您需要先安装一个扩展,例如 篡改猴、Greasemonkey 或 暴力猴,之后才能安装此脚本。
您需要先安装一个扩展,例如 篡改猴 或 暴力猴,之后才能安装此脚本。
您需要先安装一个扩展,例如 篡改猴 或 暴力猴,之后才能安装此脚本。
您需要先安装一个扩展,例如 篡改猴 或 Userscripts ,之后才能安装此脚本。
您需要先安装一款用户脚本管理器扩展,例如 Tampermonkey,才能安装此脚本。
您需要先安装用户脚本管理器扩展后才能安装此脚本。
Keep track of time spent on any website to predict sleeping time
// ==UserScript== // @name sleep prediction // @namespace http://tampermonkey.net/ // @version 0.5 // @description Keep track of time spent on any website to predict sleeping time // @icon https://www.iconsdb.com/icons/preview/gray/clock-10-xxl.png // @author moony // @match *://*/* // @grant GM_registerMenuCommand // @grant GM_setValue // @grant GM_getValue // @grant GM_listValues // @grant GM_deleteValue // @grant window.onurlchange // @require https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js // @license GPL-3.0 // ==/UserScript== (function() { 'use strict'; let sleepTimeDisplay = false; let sleepTimeDiv = document.createElement("div"); let Index_lastTime_usec = 0; let MaxIndex_lastTime_usec; let counter = 0; let typeAlgorithme = GM_getValue("typeAlgorithme", null); var predictionSleep; let diffTime = 0; let minSleep = 6; let maxSleep = 24; let CurrentDate = new Date(); let SleepingTimeArray = []; let WakeUpTimeArray = []; const tf = window.tf; let model = tf.sequential(); tf.setBackend('webgl'); let _TrainOnce = true; document.addEventListener("keypress", arrayTimeUpdate); document.addEventListener("mousemove", arrayTimeUpdate); document.addEventListener("mousedown", arrayTimeUpdate); function arrayTimeUpdate() { let wakeTimer = GM_getValue("wakeTimer", null); let oldDate = GM_getValue("oldDate", null); if (oldDate == null) { oldDate = CurrentDate.getTime(); GM_setValue("SleepingTimeArray", [8.1, 9.1, 10.1]); GM_setValue("WakeUpTimeArray", [16.1, 15.1, 14.1]); } //first time run CurrentDate = new Date(); diffTime = CurrentDate.getTime() - oldDate; diffTime = diffTime/(1000*60*60); if (diffTime > minSleep && diffTime < maxSleep) { SleepingTimeArray = GM_getValue("SleepingTimeArray", null); WakeUpTimeArray = GM_getValue("WakeUpTimeArray", null); if (SleepingTimeArray == null) { SleepingTimeArray = [diffTime]; } //first time store else { SleepingTimeArray.push(diffTime); } if (WakeUpTimeArray == null) { WakeUpTimeArray = [wakeTimer]; } else { WakeUpTimeArray.push(wakeTimer); } GM_setValue("SleepingTimeArray", SleepingTimeArray); GM_setValue("WakeUpTimeArray", WakeUpTimeArray); wakeTimer = 0; } else if (diffTime <= minSleep) { if (wakeTimer == null) { wakeTimer = diffTime; } else { wakeTimer += diffTime; } } else { wakeTimer = 0; } GM_setValue("wakeTimer", wakeTimer); GM_setValue("oldDate", CurrentDate.getTime()); } function predictSleep() { if ( typeAlgorithme == "Avg" ) { return predictSleepAvg(); } else { return predictSleepML(); } } function predictSleepML() { if (_TrainOnce) { _TrainOnce = false; let sleepingTimeArray = GM_getValue("SleepingTimeArray", null); let wakeUpTimeArray = GM_getValue("WakeUpTimeArray", null); let index = sleepingTimeArray.length - 1; while (index > -1) { sleepingTimeArray[index] = sleepingTimeArray[index] / maxSleep; wakeUpTimeArray[index] = wakeUpTimeArray[index] / maxSleep; index--; } sleepingTimeArray = tf.tensor2d(sleepingTimeArray, [sleepingTimeArray.length, 1]); wakeUpTimeArray = tf.tensor2d(wakeUpTimeArray, [wakeUpTimeArray.length, 1]); //const inputTensor = tf.stack([sleepingTimeArray, wakeUpTimeArray], 1).reshape([1, -1, 2]); //model.add(tf.layers.gru({units: 16, inputShape: [wakeUpTimeArray.length, 1]})); model.add(tf.layers.dense({ units: 128, inputShape: [1], activation: 'swish' })); // sigmoid, hardSigmoid, softplus, softsign, tanh, softmax, linear, relu, relu6, selu, elu, swish | https://www.tensorflow.org/js/tutorials/training/linear_regression model.add(tf.layers.dense({ units: 64, activation: 'softsign' })); model.add(tf.layers.dense({ units: 32, activation: 'selu' })); model.add(tf.layers.dense({ units: 1, activation: 'linear' })); model.compile({loss: 'meanSquaredError', optimizer: 'adam'}); model.fit(wakeUpTimeArray, sleepingTimeArray, {epochs: 50, batchSize: 32, optimizer: tf.train.adam(0.001), callbacks: {onEpochEnd: async(epoch, logs) => { let lossStr = logs.loss ? logs.loss.toFixed(4) : 'N/A'; console.log(`Epoch: ${epoch} - loss: ${lossStr}`);}}}); // const modelJSON = model.toJSON(); const modelString = JSON.stringify(modelJSON); GM_setValue('model', modelString); // Save the model } // <train | predict> // const modelString = GM_getValue('model'); const modelJSON = JSON.parse(modelString); const loadedModel = tf.loadLayersModel(modelJSON); // Load the model const wakeTimer = GM_getValue("wakeTimer", 0) / maxSleep; let wake = [wakeTimer]; wake = tf.tensor2d(wake, [wake.length, 1]); const predictionTensor = model.predict(wake); const prediction = predictionTensor.dataSync()[0] * maxSleep; tf.dispose(wake); // clean up: Memory management const predictionSleep = convertHours(prediction); return predictionSleep; } function predictSleepAvg() { SleepingTimeArray = GM_getValue("SleepingTimeArray", null); WakeUpTimeArray = GM_getValue("WakeUpTimeArray", null); //for read persistent storage let index = SleepingTimeArray.length - 1; let sum = 0; const nsleep = SleepingTimeArray.length; counter++ while (index > -1) { sum += SleepingTimeArray[index]; index--; } const sleepAverage = sum / nsleep; sum = 0; index = WakeUpTimeArray.length - 1; const nwake = WakeUpTimeArray.length; while (index > -1) { sum += WakeUpTimeArray[index]; index--; } const wakeAverage = sum / nwake; const ratio = sleepAverage / wakeAverage; const wakeTimer = GM_getValue("wakeTimer", 0); const predict = ratio * wakeTimer; return convertHours(predict); } function convertHours(predict) { const hours = Math.floor(predict); let remainder = predict - hours; remainder = remainder * 60; const minutes = Math.floor(remainder); remainder = remainder - minutes; remainder = remainder * 60; const seconds = Math.floor(remainder); const predictionText = `${hours} hours, ${minutes} minutes and ${seconds} seconds.(${counter})`; return predictionText; } function displaySleepTime() { sleepTimeDisplay = true; GM_setValue("sleepTimeDisplay", sleepTimeDisplay); predictionSleep = predictSleep(); let pos = GM_getValue("sleepTimeDivPos", { x: "50%", y: "50%" }); if (pos.x == "NaN" || pos.y == "NaN") pos = { x: "50%", y: "50%" }; sleepTimeDiv.style.cssText = `left: ${pos.x}; top: ${pos.y}; background-color: rgba(0,0,0,0.5); color: white; position: fixed; transform: translate(-50%, -50%); font-size: 100%; border-radius: 5px; padding: 10px; text-align: center; z-index: 9999;`; sleepTimeDiv.innerHTML = `If you sleep now, you will WakeUp in: <span id='sleepTimeSpan'>${predictionSleep}</span>`; document.body.appendChild(sleepTimeDiv); let sleepTimeSpan = document.getElementById("sleepTimeSpan"); sleepTimeSpan.style.marginRight = "30px"; let closeButton = document.createElement("button"); closeButton.style.cssText = `position: absolute; top: 5px; right: 5px; background-color: rgba(0,0,0,0.5); color: white; font-size: 100%; padding: 5px 10px; border-radius: 3px; box-shadow: 0px 0px 8px rgba(0,0,0,0.1); transition: all 0.2s ease-in-out;`; closeButton.innerHTML = "X"; sleepTimeDiv.addEventListener("dragover", (event) => { event.preventDefault(); }); sleepTimeDiv.addEventListener("drop", handleFileDrop); closeButton.addEventListener("click", function() { removeSleepTimeDisplay(); }); closeButton.addEventListener("mouseover", function() { closeButton.style.backgroundColor = "rgba(0,0,0,0.2)"; closeButton.style.color = "white"; }); closeButton.addEventListener("mouseout", function() { closeButton.style.backgroundColor = "rgba(0,0,0,0.5)"; closeButton.style.color = "white"; }); sleepTimeDiv.addEventListener("mousedown", function(event) { let currentX = event.clientX - sleepTimeDiv.offsetLeft; let currentY = event.clientY - sleepTimeDiv.offsetTop; document.addEventListener("mouseup", function() { document.removeEventListener("mousemove", moveDiv); let pos = { x: sleepTimeDiv.style.left, y: sleepTimeDiv.style.top }; GM_setValue("sleepTimeDivPos", pos); }); document.addEventListener("mousemove", moveDiv); function moveDiv(event) { sleepTimeDiv.style.left = event.clientX - currentX + "px"; sleepTimeDiv.style.top = event.clientY - currentY + "px"; } }); sleepTimeDiv.appendChild(closeButton); setInterval(function() { document.querySelector("#sleepTimeSpan") && (document.querySelector("#sleepTimeSpan").innerHTML = `${predictSleep()}`); }, 1000); } function removeSleepTimeDisplay() { if (sleepTimeDisplay) { sleepTimeDisplay = false; GM_setValue("sleepTimeDisplay", sleepTimeDisplay); const pos = { x: "50%", y: "50%" }; GM_setValue("sleepTimeDivPos", pos); sleepTimeDiv.remove(); } } function handleFileDrop(event) { // get "BrowserHistory.json" browser history from https://takeout.google.com/ event.preventDefault(); event.stopPropagation(); const reader = new FileReader(); reader.readAsArrayBuffer(event.dataTransfer.files[0]); reader.onload = () => { const data = JSON.parse(new TextDecoder().decode(reader.result)); MaxIndex_lastTime_usec = data['Browser History'].length; SleepingTimeArray = GM_getValue("SleepingTimeArray", null); WakeUpTimeArray = GM_getValue("WakeUpTimeArray", null); let lastTime_usec = 0; let wake = 0; let diff = 0; let hours = 0; let minValidSleep = 8; let maxValidSleep = 8; const minValidWakeUp = 10; const maxValidWakeUp = 30; data['Browser History'].forEach(item => { if (lastTime_usec == 0) { lastTime_usec = item.time_usec; } else { diff = lastTime_usec - item.time_usec; hours = diff / (1000 * 60 * 60); if (hours <= maxSleep && hours >= minSleep && wake >= minValidWakeUp && wake <= maxValidWakeUp) { if (hours > maxValidSleep) { maxValidSleep = hours; } else if (hours < minValidSleep) { minValidSleep = hours; } if (SleepingTimeArray == null) { SleepingTimeArray = [hours]; WakeUpTimeArray = [wake]; } else { SleepingTimeArray.push(hours); WakeUpTimeArray.push(wake); } wake = 0; Index_lastTime_usec++; } else if (hours < 6) { wake += hours; } else { wake = 0; } lastTime_usec = item.time_usec; } console.log(item.time_usec + " - " + Index_lastTime_usec + " \ " + MaxIndex_lastTime_usec); counter++; }); console.log(SleepingTimeArray); console.log(WakeUpTimeArray); GM_setValue("SleepingTimeArray", SleepingTimeArray); GM_setValue("WakeUpTimeArray", WakeUpTimeArray); }; } if (window.onurlchange === null) { console.log("URL CHANGE"); let sleepTimeDisplay = GM_getValue("sleepTimeDisplay", false); if (sleepTimeDisplay) { displaySleepTime(); } else { removeSleepTimeDisplay();} //window.addEventListener('urlchange', (info) => { console.log("newly created"); }); } GM_registerMenuCommand("Sleep Time", () => { if (sleepTimeDisplay) { removeSleepTimeDisplay(); } else { displaySleepTime(); }}); GM_registerMenuCommand("switchAlgorithm", () => { typeAlgorithme = GM_getValue("typeAlgorithme", "ML"); if (typeAlgorithme == "ML") { typeAlgorithme = "Avg"; } else { typeAlgorithme = "ML"; } GM_setValue("typeAlgorithme", typeAlgorithme); console.log("Apply: typeAlgorithme = " + typeAlgorithme); }); GM_registerMenuCommand("showDelKey", () => { const keys = GM_listValues(); const data = keys.map(key => { const value = GM_getValue(key); return { key, value }; }); console.table(data); const confirmation = confirm(`Do you want to delete all ${keys.length} values show in console?`); if (confirmation) { keys.forEach(key => { GM_deleteValue(key); }); console.log(`${keys.length} values have been deleted.`); } else { console.log(`No values have been deleted.`); } }); })();
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