lotabout

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import requests
from bs4 import BeautifulSoup
import re
import os
import json
import logging
import traceback

lotabout

// ==UserScript==
// @name         Prophet Links
// @namespace    http://tampermonkey.net/
// @version      0.2
// @description  try to take over the world!
// @author       You
// @match        http://172.27.128.87:40121/*
// @grant        none
// @require      https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js

lotabout

import org.apache.catalina.connector.Connector;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.builder.SpringApplicationBuilder;
import org.springframework.boot.web.embedded.tomcat.TomcatServletWebServerFactory;
import org.springframework.boot.web.servlet.server.ServletWebServerFactory;
import org.springframework.context.annotation.Bean;

@SpringBootApplication
public class SpringApplication {

lotabout

package me.lotabout;

import com.fasterxml.uuid.Generators;
import com.fasterxml.uuid.impl.RandomBasedGenerator;
import com.fasterxml.uuid.impl.TimeBasedGenerator;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;

import java.util.Random;
import java.util.UUID;

lotabout

import java.util.concurrent.CountDownLatch;
import java.util.concurrent.CyclicBarrier;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

public class SyncTest {

	private enum TestType {
		SYNCHRONIZED, VOLATILE, ATOMIC, LOCK, FAIR_LOCK

lotabout

import sun.misc.Unsafe;

import java.lang.reflect.Field;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.LongAdder;

public class ConcurrencyTest {
    private enum TestType {

lotabout

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# implementation of backpropogation algorithm based on
# http://neuralnetworksanddeeplearning.com/chap1.html
#
# note that mini-batches are calculated in matrix form

import numpy as np
import random

lotabout

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from datetime import datetime
import fileinput

for line in fileinput.input():
    print(f'[{datetime.now()}] {line}', end='')

lotabout

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import torch.nn as nn
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.autograd import Variable, grad
import torch.optim as optim
import torchvision.utils as vutils

lotabout

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import math
class DecisionTree(object):
    def __init__(self, data, method="naive"):
        """Learn a decision tree from data and label

        :data: List[List[val]], a list contains M sample, each sample is represented by a List
               The last column of the sample is the label