AnasBrital98

package test;

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;

import redis.clients.jedis.Jedis;

public class Main {

AnasBrital98

import redis
import random

host = '192.168.43.94'
port = 6379
db = 0
redis_client = redis.StrictRedis(host, port, db)

# créer des Hashset pour les 20 produits
first_product = 'AAAA'

AnasBrital98

# Import the necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D , AveragePooling2D , Dense , Dropout , Flatten
from tensorflow.keras.datasets import mnist
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score

AnasBrital98

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.svm import  SVC

# Create a Dataset for The Model
x , y = make_blobs(n_samples=100 , n_features=2 , centers=2 , random_state=0)
# SVM works with -1 and 1 lables this is why we convert [0,1] to [-1,1]
y = np.where(y==0 , -1 , 1)

AnasBrital98

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torchsummary import summary
from torch.autograd import Variable
import matplotlib.pyplot as plt


# Loading The Dataset and Creating The Data Loader to iterate Over The Data

AnasBrital98

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

# Loading The DataSet
train_data = datasets.MNIST(
    root = 'data',
    train = True,                         

AnasBrital98

import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose
from keras.models import Model

# Get The Data
(x_train , _) , (x_test , _ ) = mnist.load_data()

# Reshape The Data
x_train = x_train.reshape(x_train.shape[0] , x_train.shape[1] , x_train.shape[2] , 1)

AnasBrital98

import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import Model 

# Loading The Data
(x_train, _), (x_test, _) = mnist.load_data()

# PreProcess The Data
def pre_process(X):

AnasBrital98

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression

def Generate_Points(start , end , nbr_points , coefficient , noise ):
    
    #Creating X 
    x = np.arange(start , end , (end -start) / nbr_points)
    
    #calculating Y

AnasBrital98

import numpy as np
import matplotlib.pyplot as plt
from scipy import linalg

def Generate_Points(start , end , nbr_points , coefficient , noise ):
    
    #Creating X 
    x = np.arange(start , end , (end -start) / nbr_points)
    
    #calculating Y