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* Reversible Instance Normalization (RevIN).
*
* Mirrors the Python `revin()` function in torch/util.py and flax/util.py.
*
* RevIN normalizes time-series patches before feeding them into the
* transformer, then reverses the normalization on the output so forecasts
* are in the original data scale.
*
* Forward: y = (x - μ) / σ
* Reverse: y = x * σ + μ
*
* When σ < ε (1e-6), the forward pass substitutes 1.0 to avoid division
* by zero. This happens for constant-valued series.
*/
const TOLERANCE = 1e-6;
// ---------------------------------------------------------------------------
// Scalar RevIN (one batch element, one patch)
// ---------------------------------------------------------------------------
/**
* Apply RevIN to a single patch of values.
*
* @param values The (possibly multi-dimensional) value array.
* @param mu Per-dimension mean(s).
* @param sigma Per-dimension std deviation(s).
* @param reverse If true, perform inverse normalisation (denormalize).
*/
export function revin(
values: Float32Array,
mu: Float32Array | Float32Array[],
sigma: Float32Array | Float32Array[],
reverse: boolean,
): Float32Array {
const len = values.length;
const result = new Float32Array(len);
const muFlat = flattenParam(mu, len);
const sigmaFlat = flattenParam(sigma, len);
if (reverse) {
// x * σ + μ
for (let i = 0; i < len; i++) {
result[i] = values[i] * sigmaFlat[i] + muFlat[i];
}
} else {
// (x - μ) / max(σ, ε)
for (let i = 0; i < len; i++) {
const safeSigma = sigmaFlat[i] < TOLERANCE ? 1.0 : sigmaFlat[i];
result[i] = (values[i] - muFlat[i]) / safeSigma;
}
}
return result;
}
// ---------------------------------------------------------------------------
// Batch RevIN — for (batch, patches, patchLen) and (batch, patches, patchLen, q)
// ---------------------------------------------------------------------------
/**
* Apply RevIN across a batch of patched time series.
*
* Shape: `values[b][p * patchLen + i]` where b = batch, p = patch, i = offset.
*
* @param values Flat array per batch element (all patches concatenated).
* @param mu Mean per batch element, or per (batch, patch) pair.
* @param sigma Std per batch element, or per (batch, patch) pair.
* @param reverse If true, denormalize.
* @param numPatches Number of patches per batch element.
* @param patchLen Length of each patch (output: per-element).
*/
export function revinBatch(
values: Float32Array[],
mu: Float32Array[],
sigma: Float32Array[],
reverse: boolean,
numPatches: number,
patchLen: number,
): Float32Array[] {
const batchSize = values.length;
const result: Float32Array[] = [];
// Determine broadcast pattern:
// - mu/sigma length === batchSize → per-batch (apply to all patches)
// - mu/sigma length === batchSize * numPatches → per-patch
const perPatch = mu.length === batchSize * numPatches;
for (let b = 0; b < batchSize; b++) {
const totalLen = values[b].length;
const out = new Float32Array(totalLen);
for (let p = 0; p < numPatches; p++) {
const patchOffset = p * patchLen;
const muIndex = perPatch ? b * numPatches + p : b;
const m = mu[muIndex][0]; // scalar per patch/batch
const s = sigma[muIndex][0]; // scalar per patch/batch
for (let i = 0; i < patchLen && patchOffset + i < totalLen; i++) {
const idx = patchOffset + i;
if (reverse) {
out[idx] = values[b][idx] * s + m;
} else {
const safeS = s < TOLERANCE ? 1.0 : s;
out[idx] = (values[b][idx] - m) / safeS;
}
}
}
result.push(out);
}
return result;
}
// ---------------------------------------------------------------------------
// 4-D RevIN (batch, patches, patchLen, numQuantiles)
// ---------------------------------------------------------------------------
/**
* Apply RevIN to quantile-shaped outputs.
*
* Shape: (batch, patches, patchLen, numQuantiles).
*
* mu/sigma are broadcast from (batch,) or (batch, patches) with
* two trailing singleton dims added internally.
*
* **Contract**: The returned array always has exportedPatches-worth of
* elements per batch entry. When the caller only populated m < exportedPatches
* sub-patches, the trailing (exportedPatches - m) sub-patches are zero-filled.
* Callers MUST index by their known m, not by array.length. Changing this
* memory strategy requires updating all callers (notably decode-loop.ts).
*/
export function revinBatch4D(
values: Float32Array[],
mu: Float32Array[],
sigma: Float32Array[],
reverse: boolean,
numPatches: number,
patchLen: number,
numQuantiles: number,
): Float32Array[] {
const batchSize = values.length;
const result: Float32Array[] = [];
const perPatch = mu.length === batchSize * numPatches;
for (let b = 0; b < batchSize; b++) {
// Total elements per batch: numPatches * patchLen * numQuantiles
const totalLen = values[b].length;
const out = new Float32Array(totalLen);
for (let p = 0; p < numPatches; p++) {
for (let i = 0; i < patchLen; i++) {
for (let q = 0; q < numQuantiles; q++) {
const idx = (p * patchLen + i) * numQuantiles + q;
const muIndex = perPatch ? b * numPatches + p : b;
const m = mu[muIndex][0];
const s = sigma[muIndex][0];
if (reverse) {
out[idx] = values[b][idx] * s + m;
} else {
const safeS = s < TOLERANCE ? 1.0 : s;
out[idx] = (values[b][idx] - m) / safeS;
}
}
}
}
result.push(out);
}
return result;
}
// ---------------------------------------------------------------------------
// Helpers
// ---------------------------------------------------------------------------
/**
* Flatten a RevIN parameter (mu or sigma) into a per-element array
* matching the value length.
*
* Handles broadcasting:
* - length 1 → repeat to all elements
* - length === len → direct (already broadcast)
* - length === len / patchLen → per-patch (repeats within each patch)
*/
function flattenParam(param: Float32Array | Float32Array[], len: number): Float32Array {
if (param instanceof Float32Array) {
return broadcast1D(param, len);
}
// Array of scalars — each element is a Float32Array([scalar])
const flat = new Float32Array(len);
if (param.length === len) {
for (let i = 0; i < len; i++) flat[i] = param[i][0];
} else {
// Assume it's per-patch sized — repeat values evenly.
// Handle the remainder to avoid dropping trailing elements.
const ratio = Math.floor(len / param.length);
const remainder = len % param.length;
let cursor = 0;
for (let i = 0; i < param.length; i++) {
const repeatCount = ratio + (i < remainder ? 1 : 0);
for (let j = 0; j < repeatCount; j++) {
flat[cursor + j] = param[i][0];
}
cursor += repeatCount;
}
}
return flat;
}
/**
* Broadcast a scalar or 1-D array to length `len`.
*/
function broadcast1D(arr: Float32Array, len: number): Float32Array {
if (arr.length === 1) {
const result = new Float32Array(len);
result.fill(arr[0]);
return result;
}
return arr; // assume already correct length
}
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