import numpy as np
from random import randint
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
train_labels = []
train_samples = []

for i in range(50):
    random_younger = randint(13,64)
    train_samples.append(random_younger)
    train_labels.append(1)

    random_older = randint(65,100)
    train_samples.append(random_older)
    train_labels.append(0)

for i in range (1000):
    random_younger = randint(13, 64)
    train_samples.append(random_younger)
    train_labels.append(0)

    random_older = randint(65, 100)
    train_samples.append(random_older)
    train_labels.append(1)
#or i in train_samples:
#   print(i)

#or i in train_labels:
#   print(i)

train_labels= np.array(train_labels)
train_samples=np.array(train_samples)
train_labels,train_samples=shuffle(train_labels,train_samples)

scaler =MinMaxScaler(feature_range=(0,1))
scaled_train_samples= scaler.fit_transform(train_samples.reshape(-1,1))
for i in scaled_train_samples:
    print(i)

