neuralnetworktests/word2vecattempt.html at master · NeedToUpdate/neuralnetworktests · GitHub

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<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8" />
    <title>word2vec, kinda</title>
    <script type="text/javascript"
        src="lib/vector.js"></script>
    <script type="text/javascript"
        src="lib/matrix.js"></script>
    <script type="text/javascript"
        src="lib/nn.js"></script>
    <script type="text/javascript"
        src="lib/div.js"></script>
    <script type="text/javascript"
        src="lib/drawbase.js"></script>
</head>

<body style="background-color:black; overflow: hidden;">

    <div class="input"
        style="position: absolute; top: 0; left: 0;">
        <textarea name="input"
            id="input"
            cols="30"
            rows="10">the fast monkey is brown
the slow turtle is green
the monkey is fast and brown
the turtle is slow and green </textarea>
        <button id="go">go</button>
    </div>
    <script type="text/javascript">
        let height = window.innerHeight;
        let width = window.innerWidth;

        let words = '';

        let dict = {};
        let wordcount = 0;
        let mainloop = 0
        let running = false
        let brain = {}
        id('go').addEventListener('click', () => {
            if (running) {
                id('go').innerText = 'go'
                clearInterval(mainloop)
                running = false
            } else {
                running = true

                words = id('input').value
                data = prepare(words);

                createBrain()
                requestAnimationFrame(draw)
                id('go').innerText = 'stop'
                mainloop = setInterval(draw, 17)
            }
        })

        function createBrain() {
            if (Object.keys(brain).length > 0) return
            brain = new NeuralNetwork(wordcount);
            brain.addLayer(5, "dense", { //x, y , h, s, v
                activation_fn: "linear"
            });

            brain.addLayer(wordcount, "dense", {
                activation_fn: "softmax"
            });
            brain.config.loss_fn = "cross_entropy";
            brain.init();
        }

        function prepare(data) {
            let ws = 3;
            let bw = ["a", "is", "the", "are", "and", 'than', 'in'];
            let sent = data.split("\n");
            let joined = sent.map(s => {
                let t = s.split(" ").map(x => x.split('').filter(x => ![',', '.'].includes(x)).join(''));
                return t.filter(w => {
                    return !bw.includes(w);
                });
            });
            let allwords = [];
            let occs = Array(joined.length ** 2).fill(0);
            joined.forEach(w => {
                w.forEach(ww => {
                    if (!allwords.includes(ww)) {
                        allwords.push(ww);
                    }
                    occs[allwords.indexOf(ww)] += 1;
                });
            });
            occs.splice(allwords.length);
            let testdata = [];
            wordcount = allwords.length;
            allwords.forEach((w, i) => {
                dict[w] = i;
                dict[i] = w;
                let input = w;
                let outputs = [];
                joined.forEach(line => {
                    if (line.includes(w)) {
                        for (
                            j = line.indexOf(w) - ws; j <= line.indexOf(w) + ws; j++
                        ) {
                            if (line[j] && line[j] !== w) {
                                outputs.push(line[j]);
                            }
                        }
                    }
                });
                outputs.forEach(x => {
                    testdata.push({
                        input: w,
                        output: x
                    });
                });
            });
            testdata = shuffle(testdata);
            //console.log(JSON.stringify(testdata))
            return testdata;
        }

        let p = new P("Loss: ", 10, 20);

        let data = []

        let count = 0;

        function getData() {
            let x = data[count].input;
            let y = data[count].output;
            x = dict[x];
            y = dict[y];
            x = enumerate(x, wordcount);
            y = enumerate(y, wordcount);
            count++;
            if (count > wordcount - 1) {
                count = 0;
                data = shuffle(data);
            }
            return {
                input: x,
                output: y
            };
        }

        function enumerate(n, max) {
            let m = max || 8;
            let a = Array(m).fill(0);
            a[n] = 1;
            return a;
        }

        function denumerate(arr) {}

        function argmax(arr) {
            return arr.indexOf(arr.reduce((a, b) => Math.max(a, b)));
        }


        function train() {
            let d = getData();
            brain.train(d.input, d.output);
            p.shape.innerText =
                "Loss: " + ((brain.calc_loss() * 100) | 0) / 100;
        }

        let ps = [];

        function draw() {
            ps.forEach(p => {
                p.remove();
            });
            ps = [];
            loop(train, 50);
            let w2v = brain.layers[0].weight_array[0][0].copy().transpose()
                .values;
            let max = w2v.reduce((a, b) => Math.max(rmax(a), rmax(b)), [
                0,
                0,
            ]);
            let min = w2v.reduce((a, b) => Math.min(rmin(a), rmin(b)), [
                0,
                0,
            ]);
            w2v = w2v.map(pair => {
                return pair.map(v => {
                    return normalize(v, min, max);
                });
            });

            let newdict = toDict(w2v);

            Object.keys(newdict).forEach(key => {
                let p = newdict[key];
                let x = p[0] * (width - 40);
                let y = p[1] * (height - 130) + 100;
                let w = new P(key, x, y);
                w.shape.style.fontSize = "2rem"
                w.shape.style.color =
                    "hsl(" +
                    p[2] * 360 +
                    "," +
                    p[3] * 100 +
                    "%," +
                    p[4] * 100 +
                    "%)";
                w.shape.style.textShadow =
                    "hsl(" +
                    p[2] * 360 +
                    "," +
                    p[3] * 100 +
                    "%," +
                    (100 - (p[4] * 100)) +
                    "%) 0px 0px 2px";
                ps.push(w);
            });
            // if (running) {
            //     requestAnimationFrame(draw)
            // }
        };

        function normalize(n, min, max) {
            return (n - min) / (max - min);
        }

        function rmax(arr) {
            if (arr instanceof Array) {
                return arr.reduce((a, b) => Math.max(a, b));
            } else {
                return arr;
            }
        }

        function rmin(arr) {
            if (arr instanceof Array) {
                return arr.reduce((a, b) => Math.min(a, b));
            } else {
                return arr;
            }
        }

        function toDict(arr) {
            let obj = {};
            arr.forEach((x, i) => {
                let w = dict[i];
                obj[w] = x;
            });
            return obj;
        }

        function shuffle(array) {
            return array.sort(() => Math.random() - 0.5);
        }

    </script>
</body>

</html>