fix(lifelong): avoid indexing beyond dataset splits

[ali-raza-7]

Summary

Fix the lifelong learning paradigm to iterate over the actual number of dataset splits returned by split_dataset() instead of relying on the rounds value.

Changes

• Replace range(rounds + 1) with range(len(dataset_files)).
• Replace evaluation loop bounds based on rounds with len(dataset_files).
• Apply the same correction to both affected lifelong-learning execution paths.

Validation

• python3 -m py_compile core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py — PASS
• git diff --check — PASS
• Dataset splitting regression test with times=2 — PASS
• PR diff contains only lifelong_learning.py.

Rationale

The dataset splitting logic determines the actual number of dataset files returned by split_dataset(). Using rounds for indexing can result in accessing a dataset split that does not exist.

Iterating over len(dataset_files) keeps the lifelong-learning loops aligned with the dataset splits actually returned by the dataset splitter.

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This pull-request has been approved by: ali-raza-7
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[ali-raza-7]

Validation

I verified the fix locally with the following checks:

• python3 -m py_compile core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py — PASS
• git diff --check — PASS
• Regression test for Dataset.split_dataset(times=2) — PASS
• Verified that the actual diff against origin/main contains only lifelong_learning.py with 4 additions and 4 deletions.

The fix updates the lifelong-learning loops to use len(dataset_files) instead of rounds, ensuring iteration stays within the dataset splits the dataset splitter actually returns.

[ali-raza-7]

/assign @jaypume