diff --git a/js/ml/ice.js b/js/ml/ice.js
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+++ b/js/ml/ice.js
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+/**
+ **      ==============================
+ **       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:     Ankit Kuwadekar, Stefan Bosse
+ **    $INITIAL:     (C) 2014, Ankit Kuwadekar
+ **    $MODIFIED:    (C) 2006-2018 bLAB by sbosse
+ **    $VERSION:     1.3.2
+ **
+ **    $INFO:
+ **
+ ** ICE: C45/ID3 Decision Tree Algorithm supporting feature variables with eps intervals
+ **
+ ** Portable model
+ **
+ ** New:
+ **        typeof eps = number | [epsx1:number,epsx2:number,..]
+ **
+ **    $ENDOFINFO
+ */
+var Io = Require('com/io');
+var Comp = Require('com/compat');
+var current=none;
+var Aios=none;
+var that;
+
+/**
+ * Map of valid tree node types
+ * @constant
+ * @static
+ */
+var NODE_TYPES = {
+  RESULT: 'result',
+  FEATURE: 'feature',
+  FEATURE_VALUE: 'feature_value'
+};
+
+var NL ='\n'
+
+/**
+ * Creates a new tree
+ */
+function createTree(data, target, features, eps) {
+  var ml = that.ml;
+  var targets = ml.stats.unique(ml.stats.utils.column(data, target));
+  if (targets.length == 1) {
+    return {
+      type: NODE_TYPES.RESULT,
+      name: targets[0],
+    };
+  }
+
+  if (features.length == 0) {
+    var topTarget = ml.stats.mostCommon(targets);
+    return {
+      type: NODE_TYPES.RESULT,
+      name: topTarget,
+    };
+  }
+
+  
+  var split = ml.stats.splitEps(data,features,target,targets,eps);
+  var bestFeature = split.feature;
+  var index = features.indexOf(bestFeature);
+  var remainingFeatures = split.remainingFeatures;
+  var remainingEps = 
+    typeof eps == 'number'?eps:remainingFeatures.map(function (v) { return eps[features.indexOf(v)] });
+  var possibleValues = split.possibleValues;
+
+  var node = {
+    type: NODE_TYPES.FEATURE,
+    name: bestFeature,
+    index: index,
+    eps: that.ml.stats.utils.selectEps(eps,index)
+  };
+
+  node.vals = split.choices.map(function (c) {
+    var child_node = {
+      val : c.val,
+      eps : that.ml.stats.utils.selectEps(eps,index),
+      type: NODE_TYPES.FEATURE_VALUE
+    };
+
+    child_node.child = createTree(c.data, target, remainingFeatures, remainingEps);
+    return child_node;
+    
+  })
+  return node;
+}
+
+
+function depth(model) {
+  switch (model.type) {
+    case NODE_TYPES.RESULT: return 1;
+    case NODE_TYPES.FEATURE: 
+      return 1+Comp.array.max(model.vals.map(function (val) {
+        return depth(val);
+      }));
+    case NODE_TYPES.FEATURE_VALUE: 
+      return 1+depth(model.child);   
+  }
+  return 0;
+}
+
+function info(model) {
+  var vl = vars(model);
+  return {
+    depth:depth(model),
+    nodes:vl.length,
+    vars:vl.unique(),
+  }
+}
+
+function predictEps(model,sample,prob,eps) {
+  var root = model;
+  if (!prob) prob=1;
+  while (root.type !== NODE_TYPES.RESULT) {
+    var attr = root.name;
+    var sampleVal = sample[attr];
+    // kNN approximation
+    var childNode = null;
+    root.vals.forEach(function(node) {
+      var fit=Math.abs(node.val-sampleVal);
+      if (!childNode || fit < childNode.fit) childNode={fit:fit,node:node};
+    });
+    if (childNode){
+      // with fit quality propagation
+      prob = prob * (1-Math.abs(childNode.fit/that.ml.stats.utils.selectEps(eps,root.index))/4) 
+      root = childNode.node.child;
+    } else {
+      root = root.vals[0].child;
+    }
+  }
+  return {value:root.name,prob:prob};
+};
+
+
+function printModel(model,indent) {
+  var line='',sep;
+  if (indent==undefined) indent=0;
+  if (!model) return '';
+  var sp = function () {var s=''; for(var i=0;i<indent;i++) s+=' '; return s};
+  switch (model.type) {
+    case NODE_TYPES.RESULT: 
+      return sp()+'-> '+model.name+NL;
+    case NODE_TYPES.FEATURE:
+      line=sp()+'$'+model.name+'?'+NL;
+      model.vals.forEach(function (v) {
+        line += printModel(v,indent+2);
+      }); 
+      return line;
+    case NODE_TYPES.FEATURE_VALUE: 
+      line=sp()+'=['+(model.val-model.eps)+','+(model.val+model.eps)+']'+NL;
+      return line+printModel(model.child,indent+2); 
+  }
+  return 'model?';
+}
+
+function vars(model) {
+  switch (model.type) {
+    case NODE_TYPES.RESULT: return [];
+    case NODE_TYPES.FEATURE: 
+      return [model.name].concat(Comp.array.flatten(model.vals.map(vars)));
+    case NODE_TYPES.FEATURE_VALUE: 
+      return vars(model.child);   
+  }
+  return [];
+}
+
+that = module.exports = {
+  create: function (options) {
+    return createTree(options.data,
+                      options.target,
+                      options.features,
+                      options.eps)
+  },
+  depth:depth,
+  info:info,
+  ml:{},
+  predict:function (model,sample) {
+    return predictEps(model,sample,1,model.eps)
+  },
+  print:printModel,
+}