feat: sync deployed v1.0.0-rc.38-custom.42
Sync production source including routing, billing, Astra detection and public uptime. Update custom build instructions and license packaging.
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export const VALUE_MIN = 1;
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export const VALUE_MAX = 355;
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export const DIMENSION = VALUE_MAX - VALUE_MIN + 1;
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export const ALPHA = 0.5;
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export const ORDERED_BLOCK_WEIGHT = 0.25;
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export function parseNumbers(text) {
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const runs = [];
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let current = [];
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let previousEnd = 0;
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for (const match of String(text).matchAll(/\d+/g)) {
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const separator = String(text).slice(previousEnd, match.index);
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const value = Number(match[0]);
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if (current.length && /\p{L}/u.test(separator)) {
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runs.push(current);
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current = [];
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}
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if (value >= VALUE_MIN && value <= VALUE_MAX) current.push(value);
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previousEnd = match.index + match[0].length;
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}
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if (current.length) runs.push(current);
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return runs.reduce((best, run) => run.length > best.length ? run : best, []);
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}
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export function countNumbers(numbers) {
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const counts = Array(DIMENSION).fill(0);
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numbers.forEach((number) => { counts[number - VALUE_MIN] += 1; });
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return counts;
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}
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function mean(values) {
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return values.reduce((total, value) => total + value, 0) / values.length;
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}
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function standardize(values) {
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const center = mean(values);
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const variance = mean(values.map((value) => (value - center) ** 2));
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const scale = Math.max(Math.sqrt(variance), 1e-12);
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return values.map((value) => (value - center) / scale);
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}
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function dot(left, right) {
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let value = 0;
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for (let index = 0; index < left.length; index += 1) value += left[index] * right[index];
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return value;
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}
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function norm(values) {
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return Math.sqrt(dot(values, values));
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}
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function normalized(values) {
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const scale = Math.max(norm(values), 1e-12);
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return values.map((value) => value / scale);
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}
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function subtractBasis(values, basis) {
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const output = values.slice();
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for (const vector of basis || []) {
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const projection = dot(output, vector);
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for (let index = 0; index < output.length; index += 1) output[index] -= projection * vector[index];
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}
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return output;
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}
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function hellingerFeature(counts) {
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const total = counts.reduce((sum, value) => sum + value, 0) + ALPHA * DIMENSION;
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return counts.map((value) => Math.sqrt((value + ALPHA) / total));
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}
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function splitIntoFour(values) {
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const base = Math.floor(values.length / 4);
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const remainder = values.length % 4;
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const chunks = [];
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let start = 0;
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for (let index = 0; index < 4; index += 1) {
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const size = base + (index < remainder ? 1 : 0);
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chunks.push(values.slice(start, start + size));
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start += size;
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}
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return chunks;
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}
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function orderedBlockFeature(numbers) {
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const pieces = [];
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for (const chunk of splitIntoFour(numbers)) {
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const bins = Array(16).fill(0.5);
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for (const value of chunk) {
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const index = Math.min(15, Math.floor(((value - 1) / 355) * 16));
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bins[index] += 1;
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}
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const total = bins.reduce((sum, value) => sum + value, 0);
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pieces.push(...bins.map((value) => Math.sqrt(value / total)));
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}
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const lastDigits = Array(10).fill(0.5);
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numbers.forEach((value) => { lastDigits[value % 10] += 1; });
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const lastTotal = lastDigits.reduce((sum, value) => sum + value, 0);
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pieces.push(...lastDigits.map((value) => Math.sqrt(value / lastTotal)));
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return pieces;
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}
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function robustScoreCounts(counts, bank) {
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const artifact = bank.robust.hellinger;
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const feature = hellingerFeature(counts);
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let projected = feature.map((value, index) => (value - artifact.feature_mean[index]) / artifact.feature_scale[index]);
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projected = subtractBasis(projected, artifact.nuisance_basis);
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projected = normalized(projected);
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const scores = artifact.centroids.map((centroid) => dot(projected, centroid));
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return standardize(scores);
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}
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function orderedBlockScores(numbers, bank) {
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const artifact = bank.robust.ordered_blocks;
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const feature = orderedBlockFeature(numbers);
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const standardizedFeature = feature.map((value, index) => (value - artifact.feature_mean[index]) / artifact.feature_scale[index]);
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const unit = normalized(standardizedFeature);
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const environmentScores = artifact.environment_centroids.map(
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(centroids) => centroids.map((centroid) => dot(unit, centroid)),
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);
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const template = standardize(artifact.centroids.map((_, modelIndex) => Math.max(
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...environmentScores.map((scores) => scores[modelIndex]),
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)));
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const projected = normalized(subtractBasis(standardizedFeature, artifact.nuisance_basis));
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const nuisance = standardize(artifact.centroids.map((centroid) => dot(projected, centroid)));
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return standardize(template.map((value, index) => 0.5 * value + 0.5 * nuisance[index]));
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}
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function robustScoreNumbers(numbers, bank) {
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const marginal = robustScoreCounts(countNumbers(numbers), bank);
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const artifact = bank.robust.ordered_blocks;
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const weight = artifact ? Number(artifact.weight || 0) : 0;
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if (!artifact || weight === 0) return marginal;
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const ordered = orderedBlockScores(numbers, bank);
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return marginal.map((value, index) => (1 - weight) * value + weight * ordered[index]);
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}
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function softmax(values) {
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const maximum = Math.max(...values);
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const weights = values.map((value) => Math.exp(value - maximum));
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const total = weights.reduce((sum, value) => sum + value, 0);
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return weights.map((value) => value / total);
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}
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function jsSimilarity(left, right) {
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const leftTotal = left.reduce((sum, value) => sum + value, 0);
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const rightTotal = right.reduce((sum, value) => sum + value, 0) + ALPHA * DIMENSION;
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const p = left.map((value) => value / leftTotal);
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const q = right.map((value) => (value + ALPHA) / rightTotal);
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const midpoint = p.map((value, index) => (value + q[index]) / 2);
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const divergence = (values) => values.reduce(
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(total, value, index) => total + (value ? value * Math.log(value / midpoint[index]) : 0),
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0,
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);
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const js = (divergence(p) + divergence(q)) / 2;
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return 1 - Math.sqrt(js / Math.log(2));
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}
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export function analyzeGlobalOutputs(outputs, bank) {
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const modelIds = bank.models.map((model) => model.id);
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const valid = [];
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const diagnostics = [];
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outputs.forEach((item, index) => {
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const expected = Number(item.expected_count || 0);
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const numbers = parseNumbers(item.text || "");
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const minimum = expected ? Math.max(80, Math.ceil(expected * 0.55)) : 80;
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const accepted = numbers.length >= minimum;
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diagnostics.push({ index, parsed_numbers: numbers.length, minimum_numbers: minimum, accepted });
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if (accepted) valid.push({ numbers, counts: countNumbers(numbers), scores: robustScoreNumbers(numbers, bank) });
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});
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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。");
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const combinedScores = modelIds.map((_, modelIndex) => mean(valid.map((item) => item.scores[modelIndex])));
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const calibrationKey = String(Math.min(valid.length, 3));
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const beta = Number(bank.calibration[calibrationKey].beta);
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const probabilities = softmax(combinedScores.map((value) => beta * value));
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const pooledCounts = Array.from({ length: DIMENSION }, (_, index) => valid.reduce((sum, item) => sum + item.counts[index], 0));
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const modelEntries = Object.fromEntries(bank.models.map((model) => [model.id, model]));
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const familyOrder = [...new Set(bank.models.map((model) => model.family || "models"))];
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const familyNames = Object.fromEntries(familyOrder.map((family) => [
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family,
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bank.models.find((model) => (model.family || "models") === family)?.family_name || family,
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]));
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const results = modelIds.map((model, index) => ({
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model,
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display_name: modelEntries[model].display_name,
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probability: probabilities[index],
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profile_similarity: jsSimilarity(pooledCounts, modelEntries[model].counts),
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score: combinedScores[index],
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family: modelEntries[model].family || "models",
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family_name: familyNames[modelEntries[model].family || "models"],
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})).sort((left, right) => right.probability - left.probability);
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const familyProbabilities = Object.fromEntries(familyOrder.map((family) => [
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family,
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results.filter((item) => item.family === family).reduce((sum, item) => sum + item.probability, 0),
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]));
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results.forEach((item) => { item.conditional_probability = item.probability / familyProbabilities[item.family]; });
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const winningFamily = familyOrder.reduce((best, family) => familyProbabilities[family] > familyProbabilities[best] ? family : best);
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return {
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prediction: results[0].model,
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prediction_name: results[0].display_name,
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probability: results[0].probability,
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used_outputs: valid.length,
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results,
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diagnostics,
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calibration: { queries: calibrationKey, beta, cv_accuracy: bank.calibration[calibrationKey].cv_accuracy },
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family_prediction: winningFamily,
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family_prediction_name: familyNames[winningFamily],
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family_probability: familyProbabilities[winningFamily],
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family_probabilities: familyOrder.map((family) => ({ family, display_name: familyNames[family], probability: familyProbabilities[family] })),
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method: "统一全局稳健数字指纹",
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};
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}
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