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https://github.com/jung-geun/PSO.git
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jung-geun
813433000a
Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery. Constraint: Preserve one-shot official-test sealing and auditable research artifacts Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local Confidence: high Scope-risk: broad Not-tested: Production CUDA run on pieroot-server
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
from typing import Any, Literal
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import torch
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from .plugins import FitContext, InitializationPlugin
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class Particle:
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"""
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Particle Swarm Optimization particle (engine-owned state, plugin-driven movement).
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Each particle owns position, velocity, personal best score, personal best weights,
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and monitor state on device.
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"""
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def __init__(
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self,
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index: int,
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base_vector: torch.Tensor,
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context: FitContext,
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init_plugin: InitializationPlugin,
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negative: bool = False,
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):
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self.index = index
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self.negative = negative
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pos, vel = init_plugin.initialize(index, base_vector, context)
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self.position: torch.Tensor = pos
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self.velocity: torch.Tensor = vel
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self.personal_best_score: tuple[float, float, float] | None = None
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self.personal_best_weights: torch.Tensor | None = None
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self.personal_best_monitor_value: float | None = None
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def reset(
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self,
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base_vector: torch.Tensor,
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context: FitContext,
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init_plugin: InitializationPlugin,
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) -> None:
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"""
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Resets particle position, velocity, and personal best state using the initialization plugin.
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"""
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pos, vel = init_plugin.initialize(self.index, base_vector, context)
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self.position = pos
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self.velocity = vel
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self.personal_best_score = None
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self.personal_best_weights = None
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self.personal_best_monitor_value = None
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def apply_boundary_strategy(
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self,
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particle_min: float | None,
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particle_max: float | None,
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boundary_strategy: str = "clip",
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) -> None:
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"""
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Applies boundary constraints (clip or reflect) to particle position and velocity.
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"""
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if particle_min is not None and particle_max is not None:
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if boundary_strategy == "reflect":
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span = float(particle_max - particle_min)
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shift = self.position - particle_min
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q = torch.floor(shift / span).to(torch.int64)
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m = torch.remainder(shift, 2.0 * span)
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bounded_pos = torch.where(
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m <= span,
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particle_min + m,
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particle_min + (2.0 * span - m),
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)
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self.velocity = torch.where(q % 2 != 0, -self.velocity, self.velocity)
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self.position = bounded_pos
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else:
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self.position = torch.clamp(
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self.position, particle_min, particle_max
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)
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