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节点 n8

heart_jcf_peri reweighting, heart x1.4, edge x0.35 (fake Engineer, node 8)

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

来自 programs.git cdd2c2ea04:solution/METHOD.md

调研员的计划

代码改动?这个节点的程序和父节点程序的逐行差别：绿色是新增，红色是删除。

对比：父节点版本 a3fac49c2b。改动的文件：solution/METHOD.md +3 −0、solution/README.md +0 −4、solution/run.py +10 −25

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..a7ccb83--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,3 @@+heart_jcf_peri reweighting, heart x1.4, edge x0.35 (fake Engineer, node 8)++Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index ba29577..0000000--- a/solution/README.md+++ /dev/null@@ -1,4 +0,0 @@-# pseudobulk_shift--最新阶段抽样后，每个细胞加上所属类型在最后一步的伪批量差值 mean(last|type) − mean(prev|type)，夹到 ≥0；前一阶段没有的类型原样复制。-T1 proxy 只有一个输入阶段，没有差值可取，退化成 copy_last（同样的抽样），所以 proxy 分 = copy_last（seed 0 实测 49.77）。final 才真正平移；官方在真实 T1 上报的常数位移是 48.6，低于地板。diff --git a/solution/run.py b/solution/run.pyindex f3a0f25..277c530 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,33 +1,25 @@ #!/usr/bin/env python3-"""pseudobulk_shift: latest stage + per-cell-type pseudobulk delta of the last step.--The delta is mean(last|type) - mean(prev|type) over the two latest inputs,-computed on the full stages and added once to a subsample of the latest stage-(clipped at 0). Types missing from the earlier stage are copied unchanged.--With a single input stage (T1 proxy: E8.5 only) there is no step to take a-delta from, so this falls back to copy_last with the same sampling. The proxy-therefore cannot tell this seed from copy_last; that gap is expected.-"""+"""fake-8: heart_jcf_peri weights perturbed by the fake Engineer (test run)."""  from __future__ import annotations  import argparse -import numpy as np--from src.task1_temporal.baselines import shift_rows, type_deltas+from src.task1_temporal.reweight import heart_reweight from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) +HEART_WEIGHT = 1.4+EDGE_WEIGHT = 0.35+N_CELLS = 4000+  def main() -> None:     parser = argparse.ArgumentParser()@@ -35,19 +27,12 @@ def main() -> None:     parser.add_argument("--out", required=True)     parser.add_argument("--seed", type=int, default=0)     args = parser.parse_args()-     manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    stages = inputs_by_time(manifest)-    last = read_stage(args.data, stages[-1], genes)-    rng = np.random.default_rng(args.seed)-    rows = sample_rows(last.n_obs, target_n_cells(manifest, last.n_obs), rng)-    X = last.X[rows]-    if len(stages) >= 2:-        prev = read_stage(args.data, stages[-2], genes)-        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels_of(last))-        del prev-        X = shift_rows(X, labels_of(last)[rows], deltas)+    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    pass+    n = target_n_cells(manifest, N_CELLS)+    X = heart_reweight(last.X, labels_of(last), n_cells=n, heart_weight=HEART_WEIGHT, edge_weight=EDGE_WEIGHT, seed=args.seed)     write_prediction(X, genes, args.out, seed=args.seed)  

调研来源?调研员查到并用到的知识条目和文献检索结果（只列标题和编号）。

用到的知识库条目

分析结果?分析员写的 ANALYSIS.json：改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

对话摘要?每个角色和大模型对话的统计：轮数、工具调用、用时、token 数和最后的回答摘录；原始记录只给路径。

只给统计和最后回答的摘录；完整对话请到原始记录位置里列出的文件看。

分析员

ANALYSIS.json written

工程师

wrote solution/run.py with heart x1.4, edge x0.35 (ok)

调研员

PLAN.json written

审查员

verdict pass