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| """Hypervector operations for exploratory PAN state representations.""" | |
| from __future__ import annotations | |
| import math | |
| from collections.abc import Sequence | |
| BASE_AXES: tuple[tuple[str, str, str, str], ...] = ( | |
| ( | |
| "intrinsic_time", | |
| "t", | |
| "Intrinsic simulation time; never wall-clock time.", | |
| "R+", | |
| ), | |
| ( | |
| "excitability", | |
| "E", | |
| "Normalized membrane excitability proxy.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "plasticity", | |
| "P", | |
| "Current exploratory plasticity activity.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "information_proxy", | |
| "I*", | |
| "Local surprise proxy; not a validated PID quantity.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "health", | |
| "H", | |
| "Exploratory health / survival state.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "neuromodulation", | |
| "N", | |
| "Bounded exploratory neuromodulatory state.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "energy", | |
| "ATP*", | |
| "Normalized exploratory energy budget.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "embodied_position", | |
| "R", | |
| "Scalar projection of the assigned topology coordinate.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "consolidation", | |
| "C", | |
| "Activity-history consolidation proxy.", | |
| "[0,1]", | |
| ), | |
| ( | |
| "network_coupling", | |
| "S", | |
| "Normalized local coupling / degree proxy.", | |
| "[0,1]", | |
| ), | |
| ) | |
| def axis_schema(dimensions: int) -> list[dict[str, object]]: | |
| """Return neutral metadata for a PAN hyperstate with D >= 5.""" | |
| if not 5 <= dimensions <= 32: | |
| raise ValueError("PAN hypervector dimensions must be between 5 and 32") | |
| axes: list[dict[str, object]] = [] | |
| for index in range(dimensions): | |
| if index < len(BASE_AXES): | |
| name, symbol, meaning, value_range = BASE_AXES[index] | |
| else: | |
| name = f"latent_{index + 1}" | |
| symbol = f"L{index + 1}" | |
| meaning = "Unassigned exploratory latent axis." | |
| value_range = "R" | |
| axes.append( | |
| { | |
| "dimension": index + 1, | |
| "name": name, | |
| "symbol": symbol, | |
| "meaning": meaning, | |
| "range": value_range, | |
| } | |
| ) | |
| return axes | |
| def _same_width(a: Sequence[float], b: Sequence[float]) -> None: | |
| if len(a) != len(b): | |
| raise ValueError("hypervectors must have equal dimensions") | |
| def bind( | |
| a: Sequence[float], | |
| b: Sequence[float], | |
| *, | |
| method: str = "hadamard", | |
| ) -> list[float]: | |
| """Bind two equal-width hypervectors without scientific interpretation.""" | |
| _same_width(a, b) | |
| if method == "hadamard": | |
| return [float(left) * float(right) for left, right in zip(a, b)] | |
| if method == "circular_convolution": | |
| width = len(a) | |
| if width == 0: | |
| return [] | |
| return [ | |
| sum( | |
| float(a[index]) * float(b[(out - index) % width]) | |
| for index in range(width) | |
| ) | |
| for out in range(width) | |
| ] | |
| raise ValueError(f"unknown PAN binding method: {method}") | |
| def bundle(vectors: Sequence[Sequence[float]]) -> list[float]: | |
| """Bundle hypervectors by the sign of their component-wise sum.""" | |
| if not vectors: | |
| return [] | |
| width = len(vectors[0]) | |
| if any(len(vector) != width for vector in vectors): | |
| raise ValueError("all hypervectors must have equal dimensions") | |
| sums = [sum(float(vector[index]) for vector in vectors) for index in range(width)] | |
| return [0.0 if value == 0.0 else math.copysign(1.0, value) for value in sums] | |