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Sagar Dolui sagar03d

Life Is Good
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sudo apt -y install sudo wget gnupg
wget -q -O- | sudo apt-key add -
sudo apt update
echo 'deb stable main bionic' | sudo tee /etc/apt/sources.list.d/koha.list
sudo apt install koha-common
sagar03d / media-query.css
Created August 7, 2018 15:25 — forked from gokulkrishh/media-query.css
CSS Media Queries for Desktop, Tablet, Mobile.
##Device = Desktops
##Screen = 1281px to higher resolution desktops
@media (min-width: 1281px) {
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)
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:
import gym
import cv2
class Environment:
def __init__(self, params):
self.gym = gym.make(
self.observation = None
self.display = params.display
self.terminal = False
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.")
import os
import argparse
import random as rand
from environment import Environment
from train import Trainer
from dqn import DQN
#include <stdio.h>
#include <stdlib.h>
int main()
int level;
int levelcount;
int layercount;
int layer;
int star;
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
#! /usr/bin/python2
import pefile
import os
import array
import math
import pickle
from sklearn.externals import joblib
import sys
import argparse