> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embedding transforms

> Align frozen embeddings before the Bayesian head: standardize, whiten, CORAL, RPA, affine, and Euclidean alignment.

The Personalizer can apply an **embedding-space transform** on `Z` before the head. Use this when cal and stream embeddings shift, without retraining the trunk.

## Families

| Family                            | Names                                          | When                                                                                      |
| --------------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------------- |
| **Cal-fitted (unsupervised)**     | `standardize`, `whiten`, `euclidean_alignment` | Fit at `fit()` from calibration embeddings                                                |
| **Two-distribution / supervised** | `coral`, `rpa`, `affine`                       | Need a stream (or second) slice after fit — **offline/research**, not deployment defaults |

```python theme={null}
from nimbus_bci import Personalizer, wrap

enc = wrap(model.encode, model_id="partner", embedding_dim=64)

# Cal-fitted — common research path
p = Personalizer.for_research(enc, ["left", "right"], transform="whiten")
p.fit(X_cal, y_cal)

# Two-distribution maps — after fit, pass a shifted stream slice
p2 = Personalizer.for_research(enc, ["left", "right"], transform="coral")
p2.fit(X_cal, y_cal)
p2.fit_coral(X_stream)           # real two-distribution CORAL (Sun & Saenko 2016)
# p2.fit_rpa(X_stream, trim_windows=...) 
# p2.fit_affine(X_stream, y_stream)  # supervised L2 ridge toward identity
```

Or pass a stream at fit time when supported: `fit(..., X_stream=...)`.

## Quick guidance

| Transform             | Intent                                                |
| --------------------- | ----------------------------------------------------- |
| `standardize`         | Per-dim mean/std from cal                             |
| `whiten`              | Decorrelate + unit variance from cal                  |
| `euclidean_alignment` | EA-style recentering in embedding space               |
| `coral`               | Align second-order stats to a target distribution     |
| `rpa`                 | Re-centering with optional `trim_windows=` robustness |
| `affine`              | Supervised affine map; ridge toward identity          |

EMA-damped label-free CORAL refits use `ema_beta=` on supported paths.

<Warning>
  Prefer **no transform** (or cal-fitted only) for deployment. Treat `coral` / `rpa` / `affine` as research / offline alignment — they are not the product default.
</Warning>

## Types

Public types: `StandardizeTransform`, `WhitenTransform`, `CoralTransform`, `RpaTransform`, `EuclideanAlignment` (also importable from `nimbus_bci`).

## Next read

<Columns cols={2}>
  <Card title="Paradigms & profiles" icon="route" href="/personalizer/paradigms">
    MI / P300 recipes and day-2 profiles
  </Card>

  <Card title="Encoder contract" icon="plug" href="/personalizer/encoder-contract">
    wrap → Personalizer → save/load
  </Card>

  <Card title="Research / PEFT" icon="flask" href="/personalizer/research-peft">
    BaLoRA and trunk-arm adaptation
  </Card>

  <Card title="API Reference" icon="book" href="/python-sdk/api-reference">
    Full symbol list
  </Card>
</Columns>