perf(objective): compute linear_predictor distances with in-place NumPy operations

[seonghobae]

Summary

Clean recreation of #183 on the current main. The original branch's history was corrupted by a bad base merge that would have deleted several mainline modules (classification.rs, detect.rs, dif.rs, equating.rs, exposure.rs, gtheory.rs, utility.rs, classification.py, …) — verified with git merge-tree and documented on #183 before closing it.

Same two-file delta as the original Bolt optimization:

• python/fast_mlsirm/objective.py — build the linear_predictor pairwise distance with in-place NumPy operations (+=, np.maximum(..., out=...), np.sqrt(..., out=...)), avoiding several N×J intermediate allocations per objective evaluation. Numerical results are unchanged; the NumPy path stays the parity reference for the Rust core.
• .jules/bolt.md — records the in-place-operations learning per the journal convention.

Verification

• tests/test_objective.py + tests/test_math.py: 16 passed, 5 skipped (no Rust core locally; parity tests skip by design).
• The changed block matches the algebra of the previous implementation term-for-term (max(d², 0) + eps before the square root, identical clamping semantics).

Developer experience: removes redundant allocations from the hottest NumPy-path loop without touching the numeric contract shared with mlsirm-core.

User experience: identical outputs; lower peak memory on large N×J problems when running on the NumPy fallback backend.

🤖 Generated with Claude Code

https://claude.ai/code/session_01ERVynrYEjd2TwYvusVpA9f

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📥 Commits

Reviewing files that changed from the base of the PR and between 52677b5 and c397d6c.

📒 Files selected for processing (2)

• .jules/bolt.md
• python/fast_mlsirm/objective.py

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[seonghobae]

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@seonghobae: I’ll review the changes in #345.

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