| |
| (function(root){ |
| function gridValue(model,p,values){ |
| const n=model.size,x=Math.max(0,Math.min(n-1,Math.round(p.x*(n-1)))),y=Math.max(0,Math.min(n-1,Math.round(p.y*(n-1)))); |
| return values[y*n+x]; |
| } |
| function predict(model,p){return gridValue(model,p,model.grid);} |
| function weightsFor(points,models){ |
| if(!points.length)return []; |
| const logs=points.map((p,i)=>models.reduce((s,m)=>s+(p.label!==m.predictions[i]?m.alpha:0),0)); |
| const max=Math.max(...logs),weights=logs.map(v=>Math.exp(v-max)),total=weights.reduce((a,b)=>a+b,0); |
| return weights.map(w=>w/total); |
| } |
| function combine(models,prediction,probabilities){ |
| const classes=models[0]?.classes||[-1,1]; |
| const scores=new Map(classes.map(c=>[c,0])); |
| for(const m of models){ |
| if(m.forest){ |
| const values=probabilities(m); |
| m.classes.forEach((c,i)=>scores.set(c,scores.get(c)+values[i])); |
| }else{ |
| const c=prediction(m);scores.set(c,scores.get(c)+m.alpha); |
| } |
| } |
| return classes.reduce((best,c)=>scores.get(c)>scores.get(best)?c:best,classes[0]); |
| } |
| function vote(models,p){return combine(models,m=>predict(m.tree,p),m=>gridValue(m.tree,p,m.tree.probabilities));} |
| function trainingVote(models,i){return combine(models,m=>m.predictions[i],m=>m.trainingProbabilities[i]);} |
| const api={predict,weightsFor,vote,trainingVote}; |
| if(typeof module!=='undefined')module.exports=api;else root.Ensemble=api; |
| })(globalThis); |
|
|
