| """ |
| hv_intent.py |
| |
| Hypervector intent router - word-level, supervised weights, k-NN bank. |
| |
| Load: |
| from hv_intent import load_v2, predict |
| model = load_v2(".") |
| predict(model, "what is the weather today") |
| |
| Pure NumPy. No tokenizer, no gradient descent, no GPU. |
| """ |
|
|
| import json |
| import numpy as np |
| from pathlib import Path |
|
|
|
|
| DROP_THRESHOLD = 0.15 |
| K_NEIGHBOURS = 7 |
|
|
|
|
| def _encode_phrase(phrase, K, vocab_index, weights, drop_thr=DROP_THRESHOLD): |
| items = [] |
| for t in phrase.lower().split(): |
| if t not in vocab_index: |
| continue |
| w = weights.get(t, 0.5) |
| if w < drop_thr: |
| continue |
| items.append(K[vocab_index[t]].astype(np.float32) * w) |
| if not items: |
| return np.zeros(K.shape[1], dtype=np.float32) |
| v = np.stack(items).sum(axis=0) |
| n = np.linalg.norm(v) |
| return v / n if n > 1e-30 else v |
|
|
|
|
| def load_v2(path): |
| """Load the word-level model with supervised weights and k-NN bank.""" |
| path = Path(path) |
| with open(path / "config.json") as f: |
| config = json.load(f) |
| data = np.load(path / "codebooks.npz", allow_pickle=True) |
|
|
| n_cb = config["hyperparameters"]["n_codebooks"] |
| codebooks = [data[f"K_{i}"] for i in range(n_cb)] |
| vocab = list(data["vocab"]) |
| weights_arr = data["weights"] |
| phrases = list(data["phrases"]) |
| labels = data["phrase_labels"] |
| intent_labels = config["intent_labels"] |
|
|
| vocab_index = {w: i for i, w in enumerate(vocab)} |
| weights = {w: float(weights_arr[i]) for i, w in enumerate(vocab)} |
|
|
| |
| banks = [] |
| for K in codebooks: |
| H = np.stack([_encode_phrase(p, K, vocab_index, weights) |
| for p in phrases]) |
| banks.append((H, labels)) |
|
|
| return { |
| "codebooks": codebooks, |
| "vocab_index": vocab_index, |
| "weights": weights, |
| "banks": banks, |
| "intent_labels": intent_labels, |
| "config": config, |
| } |
|
|
|
|
| def predict(model, phrase, k=K_NEIGHBOURS, return_scores=False): |
| """Return the predicted intent label for a single phrase.""" |
| C = len(model["intent_labels"]) |
| vote = np.zeros(C, dtype=np.float32) |
| for K, (H, Y) in zip(model["codebooks"], model["banks"]): |
| q = _encode_phrase(phrase, K, model["vocab_index"], model["weights"]) |
| s = H @ q |
| k_eff = min(k, s.shape[0]) |
| top = np.argpartition(-s, k_eff - 1)[:k_eff] |
| for j in top: |
| vote[Y[j]] += max(s[j], 0.0) |
| pred = int(vote.argmax()) |
| if return_scores: |
| return model["intent_labels"][pred], vote |
| return model["intent_labels"][pred] |
|
|
|
|
| def predict_batch(model, phrases, k=K_NEIGHBOURS): |
| return [predict(model, p, k) for p in phrases] |
