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 sudo apt -y install sudo wget gnupg
 
 wget -q -O- https://debian.koha-community.org/koha/gpg.asc | sudo apt-key add -
 
 sudo apt update
 
 echo 'deb http://debian.koha-community.org/koha stable main bionic' | sudo tee /etc/apt/sources.list.d/koha.list
 
 sudo apt install koha-common

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/* 
  ##Device = Desktops
  ##Screen = 1281px to higher resolution desktops
*/

@media (min-width: 1281px) {
  
  //CSS
  

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data = pd.read_csv('data.csv', sep='|')
X = data.drop(['Name', 'md5', 'legitimate'], axis=1).values
y = data['legitimate'].values

print('Researching important feature based on %i total features\n' % X.shape[1])

# Feature selection using Trees Classifier
fsel = ske.ExtraTreesClassifier().fit(X, y)
model = SelectFromModel(fsel, prefit=True)
X_new = model.transform(X)

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import tensorflow as tf
import random as rand
import numpy as np
from convnet import ConvNet
from buff import Buffer
from memory import Memory


class DQN:

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import gym
import cv2

class Environment:

    def __init__(self, params):
        self.gym = gym.make(params.game)
        self.observation = None
        self.display = params.display
        self.terminal = False

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parser = argparse.ArgumentParser()
envarg = parser.add_argument_group('Environment')
envarg.add_argument("--game", type=str, default="SpaceInvaders-v0", help="Name of the atari game to test")
envarg.add_argument("--width", type=int, default=84, help="Screen width")
envarg.add_argument("--height", type=int, default=84, help="Screen height")

memarg = parser.add_argument_group('Memory')
memarg.add_argument("--size", type=int, default=100000, help="Memory size.")
memarg.add_argument("--history_length", type=int, default=4, help="Number of most recent frames experiences by the agent.")

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import os
import argparse
import random as rand
from environment import Environment
from train import Trainer
from dqn import DQN

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#include <stdio.h>
#include <stdlib.h>

int main()
{
    int level;
    int levelcount;
    int layercount;
    int layer;
    int star;

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import pandas as pd
import numpy as np
import pickle
import sklearn.ensemble as ske
from sklearn import cross_validation, tree, linear_model
from sklearn.feature_selection import SelectFromModel
from sklearn.externals import joblib
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix

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#! /usr/bin/python2
import pefile
import os
import array
import math
import pickle
from sklearn.externals import joblib
import sys
import argparse