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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\nexec(open(\"/work/rules_analysis.py\").read())\nroot=\"/work/pass_4\"\nx=np.load(root+\"/inputs.npy\").reshape(-1,81,3)\nacts=[np.load(root+f\"/act_L{k}.npy\") for k in range(1,7)]\nlogits=np.load(root+\"/logits.npy\")\nfor pos,points in [(0,[44,51,17]),(1,[50,3]),(2,[53,28]),\n                   (3,[13,21,9]),(4,[72,28]),(5,[53,55])]:\n    board=x[pos,:,0].astype(int)-x[pos,:,1].astype(int)\n    gs=groups(board)\n    print(\"\\nPOSITION\",pos)\n    for point in points:\n        print(\"Move\",point,\"recorded logit\",float(logits[pos,point]))\n        print(\"L6 [strategy,tactics,eye,quiet,self-atari,danger]:\",\n              [round(float(acts[5][pos,81*c+point]),3) for c in range(6)])\n        for j in neighbors(point):\n            if board[j]:\n                group=next((s,l) for co,s,l in gs if j in s)\n                print(\" Neighbor\",j,\"colour\",board[j],\"group size/liberties\",\n                      len(group[0]),sorted(group[1]),\n                      \"L5 channels\",\n                      [float(acts[4][pos,81*c+j]) for c in range(6)])\n```\n"}}
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