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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

sagar03d

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

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

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

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

sagar03d

 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