mirror of
https://github.com/jung-geun/PSO.git
synced 2026-09-20 14:11:48 +09:00
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
68 lines
1.3 KiB
TOML
68 lines
1.3 KiB
TOML
[build-system]
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requires = ["setuptools>=77", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "pso2keras"
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version = "4.0.0"
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description = "Particle Swarm Optimization for PyTorch models"
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readme = "README.md"
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requires-python = ">=3.10,<3.12"
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license = "MIT"
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license-files = ["LICENSE"]
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authors = [
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{ name = "pieroot", email = "jgbong0306@gmail.com" }
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]
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keywords = [
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"pso",
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"pytorch",
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"torch",
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"optimization",
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"particle swarm optimization",
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"pso2keras",
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]
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classifiers = [
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"Programming Language :: Python :: 3 :: Only",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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]
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dependencies = [
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"torch>=2.13,<3",
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"numpy<2",
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"scikit-learn",
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"tqdm",
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"tensorboard",
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"tomli>=2; python_version < '3.11'",
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]
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[project.urls]
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Homepage = "https://github.com/jung-geun/PSO"
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Repository = "https://github.com/jung-geun/PSO"
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[project.optional-dependencies]
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examples = [
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"pandas",
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"ucimlrepo",
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"torchvision>=0.28,<1",
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"matplotlib>=3.8,<4",
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]
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detection = [
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"ultralytics==8.4.142",
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"ensemble-boxes==1.0.9",
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]
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[dependency-groups]
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dev = [
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"pytest>=9,<10",
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"build>=1.3,<2",
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"twine>=6,<8",
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]
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["pso*"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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python_files = ["test_*.py"]
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