def run():
limit = 10
collection = generate_collections()
fuzzy = Fuzzy(collection)
print(fuzzy)
while True:
search = input("> ")
if not search:
return
start_time = time.time()
result = fuzzy.search(search)
has_more = len(result) - limit
if has_more == 1:
limit += 1
print("\"%s\" [%s of %s results (%.6f seconds)]" % (
search,
min(limit, len(result)),
len(result),
time.time() - start_time
))
result = result[:limit]
for text in result:
print("%s[%8.2f] %s" % (
" " * 2,
text[0],
format_text(text[1], text[2])
))
if has_more > 1:
print(" " * 2 + "...")
def getCalculation():
maximumImplicant = Fuzzy.getDefuzyfikasi()
denomi = Fuzzy.getDenoNomi()
data = {'rules' : Fuzzy.listOfUsedRule,'insideTempMember':Fuzzy.getInsideTemp(),
'outsideTempMember':Fuzzy.getOutsideTemp(),'peopleMember':Fuzzy.getPeople(),
"detail":Fuzzy.getLinguisticValue(),"maximum-Implicant":maximumImplicant,"defuzifikasi":denomi}
return jsonify(data),200
def algo_fuzzy(links, channels):
"""
@param links
@param channels
"""
# a_link is the link. NOT USED IN ALGO_FUZZY
#::TODO:: english
# INFORMACOES DISPONIVEIS:
# - taxa ocupacao canal n pelo US
# - taxa ocupacao canal n pelo US
# - canais livres
# - BER
#
channel_pu_presence = []
channel_su_presence = []
for ch in channels:
pu_occ = globs.p_up_occ_count[ch]
pu_total = globs.p_up_occ_count[ch] + globs.p_up_idle_count[ch]
channel_pu_presence.append(100.0 * pu_occ / float(pu_total) if pu_total > 0 else 0)
su_occ = globs.p_channels_count[ch]
su_total = globs.p_total_iterations
channel_su_presence.append(100.0 * su_occ / float(su_total) if su_total > 0 else 0)
rewards = Fuzzy.full_process(channel_pu_presence, channel_su_presence)
# map {ch1: reward, ch2: reward}
valid_reward = {}
for i in channels:
valid_reward[i] = rewards.pop(0)
# get higher rewards
ret = {}
for l in links:
if valid_reward:
# Search highest reward
v_cur = -1
k_cur = -1
for k, v in valid_reward.iteritems():
if v > v_cur:
k_cur = k
v_cur = v
# remove item from valid_rewards
if k_cur > -1:
ret[l] = k_cur
del valid_reward[k_cur]
return ret
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 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 start(self, quandoTermina):
if self.pb:
self.pb.show()
i = 0
while (i<len(self.listaDeArquivos)):
arquivo = self.listaDeArquivos[i]
label = str(str(arquivo).split('/')[-1])
im3 = Image.open(str(arquivo))
# Filtro 1 { Protan }
if self.pb:
self.pb.setLabel('[1/6] Aplicando filtro Protan em ' + str(label))
filtro1 = FiltroDeImagem(debug=False)
filtro1.carregarImg(str(arquivo))
filtro1.callBackPogresso(self.percentagem)
im1 = filtro1.filtrarProtan(equalizar = self.equalizar, lms = self.lms)
# Filtro 2 { Deutan }
if self.pb:
self.pb.setLabel('[2/6] Aplicando filtro Deutan em ' + str(label))
filtro2 = FiltroDeImagem(debug=False)
filtro2.carregarImg(str(arquivo))
filtro2.callBackPogresso(self.percentagem)
im2 = filtro2.filtrarDeutan(equalizar = self.equalizar, lms = self.lms)
# Fuzzy 1
fuz1 = Fuzzy(False,self.p,self.de,self.da,self.n)
if self.pb:
self.pb.setLabel('[3/6] Aplicando filtro Fuzzy 1 em ' + str(label))
fuz1.callBackProgresso(self.percentagem)
im1 = fuz1.multiplicaProtan(im1)
# Fuzzy 2
#fuz2 = Fuzzy(False,self.p,self.de,self.da,self.n)
if self.pb:
self.pb.setLabel('[4/6] Aplicando filtro Fuzzy 2 em ' + str(label))
fuz1.callBackProgresso(self.percentagem)
im2 = fuz1.multiplicaDeutan(im2)
if self.pb:
self.pb.setLabel('[5/6] Aplicando filtro Fuzzy 3 em ' + str(label))
fuz1.callBackProgresso(self.percentagem)
im3 = fuz1.multiplicaNormal(im3)
# Soma da matrizes
if self.pb:
self.pb.setLabel('[6/6] Aplicando soma de matrizes em ' + str(label))
fuz1.callBackProgresso(self.percentagem)
im4 = Image.open(str(arquivo))
im4 = fuz1.soma(im1, im2, im3, im4)
im4.save("default_output/" + str(i) + ".bmp", "BMP")
self.resultado.append((label, str(arquivo), "default_output/" + str(i) + ".bmp"))
i = i + 1
#quandoTermina(self.resultado)
#self.pb.hide()
return self.resultado
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("----------------------------------")
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()
def getAcTemp():
data = request.get_json()
Fuzzy.valueIinitialization(data['insideTemp'],data['outsideTemp'],data['people'])
bestTemp = Fuzzy.inferensi()
return jsonify({'OptimalTemperature': bestTemp}),200
