From 93d4bd43034b6b51d8641e780f54dad15bdfb6b6 Mon Sep 17 00:00:00 2001
From: sbosse <sbosse@uni-bremen.de>
Date: Mon, 21 Jul 2025 23:07:30 +0200
Subject: [PATCH] Mon 21 Jul 22:43:21 CEST 2025

---
 js/ml/cnn.js | 102 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 js/ml/cnn.js

diff --git a/js/ml/cnn.js b/js/ml/cnn.js
new file mode 100644
index 0000000..61baa81
--- /dev/null
+++ b/js/ml/cnn.js
@@ -0,0 +1,102 @@
+/**
+ **      ==============================
+ **       O           O      O   OOOO
+ **       O           O     O O  O   O
+ **       O           O     O O  O   O
+ **       OOOO   OOOO O     OOO  OOOO
+ **       O   O       O    O   O O   O
+ **       O   O       O    O   O O   O
+ **       OOOO        OOOO O   O OOOO
+ **      ==============================
+ **      Dr. Stefan Bosse http://www.bsslab.de
+ **
+ **      COPYRIGHT: THIS SOFTWARE, EXECUTABLE AND SOURCE CODE IS OWNED
+ **                 BY THE AUTHOR(S).
+ **                 THIS SOURCE CODE MAY NOT BE COPIED, EXTRACTED,
+ **                 MODIFIED, OR OTHERWISE USED IN A CONTEXT
+ **                 OUTSIDE OF THE SOFTWARE SYSTEM.
+ **
+ **    $AUTHORS:     Stefan Bosse
+ **    $CREATED:     (C) 2006-2019 bLAB by sbosse
+ **    $VERSION:     1.1.1
+ **
+ **    $INFO:
+ **
+ ** Convolutional neural network ML Algorithm
+ **
+ ** Incremental learner using ml.update! Initial training data via ml.learn (or empty data set) 
+ ** 
+ **    $ENDOFINFO
+ */
+'use strict';
+var Io = Require('com/io');
+var Comp = Require('com/compat');
+var current=none;
+var Aios=none;
+
+var convnetjs = Require('ml/convnet')
+var that;
+
+that = module.exports =  {
+  // typeof options = {x:[][],y:[],width,height,depth,normalize?:[a,b],layers:{}[]..}
+  // format x = [ [row1=[col1=[z1,z2,..],col2,..],row2,..] ]
+  create : function (options) {
+    var net = new convnetjs.Net();
+    if (options.layers)
+      net.makeLayers(options.layers);
+    if (!options.iterations) options.iterations=10;
+    if (!options.depth) options.depth=1;
+    if (!options.width) options.width=options.x[0].length,options.height=1;
+    var trainer = new convnetjs.SGDTrainer(net, options.trainer||
+                                          {method: 'adadelta', 
+                                          l2_decay: 0.001, 
+                                          batch_size: 10});
+    // convert matrix (2dim/3dim) to volume elements
+    var x = options.x;
+    if (options.normalize) {
+      var a,b,
+          c=options.normalize[0],
+          d=options.normalize[1];
+      x.forEach(function (row) {
+        var min=Math.min.apply(null,row),
+            max=Math.max.apply(null,row);
+        if (a==undefined) a=min; else a=Math.min(a,min);
+        if (b==undefined) b=max; else b=Math.max(b,max);        
+      })
+      x=x.map(function (row) {
+        return row.map(function (col) { return (((col-a)/(b-a))*(d-c))+c })  // scale [0,1] -> [c,d]
+      })
+    }
+    x=x.map(function (row) {
+      var vol = new convnetjs.Vol(options.width, options.height, options.depth, 0.0); //input volume (image)
+      vol.w = row;
+      return vol;
+    });
+    x.forEach (function (row) {
+      //net.forward(row);
+    })
+    var y = options.y;
+    if (!options.targets) {
+      options.targets=that.ml.stats.unique(y);
+    }
+    for(var iters=0;iters<options.iterations;iters++) {
+      y.forEach(function (v,i) {
+        trainer.train(x[i],options.targets.indexOf(v));
+      })
+    }
+    trainer.options= {width:options.width,height:options.height,depth:options.depth,targets:options.targets};
+    return trainer;
+  },
+  ml:{},
+  predict: function (model,sample) {
+    var options = model.options;
+    var vol = new convnetjs.Vol(options.width, options.height, options.depth, 0.0); //input volume (image)
+    vol.w = sample;
+    return model.net.forward(vol);
+  },
+  print: function () {
+  },
+  update: function (data) {
+  },
+  current:function (module) { current=module.current; Aios=module;}
+};