def repl(options=None, show_prompt=False):
while True:
if show_prompt:
sys.stdout.write("> ")
sys.stdout.flush()
line = sys.stdin.readline()
if not line: break
text = line.rstrip()
if options and not options.strict_macron_mode:
text = char.trans(text)
# textutil.analyse_text(text, analyse_sentence)
for sentence in textutil.sentence_stream(textutil.word_stream_from_text(text)):
analyse_sentence(sentence, options=options)
if show_prompt:
print
def main():
options = Options(sys.argv[1:])
if options.speech_mode:
speak_latin.init_synth('Alex')
latindic.load(auto_macron_mode=options.auto_macron_mode)
if len(options.args) == 0:
# repl mode
if select.select([sys.stdin,],[],[],0.0)[0]:
# have data from pipe. no prompt.
repl(options=options)
else:
repl(options=options, show_prompt=True)
else:
# file mode
for file in options.args:
text = textutil.load_text_from_file(file)
if options.strict_macron_mode:
text = char.trans(text)
# textutil.analyse_text(text, analyse_sentence, options=options)
for sentence in textutil.sentence_stream(textutil.word_stream_from_text(text)):
analyse_sentence(sentence, options=options)
def analyse_text(text, options=None):
# テキストを（句点などで）センテンスに切り分ける
for word_surfaces_in_a_sentence in textutil.sentence_stream(textutil.word_stream_from_text(text)):
plain_text = ' '.join(word_surfaces_in_a_sentence)
if options.echo_on:
# print plain_text + "\n"
print "\n" + ansi_color.underline(ansi_color.bold( plain_text )) + "\n"
if options.speech_mode:
speak_latin.say_latin(plain_text.decode('utf-8'))
# unicodeに変換して
word_surfaces_uc = [word_surface.decode('utf-8', 'strict') for word_surface in word_surfaces_in_a_sentence]
# 辞書を引いてから
words = lookup_all(word_surfaces_uc)
# 先にlookup結果を表示してしまう
if options and options.show_word_detail:
print " --- "
maxlen_uc = max([0] + [word.surface_len for word in words])
for i, word in enumerate(words):
text = word.surface.encode('utf-8')
print ' %2d ' % (i) + text + ' '*(maxlen_uc - word.surface_len + 1), word.detail()
print " --- "
print
# 形容詞/属格の対応
words, visited_ix = detect_and_or(words)
words, adj_ix = detect_adj_correspondances(words)
words, gen_ix = detect_genitive_correspondances(words)
for ix in visited_ix:
words[ix] = None
for ix in adj_ix:
words[ix] = None
for ix in gen_ix:
words[ix] = None
words = filter(identity, words)
words, verb_ix = detect_verbs(words)
words = detect_prep_domination(words)
words = filter(identity, words)
# print [word.surface_utf8() for word in words]
print
# 名詞句を述語動詞に結びつける
verbs_ix = []
verb_count = 0
for i, word in enumerate(words):
if isinstance(word, Predicate):
verbs_ix.append(i)
verb_count += 1
verb_surfaces = ', '.join([ansi_color.bold(words[ix].surface_utf8()) for ix in verbs_ix])
M = len(words)
groups = []
if verb_count == 0:
print ansi_color.underline("NO VERB FOUND.")
groups.append( range(M) )
elif verb_count == 1:
print ansi_color.underline("1 VERB FOUND:") + ' ' + verb_surfaces
groups.append( range(M) )
else:
print ansi_color.underline("%d VERBS FOUND:" % verb_count) + ' ' + verb_surfaces
groups.append( range(verbs_ix[0]+1) ) # [0..ix0]
for i in range(1, verb_count-1):
groups.append( [verbs_ix[i]] )
groups.append( range(verbs_ix[verb_count-1], M) )
for i in range(verb_count-1):
fr = groups[i][-1] + 1
to = groups[i+1][0] - 1
if fr == to: continue
well_divided_at = None
for j in range(fr, to+1):
if words[j].surface == u',':
well_divided_at = j
break
if well_divided_at is None:
for j in range(fr, to+1):
if words[j].surface == u'quod':
well_divided_at = j-1
break
if well_divided_at is None:
for j in range(fr, to+1):
if words[j].surface == u'et':
well_divided_at = j-1
break
if well_divided_at is not None:
groups[i] += range(fr, well_divided_at+1)
groups[i+1] = range(well_divided_at+1, to+1) + groups[i+1]
else:
print " NOT WELL: {%d..%d}" % (fr, to)
# うまく分けられない。とりあえず後の方に入れる
groups[i+1] = range(fr, to+1) + groups[i+1]
print
for i, group in enumerate(groups):
if verb_count == 0:
ws = []
for word in words:
if isinstance(word, Word) and not word.items: continue
# ws.append(word)
dump(word)
print " → ", translate(word)
print # " --"
# dump(ws)
# print translate(ws)
else:
not_solved = []
# words_in_group = [words[ix] for ix in group]
verb_ix = verbs_ix[i]
pred = words[verb_ix] # predicate
for j, ix in enumerate(group):
if ix == verb_ix: continue
word = words[ix]
if isinstance(word, AndOr):
pred.add_nominal(word.cases[0], word)
elif isinstance(word, PrepClause):
pred.add_nominal(word.prep, word)
elif isinstance(word, Word):
if not word.items: continue
first_item = word.items[0]
if j == 0 and word.surface in (u'quod', u'ut'):
if word.items[1].pos == 'conj':
first_item = word.items[1]
word.items = word.items[1:]
if first_item.pos == 'conj':
if j < 2 and not pred.conjunction:
pred.conjunction = word
else:
not_solved.append(word)
elif first_item.pos == 'adv':
if j < 2 and not pred.conjunction:
pred.conjunction = word
elif word.surface in (u'ō', u'Ō'):
# 二重になってないかチェックする or conjunction を複数取る
pred.conjunction = word
else:
pred.add_modifier(word)
elif first_item._:
cases = [x[0] for x in first_item._]
case = None
if 'Voc' in cases and ix > 0 and words[ix-1].surface in (u'ō', u'Ō'):
case = 'Voc'
# 形的にVocしかありえないケースも拾いたい
else:
for x in first_item._:
if x[0] == 'Nom':
if x[2] == 'n':
case = 'Nom/Acc'
else:
case = x[0]
break
elif x[0] == 'Acc':
case = x[0]
break
else:
if not case:
case = x[0]
# if not case: case = case_n
pred.add_nominal(case, word)
else:
# print "not solved += ", word.surface_utf8()
not_solved.append(word) #(ix, word.surface_utf8()))
if not_solved:
print " NOT SOLVED:"
# dump(not_solved, initial_indent=2)
# print translate(not_solved)
for item in not_solved:
dump(item, 4)
print " → ", translate(item)
print
dump(pred)
print
print " → ", translate(pred)
print
# 音読モードの場合、読み終わるまでウェイトを入れる
if options.speech_mode:
speak_latin.pause_while_speaking()
def main():
latindic.load(auto_macron_mode=False)
show_title('original text')
text = textutil.load_text_from_file('./latin.txt')
print text[:1000], '...'
print
show_title('texts in base-form')
texts_in_baseform = []
for word_surfaces_in_a_sentence in textutil.sentence_stream(textutil.word_stream_from_text(text)):
# print word_surfaces_in_a_sentence
bases = base_forms_of_words(word_surfaces_in_a_sentence)
texts_in_baseform.append(bases)
for sentence in texts_in_baseform[:20]:
print ' '.join([baseform.encode('utf-8') for baseform in sentence])
print '...'
print
show_title('[gensim] dictionary')
dictionary = corpora.Dictionary(texts_in_baseform)
# dictionary.save('/tmp/latintext.dict') # store the dictionary, for future reference
# print dictionary
print '{',
for token, id in dictionary.token2id.items():
print '\"%s\": %d,' % (token.encode('utf-8'), id),
print '}'
# new_doc = "In Crētā īnsulā māgnum labyrinthum Daedalus aedificāvit plēnum viārum flexuōsārum."
# new_bases = base_forms_of_words(new_doc.split())
# # print new_bases
# new_vec = dictionary.doc2bow(new_bases)
# print new_vec
show_title('[gensim] corpus')
corpus = [dictionary.doc2bow(text) for text in texts_in_baseform]
# corpora.MmCorpus.serialize('/tmp/latintext.mm', corpus)
# print corpus
for doc in corpus[:20]:
print doc
print '...'
print
show_title('tf-idf') # term frequency * inverse document frequency
tfidf = models.TfidfModel(corpus) # step 1 -- initialize a model
corpus_tfidf = tfidf[corpus]
for i, doc in enumerate(corpus_tfidf):
print doc
if i == 20: break
print '...'
print
#
def decode_result(item, delim):
def translate(token):
# print "translating \"%s\"..." % token.encode('utf-8')
items = latindic.lookup(token)
return items[0]['ja'] if items else '*'
latin_tokens = re.split(delim, item)[1::2]
jas = [translate(token) for token in latin_tokens]
return ' / '.join(jas) # print "\t", items[0]['ja']
NUM_TOPICS = 80
TOPICS_TO_TAKE = 10
show_title('LSI (Latent Semantic Indexing)')
# initialize an LSI transformation
lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=NUM_TOPICS)
# create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi
corpus_lsi = lsi[corpus_tfidf]
topics = lsi.print_topics(TOPICS_TO_TAKE)
for i, item in enumerate(topics):
print "%d) %s" % (1+i, item.encode('utf-8'))
print " ", decode_result(item, '"')
print
print
show_title('LDA (Latent Dirichlet Allocation)')
model = models.ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=NUM_TOPICS)
topics = model.show_topics(topics=TOPICS_TO_TAKE)
for i, item in enumerate(topics):
print "%d) %s" % (1+i, item.encode('utf-8'))
print " ", decode_result(item, ' ?[*+]')
print
print
