Ejemplo n.º 1
0
 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,)    
Ejemplo n.º 3
0
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...")
Ejemplo n.º 4
0
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()
Ejemplo n.º 6
0
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)
    
Ejemplo n.º 7
0
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()
Ejemplo n.º 8
0
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)
Ejemplo n.º 9
0
    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()
Ejemplo n.º 11
0
def pickle_data(D, file_name):
    filename = str(file_name)
    outfile = open(filename, 'wb')
    pickle.dumb(D, outfile)
    outfile.close()
Ejemplo n.º 12
0
def pickle_save(variable, file_name):
    with open(file_name, "wb") as saveFile:
        pickle.dumb(variable, file_name)
Ejemplo n.º 13
0
    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)
       
    
    
    
    
    
    
    
Ejemplo n.º 14
0
    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)