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extracted javascript from HTML files
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Pavel Pepeldjiyski committed May 7, 2015
1 parent 2ec6cf0 commit a029115
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501 changes: 1 addition & 500 deletions demo/autoencoder.html
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495 changes: 495 additions & 0 deletions demo/autoencoder.js
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359 changes: 1 addition & 358 deletions demo/automatic.html
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<link rel="stylesheet" href="automatic.css">

<script type="application/javascript">

// utility functions
Array.prototype.contains = function(v) {
for(var i = 0; i < this.length; i++) {
if(this[i] === v) return true;
}
return false;
};
Array.prototype.unique = function() {
var arr = [];
for(var i = 0; i < this.length; i++) {
if(!arr.contains(this[i])) {
arr.push(this[i]);
}
}
return arr;
}

function FAIL(outdivid, msg) {
$(outdivid).prepend("<div class=\"msg\" style=\"background-color:#FCC;\">"+msg+"</div>")
}
function SUCC(outdivid, msg) {
$(outdivid).prepend("<div class=\"msg\" style=\"background-color:#CFC;\">"+msg+"</div>")
}

// looks at a column i of data and guesses what's in it
// returns results of analysis: is column numeric? How many unique entries and what are they?
function guessColumn(data, c) {
var numeric = true;
var vs = [];
for(var i=0,n=data.length;i<n;i++) {
var v = data[i][c];
vs.push(v);
if(isNaN(v)) numeric = false;
}
var u = vs.unique();
if(!numeric) {
// if we have a non-numeric we will map it through uniques to an index
return {numeric:numeric, num:u.length, uniques:u};
} else {
return {numeric:numeric, num:u.length};
}
}

// returns arr (csv parse)
// and colstats, which contains statistics about the columns of the input
// parsing results will be appended to a div with id outdivid
function importData(arr, outdivid) {
$(outdivid).empty(); // flush messages

// find number of datapoints
N = arr.length;
var t = [];
SUCC(outdivid, "found " + N + " data points");
if(N === 0) { FAIL(outdivid, 'no data points found?'); return; }

// find dimensionality and enforce consistency
D = arr[0].length;
for(var i=0;i<N;i++) {
var d = arr[i].length;
if(d !== D) { FAIL(outdivid, 'data dimension not constant: line ' + i + ' has ' + d + ' entries.'); return; }
}
SUCC(outdivid, "data dimensionality is " + (D-1));

// go through columns of data and figure out what they are
var colstats = [];
for(var i=0;i<D;i++) {
var res = guessColumn(arr, i);
colstats.push(res);
if(D > 20 && i>3 && i < D-3) {
if(i==4) {
SUCC(outdivid, "..."); // suppress output for too many columns
}
} else {
SUCC(outdivid, "column " + i + " looks " + (res.numeric ? "numeric" : "NOT numeric") + " and has " + res.num + " unique elements");
}
}

return {arr: arr, colstats: colstats};
}

// process input mess into vols and labels
function makeDataset(arr, colstats) {

var labelix = parseInt($("#labelix").val());
if(labelix < 0) labelix = D + labelix; // -1 should turn to D-1

var data = [];
var labels = [];
for(var i=0;i<N;i++) {
var arri = arr[i];

// create the input datapoint Vol()
var p = arri.slice(0, D-1);
var xarr = [];
for(var j=0;j<D;j++) {
if(j===labelix) continue; // skip!

if(colstats[j].numeric) {
xarr.push(parseFloat(arri[j]));
} else {
var u = colstats[j].uniques;
var ix = u.indexOf(arri[j]); // turn into 1ofk encoding
for(var q=0;q<u.length;q++) {
if(q === ix) { xarr.push(1.0); }
else { xarr.push(0.0); }
}
}
}
var x = new convnetjs.Vol(xarr);

// process the label (last column)
if(colstats[labelix].numeric) {
var L = parseFloat(arri[labelix]); // regression
} else {
var L = colstats[labelix].uniques.indexOf(arri[labelix]); // classification
if(L==-1) {
console.log('whoa label not found! CRITICAL ERROR, very fishy.');
}
}
data.push(x);
labels.push(L);
}

var dataset = {};
dataset.data = data;
dataset.labels = labels;
return dataset;
}

// optionally provide a magic net
function testEval(optional_net) {
if (typeof optional_net !== 'undefined') {
var net = optional_net;
} else {
var net = magicNet;
}

// set options for magic net
net.ensemble_size = parseInt($("#ensemblenum").val())

// read in the data in the text field
var test_dataset = importTestData();
// use magic net to predict
var n = test_dataset.data.length;
var acc = 0.0;
for(var i=0;i<n;i++) {
var yhat = net.predict(test_dataset.data[i]);
if(yhat === -1) {
$("#testresult").html("The MagicNet is not yet ready! It must process at least one batch of candidates across all folds first. Wait a bit.");
$("#testresult").css('background-color', '#FCC');
return;
}
var l = test_dataset.labels[i];
acc += (yhat === l ? 1 : 0); // 0-1 loss
console.log('test example ' + i + ': predicting ' + yhat + ', ground truth is ' + l);
}
acc /= n;

// report accuracy
$("#testresult").html("Test set accuracy: " + acc);
$("#testresult").css('background-color', '#CFC');
}

function reinitGraph() {
var legend = [];
for(var i=0;i<magicNet.candidates.length;i++) {
legend.push('model ' + i);
}
valGraph = new cnnvis.MultiGraph(legend, {miny: 0, maxy: 1});
}

var folds_evaluated = 0;
function finishedFold() {
folds_evaluated++;
$("#foldreport").html("So far evaluated a total of " + folds_evaluated + "/" + magicNet.num_folds + " folds in current batch");
reinitGraph();
}
var batches_evaluated = 0;
function finishedBatch() {
batches_evaluated++;
$("#candsreport").html("So far evaluated a total of " + batches_evaluated + " batches of candidates");
}

var magicNet = null;
function startCV() { // takes in train_dataset global
var opts = {}
opts.train_ratio = parseInt($("#trainp").val())/100.0;
opts.num_folds = parseInt($("#foldsnum").val());
opts.num_candidates = parseInt($("#candsnum").val());
opts.num_epochs = parseInt($("#epochsnum").val());
opts.neurons_min = parseInt($("#nnmin").val());
opts.neurons_max = parseInt($("#nnmin").val());
magicNet = new convnetjs.MagicNet(train_dataset.data, train_dataset.labels, opts);
magicNet.onFinishFold(finishedFold);
magicNet.onFinishBatch(finishedBatch);

folds_evaluated = 0;
batches_evaluated = 0;
$("#candsreport").html("So far evaluated a total of " + batches_evaluated + " batches of candidates");
$("#foldreport").html("So far evaluated a total of " + folds_evaluated + "/" + magicNet.num_folds + " folds in current batch");
reinitGraph();

var legend = [];
for(var i=0;i<magicNet.candidates.length;i++) {
legend.push('model ' + i);
}
valGraph = new cnnvis.MultiGraph(legend, {miny: 0, maxy: 1});
setInterval(step, 0);
}

var fold;
var cands = [];
var dostep = false;
var valGraph;
var iter = 0;
function step() {
iter++;

magicNet.step();
if(iter % 300 == 0) {

var vals = magicNet.evalValErrors();
valGraph.add(magicNet.iter, vals);
valGraph.drawSelf(document.getElementById("valgraph"));

// print out the best models so far
var cands = magicNet.candidates; // naughty: get pointer to internal data
var scores = [];
for(var k=0;k<cands.length;k++) {
var c = cands[k];
var s = c.acc.length === 0 ? 0 : c.accv / c.acc.length;
scores.push(s);
}
var mm = convnetjs.maxmin(scores);
var cm = cands[mm.maxi];
var t = '';
if(c.acc.length > 0) {
t += 'Results based on ' + c.acc.length + ' folds:';
t += 'best model in current batch (validation accuracy ' + mm.maxv + '):<br>';
t += '<b>Net layer definitions:</b><br>';
t += JSON.stringify(cm.layer_defs);
t += '<br><b>Trainer definition:</b><br>';
t += JSON.stringify(cm.trainer_def);
t += '<br>';
}
$('#bestmodel').html(t);

// also print out the best model so far
var t = '';
if(magicNet.evaluated_candidates.length > 0) {
var cm = magicNet.evaluated_candidates[0];
t += 'validation accuracy of best model so far, overall: ' + cm.accv / cm.acc.length + '<br>';
t += '<b>Net layer definitions:</b><br>';
t += JSON.stringify(cm.layer_defs);
t += '<br><b>Trainer definition:</b><br>';
t += JSON.stringify(cm.trainer_def);
t += '<br>';
}
$('#bestmodeloverall').html(t);
}
}

// TODO: MOVE TO CONVNETJS UTILS
var randperm = function(n) {
var i = n,
j = 0,
temp;
var array = [];
for(var q=0;q<n;q++)array[q]=q;
while (i--) {
j = Math.floor(Math.random() * (i+1));
temp = array[i];
array[i] = array[j];
array[j] = temp;
}
return array;
}

var train_dataset, train_import_data; // globals
function importTrainData() {
var csv_txt = $('#data-ta').val();
var arr = $.csv.toArrays(csv_txt);
var arr_train = arr;
var arr_test = [];

var test_ratio = Math.floor($("#testsplit").val());
if(test_ratio !== 0) {
// send some lines to test set
var test_lines_num = Math.floor(arr.length * test_ratio / 100.0);
var rp = randperm(arr.length);
arr_train = [];
for(var i=0;i<arr.length;i++) {
if(i<test_lines_num) {
arr_test.push(arr[rp[i]]);
} else {
arr_train.push(arr[rp[i]]);
}
}
// enter test lines to test box
var t = "";
for(var i=0;i<arr_test.length;i++) {
t+= arr_test[i].join(",")+"\n";
}
$("#data-te").val(t);
$("#datamsgtest").empty();
}

$("#prepromsg").empty(); // flush
SUCC("#prepromsg", "Sent " + arr_test.length + " data to test, keeping " + arr_train.length + " for train.");
train_import_data = importData(arr_train,'#datamsg');
train_dataset = makeDataset(train_import_data.arr, train_import_data.colstats);
return train_dataset;
}

function importTestData() {
var csv_txt = $('#data-te').val();
var arr = $.csv.toArrays(csv_txt);
var import_data = importData(arr,'#datamsgtest');
// note important that we use colstats of train data!
test_dataset = makeDataset(import_data.arr, train_import_data.colstats);
return test_dataset;
}

function loadDB(url) {
// load a dataset from a url with ajax
$.ajax({
url: url,
dataType: "text",
success: function(txt) {
$("#data-ta").val(txt);
}
});
}

function start() {
loadDB('data/car.data.txt');
}

function exportMagicNet() {
$("#taexport").val(JSON.stringify(magicNet.toJSON()));

/*
// for debugging
var j = JSON.parse($("#taexport").val());
var m = new convnetjs.MagicNet();
m.fromJSON(j);
testEval(m);
*/
}

function changeNNRange() {
magicNet.neurons_min = parseInt($("#nnmin").val());
magicNet.neurons_max = parseInt($("#nnmax").val());
}

</script>
<script src="automatic.js"></script>
</head>
<body onload="start();">
<div id="wrap">
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