i]
homophones_sequence[i] = [{"char": filtered[i],
"prev_char": None,
"prob" : 1}]
prev_char = ""
max_prob = -math.inf
for m in homophones_sequence[i-1]:
if m['prob'] > max_prob:
max_prob = m['prob']
prev_char = m['char']
homophones_sequence[i][0]['prob'] = max_prob
homophones_sequence[i][0]['prev_char'] = prev_char
else:
total_transition_freq = cur.execute('''
SELECT sum(t.freq)
FROM transition as t
INNER JOIN pronounce as p1 ON p1.hanji = t.prev_char
INNER JOIN pronounce as p2 ON p2.hanji = t.next_char
where p2.lomaji = ? and p1.lomaji = ?''',
(small_capized[i], small_capized[i-1])).fetchall()[0][0]
for j in homophones_sequence[i]:
prev_char = None
max_prob = -math.inf
for k in homophones_sequence[i-1]:
k_to_j_freq_raw = cur.execute('''select freq from transition
where prev_char = ? and next_char = ? ''', (k["char"], j["char"])).fetchall()
if k_to_j_freq_raw == []:
den = cur.execute('''
SELECT sum(p.freq)
FROM pronounce as p
inner join pronounce as p2
on p.hanji = p2.hanji where p2.lomaji = ?''', (small_capized[i],)).fetchall()[0][0]#分母
#分子
num = cur.execute(''' SELECT sum(freq) FROM pronounce as p where hanji = ?''', (j["char"],)).fetchall()[0][0]
print("+++", num, den)
k_to_j_freq = num/den * (1-weight)
else:
num = k_to_j_freq_raw[0][0]
don = total_transition_freq
k_to_j_freq =num/don * weight
print("k_to_j_fr", k["char"], j["char"], k_to_j_freq)
if k_to_j_freq * k["prob"] > max_prob:
max_prob = k_to_j_freq * k["prob"]
prev_char = k["char"]
print("~-~_~-~-~-~-", prev_char, j["char"], max_prob)
j["prob"] = max_prob
j["prev_char"] = prev_char
max_prob = -math.inf
current = ""
prev_char = ""
for i in homophones_sequence[len(homophones_sequence)-1]:
if i["prob"] > max_prob:
max_prob = i["prob"]
current = i["char"]
prev_char = i["prev_char"]
print("~tail~~", current)
print(homophones_sequence)
return_result[len(homophones_sequence)-1] = current
for i in range(len(homophones_sequence)-2, -1, -1):
current_ls = list(filter(lambda x : x["char"] == prev_char,
homophones_sequence[i]))
print(prev_char)
return_result[i] = prev_char
current = current_ls[0]["char"]
prev_char = current_ls[0]["prev_char"]
print(return_result)
return return_result
def poj_to_tl(sentence):
return sentence
parser = argparse.ArgumentParser()
parser.add_argument('--genmod', help='generate the model', action='store_true',
required=False,)
parser.add_argument('sentence', metavar='SENTENCE', nargs='?',
help='the sentence to be converted')
parser.add_argument('--form', metavar='FORM', choices=["poj", "tl"], nargs=1,
default=['poj'],
help='the orthography to be used (poj or tl). Default is poj.')
args = parser.parse_args()
if args.genmod == True:
genmod()
elif args.sentence != None:
if args.form == ['poj']:
sentence = poj_to_tl(args.sentence)
convert(sentence)
else:
convert(args.sentence)
else:
parser.print_help()

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test2.py~ Normal file
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import re
import pandas as pd
import math
from functools import reduce
df1 = pd.read_csv('教典例句.csv', header=0, names=['漢字', '羅馬字'])
df2 = pd.read_csv('教典發音詞.csv',header=0, names=['漢字', '羅馬字'])
df = pd.concat([df1, df2]) # combine 2 csv dataframe
df['羅馬字'] = df['羅馬字'].str.lower()
new_data = []
for index, row in df.iterrows():
hanji = list(filter(lambda x : re.match("[^、（）；：，。！？「」『』]", x), list(row['漢字'])))
tl = re.split(r'(:?[!?;,.\"\'\(\):]|[-]+|\s+)', row['羅馬字'])
tl2 = list(filter(lambda x : re.match(r"([^\(\)^!:?; \'\",.\-\u3000])", x), tl))
new_data.append((hanji, tl2))
#if (len(hanji) != len(tl2)):
#print(tl2, hanji)
#print(tl2, hanji)
# char-To-Pronounciation Prossibility dict
char_to_pronounce = {}
for i in new_data:
hanji = i[0]
lomaji = i[1]
for j in range(len(i[0])):
if not hanji[j] in char_to_pronounce:
char_to_pronounce[hanji[j]] = {lomaji[j] : 1}
elif not lomaji[j] in char_to_pronounce[hanji[j]]:
char_to_pronounce[hanji[j]][lomaji[j]] = 1
else:
char_to_pronounce[hanji[j]][lomaji[j]] += 1
for char, char_reading in char_to_pronounce.items():
total_count = reduce((lambda x, y : x + y), list(char_reading.values()))
for i in char_reading.keys():
char_reading[i] = char_reading[i] / float(total_count)
#print(char_to_pronounce)
all_chars = char_to_pronounce.keys()
'''{'提': 45, '宋': 7, '完': 18, '刻': 7, '局': 9,
'巡': 8, '畫': 25, '青': 56, '尪': 13}'''
init_freq = {} #詞kap句開始ê字出現次數
for i in new_data:
head_hanji = i[0][0]
if head_hanji in init_freq:
init_freq[head_hanji] += 1
else:
init_freq[head_hanji] = 1
#補字
min_weight = 0.1
for i in all_chars:
if not i in init_freq.keys():
init_freq[i] = 0.1
#print(init_freq)
# probability of P(next=c2|this=c1)
char_transition = {}
for i in new_data:
hanji = i[0]
for j in range(len(i[0])-1):
this_hanji = hanji[j]
next_hanji = hanji[j+1]
if not this_hanji in char_transition:
char_transition[this_hanji] = {next_hanji : 1}
elif not next_hanji in char_transition[this_hanji]:
char_transition[this_hanji][next_hanji] = 1
else:
char_transition[this_hanji][next_hanji] += 1
#print(char_transition)
#補字
for i in all_chars:
if not i in char_transition.keys():
char_transition[i] = {}
for j in all_chars:
char_transition[i][j] = init_freq[j]
else:
pass
for i in char_transition.keys():
for j in all_chars:
if not j in char_transition[i].keys():
char_transition[i][j] = min_weight * (0.03+math.log(init_freq[j]))
for char, next_char in char_transition.items():
total_count = 0
[total_count := total_count + x for x in list(next_char.values())]
for i in next_char.keys():
next_char[i] = next_char[i] / float(total_count)
def get_homophones(pron):
homophones = []
for i in char_to_pronounce.keys():
if pron in char_to_pronounce[i].keys():
homophones.append(i)
else:
pass
return homophones
input_lomaji = ["guá", "kap", "tshit", "á", "lâi", "khì", "tâi", "tiong", "tshit", "thô", "sūn", "suà", "tsē", "ko", "thih"]
char_candidates = []
for i in input_lomaji:
homophones = list(map(lambda x : {"char": x,
"prev_char": None,
"prob" : None}, # probibility
get_homophones(i)))
char_candidates.append(homophones)
#print(char_candidates)
def get_max_prob(input_lmj, char_cand):
for i in range(len(input_lmj)):
if i == 0:
for j in char_cand[i]:
init_freq_sum = reduce(lambda x, y : x + y,
list(
map(lambda x : init_freq[x["char"]] ,
char_cand[0])))
print(init_freq_sum)
ch = j["char"]
init_to_char_prob = init_freq[ch] / init_freq_sum # get the ratio
char_reading_prob = char_to_pronounce[ch][input_lmj[0]]
j["prob"] = init_to_char_prob * char_reading_prob
result = ""
max_num = -math.inf
for i in char_cand[0]:
if i["prob"] >= max_num:
max_num = i["prob"]
result = i["char"]
#print(result)
else:
for j in char_cand[i]:
prob = -math.inf
prev_char = ""
for k in char_cand[i-1]:
k_prob = k["prob"]
#print(k["char"], "k_prob:", k_prob)
k_to_j_prob = char_transition[k["char"]][j["char"]]
#print(k["char"], "->",j["char"] ,"k_to_j_prob:", k_to_j_prob)
j_to_pron_prob = char_to_pronounce[j["char"]][input_lmj[i]]
total_tmp_prob = k_prob * k_to_j_prob * j_to_pron_prob
if prob < total_tmp_prob:
prob = total_tmp_prob
prev_char = k
j["prev_char"] = prev_char["char"]
j["prob"] = prob
real_last_char = ""
prev_char = ""
prob = -math.inf
for i in char_cand[-1]:
if i["prob"] > prob:
prob = i["prob"]
real_last_char = i["char"]
prev_char = i["prev_char"]
print(real_last_char)
result_hanji = [real_last_char]
for i in range(len(input_lmj)-2, -1, -1):
current = list(filter(lambda x : x["char"] == prev_char,
char_cand[i]))[0]
result_hanji.append(current["char"])
prev_char = current["prev_char"]
result_hanji.reverse()
result_hanji_string = "".join(result_hanji)
print("輸入ê羅馬字陣列(array)：", input_lomaji)
print("輸出ê漢字：", result_hanji_string)
get_max_prob(input_lomaji, char_candidates)

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test3.py~ Normal file
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import re
import pandas as pd
import math
from functools import reduce
import argparse
import os
import sqlite3
def genmod():
corpus_path = "./corpus/"
df_list = []
for file in os.listdir(corpus_path):
if file.endswith(".csv"):
df = pd.read_csv(corpus_path+file, header=0, names=['hanji', 'lomaji'])
df_list.append(df)
df = pd.concat(df_list)
df['lomaji'] = df['lomaji'].str.lower()
new_data = []
for index, row in df.iterrows():
hanji = list(filter(lambda x : re.match("[^、（）；：，。！？「」『』]", x), list(row['hanji'])))
tl = re.split(r'(:?[!?;,.\"\'\(\):]|[-]+|\s+)', row['lomaji'])
tl2 = list(filter(lambda x : re.match(r"([^\(\)^!:?; \'\",.\-\u3000])", x), tl))
new_data.append((hanji, tl2))
if (len(hanji) != len(tl2)):
raise ValueError(f"length of hanji {hanji} is different from romaji {tl2}.")
model_filename = "model.db"
try:
os.remove(model_filename)
except OSError:
pass
con = sqlite3.connect(model_filename)
cur = con.cursor()
cur.execute("CREATE TABLE pronounce(hanji, lomaji, freq)")
char_to_pronounce = {}
for i in new_data:
hanji = i[0]
lomaji = i[1]
for j in range(len(i[0])):
if not hanji[j] in char_to_pronounce:
char_to_pronounce[hanji[j]] = {lomaji[j] : 1}
elif not lomaji[j] in char_to_pronounce[hanji[j]]:
char_to_pronounce[hanji[j]][lomaji[j]] = 1
else:
char_to_pronounce[hanji[j]][lomaji[j]] += 1
print(char_to_pronounce)
for i in char_to_pronounce.keys():
hanji = char_to_pronounce[i]
for j in hanji.keys():
cur.execute("INSERT INTO pronounce VALUES(?, ?, ?)", (i,j, hanji[j]))
#con.commit()
con.commit()
con.close()
def convert(sentence):
pass
parser = argparse.ArgumentParser()
parser.add_argument('--genmod', help='generate the model', action='store_true',
required=False,)
parser.add_argument('sentence', metavar='SENTENCE', nargs='?',
help='the sentence to be converted')
parser.add_argument('--form', metavar='FORM', choices=["poj", "tl"], nargs=1,
default=['poj'],
help='the orthography to be used (poj or tl). Default is poj.')
args = parser.parse_args()
print(args)
if args.genmod == True:
genmod()
elif args.sentence != None:
if args.form == ['poj']:
sentence = poj_to_tl(args.sentence)
print(convert(sentence))
else:
print(convert(args.sentence))
else:
parser.print_help()