Add filtered KS diagnostics and feature-type plan

report.md

@@ -94,6 +94,33 @@ residual = x - trend
 
 **Two-stage training:** temporal GRU first, diffusion on residuals.
 
+### 4.3 Feature-Type Aware Strategy / 特征类型分治方案
+Based on HAI feature semantics and observed KS outliers, we classify problematic features into six types and plan separate modeling paths:
+
+1) **Type 1: Exogenous setpoints / demands** (schedule-driven, piecewise-constant)  
+   Examples: P1_B4002, P2_MSD, P4_HT_LD  
+   Strategy: program generator (HSMM / change-point), or sample from program library; condition diffusion on these.
+
+2) **Type 2: Controller outputs** (policy-like, saturation / rate limits)  
+   Example: P1_B4005  
+   Strategy: small controller emulator (PID/NARX) with clamp + rate-limit.
+
+3) **Type 3: Spiky actuators** (few operating points + long dwell)  
+   Examples: P1_PCV02Z, P1_FCV02Z  
+   Strategy: spike-and-slab + dwell-time modeling or command‑driven actuator dynamics.
+
+4) **Type 4: Quantized / digital-as-continuous**  
+   Examples: P4_ST_PT01, P4_ST_TT01  
+   Strategy: generate latent continuous then quantize or treat as ordinal discrete diffusion.
+
+5) **Type 5: Derived conversions**  
+   Examples: *FT* → *FTZ*  
+   Strategy: generate base variable and derive conversions deterministically.
+
+6) **Type 6: Aux / vibration / narrow-band**  
+   Examples: P2_24Vdc, P2_HILout  
+   Strategy: AR/ARMA or regime‑conditioned narrow-band models.
+
 ---
 
 ## 5. Diffusion Formulations / 扩散形式
@@ -201,6 +228,11 @@ Metrics (with reference):
 - 输出 `example/results/cdf_<feature>.svg`（真实 vs 生成 CDF）
 - 统计生成数据是否堆积在边界（gen_frac_at_min / gen_frac_at_max）
 
+**Filtered KS（剔除难以学习特征，仅用于诊断）：** `example/filtered_metrics.py`
+- 规则：std 过小或 KS 过高自动剔除
+- 输出 `example/results/filtered_metrics.json`
+- 只用于诊断，不作为最终指标
+
 Recent runs (Windows):
 - 2026-01-27 21:22:34 — avg_ks 0.4046 / avg_jsd 0.0376 / avg_lag1_diff 0.1449