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ad91e86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """Learned row-conditional transition head for N4 (Equations 10 and 19)."""
from __future__ import annotations
import math
from bisect import bisect_right
from collections import defaultdict
import numpy as np
import torch
from torch import nn
class TransitionHead(nn.Module):
"""Shares the belief backbone's query/candidate embeddings; has separate parameters."""
def __init__(self, hidden_dim: int = 128) -> None:
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(3 * hidden_dim + 4, hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, 1),
)
def forward(self, context: torch.Tensor, candidates: torch.Tensor,
horizon_s: torch.Tensor, candidate_mask: torch.Tensor) -> torch.Tensor:
batch, states, hidden = candidates.shape
if context.shape != (batch, hidden) or horizon_s.shape != (batch,):
raise ValueError("transition context or horizon has incorrect shape")
if torch.any(horizon_s < 0):
raise ValueError("transition horizon must be nonnegative")
phase = horizon_s.float() * (2 * math.pi / 86400.0)
time = torch.stack((torch.log1p(horizon_s.float()),
torch.sin(phase), torch.cos(phase),
horizon_s.float() / 86400.0), dim=-1)
features = torch.cat((
context[:, None, None, :].expand(-1, states, states, -1),
candidates[:, :, None, :].expand(-1, -1, states, -1),
candidates[:, None, :, :].expand(-1, states, -1, -1),
time[:, None, None, :].expand(-1, states, states, -1),
), dim=-1)
logits = self.mlp(features).squeeze(-1)
logits = logits.masked_fill(~candidate_mask[:, None, :].bool(), -1e4)
probabilities = torch.softmax(logits, dim=-1)
return probabilities * candidate_mask[:, :, None].float()
def transition_nll(kernel: torch.Tensor, source_index: torch.Tensor,
destination_index: torch.Tensor) -> torch.Tensor:
rows = kernel[torch.arange(len(kernel), device=kernel.device), source_index.long()]
selected = rows.gather(1, destination_index.long()[:, None]).squeeze(1)
return -selected.clamp_min(1e-9).log().mean()
def chronological_pairs(*, instance_ids: np.ndarray, world_ids: np.ndarray,
times_s: np.ndarray, states: np.ndarray,
max_horizon_s: float) -> list[tuple[int, int, float, int, int]]:
"""Create train-only H<=ta, s(ta), delta, s(ta+delta) labels."""
groups: dict[tuple[str, int], list[int]] = defaultdict(list)
for index, (instance, world) in enumerate(zip(instance_ids, world_ids, strict=True)):
groups[(str(instance), int(world))].append(index)
pairs = []
for indices in groups.values():
ordered = sorted(indices, key=lambda i: float(times_s[i]))
for source, destination in zip(ordered, ordered[1:], strict=False):
horizon = float(times_s[destination] - times_s[source])
if 0 < horizon <= max_horizon_s:
pairs.append((source, destination, horizon,
int(states[source]), int(states[destination])))
return pairs
def event_horizon_pairs(*, instance_ids: np.ndarray, world_ids: np.ndarray,
query_times_s: np.ndarray, query_states: np.ndarray,
events: list[dict], horizons_s: tuple[float, ...]
) -> list[tuple[int, float, float, int, int]]:
"""Sample short stationary and event-crossing tuples from one split's causal snapshots."""
if not horizons_s or any(h <= 0 for h in horizons_s):
raise ValueError("horizons must be positive")
groups: dict[tuple[str, int], list[int]] = defaultdict(list)
event_groups: dict[tuple[str, int], list[dict]] = defaultdict(list)
for index, (instance, world) in enumerate(zip(instance_ids, world_ids, strict=True)):
groups[(str(instance), int(world))].append(index)
for event in events:
event_groups[(str(event["instance_uuid"]), int(event.get("world_id", 0)))].append(event)
pairs: list[tuple[int, float, float, int, int]] = []
for (instance, world), indices in groups.items():
ordered = sorted(indices, key=lambda i: float(query_times_s[i]))
times = [float(query_times_s[i]) for i in ordered]
relevant = sorted(event_groups.get((instance, world), []),
key=lambda event: float(event["event_time_s"]))
event_times = [float(event["event_time_s"]) for event in relevant]
for position, source in enumerate(ordered):
horizon = float(horizons_s[position % len(horizons_s)])
anchor = times[position]
if anchor + horizon > times[-1]:
continue
if bisect_right(event_times, anchor + horizon) == bisect_right(event_times, anchor):
state = int(query_states[source])
pairs.append((source, anchor, horizon, state, state))
for event_index, event in enumerate(relevant):
event_time = float(event["event_time_s"])
for horizon in horizons_s:
anchor = event_time - horizon / 2.0
future = anchor + horizon
if anchor < times[0] or future > times[-1]:
continue
if (event_index > 0 and event_times[event_index - 1] >= anchor) or (
event_index + 1 < len(event_times) and event_times[event_index + 1] <= future
):
continue
source_position = bisect_right(times, anchor) - 1
if source_position < 0:
continue
pairs.append((
ordered[source_position], anchor, float(horizon),
int(event["source_state_id"]), int(event["destination_state_id"]),
))
return pairs
class NeuralTransition:
"""Inference adapter that never receives transition labels or mobility tags."""
dynamic = True
def __init__(self, belief_model: nn.Module, head: TransitionHead,
packed_batch: dict[str, torch.Tensor]) -> None:
self.model = belief_model.eval()
self.head = head.eval()
self.batch = {key: value.clone() for key, value in packed_batch.items()}
if self.batch["candidate_state_ids"].shape[0] != 1:
raise ValueError("N4 transition adapter expects one active episode")
self.elapsed_s = 0.0
def add_candidate(self, state_id: int) -> None:
present = self.batch["candidate_state_ids"][0].tolist()
if state_id in present:
return
self.batch["candidate_state_ids"] = torch.cat((
self.batch["candidate_state_ids"],
torch.tensor([[state_id]], dtype=self.batch["candidate_state_ids"].dtype),
), dim=1)
self.batch["candidate_mask"] = torch.cat((
self.batch["candidate_mask"],
torch.ones((1, 1), dtype=self.batch["candidate_mask"].dtype),
), dim=1)
def matrix(self, states: list[int], elapsed_s: float) -> np.ndarray:
if elapsed_s < 0:
raise ValueError("time cannot go backwards")
if elapsed_s == 0:
return np.eye(len(states), dtype=float)
device = next(self.model.parameters()).device
batch = {key: value.to(device) for key, value in self.batch.items()}
batch["query_time_days"] = batch["query_time_days"] + self.elapsed_s / 86400.0
batch["elapsed_since_last_positive_days"] = (
batch["elapsed_since_last_positive_days"] + self.elapsed_s / 86400.0
)
day_phase = 2 * math.pi * batch["query_time_days"]
batch["query_time_of_day_sin_cos"] = torch.stack(
(day_phase.sin(), day_phase.cos()), dim=-1
)
with torch.inference_mode():
context, candidates = self.model.backbone(batch)
kernel = self.head(
context, candidates, torch.tensor([elapsed_s], device=device),
batch["candidate_mask"],
)[0]
candidate_ids = batch["candidate_state_ids"][0].tolist()
index = [candidate_ids.index(state) for state in states]
return kernel[index][:, index].cpu().numpy()
def advance_clock(self, elapsed_s: float) -> None:
if elapsed_s < 0:
raise ValueError("time cannot go backwards")
self.elapsed_s += elapsed_s
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