def search_for_departments(keywords):
keyw = keywords.split()
dept_ids = []
try:
conn = lite.connect(db)
conn.row_factory = lite.Row
with conn:
for k in keyw:
k_stemmed = stemmer.stem(k)
print(k_stemmed)
cur = conn.cursor()
cur.execute("SELECT id FROM departments where name like ?",
("%" + k_stemmed + "%", ))
rows = cur.fetchall()
if len(rows) > 0: print("found in courses.........")
for row in rows:
dept_ids.append(row[0])
count_keywords = len(keyw)
common_ids = {x: dept_ids.count(x) for x in dept_ids}
out = []
for x in common_ids:
if common_ids[x] == count_keywords:
cur.execute("SELECT name FROM departments where id = ?",
(x, ))
rows = cur.fetchone()
out.append(str(x) + ":" + rows[0])
return out
except lite.Error as e:
print('error openning table departments', e)
return []
def search_for_departments(keywords):
keyw = keywords.split()
dept_ids = []
try:
conn = lite.connect(db)
conn.row_factory = lite.Row
with conn:
for k in keyw:
k_stemmed = stemmer.stem(k)
print( k_stemmed)
cur = conn.cursor()
cur.execute("SELECT id FROM departments where name like ?", ("%"+k_stemmed+"%",))
rows = cur.fetchall()
if len(rows)>0: print ("found in courses.........")
for row in rows:
dept_ids.append(row[0])
count_keywords = len(keyw)
common_ids = {x:dept_ids.count(x) for x in dept_ids}
out =[]
for x in common_ids:
if common_ids[x] == count_keywords:
cur.execute("SELECT name FROM departments where id = ?", (x,))
rows = cur.fetchone()
out.append(str(x)+":"+rows[0])
return out
except lite.Error as e:
print('error openning table departments', e)
return []
def pre_process(self, uid, tokens):
# adding into active terms before stemming
self.active_terms.append((self.timestamp, tokens, uid))
while len(self.active_terms) > 0:
term = self.active_terms[0]
if term[0] < self.timestamp - _ACTIVE_WINDOW_SIZE * 60:
self.active_terms.popleft()
else:
break
# stemming
tokens = map(lambda x: stemmer.stem(x), tokens)
if len(tokens) < 1:
return None
# hashing
results = [] # (counts, reserved_slot, n_words, h)
for h in range(fast_hashing.HASH_NUMBER):
results.append(({}, {}, len(tokens), h))
for token in tokens:
hash_code = np.array(fast_hashing.hash_code(token)) % _SKETCH_BUCKET_SIZE
for h in range(fast_hashing.HASH_NUMBER):
code = hash_code[h]
if code in results[h][0]:
results[h][0][code] += 1
else:
results[h][0][code] = 1
return results
def checkword(word, words, firstword=False, context=''):
if word in words or stemmer.stem(word) in words:
return True
if len(word) > 25:
return False
if firstword and (str(word[0]).upper() + word[1:] in words):
return True
print("Found a word that wasn't recognized: ", word, ", in the line: ")
print(re.sub(word, word.upper(), context), end='')
print("We're looking for close matches to this word. Please wait...")
dist = 0
editdists = [10] * 5
wordlist = {0: '', 1: '', 2: '', 3: '', 4: ''}
longest = max(editdists)
index = 1
for line in words:
'''Stuff to make it faster!'''
'''Match upper/lowercase'''
if not firstword and word[0].isupper() != line[0].isupper():
continue
'''Don't allow words that are too long or too short from the dictionary'''
if len(line) - 3 > len(word) or len(word) - 3 > len(line):
continue
'''Randomly check if some letters are contained in both,
only for long enough words. This is the part that really gets the time
down to the order of seconds, rather than minutes.'''
if len(word) > 3:
count = 0
for x in range(int(len(word) / 4)):
if word[int(random.random() * len(word))] in line:
count += 1
if count < (int(len(word) / 4)) - int(4 / len(word)):
continue
'''If we get through all that, then calculate the min edit distance'''
#ignore case if it's the first word
if firstword:
dist = mineditdist(line.lower(), word.lower())
else:
dist = mineditdist(line, word)
'''Saves the word if it's in the top 5'''
if dist < longest:
index = editdists.index(longest)
editdists.remove(longest)
wordlist[index] = line
editdists.insert(index, dist)
longest = max(editdists)
'''Ordering the list of words that are the closest to the source word'''
returnlist = []
while len(returnlist) < 5:
for val in editdists:
if len(returnlist) == 5:
break
if val == min(editdists):
returnlist.append(wordlist.get(editdists.index(val)))
wordlist.pop(editdists.index(val), 0)
editdists[editdists.index(val)] = 11
print(returnlist)
return returnlist
def search(self, query, isPhrase, isOrMatch):
results = []
stemmed = [stem(t) for t in query.split(" ")]
if (isPhrase):
results = self.phraseSearch(stemmed)
else:
results = self.termSearch(stemmed, isOrMatch)
for doc in self.removeNailPolish(results):
self.printResult(doc)
def load_text():
opinions = {}
with codecs.open('opinie1', 'r', encoding='utf-8') as my_file:
for line in my_file:
pair = line.split(";", 2)
key = pair[1]
key = split_to_words(key)
key = stemmer.stem(key)
key = " ".join(key)
value = pair[0]
opinions[key] = float(value)
return opinions
def bagOfWords(s, words):
bag = [0 for _ in range(len(words))]
s_words = nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words]
for se in s_words:
for i, w in enumerate(words):
if w == se:
bag[i] = 1
return numpy.array(bag)
def analyse_topics(self, _probs):
words = set()
for term in self.active_terms:
for word in term[1]:
words.add(word)
print "size of words:", len(words)
high_prob_words = []
for _word in words:
word = stemmer.stem(_word)
hash_code = np.array(fast_hashing.hash_code(word)) % _SKETCH_BUCKET_SIZE
min_prob_list = []
for h in range(fast_hashing.HASH_NUMBER):
prob = _probs[h][hash_code[h]]
min_prob_list.append(prob)
min_prob_list.sort()
min_prob = min_prob_list[1] # !!!
if min_prob >= _PROBABILITY_THRESHOLD:
high_prob_words.append((word, min_prob))
# rescale
s_prob = sum([p for w, p in high_prob_words])
high_prob_words = [(w, p/s_prob) for w, p in high_prob_words]
high_prob_words.sort(key=lambda x: x[1], reverse=True)
# top 20
high_prob_words = high_prob_words[:20]
post_res = postprocessor.process(high_prob_words, self.active_terms)
if eval(config.get('output', 'debug_info')):
self.output.write('high_prob_words\n')
self.output.write(str(high_prob_words)) #debugging
self.output.write('\npost_res\n')
self.output.write(str(post_res)) #debugging
self.output.write('\n')
flag, word_level_results, _ = post_res
if flag:
event = dict()
event['detection_time'] = str(datetime.utcfromtimestamp(self.timestamp))
event_words = list()
for prob_word, word_flag in zip(high_prob_words, word_level_results):
_word = prob_word[0]
if word_flag:
event_words.append(_word)
event['key_words'] = event_words
self.output.write(json.dumps(event))
self.output.write('\n')
def normalizeText(self, text):
text = text.lower()
text = re.sub(r'[^0-9a-zA-Z]+', ' ', text)
articleWords = text.split()
articleWords = self.removeStopWords(articleWords)
stemmedWords = []
for word in articleWords:
stemmed = stemmer.stem(word)
# p = stemmer.PorterStemmer()
# stemmed = p.stemWord(word)
self.reverseStemHashtable[stemmed] = word
stemmedWords.append(stemmed)
return stemmedWords
def index_stem(id, doc):
terms = doc.split()
for term in terms:
term = term.lower()
term = clean(term)
term = stemmer.stem(term, 0, len(term) - 1)
doc_ids = inverted_index.get(term)
if doc_ids:
doc_ids.add(id)
else:
inverted_index[term] = set()
inverted_index[term].add(id)
def process_word_cloud(word_cloud):
latin_numbers = "1234567890IIIVXIVIΙΙΙ"
symbols = "[]():-!?&,'//"
#create a list of stopwords
stop_words = []
with open('greek_stop_words.txt', 'r') as fo:
for line in fo:
w = line.strip()
if w[-1] == ',': w = w[:-1]
w = greek_to_upper(w)
if w not in stop_words: stop_words.append(w)
for x in symbols:
word_cloud = word_cloud.replace(x, ' ')
wc = word_cloud.split(' ')
wc_up = []
for w in wc:
wc_up.append(greek_to_upper(w))
new_wc = []
for w in wc_up:
if w not in stop_words and w not in latin_numbers and len(w) > 2:
new_wc.append(w)
else:
print("stop word eliminated:", w)
print(len(new_wc))
print(new_wc)
stems_list = {}
for w in new_wc:
st_w = stemmer.stem(w)
print(w, st_w)
if st_w not in stems_list: stems_list[st_w] = [w]
else: stems_list[st_w].append(w)
for x in stems_list:
print(x, stems_list[x])
word_frequencies = {}
for x in stems_list:
# find the shortest member of the corresponding list for the stem
word_frequencies[min(stems_list[x], key=len)] = len(stems_list[x])
#for x in word_frequencies: print (x.lower(), word_frequencies[x])
# new_text =""
# for x in word_frequencies:
# new_text += (" "+x)*word_frequencies[x]
# print(new_text.lower())
# return new_text.lower()
print(word_frequencies)
#input()
return word_frequencies
def process_word_cloud(word_cloud):
latin_numbers = "1234567890IIIVXIVIΙΙΙ"
symbols ="[]():-!?&,'//"
#create a list of stopwords
stop_words = []
with open('greek_stop_words.txt', 'r') as fo:
for line in fo:
w = line.strip()
if w[-1] == ',': w = w[:-1]
w = greek_to_upper(w)
if w not in stop_words: stop_words.append(w)
for x in symbols:
word_cloud = word_cloud.replace(x,' ')
wc = word_cloud.split(' ')
wc_up=[]
for w in wc:
wc_up.append(greek_to_upper(w))
new_wc = []
for w in wc_up:
if w not in stop_words and w not in latin_numbers and len(w)>2: new_wc.append(w)
else : print ("stop word eliminated:", w)
print(len(new_wc))
print(new_wc)
stems_list ={}
for w in new_wc:
st_w = stemmer.stem(w)
print (w, st_w)
if st_w not in stems_list: stems_list[st_w]=[w]
else: stems_list[st_w].append(w)
for x in stems_list:
print (x, stems_list[x])
word_frequencies={}
for x in stems_list:
# find the shortest member of the corresponding list for the stem
word_frequencies[min(stems_list[x], key=len)]= len ( stems_list[x])
#for x in word_frequencies: print (x.lower(), word_frequencies[x])
# new_text =""
# for x in word_frequencies:
# new_text += (" "+x)*word_frequencies[x]
# print(new_text.lower())
# return new_text.lower()
print (word_frequencies)
#input()
return word_frequencies
def preprocessing(self, text):
""" Replace the unusual character in the text """
to_replace = [
'!',
'#',
'%',
'$',
"'",
'&',
')',
'(',
'+',
'*',
'-',
',',
'/',
'.',
'1',
'0',
'3',
'2',
'5',
'4',
'7',
'6',
'9',
'8',
';',
':',
'?',
'_',
'^',
]
lowered = text.encode('ascii', 'ignore').lower()
replacing = lowered
for char_to_replace in to_replace:
replacing = replacing.replace(char_to_replace,
' ' + char_to_replace + ' ')
stemming = ' '
splited = replacing.split()
# return replacing
return stemming.join([stem(item) for item in splited])
def main(use_tfidf, opinion_text):
"""Print rating of given text
"""
opinion = " ".join(stemmer.stem(
create_vectors.split_to_words(opinion_text.decode('utf-8'))))
trainset = create_vectors.load_text()
if use_tfidf:
keywords_file = 'tfidf_keywords'
else:
keywords_file = 'keywords'
regression_file = 'regr_for_{}'.format(keywords_file)
with codecs.open(keywords_file, 'r', encoding='utf-8') as kfile:
keywords = json.load(kfile)
regr = get_regression_from_file(regression_file)
rating = get_rating(opinion, regr, trainset, keywords, use_tfidf)
if rating < 0.0:
rating = 0.0
elif rating > 5.0:
rating = 5.0
print '{:.2f}'.format(rating)
def getData(company, amount, datef, datet):
news_dates, news, news_count = downloadNews(company, amount)
writeNews(news_dates, news, news_count,
path + 'news' + sep + '{}.csv'.format(company))
#news_dates, news, news_count = readNews(path + 'news' + sep + '{}.csv'.format(company))
stocks_dates, stocks, stocks_count = downloadStock(company, datef, datet)
writeStock(stocks_dates, stocks, stocks_count,
path + 'stocks' + sep + '{}.csv'.format(company))
#stocks_dates, stocks, stocks_count = readStock(path + 'stocks' + sep + '{}.csv'.format(company))
stems_dates, stems, stems_count = stem(news_dates, news, news_count)
writeNews(stems_dates, stems, stems_count,
path + 'stems' + sep + '{}.csv'.format(company))
#stems_dates, stems, stems_count = readNews(path + 'stems' + sep + '{}.csv'.format(company))
connections_dates, connections_news, connections_stocks, connections_count = connect(
stems_dates, stems, stems_count, stocks_dates, stocks, stocks_count)
writeConnections(connections_dates, connections_news, connections_stocks,
connections_count,
path + 'connections' + sep + '{}.csv'.format(company))
def process(self, _ptweet):
self.timestamp = _ptweet.timestamp
_tokens = _ptweet.tokens
tokens = [stemmer.stem(x) for x in _tokens]
if len(tokens) < 3:
return None, None
unique_words = set(tokens)
unique_word_pairs = set()
for i in unique_words:
for j in unique_words - {i}:
# To us [a, b] = [b, a], and sorting gives us a distinct representation.
unique_word_pairs.add(tuple(sorted([i, j])))
max_sig = 0
max_sig_instance = None
sig_list = list()
for token in unique_word_pairs:
if _SIGNI_TYPE == 's':
min_instance = []
scores, codes = self.sig_scorers.get(token, self.timestamp)
for x in scores:
min_instance.append(x.observe(int(self.timestamp), 1.0))
count, ewma, ewmavar, sig = min(min_instance,
key=lambda x: x[1])
# count, ewma, ewmavar, sig = min([x.observe(int(self.timestamp), 1.0) for x in self.sig_scorers.get(token, self.timestamp)],key=lambda x:x[1])
if sig > max_sig and ewma > 0:
max_sig = sig
max_sig_instance = _ptweet.datetime(
), count, ewma, ewmavar, sig, token
if sig > _SIGNI_THRESHOLD and ewma > 0:
sig_list.append(
(_ptweet.datetime(), count, ewma, ewmavar, sig, token))
if max_sig > _SIGNI_THRESHOLD:
# print(max_sig_instance)
return max_sig_instance, sig_list
return None, None
def search_stem(tokens):
prev_doc_ids = set()
accumulate = or_comp
for token in tokens:
token = token.lower()
token = stemmer.stem(token, 0, len(token) - 1)
if operators.get(token[0:2]):
accumulate = operators[token[0:2]]
#print 'operators', accumulate
token = token[2:]
doc_ids = inverted_index.get(token)
#print token, '=', doc_ids
if doc_ids:
doc_ids = accumulate(doc_ids, prev_doc_ids)
#print accumulate, '=', doc_ids
prev_doc_ids = set(doc_ids)
l = list(doc_ids)
l.sort()
print '\t', tokens, '-->', l
def analyse_topics(self, _probs):
words = set()
for term in self.active_terms:
for word in term[1]:
words.add(word)
print "size of words:", len(words)
high_prob_words = []
for _word in words:
word = stemmer.stem(_word)
hash_code = np.array(fast_hashing.hash_code(word)) % _SKETCH_BUCKET_SIZE
min_prob_list = []
for h in range(fast_hashing.HASH_NUMBER):
prob = _probs[h][hash_code[h]]
min_prob_list.append(prob)
min_prob_list.sort()
min_prob = min_prob_list[1] # !!!
if min_prob >= _PROBABILITY_THRESHOLD:
high_prob_words.append((word, min_prob, hash_code))
high_prob_words.sort(key=lambda x: x[1], reverse=True)
high_prob_words = high_prob_words[:_MAX_NUMBER_WORDS]
print high_prob_words
_kws = list()
_kps = list()
post_result = postprocessor.process(high_prob_words, self.active_terms)
print post_result
if not post_result[0]:
return
_event = dict()
_id = event_output.getId()
_event['eid'] = _id
_event['topicID'] = _id
_event['info.dtime'] = str(datetime.datetime.utcfromtimestamp(self.timestamp))
'''
for high_prob_word in high_prob_words:
_kws.append(high_prob_word[0])
_kps.append(high_prob_word[1])'''
word_level_result = post_result[1]
for i in range(len(high_prob_words)):
high_prob_word = high_prob_words[i]
if word_level_result[i]:
_kws.append(high_prob_word[0])
_kps.append(high_prob_word[1])
_event['info.keywords'] = _kws
_event['info.probs'] = _kps
_event['info.numUsers'] = post_result[3]
_event['info.numGeoUsers'] = 0
_event['info.numTweets'] = post_result[2]
_event['info.numGeoTweets'] = 0
event_output.put(_id, _event)
if len(sys.argv) < 2:
print("Invalid argument count")
sys.exit()
else:
if len(sys.argv) >= 3:
bgColor = sys.argv[2]
if len(sys.argv) == 4:
maskFile = "py/masks/" + sys.argv[3] + ".jpg"
outFile = sys.argv[1]
inp = open("py/text/" + outFile + ".txt", encoding="utf-8", mode="r")
text = inp.read()
inp.close()
text = re.sub('[\W_]+', ' ', text)
splits = [x for x in text.split(' ') if (not x.isspace() and x)]
stemsplits = stemmer.stem(" ".join(splits)).split(' ')
dicta = {}
dicts = defaultdict(int)
dictst = defaultdict(int)
dictfull = {}
for x in range(0, len(splits)):
dicta[splits[x]] = stemsplits[x]
for x in range(0, len(splits)):
dicts[splits[x]] += 1
for x in range(0, len(stemsplits)):
dictst[stemsplits[x]] += 1
sorted_d = sorted(dicts.items(), key=operator.itemgetter(1))
brr = len(splits)
for w in range(0, len(sorted_d)):
if dicta[sorted_d[w][0]] not in dictfull:
dictfull[dicta[sorted_d[w][0]]] = (sorted_d[w][0],
"knew": "knew",
"knick": "knick",
"knif": "knif",
"knife": "knife",
"knight": "knight",
"knightly": "knight",
"knights": "knight",
"knit": "knit",
"knits": "knit",
"knitted": "knit",
"knitting": "knit",
"knives": "knive",
"knob": "knob",
"knobs": "knob",
"knock": "knock",
"knocked": "knock",
"knocker": "knocker",
"knockers": "knocker",
"knocking": "knock",
"knocks": "knock",
"knopp": "knopp",
"knot": "knot",
"knots": "knot",
}
for original in test_cases:
stemmed_term = stem(original)
expected_stem = test_cases[original]
error_msg = "stemmed %s to %s, expected value %s" % (original, stemmed_term, expected_stem)
assert expected_stem == stemmed_term, error_msg
def checkword(word, words, firstword = False, context = ''):
if word in words or stemmer.stem(word) in words:
return True
if len(word)>25:
return False
if firstword and (str(word[0]).upper()+word[1:] in words):
return True
print("Found a word that wasn't recognized: ", word, ", in the line: ")
print(re.sub(word, word.upper(), context), end = '')
print("We're looking for close matches to this word. Please wait...")
dist = 0
editdists = [10]*5
wordlist = {0:'',1:'',2:'',3:'',4:''}
longest = max(editdists)
index = 1
for line in words:
'''Stuff to make it faster!'''
'''Match upper/lowercase'''
if not firstword and word[0].isupper() != line[0].isupper():
continue
'''Don't allow words that are too long or too short from the dictionary'''
if len(line)-3 > len(word) or len(word)-3 > len(line):
continue
'''Randomly check if some letters are contained in both,
only for long enough words. This is the part that really gets the time
down to the order of seconds, rather than minutes.'''
if len(word) > 3:
count = 0
for x in range(int(len(word)/4)):
if word[int(random.random()*len(word))] in line:
count += 1
if count < (int(len(word)/4)) - int(4/len(word)):
continue
'''If we get through all that, then calculate the min edit distance'''
#ignore case if it's the first word
if firstword:
dist = mineditdist(line.lower(), word.lower())
else:
dist = mineditdist(line, word)
'''Saves the word if it's in the top 5'''
if dist < longest:
index = editdists.index(longest)
editdists.remove(longest)
wordlist[index] = line
editdists.insert(index, dist)
longest = max(editdists)
'''Ordering the list of words that are the closest to the source word'''
returnlist = []
while len(returnlist)<5:
for val in editdists:
if len(returnlist) == 5:
break
if val == min(editdists):
returnlist.append(wordlist.get(editdists.index(val)))
wordlist.pop(editdists.index(val),0)
editdists[editdists.index(val)] = 11
print(returnlist)
return returnlist
import sys
from triples import ParseTriples, Triple
import keyvalue.sqliteKVStore as sqliteKVS
import stemmer as s
imagesStore = sqliteKVS.SqliteKeyValue("images.db")
labelsStore = sqliteKVS.SqliteKeyValue("labels.db")
if (len(sys.argv) < 2):
print("Es necesario indicar la o las palabras a buscar Ejemplo:")
print("{0} palabra1".format(sys.argv[0]))
for word in sys.argv[1:]:
w = s.stem(word)
newword = labelsStore.getItem(w)
print(newword)
#if len(word) > 0:
# print(imagesStore.getItem(word[0][0]))
#@TODO Aqui debemos programar la logica de buscar las URLs
#asociadas a cada palabra que nos den via la linea de comandos.
imagesStore.close()
labelsStore.close()
dictionary[image.getSubject()] = image.getObject()
print(image.getSubject() + " -- " + image.getObject())
image = imagesDS.getNext()
for key, value in dictionary.items():
h = 0
imagsDyna.putItem(key, {"S": value})
for i in range(0, 5000):
label = labelsDS.getNext()
#if len(labelsDyna.getItem(label.getSubject())) > 0:
stemmer = s.stem(label.getObject())
#Note that label could have mutiple values so iterate thorough the list
for word in stemmer.split(" "):
if label.getSubject() in dictionary:
if word in dictionaryLabels:
dictionaryLabels[word].append({"S": label.getSubject()})
else:
dictionaryLabels[word] = [{"S": label.getSubject()}]
print(word + " is asociated with " + label.getObject() + " " +
label.getSubject())
#termsStore.putItem(key=word, value=label.getSubject())
for key, value in dictionaryLabels.items():
h = 0
labelsDyna.putItem(key, {"L": value})
def stemmed_words(doc):
return (mystem.stem(w) for w in analyzer(doc))
def correctSentence(sentence, index):
taggedS = tagger.applyMLTag(sentence)
word = taggedS[index][0]
POStag = taggedS[index][1]
stemList = stemmer.stem(word, POStag)
#remove duplicates
verifiedWords = []
for s in stemList:
#("found stem "+str(s))
tags = tagger.getTagsForWord(s[0])
if len(tags) > 0:
#print("stem added")
verifiedWords += [s]
#at this point, verifiedWords should contain only real words
if len(verifiedWords) == 0:
#print("No verified words.")
return "No Answer"
replacementWord = ""
#print "Entering while loop"
while (replacementWord == "" and len(verifiedWords) > 0):
#find the shortest word/root
root = verifiedWords[0]
numVerifiedLeft = len(verifiedWords)
for w in verifiedWords:
if len(w[0]) <= len(root[0]):
root = w
#print("shortest word is "+str(root))
#possibles should contain all words that can contain the root
possibles = tagger.getWordsWithRoot(root[0])
if (root[0][-1] == 'e'):
possibles += tagger.getWordsWithRoot(root[0][:-1])
elif (root[0][-1] == 'y'):
possibles += tagger.getWordsWithRoot(root[0][:-1] + 'i')
for row in stemmer.csvReader("irregularPastVerbs.csv"):
if (row[0] == root[0]):
possibles += tagger.getWordsWithRoot(row[1])
possibles += tagger.getWordsWithRoot(row[2])
#print("possibles for "+str(root)+" are "+str(possibles))
#actualPossibles should contain all words that can be stemmed to the root
possibles.sort(key=lambda x: len(x[0]), reverse=False)
possibles = possibles[:40]
actualPossibles = []
for word in possibles:
if (stemmer.isRootOfWord(root[0], root[1], word[0], word[1])):
actualPossibles += [word]
print("actual possibles for " + str(root) + " are " +
str(actualPossibles))
prevWord = ""
if index > 0:
prevWord = sentence[index - 1]
nextWord = ""
if index < len(sentence) - 1:
nextWord = sentence[index + 1]
replacementWord = MLWordUsingBigrams(prevWord, nextWord,
actualPossibles)
#print("replacement word found for root "+str(root)+" is "+replacementWord)
# verifiedWords.remove(root)
numVerifiedLeft -= 1
if (numVerifiedLeft == 0 and replacementWord == ""):
#print("No good replacements found. Cry now.")
return "No Answer"
#print("We highly reccomend that you replace your word with "+replacementWord)
#print("Your sentence would then become:")
sentence[index] = replacementWord
newSentence = ""
for w in sentence:
newSentence += (w + " ")
print newSentence
return sentence
border = int(connections_count * 0.75)
training_dates = total_dates[:border]
training_news = total_news_sequence[:border]
training_stocks = total_stocks[:border]
training_count = border
testing_dates = total_dates[border:]
testing_news = total_news_sequence[border:]
testing_stocks = total_stocks[border:]
testing_count = total_count - border
total_X = numpy.array(total_news_sequence)
total_y = numpy.array(total_stocks)
training_X = numpy.array(training_news)
training_y = numpy.array(training_stocks)
testing_X = numpy.array(testing_news)
testing_y = numpy.array(testing_stocks)
if sys.argv[1] == '-f':
fit(company, training_X, training_y, testing_X, testing_y)
else:
news_dates, news, news_count = readNews(path + predict_path)
stems_dates, stems, stems_count = stem(news_dates, news, news_count)
news_sequences = sequence.pad_sequences(
sequences=tokenizer.texts_to_sequences(stems))
y = predict(news_sequences, company)
print(y)
