def save(self, fileName=None):
"""Saves pickled accounts to a file. The parameter allows the user to change filenames."""
if fileName != None:
self._filename = fileName
elif fileName == None:
return
fileObj = open(self._fileName, 'wb')
for account in self._accoutns.values():
pickle.dumb(account, fileObj)
fileObj.close()
def crearArchivo():
titulos = ("El Negro Ilusionado de Nuevo",
"El Corazon Roto del Negro", "Nico Encarando", "TECNO no Trates De Entenderlo", "Vamo A La Barra",
"La Perra de la UTN", "Tengo Gastroenteritis")
m = open("series.dat","wb")
for i in range(len(titulos)):
genero = random.ranrange(6)
idioma = random.ranrange(5)
temporadas = random.ranrange(30)
duracion = random.ranrange(300)
serie = Serie(titulos[i],genero,idioma,temporadas,duracion)
pickle.dumb(serie,)
def alta(FD):
m = open(FD,"ab")
print("Ingrese Legajo: ")
legajo = validar_legajo(1,100000)
while legajo != 0:
posicion = buscar(FD,legajo)
if posicion == None or posicion == -1:
nombre = input("Ingrese Nombre: ")
nombre = nombre.ljust(30," ")
print("Ingrese promedio: ")
promedio = validar_promedio()
registro = Estudiante(legajo, nombre, promedio)
pickle.dumb(registro, m)
else:
print("Legajo Existente: Alta Rechazada")
print("Ingrese otro legajo:")
legajo = validar_legajo(1,100000)
m.close()
print("Fin opeacion de alta")
input("Ingrese <ENTER> Para Continuar...")
def Modihome(b, a): #to modify resident information
fin = open("home.DAT", "rb")
fout = open("TEMP.DAT", "ab")
ob = home()
flag = False
try:
while True:
ob = pickle.load(fin)
if ob.id == b:
flag = True
if n == 1:
a = eval(input("ENTER NEW RESIDENT DOB:"))
ob.dob = a
pickle.dump(ob, fout)
elif n == 2:
a = eval(input("ENTER NEW LAST 4 OF RESIDENT'S SSN:"))
ob.ssn = a
pickle.dumb(ob, fout)
elif n == 3:
a = eval(
input(
"DOES RESIDENT AGREE TO A BLOOD TRANSFUSION? (YES/NO):"
))
ob.transfusion = a
pickle.dump(ob, fout)
elif n == 4:
a = eval(input("DOES RESIDENT HAVE A DNR? (YES/NO):"))
ob.dnr = a
pickle.dumb(ob, fout)
elif n == 5:
a = eval(
input(
"DOES THE RESIDENT HAVE A LIVING WILL? (YES?NO):"))
ob.livwill = a
pickle.dumb(ob, fout)
elif n == 6:
a = eval(
input(
"ENTER RESIDENT'S NEW PRIMARY INSURANCE PROVIDER:")
)
ob.insname = a
pickle.dumb(ob, fout)
elif n == 7:
a = eval(input("ENTER NEW POLICY EFFECTIVE DATE:"))
ob.inseffdate = a
pickle.dumb(ob, fout)
elif n == 8:
a = eval(input("ENTER NEW POLICY NUMBER:"))
pickle.dumb(ob.fout)
elif n == 9:
print("*****UPATE RESIDENT'S ACCOUNT****")
ob.input()
pickle.dump(ob, fout)
else:
pickle.dumb(ob, fout)
except EOFError:
if not flag:
print("\n")
print("\n")
print(" ...____________________... ")
print(" | NO RECORD FOUND | ")
print(" ~~~~~~~~~~~~~~~~~~~~~ ")
fin.close()
fout.close()
os.remove("home.DAT")
os.rename("TEMP.DAT", "home.DAT")
def saveModel(self, model):
modelPklFileName = ""
modelPkl = open(modelPklFileName, 'wb')
pickle.dumb(model, modelPklFileName)
modelPkl.close()
from neuralintets import GenericAssistant
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import mplfinance as mpf
import pickle
import sys
import datetime as dt
def myfunction():
pass
mappings = {
'greetings': myfunction
}
assistant = GenericAssistant('intents.json', intent_methods=mappings)
assistant.train_model()
assistant.request("Hello")
portfolio = {'AAPL': 20, 'TSLA': 5, "GS": 10}
with open('portfolio.pkl', 'wb') as f:
pickle.dumb ( portfolio, f)
import pickle
dbfile = open('people-pickle', 'rb')
db = pickle.load(dbfile)
dbfile.close()
db['sue']['pay'] *= 1.10
db['tom']['name'] = 'tolis'
dbfile = open('people-pickle', 'wb')
pickle.dumb(db, dbfile)
dbfile.close()
clasifier_rbf = SVC(kernel='rbf') clasifier_poly = SVC(kernel='poly') clasifier_sig = SVC(kernel='sigmoid') clasifier_linear.fit(x, y) clasifier_rbf.fit(x, y) clasifier_poly.fit(x, y) clasifier_sig.fit(x, y) #plt.scatter(xindex,y,color='black',label='data') #plt.plot(xindex,clasifier_linear.predict(x),color='red',label='linear') #plt.plot(xindex,clasifier_rbf.predict(x),color='green',label='rbf') #plt.plot(xindex,clasifier_poly.predict(x),color='blue',label='polynomial') #plt.plot(xindex,clasifier_sig.predict(x),color='yellow',label='sigmoid') pickle.dumb(clasifier_linear, open(svm_linear, 'wb')) pickle.dumb(clasifier_rbf, open(svm_rbf, 'wb')) pickle.dumb(clasifier_poly, open(svm_poly, 'wb')) pickle.dumb(clasifier_sig, open(svm_sig, 'wb')) #End of Train SVC #Start Test SVC #testsvm_linear = pickle.load(open(svm_linear, 'rb')) #testsvm_poly = pickle.load(open(svm_poly, 'rb')) #testsvm_rbf = pickle.load(open(svm_rbf, 'rb')) #testsvm_sig = pickle.load(open(svm_sig, 'rb')) # ##report classification #from sklearn.metric import classification_report, confusion_matix #y_linear=clasifier_linear.predict(x) ##matrixy_lin=confusion_matix(y,y_linear)
for faces in known_faces:
print("second loop")
results = face_recognition.compare_faces(faces, face_encoding, TOLERANCE)
match = None
print("compared")
if True in results:
match = known_names[results.index(True)]
print(f"Match found : {match}")
else:
match = str(next_id)
next_id += 1
known_names.append(match)
known_faces.append(face_encoding)
os.mkdir(f"{KNOWN_FACES_DIR}/{match}")
pickle.dumb(face_encoding, open(f"{KNOWN_FACES_DIR}/{match}/{match}-{int(time.time())}.pkl", "wb"))
print("pic first seen")
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
color = [0, 255, 0]
cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)
top_left = (face_location[3], face_location[2])
bottom_right = (face_location[1], face_location[2] + 22)
cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
cv2.putText(image, match, (face_location[3] + 10, face_location[2] + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), FONT_THICKNESS)
s = pickle.dumps(data)
print(s)
# restore from a file
f = open('somefile', 'rb')
data = pickle.load(f)
# print(data)
# restore from a string
data = pickle.loads(s)
# pickle module
f = open('hello.txt', 'wb')
pickle.dump([1, 2, 3, 4], f)
pickle.dump('hello', f)
pickle.dump({'apple', 'pear', 'banana'}, f)
f.close()
# f = open('hello.txt', 'rb')
# print(pickle.load(f))
# print(pickle.load(f))
# print(pickle.load(f))
import countdown
c = countdown.Countdown(5)
print(c.run())
f = open('cstate.p', 'wb')
pickle.dumb(c, f)
f.close()
def pickle_data(D, file_name):
filename = str(file_name)
outfile = open(filename, 'wb')
pickle.dumb(D, outfile)
outfile.close()
def pickle_save(variable, file_name):
with open(file_name, "wb") as saveFile:
pickle.dumb(variable, file_name)
image=cv2.cvtColor(image,(100,100))
image=cv2.resize(image,cv2.COLOR_BGR2GRAY)
return image
images=[]
labels=[]
for i in os.listdir(img_dir):
image=cv2.imread(os.path.join(img_dir,i))
image=preprocess(image)
images.append(images)
labels.append(i.split('_')[0])
images=np.array(images)
labels=np.array(labels)
with open(os.path.join(data_dir,'images.p'),'wb') as f:
pickle.dumb(images,f)
with open(os.path.join(data_dir,'labels.p'),'wb') as f:
pickle.dumb(labels,f)
agent = DQNAgent(state_size, action_size)
scaler = get_scaler
portfolio_value = []
if args.mode == 'test':
with open(f'{models_folder}/scaler.pkl', 'rb') as f:
scaler = pickle.load(f)
env = MultiStockEnv(train_data, initial_investment)
agent.epsilon = 0.01
agent.load(f'{models_folder}/dqn.h5')
for e in range(num_episodes):
t0 = datetime.now()
val = play_one_episode(agent, env, args.mode)
dt = datetime.now() - t0
print(
f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}"
)
portfolio_value.append(val)
if args.mode == 'train':
agent.save(f'{models_folder}/dqn.h5')
with open(f'{models_folder}/scaler.pkl', 'wb') as f:
pickle.dumb(scaler, f)
np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)