def __init__(self, params, yaw = Fuzzy(mf_types, f_ssets)):
'''Initialize some variables'''
Controller.__init__(self,params)
self.heading_angle = 0
self.yaw = yaw
from rupiah import Rupiah
from fuzzy import Fuzzy
masaKerja = int(input("Masukkan lama masa kerja (tahun): "))
produkTerjual = int(input("Masukkan banyak barang terjual (unit): "))
fuzz = Fuzzy(masaKerja, produkTerjual)
bonus = Rupiah(fuzz.hitungBonus())
nilai_z = Rupiah(fuzz.nilai_z)
print("> Masa Kerja:", "{} Tahun".format(masaKerja),
"| {}".format(fuzz.displayMasaKerja()))
print("> Produk Terjual:", "{} Unit".format(produkTerjual),
"| {}".format(fuzz.displayProdukTerjual()))
print("> Bonus Penjualan:", fuzz.displayBonus())
print("----------------------------------")
print(">> Total Bonus: {}".format(bonus.konversi()), "<<")
print("----------------------------------")
from fuzzy import Fuzzy
a = Fuzzy(0.7)
b = Fuzzy(0.4)
if not a:
print('ERROR : in line 7 a should be true')
if b:
print('ERROR : in line 10 b should be false')
Fuzzy.set_truth_threshold(3)
if Fuzzy.TRUTH_TRESHOLD > 1.0:
print('ERROR : thrut treshold set above 1')
Fuzzy.set_truth_threshold(0.3)
if not b:
print('ERROR : in line 10 b should be true')
c = Fuzzy(23)
if c > 1.0:
print('ERROR : thrut value set above 1')
if -a != 0.3:
print('ERROR: negation is incorrect ({})'.format(-a))
if (a | b) != 0.7:
print('ERROR: alternative is incorrect ({})'.format(a | b))
if a & b != 0.4:
print('ERROR: conjunction is incorrect ({})'.format(a & b))
def initialize(self, article):
similarity_array = []
# similarity_array.append(article)
test = self.articleSummerization(article, 1) # in one line
# for i in summerizedSentence:
# test=str(i)
print('-------Summerized Title-------')
print(test)
sitesContainingArticle, scrapId = self.googleSearch(article)
print('sites_length_after_google search', len(sitesContainingArticle))
for index, url in enumerate(sitesContainingArticle):
print('URL ', url, scrapId[index], '\n')
raw_html = self.simple_get(url) #full page site content
try:
soup = BeautifulSoup(
raw_html, 'html.parser') #proper formattinh raw_html
# print('hua idhar')
# print(soup)
except Exception as e:
print(e)
return 0, []
_ = [s.extract() for s in soup('script')]
soup_article = soup.find_all('div', {"class": scrapId[index]})
# print(soup_article)
article_string = ''
for data in soup_article:
# print(data)
article_string += data.text
# article_string += data.text
# print(article_string)
if not article_string == '':
# print('aaya\n')
similarity_array.append(
self.articleSummerization(article_string, 5))
else:
print('nahi aaya\n')
pass
# for c in similarity_array:
# print('\n\n\n',c)
mylsa = LSA()
wmdinit = WordMoverDistance()
length = len(similarity_array)
# print(length)
if length == 0:
return 0, sitesContainingArticle
else:
count = 0
score_array = []
while (count < length):
print('\n\n', similarity_array[count])
lsa_similarity = mylsa.start([article + ' ' + article] +
similarity_array, count + 1)
wmdinit.data_accept(similarity_array[count], article)
wmddistance = wmdinit.model()
print('wordmover distance is', wmddistance)
fuzzy = Fuzzy(lsa_similarity, wmddistance)
score = fuzzy.get_score_data()
# score = score/10
print('final score ', score)
score_array.append(score)
count = count + 1
score_array = sorted(score_array, key=lambda x: x, reverse=True)
return min(100,
np.around(sum(score_array[:2]), decimals=2) *
100), sitesContainingArticle
# wmdinit=wordmover.WordMoverDistance(titles[count],titles[0])
# wmddistance=wmdinit.model()