forked from sheetjs/sheetjs
SheetJS
93f7749bec
- all formats accept `sheetRows` option (fixes #1062 h/t @prog666) - `table_to_*` support for `sheetRows` - demo cleanup
79 lines
2.4 KiB
JavaScript
79 lines
2.4 KiB
JavaScript
/* xlsx.js (C) 2013-present SheetJS -- http://sheetjs.com */
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/* eslint-env node */
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var XLSX = require('xlsx');
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var tf = require('@tensorflow/tfjs');
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var linest = require('./linest');
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/* generate linreg.xlsx with 100 random points */
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var N = 100;
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linest.generate_random_file(N);
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/* get the first worksheet as an array of arrays, skip the first row */
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var wb = XLSX.readFile('linreg.xlsx');
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var ws = wb.Sheets[wb.SheetNames[0]];
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var aoa = XLSX.utils.sheet_to_json(ws, {header:1, raw:true}).slice(1);
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/* calculate the coefficients in JS */
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(function(aoa) {
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var x_ = 0, y_ = 0, xx = 0, xy = 0, n = aoa.length;
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for(var i = 0; i < n; ++i) {
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x_ += aoa[i][0] / n;
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y_ += aoa[i][1] / n;
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xx += aoa[i][0] * aoa[i][0];
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xy += aoa[i][0] * aoa[i][1];
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}
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var m = Math.fround((xy - n * x_ * y_)/(xx - n * x_ * x_));
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console.log(m, Math.fround(y_ - m * x_), "JS Post");
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})(aoa);
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/* build X and Y vectors */
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var tensor = tf.tensor2d(aoa).transpose();
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console.log(tensor.shape);
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var xs = tensor.slice([0,0], [1,tensor.shape[1]]).flatten();
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var ys = tensor.slice([1,0], [1,tensor.shape[1]]).flatten();
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/* set up variables with initial guess */
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var x_ = xs.mean().dataSync()[0];
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var y_ = ys.mean().dataSync()[0];
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var a = tf.variable(tf.scalar(y_/x_));
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var b = tf.variable(tf.scalar(Math.random()));
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/* linear predictor */
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function predict(x) { return tf.tidy(function() { return a.mul(x).add(b); }); }
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/* mean square scoring */
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function loss(yh, y) { return yh.sub(y).square().mean(); }
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/* train */
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for(var j = 0; j < 5; ++j) {
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var learning_rate = 0.0001 /(j+1), iterations = 1000;
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var optimizer = tf.train.sgd(learning_rate);
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for(var i = 0; i < iterations; ++i) optimizer.minimize(function() {
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var pred = predict(xs);
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var L = loss(pred, ys);
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return L
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});
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/* compute the coefficient */
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var m = a.dataSync()[0], b_ = b.dataSync()[0];
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console.log(m, b_, "TF " + iterations * (j+1));
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}
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/* export data to aoa */
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var yh = predict(xs);
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var tfdata = tf.stack([xs, ys, yh]).transpose();
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var shape = tfdata.shape;
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var tfarr = tfdata.dataSync();
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var tfaoa = [];
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for(j = 0; j < shape[0]; ++j) {
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tfaoa[j] = [];
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for(i = 0; i < shape[1]; ++i) tfaoa[j][i] = tfarr[j * shape[1] + i];
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}
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/* add headers and export */
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tfaoa.unshift(["x", "y", "pred"]);
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var new_ws = XLSX.utils.aoa_to_sheet(tfaoa);
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var new_wb = XLSX.utils.book_new();
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XLSX.utils.book_append_sheet(new_wb, new_ws, "Sheet1");
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XLSX.writeFile(new_wb, "tfjs.xls");
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