Mon 21 Jul 22:43:21 CEST 2025
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js/test/learn.js
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431
js/test/learn.js
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global.TOP='/home/sbosse/proj/jam/js';
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require(TOP+'/top/module')([process.cwd(),TOP]);
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var Comp = Require('com/compat');
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var Io = Require('com/io');
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var Aios = Require('jam/aios');
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var Db = Require('db/db');
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var Fs = require('fs');
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var db = Db.Sqlc('/tmp/sqld',1);
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var Ml = Require('ml/ml');
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var ml = Ml.agent;
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var _ = Require('ml/lodash');
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var repl;
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db.init();
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var datasets=[0,1,2,3,4,5];
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var features=[];
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var eps=10;
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var class_name='load';
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var roid = 1;
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var roi0 = {x:3,y:4};
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var dataset=0;
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var training_data=[];
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var model;
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//console.log(ml.entropy([31,11,10],10));
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function entropyEps(vals,eps) {
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var uniqueVals = _.unique(vals);
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var probs = uniqueVals.map(function(x) {
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return probEps(x, vals, eps)
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});
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var logVals = probs.map(function(p) {
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return -p * log2(p)
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});
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return logVals.reduce(function(a, b) {
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return a + b
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}, 0);
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}
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// with additional 2*epsilon interval, only applicable to numerical values
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function probEps(value, list, eps) {
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var occurrences = _.filter(list, function(element) {
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return (element >= (value-eps)) && (element <= (value+eps));
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});
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var numOccurrences = occurrences.length;
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var numElements = list.length;
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return numOccurrences / numElements;
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}
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/**
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* Computes Log with base-2
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* @private
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*/
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function log2(n) {
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return Math.log(n) / Math.log(2);
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}
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var NODE_TYPES = {
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RESULT: 'result',
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FEATURE: 'feature',
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FEATURE_RANGE: 'feature_range',
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FEATURE_VALUE: 'feature_value'
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};
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function Result(key) {
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return {
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type:NODE_TYPES.RESULT,
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name:key
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}
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}
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function Feature(name,vals) {
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return {
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type:NODE_TYPES.FEATURE,
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name:name,
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vals:vals
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}
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}
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// A value can be a scalar or a range {a,b} object
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function Value(val,child) {
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return {
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type:NODE_TYPES.FEATURE_VALUE,
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val:val,
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child:child
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}
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}
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function print(model) {
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var line='',sep;
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if (!model) return '';
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switch (model.type) {
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case NODE_TYPES.RESULT:
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return ' -> '+model.name;
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case NODE_TYPES.FEATURE:
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line='('+model.name+'?';
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sep='';
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Comp.array.iter(model.vals,function (v) {
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line += sep+print(v);
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sep=',';
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});
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return line+')';
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case NODE_TYPES.FEATURE_VALUE:
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return ' '+(model.val.a==undefined?model.val:'['+model.val.a+','+model.val.b+']')+
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':'+print(model.child);
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}
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return 'model?';
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}
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/**
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* Finds element with highest occurrence in a list
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* @private
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*/
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function mostCommon(list) {
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var elementFrequencyMap = {};
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var largestFrequency = -1;
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var mostCommonElement = null;
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list.forEach(function(element) {
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var elementFrequency = (elementFrequencyMap[element] || 0) + 1;
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elementFrequencyMap[element] = elementFrequency;
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if (largestFrequency < elementFrequency) {
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mostCommonElement = element;
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largestFrequency = elementFrequency;
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}
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});
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return mostCommonElement;
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}
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function addVal(v1,v2) {
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return v1+v2; // TODO
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}
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function lowerBound(v) {
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if (v.a==undefined) return v; else return v.a;
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}
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function upperBound(v) {
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if (v.b==undefined) return v; else return v.b;
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}
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function Bounds(vl,v) {
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if (vl.length==0) return {a:v,b:v};
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else if (v==undefined) return {a:Min(vl),b:Max(vl)};
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else return {a:Min([Min(vl),v]),b:Max([Max(vl),v])};
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}
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function overlap(v1,v2) {
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return (upperBound(v1) >= lowerBound(v2) && upperBound(v1) <= upperBound(v2)) ||
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(upperBound(v2) >= lowerBound(v1) && upperBound(v2) <= upperBound(v1))
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}
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function containsVal(vl,v) {
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for (var i in vl) {
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var v2=vl[i];
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if (overlap(v,v2)) return true;
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}
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return false;
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}
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function Min(vals) {
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var min=none;
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Comp.array.iter(vals,function (val) {
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if (min==none) min=(val.a==undefined?val:val.a);
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else min=val.a==undefined?(val<min?val:min):(val.a<min?val.a:min);
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});
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return min;
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}
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function Max(vals) {
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var max=none;
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Comp.array.iter(vals,function (val) {
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if (max==none) max=(val.b==undefined?val:val.b);
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else max=(val.b==undefined?(val>max?val:max):(val.b>max?val.a:max));
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});
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return max;
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}
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function centerVal(v) {
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if (v.a==undefined) return v; else return (v.a+v.b)/2;
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}
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function distanceVal (v1,v2) {
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return Math.abs(centerVal(v1)-centerVal(v2));
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}
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function getBestFeatures(data,target,features,eps) {
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var bestfeatures=[];
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function deviation(vals) {
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var n = vals.length;
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var mu=Comp.array.sum(vals,function (val) {
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return (lowerBound(val)+upperBound(val))/2;
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})/n;
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var dev=Comp.array.sum(vals,function (val) {
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return Math.pow(((lowerBound(val)+upperBound(val))/2)-mu,2);
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})/n;
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return dev;
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}
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for (var feature in features) {
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var vals=_.pluck(data, features[feature]);
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var e = entropyEps(vals,eps);
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var d = deviation(vals);
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var min = Min(vals);
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var max = Max(vals);
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bestfeatures.push({e:e,d:d,range:{a:min,b:max},name:features[feature]});
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}
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bestfeatures.sort(function (ef1,ef2) {
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if (ef1.e > ef2.e) return -1; else return 1;
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});
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return bestfeatures;
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}
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function getPossibleValues(data,feature) {
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return Comp.array.sort(_.pluck(data, feature), function (x,y) {
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if (upperBound(x) < lowerBound(y)) return -1; else return 1; // increasing value order
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});
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}
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function add_training_set(set) {
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// Merge a data set with an existing for a specific key; create value ranges
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training_data.push(set);
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}
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function partitionVals(vals,eps) {
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var last=none;
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var partitions=[];
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var partition=[];
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for(var i in vals) {
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var val0=vals[i];
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var val1=vals[i-1];
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if (val1==undefined) partition.push(val0);
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else if ( upperBound(val0) < upperBound(addVal(val1,2*eps))) partition.push(val0);
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else {
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partitions.push(partition);
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partition=[val0];
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}
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}
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if (partition.length>0) partitions.push(partition);
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return partitions;
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}
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/**
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* Creates a new tree
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*/
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function createTree(data, target, features, eps) {
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var _newS,child_node,bounds;
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var targets = _.unique(_.pluck(data, target));
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var classes = _.unique(_.pluck(training_data, class_name));
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console.log(data);
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console.log(features);
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// Aios.aios.log('createTree:'+targets.length);
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if (targets.length == 1) return Result(targets[0]);
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if (features.length == 0) {
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var topTarget = mostCommon(targets);
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return Result(topTarget)
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}
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var bestFeatures = getBestFeatures(data, target, features, eps);
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var bestFeature = bestFeatures[0];
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var remainingFeatures = Comp.array.filtermap(bestFeatures,function (feat) {
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if (feat.name!=bestFeature.name) return feat.name;
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else return none;
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});
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var possibleValues = getPossibleValues(data,bestFeature.name);
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var vals=[];
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var partitions=partitionVals(possibleValues,eps);
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console.log(partitions);
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console.log(bestFeatures);
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//console.log(possibleValues);
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if (partitions.length==1) {
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// no further 2*eps separation possible, find best feature by largest distance
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// resort beat feature list with respect to value deviation
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bestFeatures.sort(function (ef1,ef2) {
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if (ef1.d > ef2.d) return -1; else return 1;
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});
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bestFeature = bestFeatures[0];
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possibleValues = getPossibleValues(data,bestFeature.name);
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Comp.array.iter(possibleValues,function (val,i) {
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_newS = data.filter(function(x) {
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console.log(x[bestFeature.name],val,overlap(val,x[bestFeature.name]))
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return overlap(val,x[bestFeature.name]);
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});
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child_node = Value(val);
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child_node.child = createTree(_newS, target, remainingFeatures, eps);
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//console.log(_newS);
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vals.push(child_node);
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})
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} else Comp.array.iter(partitions,function (partition,i) {
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_newS = data.filter(function(x) {
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// console.log(x[bestFeature.name],v,overlap(x[bestFeature.name],v))
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return containsVal(partition,x[bestFeature.name]);
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});
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bounds = Bounds(partition);
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child_node = Value(eps==0?v:{a:bounds.a-eps,b:bounds.b+eps});
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child_node.child = createTree(_newS, target, remainingFeatures, eps);
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//console.log(_newS);
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vals.push(child_node);
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});
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return Feature(bestFeature.name,vals);
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}
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function depth(model) {
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switch (model.type) {
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case NODE_TYPES.RESULT: return 0;
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case NODE_TYPES.FEATURE:
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return 1+Comp.array.max(model.vals,function (val) {
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return depth(val);
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});
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case NODE_TYPES.FEATURE_VALUE:
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return depth(model.child);
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}
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return 0;
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}
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function results(model) {
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var line='',sep;
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if (!model) return '';
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switch (model.type) {
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case NODE_TYPES.RESULT:
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return model.name;
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case NODE_TYPES.FEATURE:
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sep='';
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line='';
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Comp.array.iter(model.vals,function (v) {
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line += sep+results(v);
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sep=',';
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});
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return line;
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case NODE_TYPES.FEATURE_VALUE:
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return results(model.child);
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}
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return 'result?';
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}
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function nearestVal(vals,sample,fun) {
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var best=none;
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for (var v in vals) {
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var d=fun?distanceVal(fun(vals[v]),sample):distanceVal(vals[v],sample);
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if (best==none)
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best={v:vals[v],d:d};
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else if (best.d > d)
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best={v:vals[v],d:d};
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}
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if (best) return best.v;
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else return none;
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}
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function predict(model,sample) {
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var root = model;
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while (root && root.type !== NODE_TYPES.RESULT) {
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var attr = root.name;
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var sampleVal = sample[attr];
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var childNode = nearestVal(root.vals,sampleVal,function (node) {
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return node.val;
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});
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if (childNode){
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root = childNode.child;
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} else {
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root = none;
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}
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}
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if (root) return root.name||root.val;
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else return none;
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};
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var datasets=[];
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var noise=function () {
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return Comp.random.range(-eps/2,eps/2);
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};
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for (dataset=0;dataset<=5;dataset++) {
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var training_set={};
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var data={};
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var matA = db.readMatrix('sensorsA'+dataset);
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var matB = db.readMatrix('sensorsB'+dataset);
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var matAs = Aios.aios.matrix(3,3);
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var matBs = Aios.aios.matrix(3,3);
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var n=0;
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features=[];
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for (j = roi0.y-roid;j <= (roi0.y+roid);j++) {
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for (i = roi0.x-roid;i <= (roi0.x+roid);i++) {
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matAs[j-(roi0.y-roid)][i-(roi0.x-roid)]=matA[j][i];
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matBs[j-(roi0.y-roid)][i-(roi0.x-roid)]=matB[j][i];
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training_set['A'+n]=matA[j][i]+noise();
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training_set['B'+n]=matB[j][i]+noise();
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data['A'+n]=matA[j][i]+noise();
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data['B'+n]=matB[j][i]+noise();
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features.push('A'+n);
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features.push('B'+n);
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n++;
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}
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}
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training_set[class_name]='L'+dataset;
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add_training_set(training_set);
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datasets.push(data);
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}
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//console.log(training_data);
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var bestfeatures=getBestFeatures(training_data, class_name, features, eps);;
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var classes=_.unique(_.pluck(training_data, class_name));
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var model = createTree(training_data,class_name,features,eps);
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console.log(print(model));
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console.log(depth(model));
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console.log(results(model));
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console.log(predict(model,datasets[0]));
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console.log(predict(model,datasets[1]));
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console.log(predict(model,datasets[2]));
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console.log(predict(model,datasets[3]));
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console.log(predict(model,datasets[4]));
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console.log(predict(model,datasets[5]));
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