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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport os\nimport json\nimport numpy as np\n\nos.makedirs(\"/work\", exist_ok=True)\n\ndesign = r'''\nimport numpy as np\n\nDEFAULTS = dict(\n    temperature_scale=1.25,\n    capture=6.0,\n    rescue=4.2,\n    self_atari=14.0,\n    eye=19.0,\n    quiet_fill=10.0,\n    danger=1.8,\n    pass_logit=-4.0,\n    opening_length=36.0,\n    own_adjacent=-0.90,\n    own_diagonal=0.75,\n    own_jump=0.65,\n    own_knight=0.18,\n    own_third=0.10,\n    own_crowding=-0.075,\n    enemy_adjacent=0.20,\n    enemy_diagonal=0.15,\n    enemy_jump=0.25,\n    enemy_knight=0.10,\n)\n\ndef construct(parameters=None):\n    p = dict(DEFAULTS)\n    if parameters:\n        p.update(parameters)\n\n    W = [np.zeros((243 if k == 0 else 512, 512), np.float32)\n         for k in range(6)]\n    b = [np.zeros(512, np.float32) for _ in range(6)]\n    OW = np.zeros((512, 82), np.float32)\n    OB = np.zeros(82, np.float32)\n\n    def unit(channel, point):\n        return channel * 81 + point\n\n    def neighbors(point):\n        r, c = divmod(point, 9)\n        return [rr * 9 + cc for rr, cc in\n                ((r-1,c), (r+1,c), (r,c-1), (r,c+1))\n                if 0 <= rr < 9 and 0 <= cc < 9]\n\n    def diagonals(point):\n        r, c = divmod(point, 9)\n        return [rr * 9 + cc\n                for rr, cc in ((r-1,c-1), (r-1,c+1),\n                               (r+1,c-1), (r+1,c+1))\n                if 0 <= rr < 9 and 0 <= cc < 9]\n\n    adjacent = [neighbors(i) for i in range(81)]\n    diagonal = [diagonals(i) for i in range(81)]\n\n    def copy_channel(layer, source, destination=None):\n        if destination is None:\n            destination = source\n        for i in range(81):\n            W[layer][unit(source, i), unit(destination, i)] = 1.0\n\n    # A fading opening prior; this is computed from occupancy, not move history.\n    opening = 486\n    b[0][opening] = 1.0\n    for i in range(81):\n        W[0][3*i, opening] = -1.0 / p[\"opening_length\"]\n        W[0][3*i+1, opening] = -1.0 / p[\"opening_length\"]\n    for layer in range(1, 5):\n        W[layer][opening, opening] = 1.0\n\n    # Layer 1: both occupancies and each stone's immediate liberty count.\n    # Channels 2/3 are zero at points not occupied by the relevant colour.\n    for i in range(81):\n        degree = len(adjacent[i])\n        for colour in (0, 1):\n            W[0][3*i+colour, unit(colour, i)] = 1.0\n            target = unit(2+colour, i)\n            b[0][target] = degree - 4.0\n            W[0][3*i+colour, target] = 4.0\n            for j in adjacent[i]:\n                W[0][3*j, target] -= 1.0\n                W[0][3*j+1, target] -= 1.0\n\n    # Layer 2: deficiency of liberties in a stone's connected local cluster.\n    # Adjacent same-colour stones contribute their immediate liberties.\n    for colour in (0, 1):\n        copy_channel(1, colour)\n        for i in range(81):\n            target = unit(2+colour, i)\n            W[1][unit(colour, i), target] = 2.0\n            for j in [i] + adjacent[i]:\n                W[1][unit(2+colour, j), target] -= 1.0\n\n    # Layer 3: binary seeds for locally safe groups.\n    for colour in (0, 1):\n        copy_channel(2, colour)\n        for i in range(81):\n            target = unit(2+colour, i)\n            W[2][unit(colour, i), target] = 1.0\n            W[2][unit(2+colour, i), target] = -1.0\n\n    # Layer 4: spread safety through connected stones, retaining the\n    # complement: potentially critical stones. Also compute local danger.\n    for colour in (0, 1):\n        copy_channel(3, colour)\n        for i in range(81):\n            target = unit(2+colour, i)\n            W[3][unit(colour, i), target] = 1.0\n            for j in [i] + adjacent[i]:\n                W[3][unit(2+colour, j), target] -= 1.0\n\n    for i in range(81):\n        r, c = divmod(i, 9)\n\n        # Positive enemy pressure not balanced by friendly support.\n        danger = unit(4, i)\n        b[3][danger] = -1.4\n        for j in range(81):\n            rr, cc = divmod(j, 9)\n            dr, dc = abs(rr-r), abs(cc-c)\n            if dr + dc == 1:\n                strength = 1.0\n            elif dr == 1 and dc == 1:\n                strength = 0.55\n            elif (dr == 2 and dc == 0) or (dr == 0 and dc == 2):\n                strength = 0.30\n            else:\n                continue\n            W[3][unit(1, j), danger] += strength\n            W[3][unit(0, j), danger] -= strength\n\n        # max(0, 2 - number of adjacent empty points).\n        target = unit(5, i)\n        b[3][target] = 2.0 - len(adjacent[i])\n        for j in adjacent[i]:\n            W[3][unit(0, j), target] += 1.0\n            W[3][unit(1, j), target] += 1.0\n\n    # Layer 5: propagate safety another step.\n    for colour in (0, 1):\n        copy_channel(4, colour)\n        for i in range(81):\n            target = unit(2+colour, i)\n            W[4][unit(colour, i), target] = 1.0\n            for j in [i] + adjacent[i]:\n                W[4][unit(colour, j), target] -= 1.0\n                W[4][unit(2+colour, j), target] += 1.0\n    copy_channel(4, 5)\n\n    # Compress the strategic, spatial component into one unit per move.\n    # The offset keeps this signed calculation within the linear ReLU region.\n    offset = 100.0\n    opening_prior = [-3.0, -0.6, 0.8, 0.5, 0.4]\n    for i in range(81):\n        r, c = divmod(i, 9)\n        edge = min(r, c, 8-r, 8-c)\n        target = unit(4, i)\n        b[4][target] = offset - (0.35 if edge == 0 else 0.0)\n        W[4][opening, target] = opening_prior[edge]\n        W[4][unit(4, i), target] = -p[\"danger\"]\n\n        for j in range(81):\n            if i == j:\n                continue\n            rr, cc = divmod(j, 9)\n            dr, dc = abs(rr-r), abs(cc-c)\n            distance = dr + dc\n            own = 0.0\n            enemy = 0.0\n\n            if distance == 1:\n                own += p[\"own_adjacent\"]\n                enemy += p[\"enemy_adjacent\"]\n            elif dr == 1 and dc == 1:\n                own += p[\"own_diagonal\"]\n                enemy += p[\"enemy_diagonal\"]\n            elif distance == 2:\n                own += p[\"own_jump\"]\n                enemy += p[\"enemy_jump\"]\n            elif sorted((dr, dc)) == [1, 2]:\n                own += p[\"own_knight\"]\n                enemy += p[\"enemy_knight\"]\n            elif distance == 3 and min(dr, dc) == 0:\n                own += p[\"own_third\"]\n\n            if distance <= 4:\n                own += p[\"own_crowding\"]\n\n            W[4][unit(0, j), target] += own\n            W[4][unit(1, j), target] += enemy\n\n    # Layer 6: strategic score, captures, rescues, eyes, self-atari,\n    # and unnecessary filling of narrow friendly territory.\n    for i in range(81):\n        degree = len(adjacent[i])\n        r, c = divmod(i, 9)\n        boundary = r in (0, 8) or c in (0, 8)\n\n        W[5][unit(4, i), unit(0, i)] = 1.0\n\n        for j in adjacent[i]:\n            W[5][unit(3, j), unit(1, i)] += 1.0\n            W[5][unit(2, j), unit(2, i)] += 1.0\n\n        eye = unit(3, i)\n        b[5][eye] = 1.0 - degree\n        for j in adjacent[i]:\n            W[5][unit(0, j), eye] += 1.0\n        for j in diagonal[i]:\n            W[5][unit(1, j), eye] -= 1.0 if boundary else 0.5\n\n        self_atari = unit(4, i)\n        W[5][unit(5, i), self_atari] = 1.0\n        for j in adjacent[i]:\n            # A connection to a safe friendly group or a potential capture\n            # can make an otherwise cramped move playable.\n            W[5][unit(0, j), self_atari] -= 2.0\n            W[5][unit(2, j), self_atari] += 2.0\n            W[5][unit(3, j), self_atari] -= 2.0\n\n        quiet = unit(5, i)\n        b[5][quiet] = 2.0 - degree\n        for j in adjacent[i]:\n            W[5][unit(0, j), quiet] += 1.0\n            W[5][unit(1, j), quiet] -= 1.0\n        for j in diagonal[i]:\n            W[5][unit(1, j), quiet] -= 0.5\n\n        scale = p[\"temperature_scale\"]\n        coefficients = [\n            1.0, p[\"capture\"], p[\"rescue\"],\n            -p[\"eye\"], -p[\"self_atari\"], -p[\"quiet_fill\"]\n        ]\n        for channel, coefficient in enumerate(coefficients):\n            OW[unit(channel, i), i] = scale * coefficient\n        OB[i] = -scale * offset\n\n    OB[81] = p[\"temperature_scale\"] * p[\"pass_logit\"]\n\n    tensors = {}\n    for layer in range(6):\n        tensors[f\"L{layer+1}.W\"] = W[layer]\n        tensors[f\"L{layer+1}.b\"] = b[layer]\n    tensors[\"out.W\"] = OW\n    tensors[\"out.b\"] = OB\n    return tensors, p\n'''\n\nwith open(\"/work/design.py\", \"w\") as f:\n    f.write(design)\n\nnamespace = {}\nexec(design, namespace)\ntensors, parameters = namespace[\"construct\"]()\n\nfor name, array in tensors.items():\n    assert array.shape == load(name).shape, (name, array.shape, load(name).shape)\n    assert np.isfinite(array).all()\n    save(name, array)\n\nwith open(\"/work/parameters.json\", \"w\") as f:\n    json.dump(parameters, f, indent=2)\n\nprint(\"Saved all 14 tensors.\")\nprint(\"Total parameters:\", sum(a.size for a in tensors.values()))\nprint(\"Nonzero parameters:\", sum(np.count_nonzero(a) for a in tensors.values()))\nprint(\"Analytically constructed policy; no training or network evaluation performed.\")\n```"}}
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