def main():
pair = "EUR_USD"
timeframe = "H1"
#ignore last datapoint since candle is incomplete
modelPath = 'models/EUR_USD_H1_lstm.h5'
model = load_model(modelPath)
data = getData(pair, timeframe, 150)
predictions = []
for i in range(100):
inputs = data[i:50 + i]
actual = data[50 + i]
print "Inputs:\n", inputs
normalizedInput, p0 = normalize(inputs)
reshapedInput = np.reshape(normalizedInput, (1, 50, 1))
normalizedPrediction = predict(model, reshapedInput)
#print "Normalized Prediction:\n", normalizedPrediction
denormalizedPrediction = denormalize(normalizedPrediction, p0)
#print "Denormalized Prediction:\n", denormalizedPrediction
predictions.append((str(denormalizedPrediction), (inputs[49], actual)))
for prediction in predictions:
print "Prediction: ", prediction[0], "Actual: ", prediction[1][
1], "Previous: ", prediction[1][0], "Direction: ", directionTrue(
prediction[1][1], prediction[1][0], prediction[0])
numDirectionTrue(predictions)
def single(routeid):
data , MinValue= getData(routeid)
if(len(data)<50):
return 0
#print('total:',len(data_df),'train:',len(train_df),'test:',len(test_df))
######
Features , Label ,TestFeatures , TestLabel, Label_NUM = preprocess(data)
###### building model
model = Sequential()
model.add(Dense(units=32,input_dim=3,kernel_initializer='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(units=32,kernel_initializer='uniform'))
#model.add(BatchNormalization())
model.add(Activation('relu'))
#model.add(Dense(units=16,kernel_initializer='uniform',activation='relu'))
#model.add(Dense(units=16,kernel_initializer='uniform',activation='relu'))
#model.add(Dense(units=4,kernel_initializer='uniform',activation='relu'))
#model.add(Dense(units=1,kernel_initializer='uniform',activation='sigmoid'))
######
###### training
model.add(Dense(units=Label_NUM,kernel_initializer='uniform'))
#model.add(BatchNormalization())
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',optimizer='Adagrad',metrics=['accuracy'])
train_history = model.fit(x=Features,y=Label,validation_split=0.5,epochs=50,batch_size=50,verbose=2)
######
path = 'models/' + routeid + '.h5'
model.save(path)
def login():
username, password = gd.getData() #get username,password from getData function module
if vp.verifyPassword(username, password): #verifies password
print('Successfully Logged in')
return True
else:
print("Invalid username or password")
def test_api_request():
if 'credentials' not in session:
return redirect('authorize')
events_result = get_event()
events = events_result.get('items', {})
t = getData.getData(events)
return jsonify(**t)
def daily_update_loop():
# start session with database
Session = sessionmaker(bind=engine)
session = Session()
week = 1 # TODO: find way of getting week of term from date
term = 1 # TODO: find way of getting term today
# loop through each day of the week and update the predicted value on that day
for day in range(1, 6):
predData = getData(term, week, day)
for i, time in enumerate(times):
data = session.query(Data).filter_by(day=days[day - 1],
time=time).first()
# for now, update with the average of the minumum and maximum
data.jnr_expected = predData["Jnr"][i]
data.snr_expected = predData["Snr"][i]
session.commit()
# find next time until update
current = datetime.datetime.now()
new = current.replace(
day=current.day, hour=1, minute=0, second=0,
microsecond=0) + datetime.timedelta(days=1)
secs = (new - current).total_seconds()
# wait for that difference in time
threading.Timer(secs, daily_update_loop).start()
def pretreQues(question):
new_question = question
corpus, documents_tf = textBuild.unknown()
vocabulary, documents = textBuild.clean_words(corpus,
documents_tf=documents_tf)
getdata = getData.getData()
stop_words = getdata.read_stopword()
vocabulary
que_list = jieba.cut(new_question) # 可能需要事先添加一些符合业务场景的专有词,不然结巴分词无法识别
que_list = " ".join(que_list)
que_list = que_list.split(" ")
List = []
for word in que_list:
if not (word.strip() in stop_words) and len(word.strip()) > 1:
List.append(word.lower())
isin = []
for word in List:
if word in vocabulary:
# print(True)
isin.append(0)
else:
# print(False)
isin.append(1)
# print(isin)
if sum(isin) == 0:
new_document = []
for word in List:
word_loc = vocabulary.index(word)
new_document.append(word_loc)
return new_document
else:
print("词库无法匹配")
def indexId(stId):
if current_user.is_authenticated == False:
return redirect(url_for('login'))
aa = stId.split("-")
name = getID.getName(aa[0])
data = getData.getData(aa[0])
datatoday = getData.getTodayCsv(aa[0])
datalive = getData.getLive(aa[0])
dataTec = getData.getAll(aa[0])
dataFin = getData.getAllFin(aa[0])
dataPre = getData.getPreByDay(aa[0], 10)
dataNews = getData.getNewsS(aa[0], n)
dataFav = accountSql.getlike(current_user.id)
dataInd = accountSql.getIde(current_user.id)
dataArt = accountSql.getArtcile(aa[0])
dataReArt = accountSql.getReArtcile(aa[0])
lenArt = len(dataArt)
toArt = "0"
inArt = "0"
if len(aa) == 2:
toArt = aa[1]
elif len(aa) == 3:
toArt = aa[1]
inArt = aa[2]
return render_template('index.html', stock = aa[0], name = name, re = data, today = datatoday, tec = dataTec, fin = dataFin, pre = dataPre, news = dataNews, n = n, reFav = dataFav, live = datalive, ind = dataInd, art = dataArt, lenArt = lenArt, reArt = dataReArt, toArt = toArt, inArt = inArt)
def getIngrident(name):
ItemList = []
response = table.query(KeyConditionExpression=Key('recipe_name').eq(name))
for items in response['Items']:
for key in items:
if ('Ingredient' in key):
ItemList.append(items[key])
cartList = getData(ItemList)
result = ""
missed = " ingredients that are not available are "
miss_product = ""
for product in cartList:
if (cartList[product].ItemNumber == 0):
miss_product += product + ", "
else:
result += cartList[product].Description + " of brand " + cartList[
product].Brand + " and price $" + str(
cartList[product].Price) + ", "
#item = response['Item']
if (miss_product == ""):
missed = ""
else:
missed += miss_product
# result+missed
return result + missed
def RunDenseNet(batch_size, nb_epoch, depth, nb_dense_block, nb_filter,
growth_rate, dropout_rate, weight_decay):
(X_train, Y_train), (X_test,
Y_test), nb_classes, img_dim = getData.getData()
model = DenseNet.DenseNet(nb_classes, img_dim, depth, nb_dense_block,
growth_rate, nb_filter, dropout_rate,
weight_decay)
model.summary()
"""Paper Suggests using SGD"""
opt = SGD(lr=0.0, momentum=0.9, nesterov=True, decay=weight_decay)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=["accuracy"])
"""Custom learning Schedule"""
lrs = LearningRateScheduler(custom_LR, verbose=1)
print("Training")
model.fit(x=X_train,
y=Y_train,
batch_size=batch_size,
epochs=nb_epoch,
callbacks=[lrs],
verbose=2)
print("Evaluating")
scores = model.evaluate(X_test, Y_test, batch_size=64, verbose=2)
print('Test Loss: ', scores[0])
print('Test Accuracy: ', scores[1])
model.save("model.h5")
def getDailyData():
# error trapping: inputs, get data
try:
# parse year, month, day
curDate = mainDate.get().strip().split("-")
year = int(curDate[0])
month = int(curDate[1])
day = int(curDate[2])
getDataObj = getData.getData(mainCompany.get().upper(), year, month,
day, year, month, day)
getDataObj.getDailyStockData()
curStock = getDataObj.data
except:
# show warning message
messagebox.showwarning("ERROR", "Invalid SYMBOL/DATE")
else:
if curStock == []:
# show warning message
message = "Market not open on " + mainDate.get().strip()
messagebox.showwarning("ERROR", message)
else:
report = "DATE" + ": " + str(curStock[0]) + "\n" + \
"OPEN" + ": " + str(curStock[1]) + "\n" + \
"HIGH" + ": " + str(curStock[2]) + "\n" + \
"LOW" + ": " + str(curStock[3]) + "\n" + \
"CLOSE" + ": " + str(curStock[4]) + "\n" + \
"VOLUME" + ": " + str(curStock[5])
# show warning message
messagebox.showinfo(mainCompany.get().upper(), report)
def predict(routeid, C, time, rainfall):
path = '/home/user/GraduationProject/models/' + routeid + '.h5'
if (os.path.isfile(path)):
model = load_model(path)
data, MinValue = getData(routeid)
test = pd.Series([C, time, rainfall, MinValue])
newdata = pd.DataFrame(
[list(test)],
columns=["C", "datacollecttime", "rainfall", "value"])
data = pd.concat([data, newdata])
Features, Label, Label_NUM = preprocess(data)
#scores = model.evaluate(x=Features,y=Label)
#print ('scores:',scores[1])
p = model.predict(Features[-1:])
temp = MinValue * 10
pro = 0
for i in p[0]:
#print("i:",i,"temp:",temp)
pro = pro + i * temp
temp += 10
print(routeid, pro)
else:
print(routeid, "NULL")
print(" ")
def originData():
getdata = getData()
data = getdata.read_text()
stop_words = getdata.read_stopword()
doc_content = data
dict_data = doc_content.split("\n")
dict_data.remove("")
return dict_data, stop_words
def login():
uname, password = gd.getData(2)
flag = lgv.loginValidate(uname, password) #validation of user
if flag == True:
print("Successfully logged in")
return True
else:
print("Invalid credentials")
return False
def getDataAndToken(opt):
'''
获取爬虫出的已经处理好的 token list
'''
cnt, title, desc = data.getData(opt)
d_tokens = []
for d in desc:
d_nopunc = token.HandlePunctuation(d)
d_tokens += token.getToken(d_nopunc)
return d_tokens
def test_search_valid_pokemon(self):
my_data = getData()
pokemon = my_data.get('kakuna')
my_pokemon = pokemon.json()
with open('kakuna.json') as kakuna:
data = json.load(kakuna)
self.assertEqual(data['weight'], my_pokemon['weight'])
for i in my_pokemon['moves']:
#print(i['move']['name'])
self.assertTrue(i['move']['name'] in data['moves'])
def run(*args):
print ("connected.....");
while True:
wind_tenBit = 0;
Ubatt_tenBit = 0;
Ibatt_tenBit = 0;
for i in range(config.times):
wind_tenBit = wind_tenBit + getData.getData(config.windChannel)
Ubatt_tenBit = Ubatt_tenBit + getData.getData(config.UbattChannel)
Ibatt_tenBit = Ibatt_tenBit + getData.getData(config.IbattChannel)
time.sleep((config.delay) / config.times)
wind_tenBit = (wind_tenBit / (config.times));
Ubatt_tenBit = (Ubatt_tenBit / (config.times));
Ibatt_tenBit = (Ibatt_tenBit / (config.times));
ws.send('{"pw":"' + config.password + '", "data":[{"wind":' + str(wind_tenBit) + ',"Ubatt":' + str(Ubatt_tenBit) + ',"Ibatt":' + str(Ibatt_tenBit) + '}]}') # sending data in an json format to the server
def register_user():
uname, password, name, email, contact = gd.getData(1)
amount = int(input("Enter amount to be deposited "))
f = open('UserData.txt', 'a+')
f1 = open('Backup.txt', 'a+')
f.write(uname + " " + password + " " + name + " " + email + " " + contact +
" " + str(amount) + "\n") #save details in UserData.txt file
f1.write(uname + " " + password + " " + name + " " + email + " " +
contact + " " + str(amount) +
"\n") #create a Backup file concurrently
f.close()
f1.close()
print("Successfully registered ")
return True
def evaluate():
batches_in_epoch = batch_size * 10
accuracy = 0
for i in xrange(batches_in_epoch):
nextX, nextY, objCoords, _ = getData(batch_size, datasetName, img_size, img_size, hasLabel, maxNumObj)
feed_dict = {inputs_placeholder: nextX, labels_placeholder: nextY,
onehot_labels_placeholder: dense_to_one_hot(nextY)}
r = sess.run(reward, feed_dict=feed_dict)
accuracy += r
accuracy /= batches_in_epoch
print("ACCURACY: " + str(accuracy))
def input(runSettingIn, populationSizeIn, filename):
global runSetting
global populationSize
global C
global N
runSetting = runSettingIn
populationSize = populationSizeIn
tab, N, C = getData(filename)
for i in range(0, N):
Weight.append(tab[i][0])
Profits.append(tab[i][1])
populationSize = populationSizeIn
runSetting = runSettingIn
def relocate(name):
g = geocoder.google(name)
x, y = g.lat, g.lng
if g.status == 'OK':
landslide_map = getData([g.southwest, g.northeast])
currentMap[0] = landslide_map
return render_template("layout.html",
view_map=landslide_map,
location=name)
else:
print("Going to the Default")
return render_template("layout.html",
view_map=currentMap[0],
location='Not found',
error="Could not locate {}".format(name))
def verbaDeputado(numId): url = 'http://dadosabertos.almg.gov.br/ws/prestacao_contas/verbas_indenizatorias/legislatura_atual/deputados/'+str(numId)+'/datas?formato=json' deputado = [] deputado = getData(url)["list"] for dataVerba in deputado: ano = dataVerba["dataReferencia"]["$"][0]+dataVerba["dataReferencia"]["$"][1]+dataVerba["dataReferencia"]["$"][2]+dataVerba["dataReferencia"]["$"][3] mes = dataVerba["dataReferencia"]["$"][5]+dataVerba["dataReferencia"]["$"][6] if mes[0] == "0": mes = mes[1] url2 = 'http://dadosabertos.almg.gov.br/ws/prestacao_contas/verbas_indenizatorias/deputados/'+str(numId)+'/'+str(ano)+'/'+str(mes)+'?formato=json' print url2
def getValores(self):
saidaValores = []
pegaValores = getData()
for cond in self.getConditions():
for c in cond:
#print "Condicao: ", c
aplicaoExecutar = conexaoMySql.consulta(self, "SELECT * FROM aplications WHERE idaplications = " + str(c[1]) + "");
horaInicio = datetime.now() - timedelta(seconds=aplicaoExecutar[0][4])
#print "hora de Inicio ", horaInicio
valorDaCondicao = []
valorDaCondicao = pegaValores.pegaDados(c, horaInicio)
saidaValores.append({"condicao":c, "valor":valorDaCondicao})
del pegaValores
return saidaValores
def login():
"""View function for login view."""
logger.info('Logging in')
params = request.get_json()
username = params.get('username', None)
password = params.get('password', None)
if not username:
return jsonify({"msg":
"Missing username parameter"}), Status.HTTP_BAD_REQUEST
if not password:
return jsonify({"msg":
"Missing password parameter"}), Status.HTTP_BAD_REQUEST
try:
db = MySQLdb.connect(
host="localhost", # your host
user="******", # username
passwd="root", # password
db="traffic")
query = "select password from users where username='******'"
cur = db.cursor()
cur.execute(query)
count = cur.rowcount
if count == 0:
logger.info("Login Failed Due to missing username")
return jsonify({"msg": "Incorrect Username"
}), Status.HTTP_BAD_UNAUTHORIZED
data = cur.fetchall()
if password != data[0][0]:
logger.info("Login Failed Due to incorrect password")
return jsonify({"msg": "Incorrect password"
}), Status.HTTP_BAD_UNAUTHORIZED
ret = {
'jwt': create_jwt(identity=username),
'exp': datetime.utcnow() + current_app.config['JWT_EXPIRES']
}
logger.info('Getting data from S3 bucket')
global trafficData
trafficData = getData.getData()
logger.info("Data retrieval successful")
return jsonify(ret), Status.HTTP_OK_BASIC
except:
logger.info('Failed')
return jsonify({"msg": "Server Error"}), Status.HTTP_BAD_REQUEST
def main():
dataset = Dataset()
counts = {
'cpvs_not_found': 0,
'it': 0,
'not_it': 0
}
idsDictionary = getIds('jan2020.csv')
for date in idsDictionary:
ids = idsDictionary[date]
for id in ids:
data = getData(id)
# soup = BeautifulSoup(data, 'html.parser')
# cpvs = extractCPVsFromSoup(soup)
cpvs = extractCPVsFromData(data)
# print('Found CPVs:', cpvs)
if (cpvs is not None):
if(containsITCPV(cpvs)):
investments = extractInvestmentsFromData(data)
print('Adding document to dataset...')
dataset.addEntry({
'id': id,
'cpv': cpvs,
'date': date,
'investments': investments
})
counts['it'] += 1
else:
counts['not_it'] += 1
else:
raise Exception('CPVs NOT FOUND ' + id)
counts['cpvs_not_found'] += 1
print('Results:')
print('IT Licitations Found: ' + str(counts['it']))
print('Not-IT Licitations Found: ' + str(counts['not_it']))
print('Licitations with unknown CPVs: ' + str(counts['cpvs_not_found']))
print('Exporting dataset...')
dataset.exportAsCSV('./data.csv')
def regDeputado(numId):
url = 'http://dadosabertos.almg.gov.br/ws/deputados/'+str(numId)+'?formato=json'
registroDeputado = []
registroDeputado = getData(url)["deputado"]
#print "\n\tRedes Sociais\n"
# output("saida.out", "\n\tRedes Sociais\n")
# dicio = {}
for redeSoc in registroDeputado["redesSociais"]:
print "\t\t", redeSoc["redeSocial"]["nome"], "-", redeSoc["url"]
# dicio.update({redeSoc["redeSocial"]["nome"]:redeSoc["url"]})
# saida = "\t\t"+redeSoc["redeSocial"]["nome"]+" - "+redeSoc["url"]+"\n"
# output("saida.out", saida.encode('utf-8'))
saida = "\t\t{\n\t\t\"redeSocial\": {\n\t\t\t\"nome\": \""+redeSoc["redeSocial"]["nome"]+"\",\n\t\t\t\"url\": \""+redeSoc["url"]+"\",\n\t\t},\n\t}"
output("teste.out", saida.encode('utf-8'))
def evaluate():
batches_in_epoch = batch_size * 10
accuracy = 0
for i in xrange(batches_in_epoch):
nextX, nextY, objCoords, _ = getData(batch_size, datasetName, img_size,
img_size, hasLabel, maxNumObj)
feed_dict = {
inputs_placeholder: nextX,
labels_placeholder: nextY,
onehot_labels_placeholder: dense_to_one_hot(nextY)
}
r = sess.run(reward, feed_dict=feed_dict)
accuracy += r
accuracy /= batches_in_epoch
print("ACCURACY: " + str(accuracy))
def main():
default_threshold_PCA = cf.config("default_threshold_PCA")
default_threshold_label = cf.config("default_threshold_label")
print "---------------------------------------------------------------------"
mode = raw_input(
"Mode? 'e'=estimateAccuracy, 't'=testing, 'g'=getData -> ")
print "---------------------------------------------------------------------"
# estimateAccuracy mode
if (mode == "e"):
flag_PCA = raw_input(
"Principle Component Analysis? 'y'=yes, [press enter]=no -> ")
if (flag_PCA == "y"):
threshold_PCA = raw_input(
"PCA feature number (1 to 37)? [press enter]=default -> ")
if (threshold_PCA == ""): threshold_PCA = default_threshold_PCA
tr.estimateAccuracy(True, float(threshold_PCA))
else:
tr.estimateAccuracy(False, 1.1)
# getData mode
elif (mode == "g"):
fileName = raw_input("File name for saving data? -> ")
gd.getData(fileName, mode, False, 1.1, 0)
# test mode
elif (mode == "t"):
flag_PCA = raw_input(
"Principle Component Analysis? 'y'=yes, [press enter]=no -> ")
if (flag_PCA == "y"):
threshold_PCA = raw_input(
"PCA feature number (1 to 37)? [press enter]=default -> ")
if (threshold_PCA == ""): threshold_PCA = default_threshold_PCA
threshold_label = raw_input(
"Label value threshold for prediction? [press enter]=default -> "
)
if (threshold_label == ""):
threshold_label = default_threshold_label
fileName = raw_input("File name for saving data? -> ")
gd.getData(fileName, mode, True, float(threshold_PCA),
long(threshold_label))
else:
threshold_label = raw_input(
"Label value threshold for prediction? [press enter]=default -> "
)
if (threshold_label == ""):
threshold_label = default_threshold_label
fileName = raw_input("File name for saving data? -> ")
gd.getData(fileName, mode, False, 1.1, long(threshold_label))
# wrong mode
else:
print "wrong mode... program terminated"
def main(): pair = "USD_JPY" timeframe = "H1" data = getData(pair, timeframe, 51) #ignore last datapoint since candle is incomplete inputs = data[0:50] print "Inputs:\n", inputs modelPath = 'models/lstm.h5' model = load_model(modelPath) normalizedInput, p0 = normalize(inputs) reshapedInput = np.reshape(normalizedInput, (1,50,1)) normalizedPrediction = predict(model, reshapedInput) print "Normalized Prediction:\n", normalizedPrediction denormalizedPrediction = denormalize(normalizedPrediction, p0) print "Denormalized Prediction:\n", denormalizedPrediction
def TrimmingFile():
"""
Purpose:
Trim Koustav's Data file to get a list of dates
Pre-conditions:
Data files and program to read in data files must exist
Current data files include
03_10_12_diff_RKN_15_21_ut_all_data.txt ; many different days
Post-conditions:
Output a file with just the days
Return:
none
"""
FILE = "03_10_12_diff_RKN_15_21_ut_all_data.txt"
SAVE_PLOTS = False
# < -- GET DATA -- >
my_data = getData(FILE)
month = np.asarray(my_data[0])
day = np.asarray(my_data[1])
def loadCompany():
global loaded, dataList, fullDateList, dateList, openList, highList, lowList, closeList, volumeList, year
# error trapping: inputs, get data
try:
# get and save data
getDataObj = getData.getData(mainCompany.get().upper(),
int(mainYear.get()), 1, 1,
int(mainYear.get()), 12, 31)
getDataObj.getStockData()
dataList = getDataObj.dataList
year = dataList[0][0][:4]
except:
# show warning message
messagebox.showwarning("ERROR", "Invalid SYMBOL/YEAR")
else:
# insert datalist into DB table
dbTable.insertAllData(dataList)
# parse data into seprated list
fullDateList = []
dateList = []
openList = []
highList = []
lowList = []
closeList = []
volumeList = []
for x in dataList:
fullDateList.append(x[0])
dateList.append(x[0].replace("-", ".")[5:7])
openList.append(x[1])
highList.append(x[2])
lowList.append(x[3])
closeList.append(x[4])
volumeList.append(x[5])
# print to console
loaded = True
print("\nData Loaded:", mainCompany.get().upper())
def gS(filename, verbose, num_conv):
# create output file
outStats = open(r'output_files/out_stats.csv', 'w')
# write header
outStats.write("conv_id,#_messages,#_authors\n")
# read data in from xml
data = getData(filename)
# list of all conversations in the data set
conversations = range(1, 2489)
# determine how many conversations to to the analysis on.
if num_conv is None:
num_conv = len(conversations)
for i in range(1, num_conv + 1):
# iterate through the requested number of conversations
authors = {}
num_messages = 0
for msg in data[4:]:
# if not the number we want, skip to next message
if (int(msg.items()[0][1]) != i): continue
# if we have passed the message, break for efficiency
if (int(msg.items()[0][1]) > i): break
# increment number of messages
num_messages += 1
# author of the message is given in the first index
auth = msg[1].text
if (auth not in authors):
authors[auth] = 1
else:
authors[auth] += 1
# number of authors is given by the number of keys in the authors dict, which are all unique
num_authors = len(authors.keys())
# write to output file
outStats.write("{:d},{:d},{:d}\n".format(i, num_messages, num_authors))
# optional command line output.
if (verbose):
print("Conversation Number {}:".format(i))
print("{} total message(s)".format(str(num_messages)))
print("{} total author(s)".format(str(num_authors)))
print("End Conversation {}.".format(i))
print("")
def getMap(artistName, n):
ngramMap = {}
rhymeMap = {}
lyrics = getData.getData(artistName)
totalSyllables = 0
numLines = 0
rhymeWord = "not chosen yet"
lines = lyrics.split('\n')
numLines += len(lines)
for line in lines:
#line = unidecode.unidecode(line)
words = line.split()
if len(words) == 0: continue
for i in range(0, len(words)):
prevWords = [None for x in range(0, n)]
for j in range(1, n + 1):
if i - j >= 0:
prevWords[-j] = words[i - j]
totalSyllables += nsyl(words[i])[0]
ngram = tuple(prevWords)
if ngram in ngramMap:
ngramMap[ngram].append(words[i])
else:
ngramMap[ngram] = [words[i]]
if rhymeWord == "not chosen yet":
rhymeWord = words[-1]
else:
if rhymeWord in rhymeMap:
rhymeMap[rhymeWord].append(words[-1])
else:
rhymeMap[rhymeWord] = [words[-1]]
if words[-1] in rhymeMap:
rhymeMap[words[-1]].append(rhymeWord)
else:
rhymeMap[words[-1]] = [rhymeWord]
rhymeWord = "not chosen yet"
avgSyllables = totalSyllables / float(numLines)
return ngramMap, rhymeMap, avgSyllables
def rP(filename, verbose, num_conv):
# create output files
outQs = open(r'output_files/questions_found.csv', 'w')
outQs.write("conv_id,#_questions\n")
outTxtSpk = open(r'output_files/txtspk_found.csv', 'w')
outTxtSpk.write("conv_id,#_txt_abbreviations\n")
# read data in from xml
data = getData(filename)
# group data by conversation id
conversations = {}
for message in data[4:]: # ignore starting info
conv_id = int(message.items()[0][1]) # conversation number
if (conv_id not in conversations):
conversations[conv_id] = [message[2].text]
else:
conversations[conv_id] = conversations[conv_id] + [message[2].text]
if num_conv is None:
num_conv = len(conversations.keys())
for i in range(1, num_conv + 1):
if (verbose): print("Conversation Number {}:".format(i))
messages = conversations[i]
# check for questions
result = checkFor(messages, r'.*\?.*')
num_questions = sum(result)
outQs.write(str(i) + "," + str(num_questions) + "\n")
if (verbose): print("{} question(s)".format(num_questions))
# check for text speak
result = [0] * num_conv
for reg in text_speak:
temp = checkFor(messages, reg, ignorecase=True)
result = [sum(x) for x in zip(result, temp)]
num_text_speak = sum(result)
outTxtSpk.write(str(i) + "," + str(num_text_speak) + "\n")
if (verbose): print("{} text abbreviation(s)".format(num_text_speak))
if (verbose): print("End Conversation {}.".format(i))
if (verbose): print("")
outQs.close()
outTxtSpk.close()
def __init__(self):
tk.Tk.__init__(self)
data = gD.getData('*', 'customerPerformanceInfo')
rowN = 1
for n in data:
rowN += 1
t = SimpleTable(self, rowN, 6)
t.pack(side="top", fill="x")
t.set(0, 0, 'SessionID')
t.set(0, 1, 'CustomerID')
t.set(0, 2, 'Start Session')
t.set(0, 3, 'End Session')
t.set(0, 4, 'fitness Device')
t.set(0, 5, 'burnt calories')
x = 1
y = 0
for i in data:
for r in i:
t.set(x, y, r)
y += 1
if y == 6:
x = x + 1
y = 0
def __init__(self):
tk.Tk.__init__(self)
data = gD.getData('*', 'customerPerformanceInfo')
rowN = 1
for n in data:
rowN +=1
t = SimpleTable(self, rowN,6)
t.pack(side="top", fill="x")
t.set(0,0,'SessionID')
t.set(0,1,'CustomerID')
t.set(0,2,'Start Session')
t.set(0,3,'End Session')
t.set(0,4,'fitness Device')
t.set(0,5,'burnt calories')
x=1
y=0
for i in data:
for r in i:
t.set(x,y,r)
y+=1
if y==6:
x=x+1
y=0
def prepareData():
"""
function to prepare padded sequences of titles concatenated with texts for training and testing
the dates and subjects of the news have been excluded as they don't explain much the news being real of fake
:return:
trainPad: numpy array of padded sequences of titles followed by texts for training
trainLabels: numpy array of training labels [0 for real news, 1 for fake news]
testPad: numpy array of padded sequences of titles followed by texts for testing
testLabels: numpy array of testing labels [0 for real news, 1 for fake news]
"""
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
[[trainData, trainLabels], [testData, testLabels]] = getData(trainSize=0.9)
categories = list(trainData)
# Tokenize title and text
tt_tokenizer = Tokenizer(num_words=parameters["vocab_size"])
tt_tokenizer.fit_on_texts(trainData['title'] + " : " +
trainData['text'])
trainSeq = tt_tokenizer.texts_to_sequences(trainData['title'] + " : " +
trainData['text'])
trainPad = pad_sequences(trainSeq,
maxlen=parameters["max_len"],
padding=parameters["padding"],
truncating=parameters["truncate"])
testSeq = tt_tokenizer.texts_to_sequences(testData['title'] + " : " +
testData['text'])
testPad = pad_sequences(testSeq,
maxlen=parameters["max_len"],
padding=parameters["padding"],
truncating=parameters["truncate"])
return [[np.array(trainPad), np.array(trainLabels)], [np.array(testPad), np.array(testLabels)]]
def infoDeputado(status):
cont = 0
if status == 1:
listaDeputado = []
listaDeputado = getData('http://dadosabertos.almg.gov.br/ws/deputados/situacao/1?formato=json')["list"]
lista = []
dicio = {"lista":lista}
for deputado in listaDeputado:
print deputado["nome"], "-", deputado["partido"]
# saida = deputado["nome"]+" - "+deputado["partido"]
# output("saida.out", saida.encode('utf-8'))
# lista.append(regDeputado(deputado["id"])
saida = "{\n\t\"nome\": \""+deputado["nome"]+"\",\n\t\"partido\": \""+deputado["partido"]+"\",\n\t\"redesSociais\": ["
output("teste.out", saida.encode('utf-8'))
regDeputado(deputado["id"])
saida = "\t],\n},\n"
output("teste.out", saida.encode('utf-8'))
# verbaDeputado(deputado["id"])
print dicio["lista"]
# Este script captura os deputados em exercício da ALMG
# link: http://dadosabertos.almg.gov.br/ws/deputados/ajuda#Lista de Deputados em Exercício
#
# O que retornar
import urllib
import json
from StringIO import StringIO
from getData import getData
#***********************************************************************************************#
# Inicia filtrando apenas os deputados em exercício.
# Isto é, do array info[] apenas importa o IDentificador de cada deputado em exercício
info = []
info = getData('http://dadosabertos.almg.gov.br/ws/deputados/em_exercicio?formato=json')["list"]
#***********************************************************************************************#
# Tendo o IDentificador de deputados em exercício, as próximas linhas criam um array com a url
# APENAS dos deputados com o ID em questão (que estão em exercício).
# Também estou coletando PARTIDO e NOME dessa lista.
#url = []
filiacoes = []
for data in info:
# url.append("http://dadosabertos.almg.gov.br/ws/deputados/" + str(data["id"]) + "?formato=json")
j = {}
j['name'] = str(data['partido'])
# del data['partido']
import urllib
import json
from StringIO import StringIO
from getData import getData
info = []
info = getData('http://dadosabertos.almg.gov.br/ws/proposicoes/pesquisa/direcionada?tipo=PL&sitTrami=0&formato=json')["resultado"]
for data in info["listaItem"]:
print "-------------------------------------------"
print "Nome: ", data["proposicao"].encode("utf-8")
print "Autor: ", data["autor"].encode("utf-8")
print ""
if "assuntoGeral" in data:
print "Assunto Geral: ", data["assuntoGeral"].encode("uff-8")
else:
print "Assunto Geral: null"
print ""
if "indexacao" in data:
print "Indexacao: ", data["indexacao"].encode("uff-8")
else:
print "Indexacao: null"
print "-------------------------------------------"
from getData import getData
title = raw_input("Enter a movie title ")
title = title.replace(' ', '%20')
request = getData()
rawData = request.getJSON("http://www.omdbapi.com/?t=" + title)
data = request.toJSON(rawData)
keys = ["Released", "Runtime", "Genre", "Actors", "Director", "Writer", "Plot"];
for key in keys:
print data[key];
raw_input("Completed..")
import sys
import os
from getEpic import ConvertToHtml, ParseEpicFromHtml
from getData import getData
if __name__ == "__main__":
fileName = sys.argv[1]
print("Converting PDF to html")
html = ConvertToHtml(fileName)
print("Parsing Epic No from html")
ParseEpicFromHtml(html)
os.remove("output.html")
print("Getting Data from CEO site")
getData("data.xlsx")
barsSinceEntry = 0 numRuns = 0 myBPV = 0 allowPyr = 0 curShares = 0 #--------------------------------------------------------------------------------- #End of Lists and Variables #--------------------------------------------------------------------------------- #--------------------------------------------------------------------------------- #Get the raw data and its associated attributes [pointvalue,symbol,tickvalue] #Read a csv file that has at least D,O,H,L,C - V and OpInt are optional #Set up a portfolio of multiple markets #--------------------------------------------------------------------------------- dataClassList = getData() numMarkets = len(dataClassList) portfolio = portfolioClass() #--------------------------------------------------------------------------------- # SET COMMISSION, NUMBER OF BARS TO BACK TEST, AND RAMP UP FOR INDICATORS #--------------------------------------------------------------------------------- commission = 100 # deducted on a round turn basis numBarsToGoBack = 2000 # number of bars from the end of data rampUp = 100 # need this minimum of bars to calculate indicators sysName = 'TripleMA' #System Name here #//////// DO NOT CHANGE BELOW ///////////////////////////////////////////////// for marketCnt in range(0,numMarkets):
def login():
"""
Here we read a rfid card and try to log the user in.
firstly we get the rfidCards from the database.
"""
dbData = gD.getData('loggedIn, rfidNumber, customerID', 'loginInfo')
global cardA, keypad, backData, scanPass
while True:
breakOut = reader(False)
cardData = backData
cardData = str(cardData)
backData = ""
if keypad.getKey() == "*" or breakOut == True:
sleep(1)
if scanPass != "":
close(scanPass)
scanPass = ""
#turn off all led colors
GPIO.output(ledRed, False)
GPIO.output(ledGreen, False)
GPIO.output(ledBlue, False)
break
else:
rowCounter = 1
for row in dbData:
if cardData == row[1] and row[0] == 1:
# GPIO.output(ledRed, False)
# GPIO.output(ledGreen, True)
servoThread = threading.Thread(target=turnServo)
servoThread.start()
for i in range(3):
GPIO.output(ledGreen, False)
sleep(0.3)
GPIO.output(ledGreen, True)
sleep(0.3)
# GPIO.output(ledGreen, False)
# GPIO.output(ledRed, True)
updateInjection = ('customerID = ' + str(row[2]))
gD.updateData('loginInfo', 'loggedIn = false', updateInjection)# 0 en 1?
sleep(1)
elif cardData == row[1] and row[0] == 0:
servoThread = threading.Thread(target=turnServo)
servoThread.start()
for i in range(3):
GPIO.output(ledGreen, False)
sleep(0.3)
GPIO.output(ledGreen, True)
sleep(0.3)
#dataID = gD.getDataWhere('customerID', 'customerInfo', 'RFIDNumber={}'.format(row[1]))
updateInjection = ('customerID = ' + str(row[2]))
gD.updateData('loginInfo', 'loggedIn = true', updateInjection)# 0 en 1?
sleep(1)
elif cardData != row[1] and rowCounter == len(dbData):
print("niet oke")
GPIO.output(ledGreen, False)
for i in range(5):
GPIO.output(ledRed, True)
sleep(0.5)
GPIO.output(ledRed, False)
sleep(0.5)
sleep(1)
GPIO.output(ledGreen, True)
rowCounter += 1
sleep(0.1)
from getData import getData
pyplot.close("all")
showImage = 1
# datasetName = 'oneObj_big'
datasetName = 'multiObj_balanced'
hasLabel = True
img_size1 = 90
img_size2 = 90
batch_size = 10
maxNumObj = 7
selectedImgIdx = 0
imgBatch, nextY, coords, _ = getData(batch_size, datasetName, img_size1, img_size2, hasLabel, maxNumObj)
for i in xrange(batch_size):
nextY[i] = int(nextY[i])
print 'image batch dimension:'
print np.shape(imgBatch)
thiscoord = coords[selectedImgIdx]
print np.shape(coords)
print np.shape(thiscoord)
print thiscoord[:,0]
print thiscoord[:,1]
print nextY
if draw:
fig = plt.figure()
txt = fig.suptitle("-", fontsize=36, fontweight='bold')
plt.ion()
plt.show()
plt.subplots_adjust(top=0.7)
plotImgs = []
# training
for step in xrange(start_step + 1, max_iters):
start_time = time.time()
# get the next batch of examples
# nextX, nextY = dataset.train.next_batch(batch_size)
nextX, nextY, objCoords, _ = getData(batch_size, datasetName, img_size, img_size, hasLabel)
# print nextX
# print nextY
# sys.exit('STOP')
# if translateMnist:
# nextX, nextX_coord = convertTranslated(nextX, MNIST_SIZE, img_size)
feed_dict = {inputs_placeholder: nextX, labels_placeholder: nextY, \
onehot_labels_placeholder: dense_to_one_hot(nextY), b_placeholder: b_fetched}
fetches = [train_op, cost, reward, predicted_labels, correct_labels, glimpse_images, b, avg_b, rminusb, \
p_loc_orig, p_loc, mean_locs, sampled_locs, outputs[-1], lr]
# feed them to the model
results = sess.run(fetches, feed_dict=feed_dict)
_, cost_fetched, reward_fetched, prediction_labels_fetched, correct_labels_fetched, f_glimpse_images_fetched, \
b_fetched, avg_b_fetched, rminusb_fetched, p_loc_orig_fetched, p_loc_fetched, mean_locs_fetched, sampled_locs_fetched, \
#!/usr/bin/env python
import os
import sys
import re
from getFile import getFile
from getData import getData
from toXls import toXls
from getCalc import getCalc
# file = open(filePath,'r')
def display():
'''Display data in graph
Not currently implemented
'''
a = 1
if __name__=='__main__':
path = getFile()
data = getData(path)
data = getCalc(data)
toXls(data,path)
display()
#获取预测结果 preValue = n.activate(preData) #设置预测值 if preValue > 0: v = 1 else: v = -1 #将预测结果保存到数据库中 g.save(v,time) if __name__ =='__main__': #获取当天时间 time = g.todayTime() #获取最新的训练数据和预测数据 trainData,preData,num = g.getData() #构建神经网络和数据集 n = createBP(num,num+1,1) DS = getDS(num,1,trainData) #训练并预测神经网络 predict(n,DS,preData)