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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\n\n# Analytically constructed weights only: no training, search, or forward passes.\nP = 81\nWIDTH = 512\npoints = [(r, c) for r in range(9) for c in range(9)]\n\nO = np.arange(P)\nE = O + 81\nA = O + 162\nB = O + 243\nC = O + 324\nD = O + 405\n\nWHITE, TOTAL, EARLY, LATE, DEFICIT = 486, 487, 488, 489, 490\nglobals_used = [WHITE, TOTAL, EARLY, LATE, DEFICIT]\n\nix_own = np.array([inp(r, c, 0) for r, c in points])\nix_opp = np.array([inp(r, c, 1) for r, c in points])\nix_white = inp(0, 0, 2)\n\ndef neighbors(offsets):\n    result = []\n    for r, c in points:\n        result.append(np.array([\n            (r + dr) * 9 + c + dc\n            for dr, dc in offsets\n            if 0 <= r + dr < 9 and 0 <= c + dc < 9\n        ], dtype=np.int64))\n    return result\n\nadj = neighbors([(-1, 0), (1, 0), (0, -1), (0, 1)])\ndiag = neighbors([(-1, -1), (-1, 1), (1, -1), (1, 1)])\ntwo = neighbors([(-2, 0), (2, 0), (0, -2), (0, 2)])\nknight = neighbors([\n    (-2, -1), (-2, 1), (2, -1), (2, 1),\n    (-1, -2), (-1, 2), (1, -2), (1, 2)\n])\n\nparams = {}\nfor layer in range(1, 7):\n    params[f\"L{layer}.W\"] = np.zeros(\n        (243 if layer == 1 else WIDTH, WIDTH), dtype=np.float32\n    )\n    params[f\"L{layer}.b\"] = np.zeros(WIDTH, dtype=np.float32)\nparams[\"out.W\"] = np.zeros((WIDTH, 82), dtype=np.float32)\nparams[\"out.b\"] = np.zeros(82, dtype=np.float32)\n\ndef add(W, dest, sources, coefficient=1.0):\n    sources = np.asarray(sources, dtype=np.int64).reshape(-1)\n    np.add.at(W[:, int(dest)], sources, np.asarray(coefficient, np.float32))\n\ndef relay(W, indices):\n    indices = np.asarray(indices, dtype=np.int64)\n    W[indices, indices] += 1.0\n\ncoords = np.asarray(points, dtype=np.float64)\ndist2 = np.sum((coords[:, None, :] - coords[None, :, :]) ** 2, axis=2)\n\n# Static spatial kernels, not evaluations on board inputs.\ninfluence = np.exp(-np.sqrt(dist2) / 1.55)\nreference_mass = influence[40].sum()\ninfluence *= (reference_mass / influence.sum(axis=1))[:, None] ** 0.60\n\nterritory_kernel = np.exp(-dist2 / (2.0 * 1.60 ** 2))\nterritory_kernel /= territory_kernel.sum(axis=1, keepdims=True)\n\n# ----------------------------------------------------------------------\n# L1: occupancy, exact per-stone immediate liberties, diagonal information,\n#     opening/endgame clocks, and coarse regional ownership features.\n# ----------------------------------------------------------------------\nW, b = params[\"L1.W\"], params[\"L1.b\"]\n\nfor p in range(P):\n    n, d = adj[p], diag[p]\n    degree = len(n)\n\n    add(W, O[p], ix_own[p])\n    add(W, E[p], ix_opp[p])\n\n    # relu(local_empty_neighbors - 4 * (1 - own/opp_at_p)).\n    for destination, center in [(A[p], ix_own[p]), (B[p], ix_opp[p])]:\n        b[destination] = degree - 4\n        add(W, destination, center, 4.0)\n        add(W, destination, ix_own[n], -1.0)\n        add(W, destination, ix_opp[n], -1.0)\n\n    # Exactly one iff every existing diagonal is occupied.\n    b[C[p]] = 1 - len(d)\n    add(W, C[p], ix_own[d])\n    add(W, C[p], ix_opp[d])\n\nadd(W, WHITE, ix_white)\nadd(W, TOTAL, ix_own)\nadd(W, TOTAL, ix_opp)\n\nb[EARLY] = 1.0\nadd(W, EARLY, ix_own, -1.0 / 32.0)\nadd(W, EARLY, ix_opp, -1.0 / 32.0)\n\nb[LATE] = -45.0\nadd(W, LATE, ix_own)\nadd(W, LATE, ix_opp)\n\nregions = []\nfor r in (1, 4, 7):\n    for c in (1, 4, 7):\n        center = np.array([r, c], dtype=np.float64)\n        weights = 0.30 * np.exp(\n            -np.sum((coords - center) ** 2, axis=1) / (2.0 * 1.80 ** 2)\n        )\n        t = len(regions)\n        lo, hi = 490 + 2 * t, 491 + 2 * t\n        regions.append((lo, hi))\n        for destination, bias in [(lo, 1.0), (hi, -1.0)]:\n            b[destination] = bias\n            add(W, destination, ix_own, weights)\n            add(W, destination, ix_opp, -weights)\n\n# ----------------------------------------------------------------------\n# L2: conservative certificates that a connected string has >=2 liberties.\n# These certificates cannot mark a genuine one-liberty string as safe.\n# ----------------------------------------------------------------------\nW, b = params[\"L2.W\"], params[\"L2.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, [WHITE, TOTAL, EARLY, LATE])\n\nfor p in range(P):\n    n = adj[p]\n    for occupancy, liberties, seed_a, seed_b in [\n        (O, A, A, C),\n        (E, B, B, D),\n    ]:\n        # Two local liberties, or a local liberty plus a liberty on an\n        # adjacent friendly stone. Adjacent points have disjoint neighbors.\n        b[seed_a[p]] = -16.0\n        add(W, seed_a[p], liberties[p], 16.0)\n        add(W, seed_a[p], liberties[n])\n\n        # Three neighboring liberty incidences imply >=2 distinct liberties.\n        # Two suffice when no empty diagonal can be counted twice.\n        b[seed_b[p]] = -18.0\n        add(W, seed_b[p], occupancy[p], 16.0)\n        add(W, seed_b[p], liberties[n])\n        add(W, seed_b[p], C[p])\n\n# Coarse area/stone deficit, with komi, used only to discourage losing passes.\nregional_fraction = 0.65\nb[DEFICIT] = regional_fraction * 81.0 + 7.5\nfor lo, hi in regions:\n    add(W, DEFICIT, lo, -9.0 * regional_fraction)\n    add(W, DEFICIT, hi, 9.0 * regional_fraction)\nadd(W, DEFICIT, O, -(1.0 - regional_fraction))\nadd(W, DEFICIT, E, +(1.0 - regional_fraction))\nadd(W, DEFICIT, WHITE, -15.0)\n\n# ----------------------------------------------------------------------\n# L3: propagate safety once; construct signed spatial influence.\n# Unsafe flags remain binary.\n# ----------------------------------------------------------------------\nW, b = params[\"L3.W\"], params[\"L3.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, globals_used)\n\nfor p in range(P):\n    neighborhood = np.concatenate(([p], adj[p]))\n\n    add(W, A[p], O[p])\n    add(W, A[p], A[neighborhood], -1.0)\n    add(W, A[p], C[neighborhood], -1.0)\n\n    add(W, B[p], E[p])\n    add(W, B[p], B[neighborhood], -1.0)\n    add(W, B[p], D[neighborhood], -1.0)\n\n    add(W, C[p], O, influence[p])\n    add(W, C[p], E, -influence[p])\n    add(W, D[p], O, -influence[p])\n    add(W, D[p], E, influence[p])\n\n# ----------------------------------------------------------------------\n# L4: propagate safety again; estimate empty, unsettled territory and\n# distinguish true eyes from eyes spoiled by enemy diagonals.\n# ----------------------------------------------------------------------\nW, b = params[\"L4.W\"], params[\"L4.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, globals_used)\nTERRITORY_THRESHOLD = 1.35\n\nfor p, (r, c) in enumerate(points):\n    n = adj[p]\n    for occupancy, unsafe in [(O, A), (E, B)]:\n        add(W, unsafe[p], unsafe[p])\n        add(W, unsafe[p], unsafe[n])\n        add(W, unsafe[p], occupancy[n], -1.0)\n\n    b[C[p]] = TERRITORY_THRESHOLD\n    add(W, C[p], C[p], -1.0)\n    add(W, C[p], D[p], -1.0)\n    add(W, C[p], O[p], -TERRITORY_THRESHOLD)\n    add(W, C[p], E[p], -TERRITORY_THRESHOLD)\n\n    is_edge = r in (0, 8) or c in (0, 8)\n    b[D[p]] = 0.0 if is_edge else -1.0\n    add(W, D[p], E[diag[p]])\n\n# ----------------------------------------------------------------------\n# L5: final safety propagation. Enemy urgency is locally size-weighted.\n# ----------------------------------------------------------------------\nW, b = params[\"L5.W\"], params[\"L5.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, C)\nrelay(W, D)\nrelay(W, globals_used)\n\nfor p in range(P):\n    n = adj[p]\n\n    add(W, A[p], A[p])\n    add(W, A[p], A[n])\n    add(W, A[p], O[n], -1.0)\n\n    # Positive only for an enemy stone with no propagated safety certificate.\n    # Larger nearby strings receive greater capture urgency.\n    b[B[p]] = -3.0\n    add(W, B[p], B[p], 4.0)\n    add(W, B[p], B[n], 4.0)\n    add(W, B[p], E[n], -3.70)\n    add(W, B[p], E[diag[p]], 0.10)\n\n# ----------------------------------------------------------------------\n# L6: combine territory, shape, captures, viable escapes, self-atari\n# avoidance, and eye preservation.\n# ----------------------------------------------------------------------\nW, b = params[\"L6.W\"], params[\"L6.b\"]\nrelay(W, globals_used)\n\nOFFSET = 64.0\nTERRITORY_GAIN = 4.30\nCAPTURE_GAIN = 8.50\nSAVE_GAIN = 8.00\nFRIEND_LIBERTY_CREDIT = 0.80\n\nanchors = np.array([(2, 2), (2, 6), (6, 2), (6, 6), (4, 4)])\n\nfor p, (r, c) in enumerate(points):\n    n, d = adj[p], diag[p]\n    degree = len(n)\n    edge_distance = min(r, c, 8 - r, 8 - c)\n\n    # Six final feature maps.\n    base, escape_plus, escape_minus = O[p], E[p], A[p]\n    risk_one, risk_two, eye = B[p], C[p], D[p]\n\n    static_position = (-0.65, -0.15, 0.10, 0.10, 0.10)[edge_distance]\n    anchor_distance = np.min(\n        np.sum((anchors - np.array([r, c])) ** 2, axis=1)\n    )\n    opening_position = (\n        (-2.50, -1.20, 0.35, 0.25, 0.25)[edge_distance]\n        - 0.12 * anchor_distance\n    )\n\n    b[base] = (\n        OFFSET + 2.20 + static_position\n        - TERRITORY_GAIN * TERRITORY_THRESHOLD\n    )\n    add(W, base, EARLY, opening_position)\n    add(W, base, C, TERRITORY_GAIN * territory_kernel[p])\n\n    # Local shape and support, with a preference against unsupported contact.\n    add(W, base, O[n], 0.65)\n    add(W, base, E[n], -0.35)\n    add(W, base, O[d], 0.30)\n    add(W, base, E[d], 0.05)\n    add(W, base, O[two[p]], 0.14)\n    add(W, base, E[two[p]], 0.14)\n    add(W, base, O[knight[p]], 0.08)\n    add(W, base, E[knight[p]], 0.07)\n    add(W, base, O, 0.06 * influence[p])\n    add(W, base, E, -0.06 * influence[p])\n    add(W, base, B[n], CAPTURE_GAIN)\n\n    # Let a = adjacent unsafe friends, s = adjacent safe friends, and\n    # e = immediate empty neighbors.\n    # z = e + 2*s - 2 + capture_relief.\n    # The difference of these ReLUs rewards actual adjacent endangered\n    # friends only when the escape has room or joins a safe string.\n    for destination in (escape_plus, escape_minus):\n        b[destination] = degree - 2.0\n        add(W, destination, O[n], 1.0)\n        add(W, destination, E[n], -1.0)\n        add(W, destination, A[n], -2.0)\n        add(W, destination, B[n], 4.0)\n    add(W, escape_plus, A[n], 0.25)\n\n    # Conservative post-move freedom, discounting possible duplicate\n    # liberties obtained through multiple friendly contacts.\n    for destination, required in [(risk_one, 2.0), (risk_two, 3.0)]:\n        b[destination] = required - degree\n        add(W, destination, O[n], 1.0 - FRIEND_LIBERTY_CREDIT)\n        add(W, destination, E[n], 1.0)\n        add(W, destination, A[n], FRIEND_LIBERTY_CREDIT)\n        add(W, destination, B[n], -4.0)\n\n    b[eye] = 1.0 - degree\n    add(W, eye, O[n])\n    add(W, eye, D[p], -1.0)\n\n# ----------------------------------------------------------------------\n# Output: additive policy logits. Pass stays unattractive while useful\n# moves remain, especially when estimated to be behind.\n# ----------------------------------------------------------------------\nW, b = params[\"out.W\"], params[\"out.b\"]\n\nfor p, (r, c) in enumerate(points):\n    action = mv(r, c)\n    W[O[p], action] = 1.0\n    W[E[p], action] = 4.0 * SAVE_GAIN\n    W[A[p], action] = -4.0 * SAVE_GAIN\n    W[B[p], action] = -4.50\n    W[C[p], action] = -1.30\n    W[D[p], action] = -11.0\n    b[action] = -OFFSET\n\nb[PASS] = -5.50\nW[LATE, PASS] = 0.12\nW[DEFICIT, PASS] = -0.10\n\n# Validate only the weights and tensor shapes; never evaluate the network.\nassert sum(array.size for array in params.values()) == 1_480_274\nfor name, array in params.items():\n    assert array.dtype == np.float32\n    assert np.isfinite(array).all()\n    assert tuple(load(name).shape) == tuple(array.shape), name\n\nfor name, array in params.items():\n    save(name, array)\n\nprint(\"Saved all 14 tensors: 1,480,274 float32 values.\")\nprint(\"Design: conservative string-liberty propagation, captures, viable saves,\")\nprint(\"self-atari avoidance, eye preservation, territorial influence, and komi-aware passing.\")\nprint(\"No training, network evaluation, or game search was performed.\")\n```"}}
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