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{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport numpy as np\n\n# Hand-designed policy circuit. No training, game simulation, or network execution.\nN = 81\nH = 512\nOFFSET = 128.0\n\nparams = {}\nfor k in range(1, 7):\n    params[f\"L{k}.W\"] = np.zeros((243 if k == 1 else H, H), np.float32)\n    params[f\"L{k}.b\"] = np.zeros(H, np.float32)\nparams[\"out.W\"] = np.zeros((H, 82), np.float32)\nparams[\"out.b\"] = np.zeros(82, np.float32)\n\ndef unit(channel, point):\n    return channel * N + point\n\ndef point(r, c):\n    return r * 9 + c\n\ncoords = [divmod(p, 9) for p in range(N)]\nneighbors = []\ndiagonals = []\nfor r, c in coords:\n    neighbors.append([\n        point(r + dr, c + dc)\n        for dr, dc in ((-1, 0), (1, 0), (0, -1), (0, 1))\n        if 0 <= r + dr < 9 and 0 <= c + dc < 9\n    ])\n    diagonals.append([\n        point(r + dr, c + dc)\n        for dr, dc in ((-1, -1), (-1, 1), (1, -1), (1, 1))\n        if 0 <= r + dr < 9 and 0 <= c + dc < 9\n    ])\n\n# Density, friendly shape, and opponent approach kernels.\nkernels = {\n    (0, 1): (1.000, 0.550, -0.650),\n    (1, 1): (0.750, 0.700,  0.100),\n    (0, 2): (0.700, 0.750,  0.250),\n    (1, 2): (0.420, 0.450,  0.120),\n    (2, 2): (0.180, 0.120,  0.000),\n    (0, 3): (0.200, 0.200,  0.030),\n    (1, 3): (0.120, 0.120,  0.020),\n    (2, 3): (0.050, 0.040,  0.000),\n    (3, 3): (0.025, 0.015,  0.000),\n    (0, 4): (0.055, 0.030,  0.000),\n    (1, 4): (0.035, 0.020,  0.000),\n    (2, 4): (0.015, 0.000,  0.000),\n}\n\ndensity = np.zeros((N, N), np.float32)\nfriendly_shape = np.zeros((N, N), np.float32)\nenemy_shape = np.zeros((N, N), np.float32)\n\nfull_density_mass = 0.0\nfor dr in range(-4, 5):\n    for dc in range(-4, 5):\n        full_density_mass += kernels.get(\n            tuple(sorted((abs(dr), abs(dc)))), (0.0, 0.0, 0.0)\n        )[0]\n\nfor p, (r, c) in enumerate(coords):\n    for q, (s, t) in enumerate(coords):\n        d, f, e = kernels.get(\n            tuple(sorted((abs(r - s), abs(c - t)))),\n            (0.0, 0.0, 0.0),\n        )\n        density[p, q] = d\n        friendly_shape[p, q] = f\n        enemy_shape[p, q] = e\n    factor = min(\n        1.65,\n        np.sqrt(full_density_mass / float(density[p].sum()))\n    )\n    density[p] *= factor\n\nTOTAL, WHITE, OWN_COUNT, OPP_COUNT = 486, 487, 488, 489\nPHASE, LATE, AHEAD, BEHIND = 490, 491, 492, 493\n\n# L1: stones, direct liberties on stones, and territorial density penalties.\nW, b = params[\"L1.W\"], params[\"L1.b\"]\nfor p in range(N):\n    W[3*p, unit(0, p)] = 1.0\n    W[3*p+1, unit(1, p)] = 1.0\n\n    for color, channel in ((0, 2), (1, 3)):\n        j = unit(channel, p)\n        b[j] = len(neighbors[p]) - 4.0\n        W[3*p+color, j] += 4.0\n        for q in neighbors[p]:\n            W[3*q, j] -= 1.0\n            W[3*q+1, j] -= 1.0\n\n    j_own, j_opp = unit(4, p), unit(5, p)\n    b[j_own], b[j_opp] = -2.4, -2.1\n    for q in range(N):\n        d = density[p, q]\n        if d:\n            W[3*q, j_own] += d\n            W[3*q+1, j_own] -= 0.7*d\n            W[3*q+1, j_opp] += d\n            W[3*q, j_opp] -= 0.7*d\n\n    W[3*p, TOTAL] = 1.0\n    W[3*p+1, TOTAL] = 1.0\n    W[3*p+2, WHITE] = 1.0 / N\n    W[3*p, OWN_COUNT] = 1.0\n    W[3*p+1, OPP_COUNT] = 1.0\n\n# L2: certified liberty evidence and strategic move scores.\n#\n# In a same-color star, an individual liberty contributes at most eight\n# to the weighted liberty sum below. Thus sums >8 and >16 certify\n# at least two and three DISTINCT liberties, respectively.\nW, b = params[\"L2.W\"], params[\"L2.b\"]\nline_prior = [-1.30, -0.05, 0.75, 0.65, 0.55,\n               0.65, 0.75, -0.05, -1.30]\n\nfor p, (r, c) in enumerate(coords):\n    W[unit(0, p), unit(0, p)] = 1.0\n    W[unit(1, p), unit(1, p)] = 1.0\n\n    # The orientation choice preserves rotations and reflections.\n    if abs(r - 4) > abs(c - 4):\n        vertical, horizontal = 3.0, 5.0\n    elif abs(r - 4) < abs(c - 4):\n        vertical, horizontal = 5.0, 3.0\n    else:\n        vertical = horizontal = 4.0\n\n    for color, source_lib, destination, threshold in (\n        (0, 2, 2, 8.0),\n        (1, 3, 3, 8.0),\n        (0, 2, 4, 16.0),\n    ):\n        j = unit(destination, p)\n        b[j] = -threshold - 64.0\n        W[unit(color, p), j] += 64.0\n        W[unit(source_lib, p), j] += 8.0\n        for q in neighbors[p]:\n            s, t = coords[q]\n            coefficient = vertical if s != r else horizontal\n            W[unit(source_lib, q), j] += coefficient\n\n    j = unit(5, p)\n    positional = line_prior[r] + line_prior[c]\n    if r in (2, 6) and c in (2, 6):\n        positional += 0.35\n    if (r, c) == (4, 4):\n        positional += 0.15\n    b[j] = OFFSET + positional\n\n    W[unit(4, p), j] -= 2.4\n    W[unit(5, p), j] -= 2.7\n    for q in range(N):\n        W[unit(0, q), j] += friendly_shape[p, q]\n        W[unit(1, q), j] += enemy_shape[p, q]\n\nfor g in (TOTAL, WHITE, OWN_COUNT, OPP_COUNT):\n    W[g, g] = 1.0\nW[TOTAL, PHASE], b[PHASE] = 1.0, -18.0\nW[TOTAL, LATE], b[LATE] = 1.0, -52.0\nW[OWN_COUNT, AHEAD] = 1.0\nW[OPP_COUNT, AHEAD] = -1.0\nW[WHITE, AHEAD] = 7.5\nb[AHEAD] = -1.0\nW[OWN_COUNT, BEHIND] = -1.0\nW[OPP_COUNT, BEHIND] = 1.0\nW[WHITE, BEHIND] = -7.5\nb[BEHIND] = 1.0\n\ndef copy_globals(W):\n    for g in range(TOTAL, BEHIND + 1):\n        W[g, g] = 1.0\n\n# L3: propagate certified liberty evidence through same-color stones.\n# A pair (z,g) encodes stone=z-g exactly and nonnegative evidence=g.\nW, b = params[\"L3.W\"], params[\"L3.b\"]\nM = 481.0\nfor p in range(N):\n    neighborhood = [p] + neighbors[p]\n    for stone_channel, evidence_channel, z_channel, g_channel in (\n        (0, 2, 0, 1),\n        (1, 3, 2, 3),\n    ):\n        for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):\n            j = unit(destination, p)\n            W[unit(stone_channel, p), j] += M + 1.0\n            for q in neighborhood:\n                W[unit(evidence_channel, q), j] += 1.0\n            b[j] = -M - subtraction\n\n    j = unit(4, p)\n    W[unit(0, p), j] += M\n    for q in neighborhood:\n        W[unit(4, q), j] += 1.0\n    b[j] = -M\n    W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L4: another same-color propagation step.\nW, b = params[\"L4.W\"], params[\"L4.b\"]\nM = 2401.0\nfor p in range(N):\n    neighborhood = [p] + neighbors[p]\n    for z_channel, g_channel in ((0, 1), (2, 3)):\n        for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):\n            j = unit(destination, p)\n            W[unit(z_channel, p), j] += M + 1.0\n            W[unit(g_channel, p), j] -= M + 1.0\n            for q in neighborhood:\n                W[unit(g_channel, q), j] += 1.0\n            b[j] = -M - subtraction\n\n    j = unit(4, p)\n    W[unit(0, p), j] += M\n    W[unit(1, p), j] -= M\n    for q in neighborhood:\n        W[unit(4, q), j] += 1.0\n    b[j] = -M\n    W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L5: recover stones and bounded tactical classifications.\n# \"Unsafe\" means no two-liberty certificate reached this stone.\n# These flags deliberately err toward noticing threats.\nW, b = params[\"L5.W\"], params[\"L5.b\"]\nfor p in range(N):\n    neighborhood = [p] + neighbors[p]\n    for z_channel, g_channel, stone_channel, unsafe_channel in (\n        (0, 1, 0, 2),\n        (2, 3, 1, 3),\n    ):\n        j = unit(stone_channel, p)\n        W[unit(z_channel, p), j] += 1.0\n        W[unit(g_channel, p), j] -= 1.0\n\n        j = unit(unsafe_channel, p)\n        W[unit(z_channel, p), j] += 1.0\n        W[unit(g_channel, p), j] -= 1.0\n        for q in neighborhood:\n            W[unit(g_channel, q), j] -= 1.0\n\n    j = unit(4, p)  # Friendly stone lacking a three-liberty certificate.\n    W[unit(0, p), j] += 1.0\n    W[unit(1, p), j] -= 1.0\n    for q in neighborhood:\n        W[unit(4, q), j] -= 1.0\n\n    W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L6: move-level capture/rescue, shape, eyes, self-atari, and safe contact.\nW, b = params[\"L6.W\"], params[\"L6.b\"]\nfor p, (r, c) in enumerate(coords):\n    degree = len(neighbors[p])\n\n    for q in neighbors[p]:\n        W[unit(2, q), unit(0, p)] += 1.0  # Rescue urgency.\n        W[unit(3, q), unit(1, p)] += 1.0  # Capture urgency.\n\n    j = unit(2, p)\n    W[unit(5, p), j] = 1.0\n    edge_phase_weight = 0.0\n    for d in (min(r, 8-r), min(c, 8-c)):\n        if d == 0:\n            edge_phase_weight += 0.030\n        elif d == 1:\n            edge_phase_weight += 0.012\n    W[PHASE, j] += edge_phase_weight\n    for q in neighbors[p]:\n        W[unit(1, q), j] += 1.1\n\n    # True-eye preservation, with diagonal false-eye exceptions.\n    j = unit(3, p)\n    b[j] = 1.0 - degree\n    for q in neighbors[p]:\n        W[unit(0, q), j] += 1.0\n    diagonal_penalty = 0.5 if degree == 4 else 1.0\n    for q in diagonals[p]:\n        W[unit(1, q), j] -= diagonal_penalty\n\n    # Conservative self-atari / futile-extension penalty.\n    # Capturing or connecting to a certified strong group exempts a move.\n    j = unit(4, p)\n    b[j] = 2.0 - 1.6 * degree\n    for q in neighbors[p]:\n        W[unit(0, q), j] += -3.2\n        W[unit(1, q), j] += 1.6\n        W[unit(2, q), j] += 1.4\n        W[unit(3, q), j] -= 4.0\n        W[unit(4, q), j] += 4.0\n\n    # Enemy contact is rewarded primarily when backed by a strong group.\n    j = unit(5, p)\n    for q in neighbors[p]:\n        W[unit(1, q), j] += 1.0\n        W[unit(0, q), j] -= 2.0\n        W[unit(4, q), j] += 2.0\n\ncopy_globals(W)\n\n# Output policy.\nW, b = params[\"out.W\"], params[\"out.b\"]\nfor p in range(N):\n    b[p] = -OFFSET\n    for channel, coefficient in (\n        (0, 4.6),\n        (1, 7.1),\n        (2, 1.0),\n        (3, -64.0),\n        (4, -9.0),\n        (5, -1.6),\n    ):\n        W[unit(channel, p), p] = coefficient\n\n# Passing remains unlikely while constructive moves exist.\nb[81] = -5.6\nW[PHASE, 81] = 0.050\nW[LATE, 81] = 0.045\nW[AHEAD, 81] = 0.025\nW[BEHIND, 81] = -0.035\nW[WHITE, 81] = 0.150\n\n# Only inspect and save weights; do not evaluate the policy.\nfor name, array in params.items():\n    assert array.dtype == np.float32\n    assert np.isfinite(array).all()\n    save(name, array)\n\nprint(\"Saved all 14 tensors.\")\nprint(\"Parameters:\", sum(a.size for a in params.values()))\nprint(\"Nonzero parameters:\", sum(np.count_nonzero(a) for a in params.values()))\nprint(\"Design: certified local liberty propagation, capture/rescue priorities,\")\nprint(\"self-atari filtering, eye preservation, territorial shape, and late passing.\")\n```"}}
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