103 lines
3.4 KiB
JavaScript
103 lines
3.4 KiB
JavaScript
/**
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** ==============================
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** O O O OOOO
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** O O O O O O
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** O O O O O O
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** OOOO OOOO O OOO OOOO
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** O O O O O O O
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** O O O O O O O
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** OOOO OOOO O O OOOO
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** ==============================
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** Dr. Stefan Bosse http://www.bsslab.de
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**
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** COPYRIGHT: THIS SOFTWARE, EXECUTABLE AND SOURCE CODE IS OWNED
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** BY THE AUTHOR(S).
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** THIS SOURCE CODE MAY NOT BE COPIED, EXTRACTED,
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** MODIFIED, OR OTHERWISE USED IN A CONTEXT
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** OUTSIDE OF THE SOFTWARE SYSTEM.
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**
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** $AUTHORS: Stefan Bosse
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** $CREATED: (C) 2006-2019 bLAB by sbosse
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** $VERSION: 1.1.1
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**
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** $INFO:
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**
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** Convolutional neural network ML Algorithm
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**
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** Incremental learner using ml.update! Initial training data via ml.learn (or empty data set)
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**
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** $ENDOFINFO
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*/
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'use strict';
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var Io = Require('com/io');
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var Comp = Require('com/compat');
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var current=none;
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var Aios=none;
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var convnetjs = Require('ml/convnet')
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var that;
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that = module.exports = {
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// typeof options = {x:[][],y:[],width,height,depth,normalize?:[a,b],layers:{}[]..}
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// format x = [ [row1=[col1=[z1,z2,..],col2,..],row2,..] ]
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create : function (options) {
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var net = new convnetjs.Net();
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if (options.layers)
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net.makeLayers(options.layers);
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if (!options.iterations) options.iterations=10;
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if (!options.depth) options.depth=1;
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if (!options.width) options.width=options.x[0].length,options.height=1;
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var trainer = new convnetjs.SGDTrainer(net, options.trainer||
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{method: 'adadelta',
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l2_decay: 0.001,
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batch_size: 10});
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// convert matrix (2dim/3dim) to volume elements
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var x = options.x;
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if (options.normalize) {
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var a,b,
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c=options.normalize[0],
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d=options.normalize[1];
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x.forEach(function (row) {
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var min=Math.min.apply(null,row),
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max=Math.max.apply(null,row);
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if (a==undefined) a=min; else a=Math.min(a,min);
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if (b==undefined) b=max; else b=Math.max(b,max);
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})
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x=x.map(function (row) {
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return row.map(function (col) { return (((col-a)/(b-a))*(d-c))+c }) // scale [0,1] -> [c,d]
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})
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}
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x=x.map(function (row) {
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var vol = new convnetjs.Vol(options.width, options.height, options.depth, 0.0); //input volume (image)
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vol.w = row;
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return vol;
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});
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x.forEach (function (row) {
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//net.forward(row);
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})
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var y = options.y;
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if (!options.targets) {
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options.targets=that.ml.stats.unique(y);
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}
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for(var iters=0;iters<options.iterations;iters++) {
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y.forEach(function (v,i) {
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trainer.train(x[i],options.targets.indexOf(v));
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})
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}
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trainer.options= {width:options.width,height:options.height,depth:options.depth,targets:options.targets};
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return trainer;
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},
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ml:{},
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predict: function (model,sample) {
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var options = model.options;
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var vol = new convnetjs.Vol(options.width, options.height, options.depth, 0.0); //input volume (image)
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vol.w = sample;
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return model.net.forward(vol);
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},
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print: function () {
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},
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update: function (data) {
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},
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current:function (module) { current=module.current; Aios=module;}
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};
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