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):
    # the 5% of younger individuals who did experience side effects
    random_younger = randint(13, 64)
    Train_samples.append(random_younger)
    Train_labels.append(1)

    # the 5% of older individuals who did not experience side effects 
    random_older = randint(65, 100)
    Train_samples.append(random_older)
    Train_labels.append(0)

for i in range(1000):
    # the 95% of younger indviduals who did not experience side effects
    random_younger = randint(13, 64)
    Train_samples.append(random_younger)
    Train_labels.append(0)

    # the 95% of older individuals who did experience the side effects
    random_older = randint(65, 100)
    Train_samples.append(random_older)
    Train_labels.append(1)

for i in Train_labels:
    print(i)
for i in Train_samples:
    print(i)

#the next step is to take these two lists and processing them
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)





