/**
 * End-to-end pipeline test without a microphone: three synthetic event
 * types (a low ringing tone, a high short tone, a noise burst) stand in
 * for recorded taps. Builds a snippet library, synthesizes datasets across
 * runs, executes each baseline algorithm through numbl, and prints scores.
 * The matched filter and the kNN sorter should sit far above the naive
 * floor; the run fails loudly if they do not.
 */
import { FS, type SynthConfig } from '../src/bench/types.ts';
import { synthesize } from '../src/bench/synthesize.ts';
import { score } from '../src/bench/score.ts';
import { buildTraining } from '../src/bench/training.ts';
import { runAlgorithm } from '../src/bench/execute.ts';
import { makeSyntheticLibrary } from './synthetic-lib.ts';
import matchedFilterSrc from '../src/algorithms/template_matched_filter.m?raw';
import onsetKnnSrc from '../src/algorithms/onset_knn.m?raw';
import naiveSrc from '../src/algorithms/naive_threshold.m?raw';

const lib = makeSyntheticLibrary();

const cfg: SynthConfig = {
  durationS: 30,
  eventsPerType: 30,
  gainMinDb: -12,
  overlap: 0.2,
  trainFraction: 0.3,
  seed: 1,
};

const algos = [
  { name: 'template_matched_filter', source: matchedFilterSrc },
  { name: 'onset_knn', source: onsetKnnSrc },
  { name: 'naive_threshold', source: naiveSrc },
];

const NRUNS = 2;
const TOL = 0.02;
const acc: Record<string, number[]> = {};
for (let r = 0; r < NRUNS; r++) {
  const ds = synthesize(lib, cfg, r);
  const training = buildTraining(lib, ds.trainIndices);
  console.log(`run ${r}: ${ds.truth.length} ground-truth events, ${ds.x.length} samples`);
  for (const a of algos) {
    const t0 = Date.now();
    try {
      const out = runAlgorithm(a.source, ds.x, FS, training);
      const s = score(ds.truth, out.detections, lib.names.length, TOL);
      (acc[a.name] ??= []).push(s.accuracy);
      console.log(
        `  ${a.name.padEnd(26)} acc=${s.accuracy.toFixed(3)}  correct=${s.correct}` +
        ` miss=${s.missed} fp=${s.falsePositives} misclass=${s.misclassified}` +
        `  (${Date.now() - t0} ms)`,
      );
    } catch (err) {
      console.log(`  ${a.name.padEnd(26)} ERROR: ${(err as Error).message}`);
    }
  }
}

const mean = (v: number[]) => v.reduce((a, b) => a + b, 0) / Math.max(v.length, 1);
console.log('\nmean accuracy over runs:');
for (const a of algos) console.log(`  ${a.name.padEnd(26)} ${mean(acc[a.name] ?? []).toFixed(3)}`);

const mf = mean(acc['template_matched_filter'] ?? []);
const knn = mean(acc['onset_knn'] ?? []);
if (!(mf > 0.7)) { console.error('FAIL: matched filter accuracy too low'); process.exit(1); }
if (!(knn > 0.5)) { console.error('FAIL: onset_knn accuracy too low'); process.exit(1); }
console.log('PASS');