ZJUGuoShuai

#include <iostream>
#include <sstream>
#include <fstream>

std::string readFileToStr(std::string file)
{
  std::ifstream input(file);
  std::stringstream sstr;

  while(input >> sstr.rdbuf());

ZJUGuoShuai

[Unit]
Description=Shadowsocks Python local client

[Service]
Type=forking
ExecStart=/path/to/sslocal -c /path/to/config.json -d start
ExecReload=/path/to/sslocal -c /path/to/config.json -d restart
ExecStop=/path/to/sslocal -c /path/to/config.json -d stop

[Install]

ZJUGuoShuai

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

from_addr = "[email protected]"
to_addr = "[email protected]"

msg = MIMEMultipart()
msg['From'] = from_addr
msg['To'] = to_addr

ZJUGuoShuai

#include <windows.h>
#include <stdio.h>

void normal(char* s) {
    HANDLE hOut = GetStdHandle(STD_OUTPUT_HANDLE);
    WORD info_style = FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE;
    SetConsoleTextAttribute(hOut, info_style);
    printf(s);
}

ZJUGuoShuai

\HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows\CurrentVersion\Themes\DefaultColors\Standard

\HKEY_CURRENT_USER\Control Panel\Colors

ZJUGuoShuai

import requests
from bs4 import BeautifulSoup

base_url = 'https://zzk.cnblogs.com/s/blogpost'
cookies = dict(
    ZzkNoRobotCookie=
    'CfDJ8KlpyPucjmhMuZTmH8oiYTOsZWLqcxSx0sRuNdfc35P334ttmwTqPekgb1OOGtp_JeXby7PZgQla4HC63Y3_nnWwF8kvdklA71DbLOQ2ADfziUqy4BkuXQUgJEB4y2kj6w'
)

languages = [

ZJUGuoShuai

from scipy.misc import imread
from scipy.linalg import svd
import matplotlib.pyplot as plt
import numpy as np


if __name__ == '__main__':
    img = imread('mm.jpg')
    h, w, c = img.shape

ZJUGuoShuai

from collections import namedtuple
from operator import itemgetter
from pprint import pformat


class Node(namedtuple('Node', 'location left_child right_child')):
    def __repr__(self):
        return pformat(tuple(self))

ZJUGuoShuai

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

torch.manual_seed(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

CONTEXT_SIZE = 2
EMBEDDING_DIM = 10

ZJUGuoShuai

import torch
import torch.nn as nn
import numpy as np

def make_context_vector(context, word_to_ix):
    idxs = [word_to_ix[w] for w in context]
    return torch.tensor(idxs, dtype=torch.long)

def get_index_of_max(input):
    index = 0