| const {test}=require('node:test'); |
| const assert=require('node:assert/strict'); |
| const E=require('./ensemble.js'); |
| test('grid lookup respects coordinates and clamps plot edges',()=>{ |
| const grid={size:2,grid:[-1,1,2,-1]}; |
| assert.equal(E.predict(grid,{x:0,y:0}),-1); |
| assert.equal(E.predict(grid,{x:0,y:1}),2); |
| assert.equal(E.predict(grid,{x:2,y:2}),-1); |
| }); |
| test('SAMME upweights mistakes and restores weights after removal',()=>{ |
| const points=[{label:-1},{label:1},{label:2}]; |
| const models=[{alpha:Math.log(4),predictions:[-1,-1,2]}]; |
| const before=E.weightsFor(points,models); |
| assert.ok(Math.abs(before[1]-2/3)<1e-12); |
| models.push({alpha:1,predictions:[-1,1,2]});models.pop(); |
| assert.deepEqual(E.weightsFor(points,models),before); |
| }); |
| test('three-class voting and forests average probabilities across all classes',()=>{ |
| const classes=[-1,1,2]; |
| const models=[{classes,alpha:1,predictions:[1],tree:{size:1,grid:[1]}},{classes,alpha:2,predictions:[2],tree:{size:1,grid:[2]}}]; |
| assert.equal(E.vote(models,{x:0,y:0}),2);assert.equal(E.trainingVote(models,0),2); |
| const forest=[[.45,.05,.5],[.6,.1,.3]].map(p=>({classes,forest:true,alpha:1,trainingProbabilities:[p],tree:{size:1,probabilities:[p]}})); |
| assert.equal(E.vote(forest,{x:0,y:0}),-1);forest.pop();assert.equal(E.trainingVote(forest,0),2); |
| }); |
| test('training supports a pair of classes that excludes A',()=>{ |
| assert.equal(E.trainingVote([{classes:[1,2],alpha:1,predictions:[2]}],0),2); |
| }); |
|
|
