def compute_p_word_given_class(data_paths, vocab_size):
"""
Return a dictionary of word probabilities, P(word | class). All datapaths belong to the same class.
Incorporate Laplacian Smoothing with k=1 here. p_word_given_class should include the probability of UNKNOWN_WORD,
any word that doesn't appear in the training set
"""
p_word_given_class = dict()
# compute number of words in the given class
class_size = 0
for path in data_paths:
message = open_file(path)
words = get_words(message)
class_size += len(words)
# add elements to dictionary
for path in data_paths:
message = open_file(path)
words = get_words(message)
for word in words:
if word in p_word_given_class:
p_word_given_class[word] += 1 / (class_size + vocab_size + 1)
else:
p_word_given_class[word] = 2 / (class_size + vocab_size + 1)
p_word_given_class['UNKNOWN_WORD'] = 1 / (class_size + vocab_size + 1)
return p_word_given_class
def __get_equals_words_atr(self, summary_sentence, document_sentence):
''' Return the proportions of equals words '''
words_summary = utils.get_words(summary_sentence, stop_words=True)
words_document = utils.get_words(document_sentence, stop_words=True)
total_words = len(words_summary)
equals_words = 0
tmp_dict = dict()
for word_summary in words_summary:
for word_document in words_document:
if word_summary == word_document and word_summary not in tmp_dict:
tmp_dict[word_summary] = 1
equals_words += 1
return equals_words / total_words
def analyze_text(self, text):
words = get_words(text)
char_count = get_char_count(words)
word_count = len(words)
sentence_count = len(get_sentences(text))
syllable_count = count_syllables(words)
complexwords_count = count_complex_words(text)
avg_words_p_sentence = word_count / sentence_count
self.analyzedVars = {
'words': words,
'char_cnt': float(char_count),
'word_cnt': float(word_count),
'sentence_cnt': float(sentence_count),
'syllable_cnt': float(syllable_count),
'complex_word_cnt': float(complexwords_count),
'avg_words_p_sentence': float(avg_words_p_sentence)
}
outData = {
'char_cnt': float(char_count),
'word_cnt': float(word_count),
'sentence_cnt': float(sentence_count),
'syllable_cnt': float(syllable_count),
'complex_word_cnt': float(complexwords_count),
'avg_words_p_sentence': float(avg_words_p_sentence)
}
return outData
def compute(test_id='test'):
pic_path = 'static/' + test_id + '.png'
truth_path = 'static/' + test_id + '.txt'
base_image, pic_with_box, word_pics = get_words(pic_path)
word_pics = np.stack(word_pics)
pred = F.softmax(net_recog(torch.from_numpy(word_pics)), dim=1)
_, idxes = torch.max(pred, dim=1)
res = []
for idx in idxes:
res.append(ug_dict[idx])
f = open(truth_path, 'r')
lines = f.readlines()
f.close()
lines = [l.strip() for l in lines]
truth = []
for l in lines:
truth += l.split()
for i, w in enumerate(res):
if w not in truth:
res[i] = '<span style="color:red">' + res[i] + '</span>'
truth = ' '.join(truth)
res = ' '.join(res[::-1])
score = difflib.SequenceMatcher(a=truth, b=res).ratio()
return img2str(base_image), img2str(pic_with_box), res, truth, score
def p_word_given_class(self, data_files, vocab_size):
"""
helper function
return dictionary representation of P(word | class)
"""
word_counter = Counter()
for file in data_files:
words = get_words(open_file(file))
for i, word in enumerate(words):
if unigrams:
if word not in self.vocab:
continue
word_counter[word] += 1
if bigrams:
if i != 0:
bigram = words[i - 1] + ' ' + word
if bigram not in self.vocab:
continue
word_counter[bigram] += 1
word_counter["UNK"] = 0
total_count = sum(word_counter.values())
for word in word_counter:
word_counter[word] = (word_counter[word] +
self.k) / (total_count + vocab_size * self.k)
p_word_given_class = dict(word_counter)
return p_word_given_class
def construct_libsvm_line(line):
global target_index, value_indexes, headers, indexes2binarize, indexes2tokenize, indexes2filter
label = target_index
new_line = []
for i in sorted(indexes2binarize + indexes2tokenize):
col_name = headers[i]
if i in indexes2binarize:
value = line[i]
try:
value_index = value_indexes[i][value]
except KeyError:
continue
new_item = "%s:1" % (value_index)
new_line.append(new_item)
else:
text = line[i]
words = get_words(text)
# word_indexes = map( lambda x: value_indexes[i][x], words )
word_indexes = get_word_indexes(words, i)
for word_index in sorted(word_indexes):
new_item = "%s:1" % (word_index)
new_line.append(new_item)
new_line.insert(0, label)
new_line = " ".join(new_line)
return new_line
def p_class_given_input(self, input_path, p_word_given_class, p_class):
"""
helper function
return P(class | input)
"""
p_class_given_input = 0
words = get_words(open_file(input_path))
if bigrams:
sample_bigrams = set()
for i, word in enumerate(words):
if i != 0:
sample_bigrams.add(words[i - 1] + ' ' + word)
for bigram in sample_bigrams:
if bigram not in self.vocab:
continue
if bigram not in p_word_given_class:
bigram = "UNK"
p = p_word_given_class[bigram]
if p > 0:
p_class_given_input += math.log(p_word_given_class[bigram])
if unigrams:
for word in set(words):
if word not in self.vocab:
continue
if word not in p_word_given_class:
word = "UNK"
p = p_word_given_class[word]
if p > 0:
p_class_given_input += math.log(p_word_given_class[word])
p_class_given_input += math.log(p_class)
return p_class_given_input
def construct_libsvm_line( line ): global target_index, value_indexes, headers, indexes2binarize, indexes2tokenize, indexes2filter label = target_index new_line = [] for i in sorted( indexes2binarize + indexes2tokenize ): col_name = headers[i] if i in indexes2binarize: value = line[i] try: value_index = value_indexes[i][value] except KeyError: continue new_item = "%s:1" % ( value_index ) new_line.append( new_item ) else: text = line[i] words = get_words( text ) # word_indexes = map( lambda x: value_indexes[i][x], words ) word_indexes = get_word_indexes( words, i ) for word_index in sorted( word_indexes ): new_item = "%s:1" % ( word_index ) new_line.append( new_item ) new_line.insert( 0, label ) new_line = " ".join( new_line ) return new_line
def compute_p_word_given_class(data_paths, vocab_size):
"""
Return a dictionary of word probabilities, P(word | class). All datapaths belong to the same class.
Incorporate Laplacian Smoothing with k=1 here. p_word_given_class should include the probability of UNKNOWN_WORD,
any word that doesn't appear in the training set
"""
###########################################################
# Implement your solution here
###########################################################
count_words = {}
total_num_words = 0
for path in data_paths:
words = get_words(open_file(path))
total_num_words += len(words)
for word in words:
if word in count_words:
count_words[word] += 1
else:
count_words[word] = 1
p_word_given_class = {}
for word in count_words:
prob = (count_words[word] + 1) / (total_num_words + vocab_size + 1)
p_word_given_class[word] = prob
#print(word + " ", end="")
#print(p_word_given_class[word])
p_word_given_class["UNKNOWN_WORD"] = 1 / (total_num_words + vocab_size + 1)
#print(p_word_given_class["UNKNOWN_WORD"])
return p_word_given_class
def get_cut_points(
time_dict: Dict[datetime.datetime, List[str]],
up_ratio: float = 2,
down_ratio: float = 0.75,
topK: int = 5
) -> List[Tuple[datetime.datetime, datetime.datetime, List[str]]]:
status = 0
cut_points = []
prev_num = None
start_time = None
temp_texts = []
for time, texts in time_dict.items():
if prev_num is None:
start_time = time
temp_texts = copy.copy(texts)
elif status == 0 and len(texts) >= prev_num * up_ratio:
status = 1
temp_texts.extend(texts)
elif status == 1 and len(texts) < prev_num * down_ratio:
tags = utils.get_words("。".join(texts), topK=topK)
cut_points.append((start_time, time, tags))
status = 0
start_time = time
temp_texts = copy.copy(texts)
elif status == 0:
start_time = time
temp_texts = copy.copy(texts)
prev_num = len(texts)
return cut_points
def analyze_text(self, text):
words = get_words(text)
char_count = int(get_char_count(words))
word_count = int(len(words))
sentences = get_sentences(text)
len_sentences = len(sentences)
sentence_count = int(len_sentences)
# sentence_count = int(len(get_sentences(text)))
syllable_count = count_syllables(words)
complexwords_count = count_complex_words(text.decode('utf-8'))
avg_words_p_sentence = word_count / sentence_count
encoding_dict = detect_encoding(self.filename)
self.analyzedVars = {
'filename': self.filename,
# 'text_truncated': text[:200].replace("\n", " "),
'words': words,
'char_cnt': float(char_count),
'word_cnt': float(word_count),
'sentence_cnt': float(sentence_count),
'syllable_cnt': float(syllable_count),
'complex_word_cnt': float(complexwords_count),
'avg_words_p_sentence': float(avg_words_p_sentence),
'encoding': encoding_dict['encoding'],
'encoding_confidence': encoding_dict['confidence']
}
def SPIMI(docs, details=False):
ans = {}
cur = 0
tmp = []
for doc in docs:
cnt = 0
with open(doc, 'r') as f:
s = f.read()
stop_list = gen_default_stop(s)
words = get_words(s, stop_list)
indexing = invert_index(words, delta=False)
for key, value in indexing.items():
if key not in ans:
ans[key] = list(np.array(value) + cur)
else:
ans[key] += list(np.array(value) + cur)
cnt += len(value)
tmp.append(cnt)
cur += cnt
for key, value in ans.items():
for i in range(1, len(value)):
value[i] -= value[i - 1]
if details:
res = {'词项数目': len(ans), '文档数量': len(docs), \
'词条数量': cur, '文档长度': tmp, '文档平均长度': np.average(tmp)}
return ans, res
return ans
def __get_words_summary(self, summary_text):
''' Gets unique words of the summary '''
self.__words_summary = utils.get_words(summary_text)
for word in self.__words_summary:
if word in self.__unique_words_summary:
self.__unique_words_summary[word] += 1
else:
self.__unique_words_summary[word] = 1
def __init__(self, fold):
self.fold = fold
#print('fold {}'.format(fold))
#print('getting data paths')
self.X_train_pos, self.X_test_pos = get_data_paths(
data_positive, test_data_start, numfolds, self.fold)
self.X_train_neg, self.X_test_neg = get_data_paths(
data_negative, test_data_start, numfolds, self.fold)
#print('building vocabulary')
self.vocab = Counter()
# filtering words under threshold
words_to_delete = set()
ft = 0
if bigrams:
# print('generating vocabulary for bigrams')
for path in self.X_train_pos + self.X_train_neg:
message = open_file(path)
words = get_words(message)
for i, word in enumerate(words):
if i != 0:
bigram = words[i - 1] + ' ' + word
self.vocab[bigram] += 1
ft = frequency_cutoff_bigram
for word in self.vocab:
if self.vocab[word] < ft:
words_to_delete.add(word)
if unigrams:
# print('generating vocabulary for unigrams')
for path in self.X_train_pos + self.X_train_neg:
message = open_file(path)
words = get_words(message)
for word in words:
self.vocab[word] += 1
ft = frequency_cutoff_unigram
for word in self.vocab:
if self.vocab[word] < ft:
words_to_delete.add(word)
for word in words_to_delete:
del (self.vocab[word])
#print('initializing rest of variables')
self.vocab_size = len(set(self.vocab))
self.p_word_given_pos = dict()
self.p_word_given_neg = dict()
self.p_pos = 0
self.p_neg = 0
self.k = k
def __get_words_summary(self, summary_text):
''' Gets unique words of the summary '''
self.__words_summary = utils.get_words(summary_text)
for word in self.__words_summary:
if word in self.__unique_words_summary:
self.__unique_words_summary[word] += 1
else:
self.__unique_words_summary[word] = 1
def __get_keywords_positions(self, text_document):
word_list = utils.get_words(text_document)
position_list = dict()
for i in range(len(word_list)):
if word_list[i] in self.__keywords_list:
if not word_list[i] in position_list:
position_list[word_list[i]] = list()
position_list[word_list[i]].append(i)
return position_list
def relable_transformer(val, keywords, no_hit_to_null=True):
val_clean = ' '.join(get_words(val))
for keyword, repl in keywords:
if keyword in val_clean:
return repl
if no_hit_to_null:
return na_value
else:
return val
def __frequency_word(self, text_document):
word_list = utils.get_words(text_document)
frequency_word = dict()
for word in word_list:
if word in frequency_word:
frequency_word[word] += 1
else:
frequency_word[word] = 1
return frequency_word
def create_position_list(self):
''' Obtains the positions in the document of the words summary: 'S1_W1'
S1 = position of the sentence, W1 = position of word in the sentences '''
for i in range(len(self.__document_sentence_list)):
words = utils.get_words(self.__document_sentence_list[i])
for j in range(len(words)):
if words[j] in self.__unique_words_summary:
if not words[j] in self.__position_word_list:
self.__position_word_list[words[j]] = list()
self.__position_word_list[words[j]].append(str(i+1)+'_'+str(j+1))
def prepare_chn_data(args):
settings = parse_settings(args.setting)
try:
chn_file = os.path.join(ENV.data_dir, "%s" % settings['chn'])
combinations = os.path.join(ENV.data_dir, "combination_%s" % settings['chn'])
words = utils.get_words(chn_file)
for i in range(2,3):
for expression in list(itertools.combinations(words, i)):
utils.push_word_back(combinations, ''.join(expression))
except Exception as e:
print e
def __get_sense_units_atr(self, summary_sentence, document_sentence):
''' Return the proportions of equals words'''
words_summary = utils.get_words(summary_sentence, stop_words=True)
words_document = utils.get_words(document_sentence, stop_words=True)
#print("words_summary", words_summary)
#print("words_sentence", words_sentence)
unique_words = dict()
cont = 0
tmp = list()
for word_summary in words_summary:
if not word_summary in unique_words: # to avoid repetitions
unique_words[word_summary] = 1
for word_document in words_document:
if word_summary in self.__tep_synonyms and word_document in self.__tep_synonyms[
word_summary]:
#print("synonyms", word_summary, word_sentence)
tmp.append((word_summary, word_document))
cont += 1
return cont #(cont, tmp)
def create_position_list(self):
''' Obtains the positions in the document of the words summary: 'S1_W1'
S1 = position of the sentence, W1 = position of word in the sentences '''
for i in range(len(self.__document_sentence_list)):
words = utils.get_words(self.__document_sentence_list[i])
for j in range(len(words)):
if words[j] in self.__unique_words_summary:
if not words[j] in self.__position_word_list:
self.__position_word_list[words[j]] = list()
self.__position_word_list[words[j]].append(
str(i + 1) + '_' + str(j + 1))
def build_markov(self, file_name):
previous = None
self.word_list = utils.get_words(file_name)
for word in self.word_list:
if previous is None:
previous = word
continue
if previous not in self:
self[previous] = Dictogram()
self[previous].add_count(word)
previous = word
def count_file(filename):
freqs = Counter()
with open(filename, 'r') as file:
chunk_reader = partial(_read_chunk_full_words, file)
for chunk in iter(chunk_reader, ''):
words = get_words(chunk)
freqs.update(Counter(words))
update_data(freqs)
return frequencies_string(freqs)
def start_classify(path, classifier):
output_folder = prepare_output()
words = utils.get_words(path)
for word in words:
#word = post_processing
if classifier.classify(word) == 'words':
utils.push_word_back(os.path.join(output_folder, "words.txt"), word)
elif classifier.classify(word) == 'pinyin':
utils.push_word_back(os.path.join(output_folder, "pinyin.txt"), word)
else:
pass
def get_bag_of_symbols(max_len, string):
words = get_words(string)
bag = []
window_size = 1
while window_size <= max_len:
for s_i in range(0, len(words) - window_size + 1):
symbol = []
for win_i in range(s_i, s_i + window_size):
symbol.append(words[win_i])
bag.append(symbol)
window_size = window_size + 1
return bag
def __init__(self, board=None):
if board is None:
words = get_words()
sample = random.sample(words, 25)
labels = Board.get_label_list()
self.board = [
Word(word, label) for (word, label) in zip(sample, labels)
]
else:
self.board = board
self.starting_team = Tags.BLUE if len(self.get_words(Tags.BLUE)) > len(
self.get_words(Tags.RED)) else Tags.RED
def frequency_analysis():
global result
words = utils.get_words(search_string)
frequencies = utils.get_frequencies()
input_frequencies = utils.get_frequencies(words, "input")
input_first_letter = input_frequencies[0]
for i in frequencies:
delta = ord(input_first_letter[0]) - ord(i[0])
check_with_shift(delta)
if result.percentage == 100:
break
def do_preprocess(self, url_list, label_list):
"""
进行预处理
:param url_list:
:param label_list:
:return:
"""
if MIN_WORD_FREQ > 0:
x__, word_reverse_dict = get_word_vocab(url_list, MAX_LENGTH_WORDS,
MIN_WORD_FREQ)
self.high_freq_words = sorted(list(word_reverse_dict.values()))
self.x, self.word_reverse_dict = get_word_vocab(
url_list, MAX_LENGTH_WORDS)
word_x = get_words(self.x, self.word_reverse_dict, DELIMIT_MODE,
url_list)
self.ngramed_id_x, self.ngrams_dict, self.worded_id_x, self.words_dict = \
ngram_id_x(word_x, MAX_LENGTH_SUBWORDS, self.high_freq_words)
self.chars_dict = self.ngrams_dict
self.chared_id_x = get_char_id_x(url_list, self.chars_dict,
MAX_LENGTH_CHARS)
pos_x, neg_x = list(), list()
for index in range(len(label_list)):
label = label_list[index]
if label == 1:
pos_x.append(index)
else:
neg_x.append(index)
print("Overall Mal/Ben split: {}/{}".format(len(pos_x), len(neg_x)))
pos_x = np.array(pos_x)
neg_x = np.array(neg_x)
self.x_train, self.y_train, self.x_test, self.y_test = prep_train_test(
pos_x, neg_x, DEV_PERCENTAGE)
self.x_train_char = get_ngramed_id_x(self.x_train, self.ngramed_id_x)
self.x_test_char = get_ngramed_id_x(self.x_test, self.ngramed_id_x)
self.x_train_word = get_ngramed_id_x(self.x_train, self.worded_id_x)
self.x_test_word = get_ngramed_id_x(self.x_test, self.worded_id_x)
self.x_train_char_seq = get_ngramed_id_x(self.x_train,
self.chared_id_x)
self.x_test_char_seq = get_ngramed_id_x(self.x_test, self.chared_id_x)
self.dump_dict(self.ngrams_dict, NGRAMS_DICT_FILE)
self.dump_dict(self.words_dict, WORDS_DICT_FILE)
self.dump_dict(self.chars_dict, CHARS_DICT_FILE)
def __read_documents(self, id_class, class_path):
size = len(os.listdir(class_path))
for name_document in os.listdir(class_path):
text_document = utils.read_file(
os.path.join(class_path, name_document))
word_list = utils.get_words(text_document)
for word in word_list:
if word in self.__classes[id_class]:
self.__classes[id_class][word] += 1
else:
self.__classes[id_class][word] = 1
for key in self.__classes[id_class].keys():
self.__classes[id_class][key] /= size
def __fill_class(self, id_class, name_document, text_document):
word_list = utils.get_words(text_document)
for word in word_list:
tag = nltk.pos_tag([word])
if tag[0][1].startswith('N') or tag[0][1].startswith(
'V') or tag[0][1].startswith('S') or tag[0][1].startswith(
'F') or tag[0][1].startswith('J'):
if word in self.__classes[id_class]:
if name_document in self.__classes[id_class][word]:
self.__classes[id_class][word][name_document] += 1
else:
self.__classes[id_class][word][name_document] = 1
else:
self.__classes[id_class][word] = dict()
self.__classes[id_class][word][name_document] = 1
def compute_p_class_given_input(input_path, p_word_given_class, p_class):
"""
Return P(class | input).
"""
message = open_file(input_path)
words = get_words(message)
p_class_given_input = 0.0
for word in words:
if (word in p_word_given_class):
p_class_given_input += ln(p_word_given_class[word] * p_class)
else:
p_class_given_input += ln(p_word_given_class['UNKNOWN_WORD'] *
p_class)
return p_class_given_input
def do_preprocess(self, url_list):
"""
测试数据预处理
:param url_list:
:return:
"""
self.chars_dict = self.load_dict(CHARS_DICT_FILE)
self.ngrams_dict = self.load_dict(NGRAMS_DICT_FILE)
self.words_dict = self.load_dict(WORDS_DICT_FILE)
x, word_reverse_dict = get_word_vocab(url_list, MAX_LENGTH_WORDS)
word_x = get_words(x, word_reverse_dict, DELIMIT_MODE, url_list)
self.ngramed_id_x, self.worded_id_x = \
ngram_id_x_from_dict(word_x, MAX_LENGTH_SUBWORDS, self.ngrams_dict, self.words_dict)
self.chared_id_x = get_char_id_x(url_list, self.chars_dict,
MAX_LENGTH_CHARS)
def analyze_text(self, text):
words = get_words(text)
char_count = get_char_count(words)
word_count = len(words)
sentence_count = len(get_sentences(text))
syllable_count = count_syllables(words)
complexwords_count = count_complex_words(text)
avg_words_p_sentence = word_count/sentence_count
self.analyzedVars = {
'words': words,
'char_cnt': float(char_count),
'word_cnt': float(word_count),
'sentence_cnt': float(sentence_count),
'syllable_cnt': float(syllable_count),
'complex_word_cnt': float(complexwords_count),
'avg_words_p_sentence': float(avg_words_p_sentence)
}
def analyze_text(self, text):
words = get_words(text)
char_count = get_char_count(words)
words_count = len(words)
sentence_count = len(get_sentences(text))
syllable_count = count_syllables(words)
print("syllable_count:", syllable_count)
complex_words_count = count_complex_words(text)
avg_words_per_sentence = int(words_count / sentence_count)
print("avg_words_per_sentence", avg_words_per_sentence)
self.ana_vars = {
'words': words,
'char_count': float(char_count),
'words_count': float(words_count),
'sentence_count': float(sentence_count),
'syllable_count': float(syllable_count),
'complex_words_count': float(complex_words_count),
'avg_words_per_sentence': float(avg_words_per_sentence)
}
def analyze_text(self, text):
words = get_words(text)
char_count = int(get_char_count(words))
word_count = int(len(words))
sentence_count = int(len(get_sentences(text)))
syllable_count = count_syllables(words)
complexwords_count = count_complex_words(text)
avg_words_p_sentence = word_count / sentence_count
self.analyzedVars = {
'filename': self.filename,
# 'text_truncated': text[:200].replace("\n", " "),
'words': words,
'char_cnt': float(char_count),
'word_cnt': float(word_count),
'sentence_cnt': float(sentence_count),
'syllable_cnt': float(syllable_count),
'complex_word_cnt': float(complexwords_count),
'avg_words_p_sentence': float(avg_words_p_sentence)
}
def compute_p_class_given_input(input_path, p_word_given_class, p_class):
"""
Return P(class | input).
"""
###########################################################
# Implement your solution here
###########################################################
words = get_words(open_file(input_path))
p_class_given_input = 0
for word in words:
prob = p_word_given_class["UNKNOWN_WORD"]
if word in p_word_given_class:
prob = p_word_given_class[word]
p_class_given_input += math.log(prob)
p_class_given_input += math.log(p_class)
return p_class_given_input
def create_corpus(corpus_path):
if os.path.exists(corpus_path):
print(f'Using corpus {corpus_path}')
else:
print(f'Creating corpus {corpus_path}')
word_list = [w.lower() for w in get_words(prefix='../')]
replacements = {}
for word in word_list:
if ' ' in word:
replacements[word] = word.replace(' ', '_')
corpus_files = []
for path, dirs, files in os.walk(CORPUS_FILE_PATH):
corpus_files = [f for f in files if f.endswith('.gz')]
break
random.shuffle(corpus_files)
sentences = []
for corpus_file in tqdm(corpus_files):
with gzip.open(f'{CORPUS_FILE_PATH}/{corpus_file}', 'rb') as f_in:
for line in f_in:
line = line.decode('utf-8').rstrip()
for r in replacements:
line = re.sub(rf'\b{r}\b', replacements[r], line)
sentences.append(line)
sentence_order = list(range(len(sentences)))
random.shuffle(sentence_order)
print('writing corpus to file...')
f_out = gzip.open(corpus_path, 'wb')
for i in sentence_order:
f_out.write((sentences[i] + '\n').encode('utf-8'))
f_out.close()
print('done writing corpus to file')
def flesch_kincaid_score(article):
xml_url = '&titles='.join([xml_api_url, title])
try:
xml = requests.get(xml_url).content
bs = BeautifulSoup(xml)
try:
text = str(bs.find('extract').contents[0].encode('utf-8')) # convert NavigableString to string after encoding
non_text = ['== See also ==\n', '== References ==\n', ' === Further references ===\n', '== External links ==\n', '== Notes ==\n']
for ele in non_text:
text = text.split(ele, 1)[0]
text = re.sub('==.*==', '', text)
words = get_words(text)
syllableCount = count_syllables(text)
sentences = get_sentences(text)
fk = 206.835 - 1.015*len(words)/len(sentences) - 84.6*(syllableCount)/len(words)
return float(format(fk,'.2f'))
except:
print 'Error while computing fk score of ' + article
print format_exc()
except:
print 'Error while fetching xml content of ' + article
print format_exc()
def main():
words = get_words()[:100]
item = words[0]
hashtable = HashTable(words, hashsize=10)
print hashtable
print hashtable.search(item)
import graph
import codecs
import utils
import rhymes
import generator
def get_args():
parser = argparse.ArgumentParser(description="Generate a nice poem :)")
parser.add_argument('--source_text', default='data/PanTadeusz.txt')
parser.add_argument('--syllable_count', type=int, default=13)
parser.add_argument('--rhyme_span', type=int, default=2)
parser.add_argument('--length', type=int, default=4)
parser.add_argument('--markov_order', type=int, default=1)
# parser.add_argument('--rhyme_pattern')
# parser.add_argument('--keyword_file')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
words = []
with codecs.open(args.source_text, 'rb', encoding='utf8') as f:
for l in f:
words += utils.get_words(l.rstrip())
wg = graph.from_file(words, args.markov_order)
rhs = rhymes.from_file(words, args.rhyme_span)
poem = generator.create_poem(wg, rhs, args.syllable_count, args.length)
unique_values = defaultdict( set ) n = 0 for line in reader: for i in indexes2binarize: value = line[i] if pass_filter( value, headers[i] ): unique_values[i].add( value ) # the same, but first get unique words from the column text # could also use non-unique words, as unique_values[i] is a set # using approach no2 for i in indexes2tokenize: text = line[i] words = get_words( text ) # filter words = filter_words( words, headers[i] ) for w in words: unique_values[i].add( w ) n += 1 if n % 10000 == 0: print n for i in unique_values: print "%s: %s" % ( i, len( unique_values[i] )) # calculate column offsets
