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생산성 및 협업 · SKILL.md

minibatch_ot_coupling

minibatch_ot_coupling

Compute deterministic squared-cost minibatch optimal transport pairings for OT conditional flow matching recovery runs.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: minibatch_ot_coupling description: Compute deterministic squared-cost minibatch optimal transport pairings for OT conditional flow matching recovery runs.

Minibatch OT Coupling

Use this skill when implementing OT-CFM or validating whether a recovery experiment pairs source and target samples according to a minibatch optimal transport plan. Do not use the exact enumerator for large minibatches.

Inputs

  • Equal-sized source and target minibatches of numeric vectors.
  • Optional comparison pairing, such as an independent or shuffled permutation.

Outputs

  • Minimum-cost permutation mapping each source index to a target index.
  • Squared Euclidean transport cost for the assignment.
  • Pair records that downstream CFM objective code can consume.

Workflow

  1. Build the squared Euclidean cost matrix.
  2. Solve a one-to-one minimum assignment. The bundled script enumerates permutations and is intended for small deterministic tests.
  3. Return paired source/target indices and costs.
  4. Compare against independent pairings when validating the OT-CFM mechanism.

Validation

Run python tests/test_minibatch_ot.py or the Distiller skill-tree validator with tests enabled.

Limitations

The bundled solver is factorial in batch size and should be replaced with a Hungarian, Sinkhorn, or POT implementation for real-scale training.

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