Chris Ismael ismaelc

## trainModel.js
function trainModel(data) {
  classifier = new Classifier({
    nGramMin: 1,
    nGramMax: 3,
  });

  for (let key in data) {
    classifier.train(data[key], key);
  }
}

## capturerange.02.js
  let range = resolveRange(selectedRange.address, usedRange.address)

  range = book.worksheets
    .getActiveWorksheet()
    .getRange(range);

  range.load("values");
  await context.sync();

  processedData = processData(range.values);

## capturerange.01.js
let book = context.workbook
let selectedRange = book.getSelectedRange()
let usedRange = book.worksheets
  .getActiveWorksheet()
  .getUsedRange();
selectedRange.load("address");
usedRange.load("address");
await context.sync();

## capturerange.js
export async function captureRange() {
  try {
    await Excel.run(async context => {
      let book = context.workbook
      let selectedRange = book.getSelectedRange()
      let usedRange = book.worksheets
        .getActiveWorksheet()
        .getUsedRange();
      selectedRange.load("address");
      usedRange.load("address");

## taskpane.02.js
import Classifier from 'ml-classify-text'
let processedData = []
let classifier = {}

## taskpane.01.js
    //document.getElementById("run").onclick = run;
    document.getElementById("capture").onclick = captureRange;
    document.getElementById("predict").onclick = predictRange;

## taskpane.html
<body class="ms-font-m ms-welcome ms-Fabric">
    <section id="sideload-msg" class="ms-welcome__main">
        <h2 class="ms-font-xl">Please sideload your add-in to see app body.</h2>
    </section>
    <main id="app-body" class="ms-welcome__main" style="display: none;">
        <div role="button" id="capture" class="ms-welcome__action ms-Button ms-Button--hero ms-font-xl">
            <span class="ms-Button-label">Capture training data</span>
        </div>
        <div role="button" id="predict" class="ms-welcome__action ms-Button ms-Button--hero ms-font-xl">
            <span class="ms-Button-label">Predict</span>

## sentiment.js
let positive = [
    'This is great, so cool!',
    'Wow, I love it!',
    'It really is amazing',
]

let negative = [
    'This is really bad',
    'I hate it with a passion',
    'Just terrible!',

## processdata.js
// Formats data into key:array
function processData(arrayData) {
  let processed = {};

  for (let [i, [text, label]] of arrayData.entries()) {
    // TODO: Deal with headers
    if (i == 0 && this.excludeFirstRow) continue;
    // Do not add empty rows
    if (text.trim() == '' || label.trim() == '') continue;

## resolverange.js
// Deal with column vs range select
function resolveRange(sheet) {
  let range = sheet.getActiveRange().getA1Notation();
  let [first, second] = range.split(':')
  if (!/\d/.test(second)) {
    range = `${first}1:${second}${sheet.getLastRow()}`
  }

  return range
}
	function trainModel(data) {
	classifier = new Classifier({
	nGramMin: 1,
	nGramMax: 3,
	});

	for (let key in data) {
	classifier.train(data[key], key);
	}
	}
	let range = resolveRange(selectedRange.address, usedRange.address)

	range = book.worksheets
	.getActiveWorksheet()
	.getRange(range);

	range.load("values");
	await context.sync();

	processedData = processData(range.values);
	let book = context.workbook
	let selectedRange = book.getSelectedRange()
	let usedRange = book.worksheets
	.getActiveWorksheet()
	.getUsedRange();
	selectedRange.load("address");
	usedRange.load("address");
	await context.sync();
	export async function captureRange() {
	try {
	await Excel.run(async context => {
	let book = context.workbook
	let selectedRange = book.getSelectedRange()
	let usedRange = book.worksheets
	.getActiveWorksheet()
	.getUsedRange();
	selectedRange.load("address");
	usedRange.load("address");
	import Classifier from 'ml-classify-text'
	let processedData = []
	let classifier = {}
	//document.getElementById("run").onclick = run;
	document.getElementById("capture").onclick = captureRange;
	document.getElementById("predict").onclick = predictRange;
	<body class="ms-font-m ms-welcome ms-Fabric">
	<section id="sideload-msg" class="ms-welcome__main">
	<h2 class="ms-font-xl">Please sideload your add-in to see app body.</h2>
	</section>
	<main id="app-body" class="ms-welcome__main" style="display: none;">
	<div role="button" id="capture" class="ms-welcome__action ms-Button ms-Button--hero ms-font-xl">
	<span class="ms-Button-label">Capture training data</span>
	</div>
	<div role="button" id="predict" class="ms-welcome__action ms-Button ms-Button--hero ms-font-xl">
	<span class="ms-Button-label">Predict</span>
	let positive = [
	'This is great, so cool!',
	'Wow, I love it!',
	'It really is amazing',
	]

	let negative = [
	'This is really bad',
	'I hate it with a passion',
	'Just terrible!',
	// Formats data into key:array
	function processData(arrayData) {
	let processed = {};

	for (let [i, [text, label]] of arrayData.entries()) {
	// TODO: Deal with headers
	if (i == 0 && this.excludeFirstRow) continue;
	// Do not add empty rows
	if (text.trim() == '' \|\| label.trim() == '') continue;
	// Deal with column vs range select
	function resolveRange(sheet) {
	let range = sheet.getActiveRange().getA1Notation();
	let [first, second] = range.split(':')
	if (!/\d/.test(second)) {
	range = `${first}1:${second}${sheet.getLastRow()}`
	}

	return range
	}