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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
30 lines
584 B
Python
30 lines
584 B
Python
from ._version import __version__
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from .optimizer import Optimizer
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from .particle import Particle
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from .plugins import (
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BasePlugin,
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InitializationPlugin,
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EvaluationPlugin,
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MovementPlugin,
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ConvergencePlugin,
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RefinementPlugin,
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PluginMetadata,
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SwarmState,
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available_plugins,
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)
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__all__ = [
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"Optimizer",
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"Particle",
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"__version__",
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"BasePlugin",
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"InitializationPlugin",
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"EvaluationPlugin",
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"MovementPlugin",
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"ConvergencePlugin",
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"RefinementPlugin",
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"PluginMetadata",
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"SwarmState",
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"available_plugins",
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]
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