new-api-custom/pkg/model_fingerprint/scorer.js

export const VALUE_MIN = 1;
export const VALUE_MAX = 355;
export const DIMENSION = VALUE_MAX - VALUE_MIN + 1;
export const ALPHA = 0.5;
export const ORDERED_BLOCK_WEIGHT = 0.25;

export function parseNumbers(text) {
  const runs = [];
  let current = [];
  let previousEnd = 0;
  for (const match of String(text).matchAll(/\d+/g)) {
    const separator = String(text).slice(previousEnd, match.index);
    const value = Number(match[0]);
    if (current.length && /\p{L}/u.test(separator)) {
      runs.push(current);
      current = [];
    }
    if (value >= VALUE_MIN && value <= VALUE_MAX) current.push(value);
    previousEnd = match.index + match[0].length;
  }
  if (current.length) runs.push(current);
  return runs.reduce((best, run) => run.length > best.length ? run : best, []);
}

export function countNumbers(numbers) {
  const counts = Array(DIMENSION).fill(0);
  numbers.forEach((number) => { counts[number - VALUE_MIN] += 1; });
  return counts;
}

function mean(values) {
  return values.reduce((total, value) => total + value, 0) / values.length;
}

function standardize(values) {
  const center = mean(values);
  const variance = mean(values.map((value) => (value - center) ** 2));
  const scale = Math.max(Math.sqrt(variance), 1e-12);
  return values.map((value) => (value - center) / scale);
}

function dot(left, right) {
  let value = 0;
  for (let index = 0; index < left.length; index += 1) value += left[index] * right[index];
  return value;
}

function norm(values) {
  return Math.sqrt(dot(values, values));
}

function normalized(values) {
  const scale = Math.max(norm(values), 1e-12);
  return values.map((value) => value / scale);
}

function subtractBasis(values, basis) {
  const output = values.slice();
  for (const vector of basis || []) {
    const projection = dot(output, vector);
    for (let index = 0; index < output.length; index += 1) output[index] -= projection * vector[index];
  }
  return output;
}

function hellingerFeature(counts) {
  const total = counts.reduce((sum, value) => sum + value, 0) + ALPHA * DIMENSION;
  return counts.map((value) => Math.sqrt((value + ALPHA) / total));
}

function splitIntoFour(values) {
  const base = Math.floor(values.length / 4);
  const remainder = values.length % 4;
  const chunks = [];
  let start = 0;
  for (let index = 0; index < 4; index += 1) {
    const size = base + (index < remainder ? 1 : 0);
    chunks.push(values.slice(start, start + size));
    start += size;
  }
  return chunks;
}

function orderedBlockFeature(numbers) {
  const pieces = [];
  for (const chunk of splitIntoFour(numbers)) {
    const bins = Array(16).fill(0.5);
    for (const value of chunk) {
      const index = Math.min(15, Math.floor(((value - 1) / 355) * 16));
      bins[index] += 1;
    }
    const total = bins.reduce((sum, value) => sum + value, 0);
    pieces.push(...bins.map((value) => Math.sqrt(value / total)));
  }
  const lastDigits = Array(10).fill(0.5);
  numbers.forEach((value) => { lastDigits[value % 10] += 1; });
  const lastTotal = lastDigits.reduce((sum, value) => sum + value, 0);
  pieces.push(...lastDigits.map((value) => Math.sqrt(value / lastTotal)));
  return pieces;
}

function robustScoreCounts(counts, bank) {
  const artifact = bank.robust.hellinger;
  const feature = hellingerFeature(counts);
  let projected = feature.map((value, index) => (value - artifact.feature_mean[index]) / artifact.feature_scale[index]);
  projected = subtractBasis(projected, artifact.nuisance_basis);
  projected = normalized(projected);
  const scores = artifact.centroids.map((centroid) => dot(projected, centroid));
  return standardize(scores);
}

function orderedBlockScores(numbers, bank) {
  const artifact = bank.robust.ordered_blocks;
  const feature = orderedBlockFeature(numbers);
  const standardizedFeature = feature.map((value, index) => (value - artifact.feature_mean[index]) / artifact.feature_scale[index]);
  const unit = normalized(standardizedFeature);
  const environmentScores = artifact.environment_centroids.map(
    (centroids) => centroids.map((centroid) => dot(unit, centroid)),
  );
  const template = standardize(artifact.centroids.map((_, modelIndex) => Math.max(
    ...environmentScores.map((scores) => scores[modelIndex]),
  )));
  const projected = normalized(subtractBasis(standardizedFeature, artifact.nuisance_basis));
  const nuisance = standardize(artifact.centroids.map((centroid) => dot(projected, centroid)));
  return standardize(template.map((value, index) => 0.5 * value + 0.5 * nuisance[index]));
}

function robustScoreNumbers(numbers, bank) {
  const marginal = robustScoreCounts(countNumbers(numbers), bank);
  const artifact = bank.robust.ordered_blocks;
  const weight = artifact ? Number(artifact.weight || 0) : 0;
  if (!artifact || weight === 0) return marginal;
  const ordered = orderedBlockScores(numbers, bank);
  return marginal.map((value, index) => (1 - weight) * value + weight * ordered[index]);
}

function softmax(values) {
  const maximum = Math.max(...values);
  const weights = values.map((value) => Math.exp(value - maximum));
  const total = weights.reduce((sum, value) => sum + value, 0);
  return weights.map((value) => value / total);
}

function jsSimilarity(left, right) {
  const leftTotal = left.reduce((sum, value) => sum + value, 0);
  const rightTotal = right.reduce((sum, value) => sum + value, 0) + ALPHA * DIMENSION;
  const p = left.map((value) => value / leftTotal);
  const q = right.map((value) => (value + ALPHA) / rightTotal);
  const midpoint = p.map((value, index) => (value + q[index]) / 2);
  const divergence = (values) => values.reduce(
    (total, value, index) => total + (value ? value * Math.log(value / midpoint[index]) : 0),
    0,
  );
  const js = (divergence(p) + divergence(q)) / 2;
  return 1 - Math.sqrt(js / Math.log(2));
}

export function analyzeGlobalOutputs(outputs, bank) {
  const modelIds = bank.models.map((model) => model.id);
  const valid = [];
  const diagnostics = [];
  outputs.forEach((item, index) => {
    const expected = Number(item.expected_count || 0);
    const numbers = parseNumbers(item.text || "");
    const minimum = expected ? Math.max(80, Math.ceil(expected * 0.55)) : 80;
    const accepted = numbers.length >= minimum;
    diagnostics.push({ index, parsed_numbers: numbers.length, minimum_numbers: minimum, accepted });
    if (accepted) valid.push({ numbers, counts: countNumbers(numbers), scores: robustScoreNumbers(numbers, bank) });
  });
  if (!valid.length) throw new Error("没有可用回答：请粘贴完整数字序列；拒答或严重截断的回答不会计入。");

  const combinedScores = modelIds.map((_, modelIndex) => mean(valid.map((item) => item.scores[modelIndex])));
  const calibrationKey = String(Math.min(valid.length, 3));
  const beta = Number(bank.calibration[calibrationKey].beta);
  const probabilities = softmax(combinedScores.map((value) => beta * value));
  const pooledCounts = Array.from({ length: DIMENSION }, (_, index) => valid.reduce((sum, item) => sum + item.counts[index], 0));
  const modelEntries = Object.fromEntries(bank.models.map((model) => [model.id, model]));
  const familyOrder = [...new Set(bank.models.map((model) => model.family || "models"))];
  const familyNames = Object.fromEntries(familyOrder.map((family) => [
    family,
    bank.models.find((model) => (model.family || "models") === family)?.family_name || family,
  ]));
  const results = modelIds.map((model, index) => ({
    model,
    display_name: modelEntries[model].display_name,
    probability: probabilities[index],
    profile_similarity: jsSimilarity(pooledCounts, modelEntries[model].counts),
    score: combinedScores[index],
    family: modelEntries[model].family || "models",
    family_name: familyNames[modelEntries[model].family || "models"],
  })).sort((left, right) => right.probability - left.probability);
  const familyProbabilities = Object.fromEntries(familyOrder.map((family) => [
    family,
    results.filter((item) => item.family === family).reduce((sum, item) => sum + item.probability, 0),
  ]));
  results.forEach((item) => { item.conditional_probability = item.probability / familyProbabilities[item.family]; });
  const winningFamily = familyOrder.reduce((best, family) => familyProbabilities[family] > familyProbabilities[best] ? family : best);
  return {
    prediction: results[0].model,
    prediction_name: results[0].display_name,
    probability: results[0].probability,
    used_outputs: valid.length,
    results,
    diagnostics,
    calibration: { queries: calibrationKey, beta, cv_accuracy: bank.calibration[calibrationKey].cv_accuracy },
    family_prediction: winningFamily,
    family_prediction_name: familyNames[winningFamily],
    family_probability: familyProbabilities[winningFamily],
    family_probabilities: familyOrder.map((family) => ({ family, display_name: familyNames[family], probability: familyProbabilities[family] })),
    method: "统一全局稳健数字指纹",
  };
}