feat(sense): T-G4 线性头（离线 softmax 回归 + 纯 Python 推理）

- classifier.py：LinearHead.load（JSON 工件，缺失/损坏 -> ArtifactMissing）+
  predict（纯 Python 点积 + 稳定 softmax，D-G5 serving 零依赖）；LoraRemote 预留
- scripts/train_tier_head.py：numpy softmax 回归（类别权重均衡/L2），
  70/15/15 切分，head.json + metrics.json（accuracy/macro-F1/逐档 F1）+ 工件登记；
  --synthetic 合成自测链路；CLI 出口 UTF-8 reconfigure
- requirements-ml.txt 登记 numpy（离线训练用，D-P6/D-G5 说明理由）
- 测试 +5（黄金向量 softmax 一致/argmax/缺失/损坏/合成训练-推理链路），全量 400 passed

tests/test_sense_classifier.py

@@ -0,0 +1,83 @@
+"""线性头测试（T-G4）：纯 Python 推理与黄金向量一致 / 工件缺失 ArtifactMissing。"""
+import json
+import math
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from gateway.sense.classifier import LinearHead
+from gateway.sense.errors import ArtifactMissing
+
+W = [[0.5, -0.5, 0.0], [0.0, 0.5, -0.5], [-0.5, 0.0, 0.5]]
+B = [0.1, 0.0, -0.1]
+
+
+def _head(tmp_path):
+    p = tmp_path / "head.json"
+    p.write_text(json.dumps({"version": "test", "dim": 3, "labels": ["t1", "t2", "t3"],
+                             "weights": W, "bias": B}), encoding="utf-8")
+    return p
+
+
+def test_predict_matches_reference_softmax(tmp_path):
+    """纯 Python 点积 + softmax 与手工参考实现一致（黄金向量）。"""
+    head = LinearHead.load(_head(tmp_path))
+    vec = [1.0, 2.0, 3.0]
+    logits = [sum(wi * vi for wi, vi in zip(w, vec)) + b for w, b in zip(W, B)]
+    m = max(logits)
+    exps = [math.exp(z - m) for z in logits]
+    total = sum(exps)
+    expected = {"t1": exps[0] / total, "t2": exps[1] / total, "t3": exps[2] / total}
+    probs = head.predict(vec)
+    assert sum(probs.values()) == pytest.approx(1.0, abs=1e-9)
+    for k, v in expected.items():
+        assert probs[k] == pytest.approx(v, rel=1e-9)
+
+
+def test_predict_argmax_consistent_with_logit_order(tmp_path):
+    head = LinearHead.load(_head(tmp_path))
+    # [3,1,2] 点积最大维度是 0 -> t1 概率最大
+    probs = head.predict([3.0, 1.0, 2.0])
+    assert max(probs, key=probs.get) == "t1"
+
+
+def test_load_missing_raises_artifact_missing(tmp_path):
+    with pytest.raises(ArtifactMissing):
+        LinearHead.load(tmp_path / "ghost.json")
+
+
+def test_load_corrupted_raises_artifact_missing(tmp_path):
+    p = tmp_path / "bad.json"
+    p.write_text('{"weights": [], "bias": []}', encoding="utf-8")
+    with pytest.raises(ArtifactMissing):
+        LinearHead.load(p)
+
+
+def test_train_synthetic_roundtrip(tmp_path):
+    """T-G4 验收：合成集训练 macro-F1 > 0.9，head.json 可被 LinearHead 推理。"""
+    import importlib.util
+    import numpy as np
+
+    script = Path(__file__).resolve().parent.parent / "scripts" / "train_tier_head.py"
+    spec = importlib.util.spec_from_file_location("train_tier_head", script)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+
+    rng = np.random.default_rng(7)
+    n, d = 150, 32
+    centers = [rng.normal(0, 1, d), rng.normal(5, 1, d), rng.normal(-5, 1, d)]
+    X = np.vstack([c + rng.normal(0, 0.7, (n, d)) for c in centers])
+    y = np.repeat([0, 1, 2], n)
+    W, b = mod.train(X, y, epochs=150)
+    m = mod.metrics(X, y, W, b)
+    assert m["macro_f1"] > 0.9, m
+
+    out = tmp_path / "head.json"
+    out.write_text(json.dumps({"version": "synthetic", "dim": d,
+                               "labels": ["t1", "t2", "t3"],
+                               "weights": W.T.tolist(), "bias": b.tolist()}),
+                   encoding="utf-8")
+    head = LinearHead.load(out)
+    probs = head.predict(X[0].tolist())
+    assert sum(probs.values()) == pytest.approx(1.0, abs=1e-9)