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from sklearn import datasets
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split 
import numpy as np

# prepare data
bc = datasets.load_breast_cancer()
X,y = bc.data,bc.target
n_samples,n_features = X.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1234)

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import numpy as np
X = np.array(
    [[1,2,3,1]
    ,[2,3,4,1]
    ,[3,4,5,1]
    ,[4,3,2,1]
    ,[12,13,14,1]
    ,[7,8,9,1]
    ,[5,2,10,1]]
    ,dtype=np.float32

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import numpy as np
# model = y = 2*x + 3
X = np.array([1,2,3,4],dtype=np.float32)
Y = [(2*x + 3) for x in X]
w,b = 0.0,0.0

def forward(x):
    return w*x+b

def loss(y,y_pred):

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from PySide6.QtWidgets import (
    QApplication
    ,QMessageBox
    ,QWidget
    ,QLabel
    ,QPushButton
    ,QVBoxLayout
)

app         = QApplication([])

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import time
from threading import Thread
from concurrent.futures import ThreadPoolExecutor,as_completed

def factorize(number):
    result = []
    for i in range(1, number+1):
        if number % i == 0:
            result.append(i)
    return result