AMAbot.js (2)

var http = require('http');
var natural = require('natural');
fs = require('fs')
brain = require('brain')

// Create the configuration
var config = {
    channels: ["##bottest", "#futurology", "#mybots", "#reddit"],
    server: "irc.snoonet.org",
    botName: "AMAbot",
    sasl: false,
    secure: false,
    password: 'password here if any'
};

var maxLength = 500;
var released = 0;
var subredditList = ["askreddit+AskHistorians+askscience+explainlikeimfive", "askmen+askwomen+askculinary", "futurology+philosophy+psychonaut+MachineLearning+learnprogramming", "all"];
var activeChannels = {};
var rawDataset = [];
var defaultChannelSetting = {};
var networkSettings = {
    layers: [4, 3, 2],
    learningRate: 0.1,
    threshold: 0.000001,
    iterations: 20000
}
/*var networkSettings = {"layers":[3,6,3,3,3,5,3,1],"learningRate":0.009193395357579001,"threshold":0.2702860541099906,"iterations":19212.502439972013};
var networkSettings = {"layers":[8,19,9,22,24,1,6,7,2,16,3,2,5,7,1,2,4,1],"learningRate":0.0033243911806494705,"threshold":0.7047150845097602,"iterations":40797.12532903068};
var networkSettings = {"layers":[9,20,9,21,24,1,10,9,1,11,1,2,5,6,3],"learningRate":0.002924735611304713,"threshold":0.7961916553246081,"iterations":43209.032842190936};
var networkSettings = {"layers":[2,4,1,2],"learningRate":0.008147467230446723,"threshold":0.22654134919105473,"iterations":46747.36615619622};
*/
//var networkSettings = {"layers":[4,5,2],"learningRate":0.00023194521199911694,"threshold":0.000001078763395664282,"iterations":20034.78908073157};
//var networkSettings = {"layers":[4,5,2],"learningRate":0.00023194521199911694,"threshold":0.0000010562829725909978,"iterations":19797.069663880393};
/*var networkSettings = {"layers":[3,5,2,1,2,1,4],"learningRate":0.14381794724613428,"threshold":0.0000010224730655085295,"iterations":18688.28935455531};
var networkSettings = {"layers":[3,5,2,1,2,1],"learningRate":0.1711220392258838,"threshold":8.262058848747984e-7,"iterations":18793.842338956892};
var networkSettings = {"layers":[3,3,3,1,3],"learningRate":0.0035125559894367345,"threshold":0.0010001768320446372,"iterations":20093.089742818847};
*/
//var networkSettings = {"layers":[4,2,2],"learningRate":0.00023194521199911694,"threshold":0.0000012831276768585669,"iterations":19920.421788701788};
//var networkSettings = {"layers":[],"learningRate":0.0002504901404011595,"threshold":0.0000012533658032377527,"iterations":25414.23598228595};


var networkSettings = {"layers":[],"learningRate":0.019543054905444283,"threshold":0.1,"iterations":1000.198314515295};
var networkSettings = {"layers":[],"learningRate":0.02371273614254603,"threshold":0.1080865932999755,"iterations":6808.866671373775};
var networkSettings = {"layers":[1],"learningRate":0.031357327018297194,"threshold":0.0757713085276743,"iterations":7010.153680644004};
var networkSettings = {"layers":[1],"learningRate":0.02930038359341617,"threshold":0.06959408669598949,"iterations":7586.342043816811};
var networkSettings = {"layers":[2,1],"learningRate":0.02991200765536878,"threshold":0.062440579693873156,"iterations":8036.404564736056};
var networkSettings = {"layers":[2],"learningRate":0.02710111697056981,"threshold":0.057042649478990404,"iterations":8703.008759251212};
var networkSettings = {"layers":[4,3],"learningRate":0.04114500238704092,"threshold":0.0611729400814995,"iterations":5443.051571087624}
var networkSettings = {"layers":[],"learningRate":0.03521817724270217,"threshold":0.05562827149940124,"iterations":6551.885643714466};
var networkSettings = {"layers":[3,2,1],"learningRate":0.026519552102619342,"threshold":0.035693828430759994,"iterations":6965.243219611451};
var networkSettings = {"layers":[],"learningRate":0.023396226697915284,"threshold":0.035693828430759994,"iterations":6628.1487504702045};
var networkSettings = {"layers":[],"learningRate":0.02523913035089666,"threshold":0.036409500997588884,"iterations":7160.281104233691};
var networkSettings = {"layers":[6,2,4,2],"learningRate":0.030078094240842526,"threshold":0.05356082839949051,"iterations":11418.317464719004};
var networkSettings = {"layers":[4,4],"learningRate":0.031054105722877038,"threshold":0.05066490997169323,"iterations":12599.905669422802};
var networkSettings = {"layers":[4,4],"learningRate":0.031054105722877038,"threshold":0.05066490997169323,"iterations":12599.905669422802};
var networkSettings =  {"layers":[4,4],"learningRate":0.031054105722877038,"threshold":0.05066490997169323,"iterations":12599.905669422802};
var networkSettings = {"layers":[],"learningRate":0.029689335672511794,"threshold":0.051498690923560554,"iterations":16635.94919009061};
var net;

function makeNet(networkSettings, dataset){
    var net = new brain.NeuralNetwork({
        hiddenLayers: networkSettings.layers,
        learningRate: networkSettings.learningRate
    });
    var result = net.train(dataset, {
      errorThresh: networkSettings.threshold,  // error threshold to reach
      iterations: networkSettings.iterations,   // maximum training iterations
      log: false,           // console.log() progress periodically
      logPeriod: 5000        // number of iterations between logging
    })
    console.log(JSON.stringify(result));
    return net;
}

//randomly split dataset some percent
//create a random network setup
//train network on training data
//test on validation set
//mutate network
//repeat
//if mutated network settings do better, choose previous
//repeat
function hillClimb(rawDataset, percentValidation, gens, mutationRate, callback, timeinterval, maxtime){
    var dataset = extract(rawDataset);
    var validationset = [];
    var trainingset = [];
    for (key in dataset){
        value = dataset[key]
        if (Math.random() < percentValidation){
            validationset.push(value);
        } else {
            trainingset.push(value);
        }
    }
    //
    function testNet(net){
        var err = 0;
        for (key in validationset){
            var result = net.run(validationset[key].input)[0];
            err += Math.abs(validationset[key].output - result);
        }
        return err;
    }
    function testNets(newNet, oldNet){
        var probNew = 0.5;
        for (key in validationset){
            var dat = validationset[key];
            var oldResult = newNet.run(dat.input)[0];
            var newResult = oldNet.run(dat.input)[0];
            var probDataOld = 1 - Math.abs(dat.output - oldResult);
            var probDataNew = 1 - Math.abs(dat.output - newResult);
            probNew = (probDataNew*probNew)/(probDataOld*(1-probNew)+probDataNew*probNew);
            //console.log(probNew, probDataOld, probDataNew);
        }
        return probNew;
    }
    function mutate(networkSettings){
        function mutateVal(val, amount, min, max){
            var newVal = val;
            if (Math.random()<mutationRate){
                newVal += (Math.random()*val/4)-(val/4)/2;
                if (newVal > max || newVal < min){
                    newVal = val;
                }
            }
            return newVal;
        }
        var layers = networkSettings.layers
        var newLayers = []
        for (key in layers){
            var nodes;
            if (Math.random()<mutationRate/layers.length){
                nodes = layers[key]+Math.round((Math.random()*4)-2);
                if (nodes <= 1){
                    nodes = 1;
                }
            } else {
                nodes = layers[key]
            }
            newLayers[key] = nodes;
        }
        if (Math.random()<mutationRate){
            newLayers.push(Math.round(Math.random()*3+1));
        }
        if (Math.random()<mutationRate){
            newLayers.pop();
        }
        var newLearningRate = mutateVal(networkSettings.learningRate, 0.1, 0, 1);
        var newThreshold = mutateVal(networkSettings.threshold, 0.0000003, 0, 1);
        var newIterations = mutateVal(networkSettings.iterations, 1000, 1, 10000000);

        return {layers: newLayers, learningRate: newLearningRate, threshold: newThreshold, iterations: newIterations};
    }
    function begin(i, callback){
        var newSettings = mutate(networkSettings);
        console.log("NewSettings:", JSON.stringify(newSettings));
        var clock = Date.now()
        var newNet = makeNet(newSettings, trainingset);
        var newTime = Date.now() - clock;
        function callback2(){
            var clock = Date.now()
            var oldNet = makeNet(networkSettings, trainingset);
            var oldTime = Date.now() - clock;
            console.log("NewTime: ", newTime/1000, "OldTime:", oldTime/1000);
            //var errNew = testNet(newNet);
            //var errOld = testNet(oldNet);
            var probNew = testNets(newNet, oldNet)
            var newBetter = Math.random()<probNew;
            console.log("Probability of New settings:", probNew)
            if ((newBetter && newTime < maxtime*1000) || (oldTime > maxtime*1000 && newTime < oldTime && newBetter)){
                networkSettings = newSettings;
                console.log("Switching to new settings!")
                //console.log(JSON.stringify(newSettings));
                //console.log(errNew/validationset.length);
            }
            if (i<gens){
                setTimeout(function(){begin(i+1, callback);},timeinterval)
            } else {
                callback();
            }
        }
        setTimeout(callback2, timeinterval);
    }
    begin(1, callback);
}

function test(){
    console.log("done saving");

    for (k in rawDataset){
        if (!("threadNum" in rawDataset[k])){
            rawDataset[k].threadNum = 1;
            rawDataset[k].commentNum = 1;
            console.log("FIXING?")
        }
    }

    /*var dataset = extract(rawDataset)
    console.log(dataset)
    for (k in dataset){
        console.log(dataset[k]);
    }*/

    /*for (k in rawDataset){
        if (rawDataset[k].thread.id == "feqqk"){
            console.log("Removing Entry About Moon.");
        }
    }*/

    //hillClimb(rawDataset, 0.3, 10, 0.5)
    var dataset = extract(rawDataset);
    /*var result = net.train(datasets, {
      errorThresh: 0.000001,  // error threshold to reach
      iterations: 20000,   // maximum training iterations
      log: true,           // console.log() progress periodically
      logPeriod: 500        // number of iterations between logging
    })*/
    net = makeNet(networkSettings, dataset)
    //var json = net.toJSON();

    //net = net.fromJSON(json);

    //console.log(result);
    //setTimeout(function(){bot.say("NickServ", "identify 1robot");}, 5000);
}

function load(){
    fs.readFile("C:\\Users\\Daniel\\Documents\\Programming\\AMAbotData.txt", "utf8", function (err,data) {
      if (err) {
        return console.log(err);
      }
      rawDataset = JSON.parse(data);
      console.log("loadedData")
      test();
    });
}

function arraysEqual(arr1, arr2) {
    if(arr1.length !== arr2.length)
        return false;
    for(var i = arr1.length; i--;) {
        if(arr1[i] !== arr2[i])
            return false;
    }

    return true;
}

var tokenizer = new natural.TreebankWordTokenizer();
var NGrams = natural.NGrams;
function tokenize(text){
    return tokenizer.tokenize(text.toLowerCase())
}

function matchMarkov(search, text, n){
    var search = tokenize(search);
    var text = tokenize(text);
    var searchgrams = NGrams.ngrams(search, n);
    var textgrams = NGrams.ngrams(text, n);
    var count = 0;
for (textgramNum in textgrams){
        for (searchgramNum in searchgrams){
            if (arraysEqual(searchgrams[searchgramNum], textgrams[textgramNum])){
                count += 1;
            }
        }
    }
    return count;
}

function matchMarkovPercent(search, text, n){
    var search = tokenize(search);
    var text = tokenize(text);
    var searchgrams = NGrams.ngrams(search, n);
    var textgrams = NGrams.ngrams(text, n);
    var count = 0;
    for (searchgramNum in searchgrams){
        for (textgramNum in textgrams){
            if (arraysEqual(searchgrams[searchgramNum], textgrams[textgramNum])){
                count += 1;
                break;
            }
        }
    }
    return (searchgrams.length > 0 ? count/searchgrams.length : 0);
}

function numwords(text){
    return tokenize(text).length;
}

function segmentMatches(search, text, n){
    search = search.toLowerCase()
    text = text.toLowerCase()
    for (i=0;i<n;i++){
        var rand1 = Math.round(Math.random()*search.length-1);
        var rand2 = Math.round(Math.random()*search.length-1);
        if (rand1 > rand2){
            text.indexOf(search.splice(rand1, search.length));
        } else {

        }
    }
}

function wordMatches(words, text){
    words = words.toLowerCase();
    text = text.toLowerCase();
    wordsArray = words.split(" ");
    textArray = text.split(" ");
    wordsArray = wordsArray.filter(function(elem, pos) {
        return wordsArray.indexOf(elem) == pos;
    })
    var count = 0;
    for (k in wordsArray){
        for(var i = 0; i < textArray.length; ++i){
            if(textArray[i] == wordsArray[k])
                count++;
        }
    }
    return count
}

function wordMatchesPercent(words, text){
    words = words.toLowerCase();
    text = text.toLowerCase();
    wordsArray = words.split(" ");
    textArray = text.split(" ");
    wordsArray = wordsArray.filter(function(elem, pos) {
        return wordsArray.indexOf(elem) == pos;
    })
    var count = 0;
    for (k in wordsArray){
        for(var i = 0; i < textArray.length; ++i){
            if(textArray[i] == wordsArray[k])
                count++;
        }
    }
    return (textArray.length > 0 ? count/textArray.length : 0)
}

function sigmoid(t) {
    return 1/(1+Math.pow(Math.E, -t));
}

function scale(n, max){
    return (n > max ? 1 : (n < 0 ? 0 : n/max));
}

function getInput(rawExample){
    var example = rawExample.thread;
    var question = rawExample.question;
    var featureVector = {
        totalResults: (rawExample.totalResults ? rawExample.totalResults : 0),
        resultsRecorded: (rawExample.totalResults ? 1 : 0),
        /*isFirst: (rawExample.threadNum == 0 ? 1 : 0),
        firstAnswers: 1-scale(rawExample.threadNum, 7),*/
        noComments: (example.num_comments == 0 ? 1 : 0),
        manyComments: scale(example.num_comments, 5),
        searchRank: 1-rawExample.threadNum/100,
        score: scale(example.score, 3000),
        /*downs: scale(example.downs, 3000),
        ups: scale(example.ups, 3000),
        edited: (example.edited ? 1 : 0),*/
        is_self: (example.is_self ? 1 : 0),
        comments: scale(example.num_comments,20000),
        over_18: (example.over_18 ? 1 : 0),
        questionLength: scale(numwords(question),200/5),
        selfTextLength: scale(numwords(example.selftext),10000/5),
        titleLength: scale(numwords(example.title), 300/5),
        Title1gramMatches: matchMarkovPercent(question, example.title, 1),
        Title2gramMatches: matchMarkovPercent(question, example.title, 2),
        Title3gramMatches: matchMarkovPercent(question, example.title, 3),
        Self1gramMatches: matchMarkovPercent(question, example.selftext, 1),
        Self2gramMatches: matchMarkovPercent(question, example.selftext, 2),
        Self3gramMatches: matchMarkovPercent(question, example.selftext, 3),
        /*titleWordMatches: scale(wordMatches(question, example.title), 15),
        selfTextWordMatches: scale(wordMatches(question, example.selftext),30),
        titleWordMatchesPercent: wordMatchesPercent(question, example.title),
        selfTextWordMatchesPercent: wordMatchesPercent(question, example.selftext)*/
    };
    return featureVector;
}

//extract features
function extract(rawDataset){
    var dataset = [];
    for (k in rawDataset){
        var example = rawDataset[k].thread;
        if ("label" in rawDataset[k]){
            var question = rawDataset[k].question;
            var featureVector = getInput(rawDataset[k]);
                var trainExample = {input: featureVector, output: (rawDataset[k].label == "positive" ? [1] : [0])};
            dataset.push(trainExample);
        }
    }
    console.log("Done extracting.");
    return dataset;
}

function save(data, filename){
    fs.writeFile(filename, data, function(err) {
    if(err) {
        console.log(err);
    } else {
        console.log("The file was saved!");
    }
});
}

// Get the lib
var irc = require("irc");

// Create the bot name
var bot = new irc.Client(config.server, config.botName, {
    channels: config.channels,
    sasl: config.sasl,
    secure: config.secure,
    userName: config.botName,
    nick: config.botName,
    password: config.password
});

function getCommand(text){
    if (text.indexOf("!") == 0){
        if (text.indexOf(" ") > 0){
            return [text.slice(1, text.indexOf(" ")), text.slice(text.indexOf(" ")+1, text.length)];
        } else {
            return [text.slice(1, text.length), ""];
        }
    } else {
        return ["", text];
    }
}

function answer(to, question){
    console.log("answering")
    var callback1 = function(output, threadDat, threadNum, commentDat, commentNum, totalResults, prob){
        if (threadDat){
            rawDataset.push({question: question, thread: threadDat, comment: commentDat, threadNum: threadNum, commentNum: commentNum, totalResults: totalResults});
            if (!commentDat){
                rawDataset[rawDataset.length-1].label = "negative";
            }
            if (!(to in activeChannels)){
                activeChannels[to] = {};
                //console.log("adding " + to + " to active channels");
            }
            activeChannels[to].last = rawDataset.length-1;
            //console.log("Changing " + to + ".last to " + (rawDataset.length-1));
            //console.log(JSON.stringify(activeChannels));
            activeChannels[to].details = {Title: threadDat.title, Link: "http://www.reddit.com"+threadDat.permalink, SearchResultNumber: threadNum+1, CommentNumber: commentNum+1, TotalResults: totalResults, EstimatedProbability: prob};
            //console.log(JSON.stringify(activeChannels));
            //console.log("Changing " + to + ".details to " + activeChannels[to].details);
        }
        bot.say(to, output);
    }
    if (released < 2000){
        getSearch(question, subredditList, callback1);
    } else {
        setTimeout(function(){answer(to, question);}, released+10);
    }
}

// Listen for any message
bot.addListener("message", function(from, to, text, message) {
    //detect command and splice command
    var commandCut = getCommand(text);
    var command = commandCut[0];
    var text = commandCut[1];
    console.log(command, text);
    if (to == config.botName){
        var replyTo = from;
    } else {
        var replyTo = to;
    }
    if (command == "boobs"){
        bot.say(replyTo, "(.Y.)")
    }
    if (command == "hillclimb"){
        bot.say(replyTo, "Climbing hills!...");
        var gens = 100;
        if (text){
            vals = JSON.parse("["+text+"]");
            if (vals[0]) {
                gens = vals[0];
            }
        }
        var callback = function(){bot.say(replyTo, JSON.stringify(networkSettings));};
        hillClimb(rawDataset, 0.3, gens, 0.5, callback, (vals[1] ? vals[1]*1000 : 0), (vals[2] ? vals[2] : 60))
    }
    //if (command == "review")

    //
    if (command == "say"){
        var txt = text.split(" ");
        bot.say(txt[0], txt.splice(1,text.length).join(" "))
    }
    if (command == "results"){
        if (replyTo in activeChannels && "details" in activeChannels[replyTo]){
            bot.say(replyTo, "Picked " + activeChannels[replyTo].details.SearchResultNumber.toString() + " of " + activeChannels[replyTo].details.TotalResults.toString() + " results.");
        }
    }
    if (command == "prob"){
        if (replyTo in activeChannels && "details" in activeChannels[replyTo]){
            bot.say(replyTo, "Estimated Probability: " + (activeChannels[replyTo].details.EstimatedProbability *100).toString().slice(0, 3) + "%");
        }
    }
    if (command == "title"){
        if (replyTo in activeChannels && "details" in activeChannels[replyTo]){
            bot.say(replyTo, activeChannels[replyTo].details.Title);
        }
    }
    if (command == "KickTheBucket"){
        bot.say(replyTo, "/me kicks pail.");
    }
    if (command == "train"){
        if (text.length > 0){
            var value = JSON.parse("["+text+"]");
            networkSettings.layers = value[0];
            networkSettings.learningRate = (value[1] ? value[1] : networkSettings.learningRate);
            networkSettings.threshold = (value[2] ? value[2] : networkSettings.threshold);
            /*net = new brain.NeuralNetwork({
                hiddenLayers: values[0],
                learningRate: (values[1] ? values[1] : 0.3)
            })*/;}
        var dataset = extract(rawDataset);
        //net = makeNet(networkSettings, dataset);
        var result = net.train(dataset, {
          errorThresh: networkSettings.threshold,  // error threshold to reach
          iterations: networkSettings.iterations,   // maximum training iterations
          log: true,           // console.log() progress periodically
          logPeriod: 5000        // number of iterations between logging
        })
        bot.say(replyTo, JSON.stringify(result));
        //bot.say(replyTo, "Trained");
    }
    if (command == "details"){
        console.log("This part is working, command details recognized.")
        //console.log(JSON.stringify(activeChannels));
        if (replyTo in activeChannels && "details" in activeChannels[replyTo]){
            bot.say(replyTo, JSON.stringify(activeChannels[replyTo].details));
        }
    }
    if (command == 'ShowMeYourBrain'){
        bot.say(replyTo, JSON.stringify(net.toJSON()));
    }
    if (command == "login"){
        bot.say("NickServ", "nick AMAbot");
        setTimeout(function(){bot.say("NickServ", "identify 1robot");}, 5000);
    }
    if (command == "save"){
        console.log("saving...");
        bot.say(replyTo, "Saving...")
        save(JSON.stringify(rawDataset), "C:\\Users\\Daniel\\Documents\\Programming\\AMAbotData.txt");
    }
    if (command == "yes" || command == "no"){
        if (replyTo in activeChannels && "last" in activeChannels[replyTo]){
            rawDataset[activeChannels[replyTo].last].label = (command == "yes" ? "positive" : "negative")
        }
    } else if (to == config.botName && from != config.botName && command == ""){
        console.log(from, command, text);
        answer(from, text);
    } else if (command == "ask"){
        console.log(from, command, text);
        answer(replyTo, text);
    }
});

function getSearch(question, subreddits, callback1){
    question2 = encodeURIComponent(question);
    var options = {
      host: 'www.reddit.com',
      path: '/r/'+subreddits[0]+'/search.json?q=' + question2 + '&restrict_sr=on&sort=relevance&t=all&limit=100',
      headers: {'user-agent': '/u/Noncomment IRC bot \'AMAbot\'; #futurology, #bottest'}
    };

    callback = function(response) {
      var str = '';

      //another chunk of data has been recieved, so append it to `str`
      response.on('data', function (chunk) {
        str += chunk;
      });

      //the whole response has been recieved
      response.on('end', function () {
        var search = JSON.parse(str);
        if (search.data.children.length < 1){
            if (subreddits.length>1){
                subreddits = subreddits.slice(1, subreddits.length);
                setTimeout(function(){getSearch(question, subreddits, callback1);}, 50);
            } else {
                callback1("Sorry I don't know.", false, 0, false, 0, 0, 0);
            }
        } else {
            var children = search.data.children;
            var best = 0;
            var bestNum = 0;
            var bestThread = children[0].data;
            var bestDat;
            for (num=0;num<children.length;num++){
                var threadDat = children[num].data;
                var example = {question: question, thread: threadDat, threadNum: num};
                var test = getInput(example);
                var prob = net.run(test)[0];
                console.log(num, prob);
                if (prob > best){
                    best = prob;
                    bestNum = num;
                    bestThread = threadDat;
                    bestDat = test;
                }
            }
            console.log(bestDat);
            console.log("Picking: ", bestNum, best);
            setTimeout(function(){getComment(bestThread.permalink, callback1, bestThread, bestNum, children.length, best);}, 50);
        }
      });
    }
    released += 2000;
    setTimeout(function(){released += -2000;}, 2000);
    http.request(options, callback).end();
}

function getComment(permalink, callback1, threadDat, threadNum, totalResults, prob){
    console.log(permalink)
    var options = {
      host: 'www.reddit.com',
      path: permalink + ".json",
      headers: {'user-agent': '/u/Noncomment IRC bot \'AMAbot\'; #futurology, #bottest'}
    };

    callback = function(response) {
      var str = '';

      //another chunk of data has been recieved, so append it to `str`
      response.on('data', function (chunk) {
        str += chunk;
      });

      //the whole response has been recieved
      response.on('end', function () {
        var search = JSON.parse(str);
        var i = 0;
        var comments = search[1].data.children;
        while (i<comments.length){
            var comment = comments[i].data.body;
            console.log(comment);
            var completed = false
            if (!(comment == "[deleted]") && comment.length < maxLength){
                commentDat = comments[i].data;
                callback1(comment, threadDat, threadNum, commentDat, i, totalResults, prob);
                completed = true;
                break;
            }
            i++
        }
        if (!completed){
            callback1("Sorry this knowledge is forbidden.", threadDat, threadNum, false, 0, totalResults, prob)
        }
      });
    }
    released += 2000;
    setTimeout(function(){released += -2000;}, 2000);
    http.request(options, callback).end();
}
load()