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Created February 7, 2026 22:32
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solution-draft-log-pr-1770503556105.txt
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# Solve.mjs Log - 2026-02-07T22:18:51.055Z
[2026-02-07T22:18:51.056Z] [INFO] 📁 Log file: /home/hive/solve-2026-02-07T22-18-51-055Z.log
[2026-02-07T22:18:51.057Z] [INFO] (All output will be logged here)
[2026-02-07T22:18:51.567Z] [INFO]
[2026-02-07T22:18:51.569Z] [INFO] 🚀 solve v1.16.0
[2026-02-07T22:18:51.569Z] [INFO] 🔧 Raw command executed:
[2026-02-07T22:18:51.570Z] [INFO] /home/hive/.nvm/versions/node/v20.20.0/bin/node /home/hive/.bun/bin/solve https://github.com/konard/tsp/issues/65 --model opus --attach-logs --verbose --no-tool-check --auto-resume-on-limit-reset --tokens-budget-stats
[2026-02-07T22:18:51.570Z] [INFO]
[2026-02-07T22:18:51.582Z] [INFO]
[2026-02-07T22:18:51.583Z] [WARNING] ⚠️ SECURITY WARNING: --attach-logs is ENABLED
[2026-02-07T22:18:51.583Z] [INFO]
[2026-02-07T22:18:51.584Z] [INFO] This option will upload the complete solution draft log file to the Pull Request.
[2026-02-07T22:18:51.584Z] [INFO] The log may contain sensitive information such as:
[2026-02-07T22:18:51.584Z] [INFO] • API keys, tokens, or secrets
[2026-02-07T22:18:51.584Z] [INFO] • File paths and directory structures
[2026-02-07T22:18:51.584Z] [INFO] • Command outputs and error messages
[2026-02-07T22:18:51.585Z] [INFO] • Internal system information
[2026-02-07T22:18:51.585Z] [INFO]
[2026-02-07T22:18:51.585Z] [INFO] ⚠️ DO NOT use this option with public repositories or if the log
[2026-02-07T22:18:51.585Z] [INFO] might contain sensitive data that should not be shared publicly.
[2026-02-07T22:18:51.585Z] [INFO]
[2026-02-07T22:18:51.585Z] [INFO] Continuing in 5 seconds... (Press Ctrl+C to abort)
[2026-02-07T22:18:51.586Z] [INFO]
[2026-02-07T22:18:56.591Z] [INFO]
[2026-02-07T22:18:56.610Z] [INFO] 💾 Disk space check: 57944MB available (2048MB required) ✅
[2026-02-07T22:18:56.611Z] [INFO] 🧠 Memory check: 10674MB available, swap: 4095MB (0MB used), total: 14769MB (256MB required) ✅
[2026-02-07T22:18:56.624Z] [INFO] ⏩ Skipping tool connection validation (dry-run mode or skip-tool-connection-check enabled)
[2026-02-07T22:18:56.625Z] [INFO] ⏩ Skipping GitHub authentication check (dry-run mode or skip-tool-connection-check enabled)
[2026-02-07T22:18:56.625Z] [INFO] 📋 URL validation:
[2026-02-07T22:18:56.625Z] [INFO] Input URL: https://github.com/konard/tsp/issues/65
[2026-02-07T22:18:56.625Z] [INFO] Is Issue URL: true
[2026-02-07T22:18:56.626Z] [INFO] Is PR URL: false
[2026-02-07T22:18:56.626Z] [INFO] 🔍 Checking repository access for auto-fork...
[2026-02-07T22:18:57.410Z] [INFO] Repository visibility: public
[2026-02-07T22:18:57.410Z] [INFO] ✅ Auto-fork: Write access detected to public repository, working directly on repository
[2026-02-07T22:18:57.411Z] [INFO] 🔍 Checking repository write permissions...
[2026-02-07T22:18:57.776Z] [INFO] ✅ Repository write access: Confirmed
[2026-02-07T22:18:58.099Z] [INFO] Repository visibility: public
[2026-02-07T22:18:58.100Z] [INFO] Auto-cleanup default: false (repository is public)
[2026-02-07T22:18:58.101Z] [INFO] 🔍 Auto-continue enabled: Checking for existing PRs for issue #65...
[2026-02-07T22:18:58.101Z] [INFO] 🔍 Checking for existing branches in konard/tsp...
[2026-02-07T22:18:58.796Z] [INFO] 📝 No existing PRs found for issue #65 - creating new PR
[2026-02-07T22:18:58.797Z] [INFO] 📝 Issue mode: Working with issue #65
[2026-02-07T22:18:58.798Z] [INFO]
Creating temporary directory: /tmp/gh-issue-solver-1770502738798
[2026-02-07T22:18:58.801Z] [INFO]
📥 Cloning repository: konard/tsp
[2026-02-07T22:18:59.665Z] [INFO] ✅ Cloned to: /tmp/gh-issue-solver-1770502738798
[2026-02-07T22:18:59.813Z] [INFO]
📌 Default branch: main
[2026-02-07T22:18:59.868Z] [INFO]
🌿 Creating branch: issue-65-37a562943dfc from main (default)
[2026-02-07T22:18:59.915Z] [INFO] 🔍 Verifying: Branch creation...
[2026-02-07T22:18:59.950Z] [INFO] ✅ Branch created: issue-65-37a562943dfc
[2026-02-07T22:18:59.951Z] [INFO] ✅ Current branch: issue-65-37a562943dfc
[2026-02-07T22:18:59.951Z] [INFO] Branch operation: Create new branch
[2026-02-07T22:18:59.952Z] [INFO] Branch verification: Matches expected
[2026-02-07T22:18:59.955Z] [INFO]
🚀 Auto PR creation: ENABLED
[2026-02-07T22:18:59.956Z] [INFO] Creating: Initial commit and draft PR...
[2026-02-07T22:18:59.956Z] [INFO]
[2026-02-07T22:18:59.995Z] [INFO] Using CLAUDE.md mode (--claude-file=true, --gitkeep-file=false, --auto-gitkeep-file=true)
[2026-02-07T22:18:59.996Z] [INFO] 📝 Creating: CLAUDE.md with task details
[2026-02-07T22:18:59.996Z] [INFO] Issue URL from argv['issue-url']: https://github.com/konard/tsp/issues/65
[2026-02-07T22:18:59.997Z] [INFO] Issue URL from argv._[0]: undefined
[2026-02-07T22:18:59.997Z] [INFO] Final issue URL: https://github.com/konard/tsp/issues/65
[2026-02-07T22:18:59.997Z] [INFO] ✅ File created: CLAUDE.md
[2026-02-07T22:18:59.997Z] [INFO] 📦 Adding file: To git staging
[2026-02-07T22:19:00.099Z] [INFO] Git status after add: A CLAUDE.md
[2026-02-07T22:19:00.099Z] [INFO] 📝 Creating commit: With CLAUDE.md file
[2026-02-07T22:19:00.150Z] [INFO] ✅ Commit created: Successfully with CLAUDE.md
[2026-02-07T22:19:00.151Z] [INFO] Commit output: [issue-65-37a562943dfc 27bf0a7] Initial commit with task details
1 file changed, 5 insertions(+)
create mode 100644 CLAUDE.md
[2026-02-07T22:19:00.191Z] [INFO] Commit hash: 27bf0a7...
[2026-02-07T22:19:00.235Z] [INFO] Latest commit: 27bf0a7 Initial commit with task details
[2026-02-07T22:19:00.274Z] [INFO] Git status: clean
[2026-02-07T22:19:00.313Z] [INFO] Remotes: origin https://github.com/konard/tsp.git (fetch)
[2026-02-07T22:19:00.349Z] [INFO] Branch info: * issue-65-37a562943dfc 27bf0a7 [origin/main: ahead 1] Initial commit with task details
main f8de435 [origin/main] Update README screenshot [skip ci]
[2026-02-07T22:19:00.349Z] [INFO] 📤 Pushing branch: To remote repository...
[2026-02-07T22:19:00.350Z] [INFO] Push command: git push -u origin issue-65-37a562943dfc
[2026-02-07T22:19:01.126Z] [INFO] Push exit code: 0
[2026-02-07T22:19:01.127Z] [INFO] Push output: remote:
remote: Create a pull request for 'issue-65-37a562943dfc' on GitHub by visiting:
remote: https://github.com/konard/tsp/pull/new/issue-65-37a562943dfc
remote:
To https://github.com/konard/tsp.git
* [new branch] issue-65-37a562943dfc -> issue-65-37a562943dfc
branch 'issue-65-37a562943dfc' set up to track 'origin/issue-65-37a562943dfc'.
[2026-02-07T22:19:01.127Z] [INFO] ✅ Branch pushed: Successfully to remote
[2026-02-07T22:19:01.127Z] [INFO] Push output: remote:
remote: Create a pull request for 'issue-65-37a562943dfc' on GitHub by visiting:
remote: https://github.com/konard/tsp/pull/new/issue-65-37a562943dfc
remote:
To https://github.com/konard/tsp.git
* [new branch] issue-65-37a562943dfc -> issue-65-37a562943dfc
branch 'issue-65-37a562943dfc' set up to track 'origin/issue-65-37a562943dfc'.
[2026-02-07T22:19:01.127Z] [INFO] Waiting for GitHub to sync...
[2026-02-07T22:19:03.518Z] [INFO] Compare API check: 1 commit(s) ahead of main
[2026-02-07T22:19:03.518Z] [INFO] GitHub compare API ready: 1 commit(s) found
[2026-02-07T22:19:03.817Z] [INFO] Branch verified on GitHub: issue-65-37a562943dfc
[2026-02-07T22:19:04.197Z] [INFO] Remote commit SHA: 27bf0a7...
[2026-02-07T22:19:04.197Z] [INFO] 📋 Getting issue: Title from GitHub...
[2026-02-07T22:19:04.487Z] [INFO] Issue title: "Implement U-fork fractal algorithm"
[2026-02-07T22:19:04.488Z] [INFO] 👤 Getting user: Current GitHub account...
[2026-02-07T22:19:04.760Z] [INFO] Current user: konard
[2026-02-07T22:19:04.999Z] [INFO] User has collaborator access
[2026-02-07T22:19:04.999Z] [INFO] User has collaborator access
[2026-02-07T22:19:04.999Z] [INFO] 🔄 Fetching: Latest main branch...
[2026-02-07T22:19:05.306Z] [INFO] ✅ Base updated: Fetched latest main
[2026-02-07T22:19:05.307Z] [INFO] 🔍 Checking: Commits between branches...
[2026-02-07T22:19:05.342Z] [INFO] Commits ahead of origin/main: 1
[2026-02-07T22:19:05.343Z] [INFO] ✅ Commits found: 1 commit(s) ahead
[2026-02-07T22:19:05.343Z] [INFO] 🔀 Creating PR: Draft pull request...
[2026-02-07T22:19:05.343Z] [INFO] 🎯 Target branch: main (default)
[2026-02-07T22:19:05.343Z] [INFO] PR Title: [WIP] Implement U-fork fractal algorithm
[2026-02-07T22:19:05.343Z] [INFO] Base branch: main
[2026-02-07T22:19:05.343Z] [INFO] Head branch: issue-65-37a562943dfc
[2026-02-07T22:19:05.344Z] [INFO] Assignee: konard
[2026-02-07T22:19:05.344Z] [INFO] PR Body:
## 🤖 AI-Powered Solution Draft
This pull request is being automatically generated to solve issue #65.
### 📋 Issue Reference
Fixes #65
### 🚧 Status
**Work in Progress** - The AI assistant is currently analyzing and implementing the solution draft.
### 📝 Implementation Details
_Details will be added as the solution draft is developed..._
---
*This PR was created automatically by the AI issue solver*
[2026-02-07T22:19:05.345Z] [INFO] Command: cd "/tmp/gh-issue-solver-1770502738798" && gh pr create --draft --title "$(cat '/tmp/pr-title-1770502745345.txt')" --body-file "/tmp/pr-body-1770502745345.md" --base main --head issue-65-37a562943dfc --assignee konard
[2026-02-07T22:19:07.873Z] [INFO] 🔍 Verifying: PR creation...
[2026-02-07T22:19:08.172Z] [INFO] ✅ Verification: PR exists on GitHub
[2026-02-07T22:19:08.173Z] [INFO] ✅ PR created: #68
[2026-02-07T22:19:08.173Z] [INFO] 📍 PR URL: https://github.com/konard/tsp/pull/68
[2026-02-07T22:19:08.173Z] [INFO] 👤 Assigned to: konard
[2026-02-07T22:19:08.173Z] [INFO] 🔗 Linking: Issue #65 to PR #68...
[2026-02-07T22:19:08.469Z] [INFO] Issue node ID: I_kwDORDRIBs7pIzMo
[2026-02-07T22:19:08.780Z] [INFO] PR node ID: PR_kwDORDRIBs7CO0O1
[2026-02-07T22:19:09.101Z] [INFO]
[2026-02-07T22:19:09.101Z] [WARNING] ⚠️ ISSUE LINK MISSING: PR not linked to issue
[2026-02-07T22:19:09.101Z] [INFO]
[2026-02-07T22:19:09.102Z] [WARNING] The PR wasn't linked to issue #65
[2026-02-07T22:19:09.102Z] [WARNING] Expected: "Fixes #65" in PR body
[2026-02-07T22:19:09.102Z] [INFO]
[2026-02-07T22:19:09.102Z] [WARNING] To fix manually:
[2026-02-07T22:19:09.102Z] [WARNING] 1. Edit the PR description at: https://github.com/konard/tsp/pull/68
[2026-02-07T22:19:09.102Z] [WARNING] 2. Ensure it contains: Fixes #65
[2026-02-07T22:19:09.102Z] [INFO]
[2026-02-07T22:19:14.367Z] [INFO] 👤 Current user: konard
[2026-02-07T22:19:14.368Z] [INFO]
📊 Comment counting conditions:
[2026-02-07T22:19:14.368Z] [INFO] prNumber: 68
[2026-02-07T22:19:14.368Z] [INFO] branchName: issue-65-37a562943dfc
[2026-02-07T22:19:14.368Z] [INFO] isContinueMode: false
[2026-02-07T22:19:14.368Z] [INFO] Will count comments: true
[2026-02-07T22:19:14.369Z] [INFO] 💬 Counting comments: Checking for new comments since last commit...
[2026-02-07T22:19:14.369Z] [INFO] PR #68 on branch: issue-65-37a562943dfc
[2026-02-07T22:19:14.369Z] [INFO] Owner/Repo: konard/tsp
[2026-02-07T22:19:14.748Z] [INFO] 📅 Last commit time (from API): 2026-02-07T22:19:00.000Z
[2026-02-07T22:19:15.663Z] [INFO] 💬 New PR comments: 0
[2026-02-07T22:19:15.663Z] [INFO] 💬 New PR review comments: 0
[2026-02-07T22:19:15.664Z] [INFO] 💬 New issue comments: 0
[2026-02-07T22:19:15.664Z] [INFO] Total new comments: 0
[2026-02-07T22:19:15.664Z] [INFO] Comment lines to add: No (saving tokens)
[2026-02-07T22:19:15.664Z] [INFO] PR review comments fetched: 0
[2026-02-07T22:19:15.665Z] [INFO] PR conversation comments fetched: 0
[2026-02-07T22:19:15.665Z] [INFO] Total PR comments checked: 0
[2026-02-07T22:19:18.047Z] [INFO] Feedback info will be added to prompt:
[2026-02-07T22:19:18.048Z] [INFO] - Pull request description was edited after last commit
[2026-02-07T22:19:18.048Z] [INFO] 📅 Getting timestamps: From GitHub servers...
[2026-02-07T22:19:18.363Z] [INFO] 📝 Issue updated: 2026-02-07T22:16:14.000Z
[2026-02-07T22:19:18.646Z] [INFO] 💬 Comments: None found
[2026-02-07T22:19:18.960Z] [INFO] 🔀 Recent PR: 2026-02-07T22:19:06.000Z
[2026-02-07T22:19:18.963Z] [INFO]
✅ Reference time: 2026-02-07T22:19:06.000Z
[2026-02-07T22:19:18.964Z] [INFO]
🔍 Checking for uncommitted changes to include as feedback...
[2026-02-07T22:19:18.998Z] [INFO] ✅ No uncommitted changes found
[2026-02-07T22:19:21.546Z] [INFO] 🎭 Playwright MCP detected - enabling browser automation hints
[2026-02-07T22:19:21.908Z] [INFO] 👁️ Model vision capability: supported
[2026-02-07T22:19:21.909Z] [INFO]
📝 Final prompt structure:
[2026-02-07T22:19:21.909Z] [INFO] Characters: 244
[2026-02-07T22:19:21.909Z] [INFO] System prompt characters: 12570
[2026-02-07T22:19:21.909Z] [INFO] Feedback info: Included
[2026-02-07T22:19:21.911Z] [INFO]
🤖 Executing Claude: OPUS
[2026-02-07T22:19:21.912Z] [INFO] Model: opus
[2026-02-07T22:19:21.912Z] [INFO] Working directory: /tmp/gh-issue-solver-1770502738798
[2026-02-07T22:19:21.912Z] [INFO] Branch: issue-65-37a562943dfc
[2026-02-07T22:19:21.912Z] [INFO] Prompt length: 244 chars
[2026-02-07T22:19:21.912Z] [INFO] System prompt length: 12570 chars
[2026-02-07T22:19:21.913Z] [INFO] Feedback info included: Yes (1 lines)
[2026-02-07T22:19:21.961Z] [INFO] 📈 System resources before execution:
[2026-02-07T22:19:21.962Z] [INFO] Memory: MemFree: 6484616 kB
[2026-02-07T22:19:21.962Z] [INFO] Load: 0.88 0.53 0.30 1/352 31087
[2026-02-07T22:19:21.962Z] [INFO]
📝 Raw command:
[2026-02-07T22:19:21.962Z] [INFO] (cd "/tmp/gh-issue-solver-1770502738798" && claude --output-format stream-json --verbose --dangerously-skip-permissions --model claude-opus-4-6 -p "Issue to solve: https://github.com/konard/tsp/issues/65
Your prepared branch: issue-65-37a562943dfc
Your prepared working directory: /tmp/gh-issue-solver-1770502738798
Your prepared Pull Request: https://github.com/konard/tsp/pull/68
Proceed.
" --append-system-prompt "You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements.
General guidelines.
- When you execute commands, always save their logs to files for easier reading if the output becomes large.
- When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file.
- When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command.
- When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps:
Step 1: List recent runs with timestamps using: gh run list --repo konard/tsp --branch issue-65-37a562943dfc --limit 5 --json databaseId,conclusion,createdAt,headSha
Step 2: Verify runs are after the latest commit by checking timestamps and SHA
Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo konard/tsp --log > ci-logs/{workflow}-{run-id}.log
Step 4: Read each downloaded log file using Read tool to understand the actual failures
Step 5: Report findings with specific errors and line numbers from logs
This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR.
Note: If user says \"failing\" but tools show \"passing\", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy.
- When a code or log file has more than 1500 lines, read it in chunks of 1500 lines.
- When facing a complex problem, do as much tracing as possible and turn on all verbose modes.
- When you create debug, test, or example/experiment scripts for fixing, always keep them in an ./examples and/or ./experiments folders so you can reuse them later.
- When testing your assumptions, use the experiment scripts, and add it to ./experiments folder.
- When your experiments can show real world use case of the software, add it to ./examples folder.
- When you face something extremely hard, use divide and conquer — it always helps.
Initial research.
- When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality.
- When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs.
- When you read issue, read all details and comments thoroughly.
- When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause \"Could not process image\" errors and may crash the AI solver process. If the file command shows \"HTML\" or \"text\", the download failed and you should retry or skip the image.
- When you need issue details, use gh issue view https://github.com/konard/tsp/issues/65.
- When you need related code, use gh search code --owner konard [keywords].
- When you need repo context, read files in your working directory.
- When you study related work, study the most recent related pull requests.
- When issue is not defined enough, write a comment to ask clarifying questions.
- When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication.
- When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed.
- When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default.
- When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints:
1. PR review comments (inline code comments): gh api repos/konard/tsp/pulls/68/comments --paginate
2. PR conversation comments (general discussion): gh api repos/konard/tsp/issues/68/comments --paginate
3. PR reviews (approve/request changes): gh api repos/konard/tsp/pulls/68/reviews --paginate
IMPORTANT: The command \"gh pr view --json comments\" ONLY returns conversation comments and misses review comments!
- When you need latest comments on issue, use gh api repos/konard/tsp/issues/65/comments --paginate.
Solution development and testing.
- When issue is solvable, implement code with tests.
- When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch.
- When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request.
- When you test:
start from testing of small functions using separate scripts;
write unit tests with mocks for easy and quick start.
- When you test integrations, use existing framework.
- When you test solution draft, include automated checks in pr.
- When issue is unclear, write comment on issue asking questions.
- When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help.
- When you need human help, use gh pr comment 68 --body \"your message\" to comment on existing PR.
Preparing pull request.
- When you code, follow contributing guidelines.
- When you commit, write clear message.
- When you need examples of style, use gh pr list --repo konard/tsp --state merged --search [keywords].
- When you open pr, describe solution draft and include tests.
- When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release.
- When you update existing pr 68, use gh pr edit to modify title and description.
- When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing.
- When you finalize the pull request:
follow style from merged prs for code, title, and description,
make sure no uncommitted changes corresponding to the original requirements are left behind,
make sure the default branch is merged to the pull request's branch,
make sure all CI checks passing if they exist before you finish,
check for latest comments on the issue and pull request to ensure no recent feedback was missed,
double-check that all changes in the pull request answer to original requirements of the issue,
make sure no new new bugs are introduced in pull request by carefully reading gh pr diff,
make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments.
- When you finish implementation, use gh pr ready 68.
Workflow and collaboration.
- When you check branch, verify with git branch --show-current.
- When you push, push only to branch issue-65-37a562943dfc.
- When you finish, create a pull request from branch issue-65-37a562943dfc. (Note: PR 68 already exists, update it instead)
- When you organize workflow, use pull requests instead of direct merges to default branch (main or master).
- When you manage commits, preserve commit history for later analysis.
- When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed.
- When you face conflict that you cannot resolve yourself, ask for help.
- When you collaborate, respect branch protections by working only on issue-65-37a562943dfc.
- When you mention result, include pull request url or comment url.
- When you need to create pr, remember pr 68 already exists for this branch.
Self review.
- When you check your solution draft, run all tests locally.
- When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes.
- When you compare with repo style, use gh pr diff [number].
- When you finalize, confirm code, tests, and description are consistent.
GitHub CLI command patterns.
- IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default).
- When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate.
- When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate.
- When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate.
- When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate.
- When adding PR comment, use gh pr comment NUMBER --body \"text\" --repo OWNER/REPO.
- When adding issue comment, use gh issue comment NUMBER --body \"text\" --repo OWNER/REPO.
- When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO.
- When filtering with jq, use gh api repos/\${owner}/\${repo}/pulls/\${prNumber}/comments --paginate --jq 'reverse | .[0:5]'.
Playwright MCP usage (browser automation via mcp__playwright__* tools).
- When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser.
- When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing.
- When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools.
- When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP.
- When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form).
- When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP.
- When you finish using the browser, always close it with browser_close to free resources.
Visual UI work and screenshots.
- When you work on visual UI changes (frontend, CSS, HTML, design), include a render or screenshot of the final result in the pull request description.
- When you need to show visual results, take a screenshot and save it to the repository (e.g., in a docs/screenshots/ or assets/ folder).
- When you save screenshots to the repository, use permanent raw file links in the pull request description markdown (e.g., https://raw.githubusercontent.com/konard/tsp/issue-65-37a562943dfc/docs/screenshots/result.png).
- When uploading images, commit them to the branch first, then reference them using the raw GitHub URL format.
- When the visual result is important for review, mention it explicitly in the pull request description with the embedded image." | jq -c .)
[2026-02-07T22:19:21.963Z] [INFO]
[2026-02-07T22:19:21.963Z] [INFO] 📋 User prompt:
[2026-02-07T22:19:21.963Z] [INFO] ---BEGIN USER PROMPT---
[2026-02-07T22:19:21.963Z] [INFO] Issue to solve: https://github.com/konard/tsp/issues/65
Your prepared branch: issue-65-37a562943dfc
Your prepared working directory: /tmp/gh-issue-solver-1770502738798
Your prepared Pull Request: https://github.com/konard/tsp/pull/68
Proceed.
[2026-02-07T22:19:21.964Z] [INFO] ---END USER PROMPT---
[2026-02-07T22:19:21.964Z] [INFO]
[2026-02-07T22:19:21.964Z] [INFO] 📋 System prompt:
[2026-02-07T22:19:21.964Z] [INFO] ---BEGIN SYSTEM PROMPT---
[2026-02-07T22:19:21.964Z] [INFO] You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements.
General guidelines.
- When you execute commands, always save their logs to files for easier reading if the output becomes large.
- When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file.
- When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command.
- When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps:
Step 1: List recent runs with timestamps using: gh run list --repo konard/tsp --branch issue-65-37a562943dfc --limit 5 --json databaseId,conclusion,createdAt,headSha
Step 2: Verify runs are after the latest commit by checking timestamps and SHA
Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo konard/tsp --log > ci-logs/{workflow}-{run-id}.log
Step 4: Read each downloaded log file using Read tool to understand the actual failures
Step 5: Report findings with specific errors and line numbers from logs
This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR.
Note: If user says "failing" but tools show "passing", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy.
- When a code or log file has more than 1500 lines, read it in chunks of 1500 lines.
- When facing a complex problem, do as much tracing as possible and turn on all verbose modes.
- When you create debug, test, or example/experiment scripts for fixing, always keep them in an ./examples and/or ./experiments folders so you can reuse them later.
- When testing your assumptions, use the experiment scripts, and add it to ./experiments folder.
- When your experiments can show real world use case of the software, add it to ./examples folder.
- When you face something extremely hard, use divide and conquer — it always helps.
Initial research.
- When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality.
- When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs.
- When you read issue, read all details and comments thoroughly.
- When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause "Could not process image" errors and may crash the AI solver process. If the file command shows "HTML" or "text", the download failed and you should retry or skip the image.
- When you need issue details, use gh issue view https://github.com/konard/tsp/issues/65.
- When you need related code, use gh search code --owner konard [keywords].
- When you need repo context, read files in your working directory.
- When you study related work, study the most recent related pull requests.
- When issue is not defined enough, write a comment to ask clarifying questions.
- When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication.
- When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed.
- When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default.
- When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints:
1. PR review comments (inline code comments): gh api repos/konard/tsp/pulls/68/comments --paginate
2. PR conversation comments (general discussion): gh api repos/konard/tsp/issues/68/comments --paginate
3. PR reviews (approve/request changes): gh api repos/konard/tsp/pulls/68/reviews --paginate
IMPORTANT: The command "gh pr view --json comments" ONLY returns conversation comments and misses review comments!
- When you need latest comments on issue, use gh api repos/konard/tsp/issues/65/comments --paginate.
Solution development and testing.
- When issue is solvable, implement code with tests.
- When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch.
- When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request.
- When you test:
start from testing of small functions using separate scripts;
write unit tests with mocks for easy and quick start.
- When you test integrations, use existing framework.
- When you test solution draft, include automated checks in pr.
- When issue is unclear, write comment on issue asking questions.
- When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help.
- When you need human help, use gh pr comment 68 --body "your message" to comment on existing PR.
Preparing pull request.
- When you code, follow contributing guidelines.
- When you commit, write clear message.
- When you need examples of style, use gh pr list --repo konard/tsp --state merged --search [keywords].
- When you open pr, describe solution draft and include tests.
- When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release.
- When you update existing pr 68, use gh pr edit to modify title and description.
- When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing.
- When you finalize the pull request:
follow style from merged prs for code, title, and description,
make sure no uncommitted changes corresponding to the original requirements are left behind,
make sure the default branch is merged to the pull request's branch,
make sure all CI checks passing if they exist before you finish,
check for latest comments on the issue and pull request to ensure no recent feedback was missed,
double-check that all changes in the pull request answer to original requirements of the issue,
make sure no new new bugs are introduced in pull request by carefully reading gh pr diff,
make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments.
- When you finish implementation, use gh pr ready 68.
Workflow and collaboration.
- When you check branch, verify with git branch --show-current.
- When you push, push only to branch issue-65-37a562943dfc.
- When you finish, create a pull request from branch issue-65-37a562943dfc. (Note: PR 68 already exists, update it instead)
- When you organize workflow, use pull requests instead of direct merges to default branch (main or master).
- When you manage commits, preserve commit history for later analysis.
- When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed.
- When you face conflict that you cannot resolve yourself, ask for help.
- When you collaborate, respect branch protections by working only on issue-65-37a562943dfc.
- When you mention result, include pull request url or comment url.
- When you need to create pr, remember pr 68 already exists for this branch.
Self review.
- When you check your solution draft, run all tests locally.
- When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes.
- When you compare with repo style, use gh pr diff [number].
- When you finalize, confirm code, tests, and description are consistent.
GitHub CLI command patterns.
- IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default).
- When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate.
- When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate.
- When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate.
- When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate.
- When adding PR comment, use gh pr comment NUMBER --body "text" --repo OWNER/REPO.
- When adding issue comment, use gh issue comment NUMBER --body "text" --repo OWNER/REPO.
- When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO.
- When filtering with jq, use gh api repos/${owner}/${repo}/pulls/${prNumber}/comments --paginate --jq 'reverse | .[0:5]'.
Playwright MCP usage (browser automation via mcp__playwright__* tools).
- When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser.
- When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing.
- When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools.
- When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP.
- When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form).
- When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP.
- When you finish using the browser, always close it with browser_close to free resources.
Visual UI work and screenshots.
- When you work on visual UI changes (frontend, CSS, HTML, design), include a render or screenshot of the final result in the pull request description.
- When you need to show visual results, take a screenshot and save it to the repository (e.g., in a docs/screenshots/ or assets/ folder).
- When you save screenshots to the repository, use permanent raw file links in the pull request description markdown (e.g., https://raw.githubusercontent.com/konard/tsp/issue-65-37a562943dfc/docs/screenshots/result.png).
- When uploading images, commit them to the branch first, then reference them using the raw GitHub URL format.
- When the visual result is important for review, mention it explicitly in the pull request description with the embedded image.
[2026-02-07T22:19:21.965Z] [INFO] ---END SYSTEM PROMPT---
[2026-02-07T22:19:21.965Z] [INFO]
[2026-02-07T22:19:21.966Z] [INFO] 📊 CLAUDE_CODE_MAX_OUTPUT_TOKENS: 128000
[2026-02-07T22:19:21.966Z] [INFO] 📊 MCP_TIMEOUT: 900000ms (server startup)
[2026-02-07T22:19:21.966Z] [INFO] 📊 MCP_TOOL_TIMEOUT: 900000ms (tool execution)
[2026-02-07T22:19:21.967Z] [INFO] 📋 Command details:
[2026-02-07T22:19:21.967Z] [INFO] 📂 Working directory: /tmp/gh-issue-solver-1770502738798
[2026-02-07T22:19:21.967Z] [INFO] 🌿 Branch: issue-65-37a562943dfc
[2026-02-07T22:19:21.967Z] [INFO] 🤖 Model: Claude OPUS
[2026-02-07T22:19:21.967Z] [INFO]
▶️ Streaming output:
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"TodoWrite",
"WebSearch",
"TaskStop",
"AskUserQuestion",
"Skill",
"EnterPlanMode",
"ToolSearch",
"mcp__playwright__browser_close",
"mcp__playwright__browser_resize",
"mcp__playwright__browser_console_messages",
"mcp__playwright__browser_handle_dialog",
"mcp__playwright__browser_evaluate",
"mcp__playwright__browser_file_upload",
"mcp__playwright__browser_fill_form",
"mcp__playwright__browser_install",
"mcp__playwright__browser_press_key",
"mcp__playwright__browser_type",
"mcp__playwright__browser_navigate",
"mcp__playwright__browser_navigate_back",
"mcp__playwright__browser_network_requests",
"mcp__playwright__browser_run_code",
"mcp__playwright__browser_take_screenshot",
"mcp__playwright__browser_snapshot",
"mcp__playwright__browser_click",
"mcp__playwright__browser_drag",
"mcp__playwright__browser_hover",
"mcp__playwright__browser_select_option",
"mcp__playwright__browser_tabs",
"mcp__playwright__browser_wait_for"
],
"mcp_servers": [
{
"name": "playwright",
"status": "connected"
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],
"model": "claude-opus-4-6",
"permissionMode": "bypassPermissions",
"slash_commands": [
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"debug",
"compact",
"context",
"cost",
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"pr-comments",
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"review",
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"apiKeySource": "none",
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"output_style": "default",
"agents": [
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"skills": [
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"plugins": [],
"uuid": "c5d45b89-754c-464b-8bcf-996ffc905f30"
}
[2026-02-07T22:19:24.614Z] [INFO] 📌 Session ID: 13a08b48-b521-4f16-bcc0-b181d94e47a4
[2026-02-07T22:19:24.614Z] [INFO] 📁 Log renamed to: /home/hive/13a08b48-b521-4f16-bcc0-b181d94e47a4.log
[2026-02-07T22:19:26.572Z] [INFO] {
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"type": "message",
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[2026-02-07T22:19:27.488Z] [INFO] {
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"type": "message",
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"content": [
{
"type": "tool_use",
"id": "toolu_0113CFw8DWMexhy2Hx1CgjRb",
"name": "Bash",
"input": {
"command": "gh issue view 65 --repo konard/tsp",
"description": "View issue 65 details"
}
}
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"stop_reason": null,
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[2026-02-07T22:19:28.160Z] [INFO] {
"type": "assistant",
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"id": "msg_01AnEz1w4Gar93V2ruN6UDhh",
"type": "message",
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{
"type": "tool_use",
"id": "toolu_01DnrwYqXt9WBrnRwM71roKK",
"name": "Bash",
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[2026-02-07T22:19:28.877Z] [INFO] {
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[2026-02-07T22:19:28.888Z] [INFO] {
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{
"type": "tool_use",
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[2026-02-07T22:19:30.283Z] [INFO] {
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"content": " 1→/**\n 2→ * Atomic Moore Curve Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Moore curve (L-system) that fills the grid space\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * Time Complexity: O(n log n)\n 13→ * Space Complexity: O(n)\n 14→ */\n 15→\n 16→import { distance } from '../../utils.js';\n 17→\n 18→/**\n 19→ * Generate Moore curve using L-system\n 20→ * L-system rules for Moore curve:\n 21→ * Axiom: LFL+F+LFL\n 22→ * L -> -RF+LFL+FR-\n 23→ * R -> +LF-RFR-FL+\n 24→ *\n 25→ * @param {number} order - Order of the Moore curve\n 26→ * @returns {string} L-system sequence\n 27→ */\n 28→export const generateMooreCurve = (order) => {\n 29→ let sequence = 'LFL+F+LFL';\n 30→\n 31→ for (let i = 0; i < order; i++) {\n 32→ let newSequence = '';\n 33→ for (const char of sequence) {\n 34→ if (char === 'L') {\n 35→ newSequence += '-RF+LFL+FR-';\n 36→ } else if (char === 'R') {\n 37→ newSequence += '+LF-RFR-FL+';\n 38→ } else {\n 39→ newSequence += char;\n 40→ }\n 41→ }\n 42→ sequence = newSequence;\n 43→ }\n 44→\n 45→ return sequence;\n 46→};\n 47→\n 48→/**\n 49→ * Convert Moore curve L-system sequence to coordinate points\n 50→ *\n 51→ * @param {string} sequence - L-system sequence\n 52→ * @param {number} mooreGridSize - Size of the grid\n 53→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 54→ */\n 55→export const mooreCurveToPoints = (sequence, mooreGridSize) => {\n 56→ const stepSize = 1;\n 57→\n 58→ const curvePoints = [];\n 59→ let x = 0;\n 60→ let y = 0;\n 61→\n 62→ // Direction: 0=up, 1=right, 2=down, 3=left\n 63→ let direction = 0;\n 64→\n 65→ curvePoints.push({ x, y });\n 66→\n 67→ for (const char of sequence) {\n 68→ if (char === 'F') {\n 69→ if (direction === 0) {\n 70→ y -= stepSize;\n 71→ } else if (direction === 1) {\n 72→ x += stepSize;\n 73→ } else if (direction === 2) {\n 74→ y += stepSize;\n 75→ } else if (direction === 3) {\n 76→ x -= stepSize;\n 77→ }\n 78→\n 79→ curvePoints.push({ x, y });\n 80→ } else if (char === '+') {\n 81→ direction = (direction + 1) % 4;\n 82→ } else if (char === '-') {\n 83→ direction = (direction + 3) % 4;\n 84→ }\n 85→ }\n 86→\n 87→ // Find bounding box of the curve\n 88→ let minX = Infinity,\n 89→ maxX = -Infinity,\n 90→ minY = Infinity,\n 91→ maxY = -Infinity;\n 92→ for (const p of curvePoints) {\n 93→ minX = Math.min(minX, p.x);\n 94→ maxX = Math.max(maxX, p.x);\n 95→ minY = Math.min(minY, p.y);\n 96→ maxY = Math.max(maxY, p.y);\n 97→ }\n 98→\n 99→ const curveWidth = maxX - minX;\n 100→ const curveHeight = maxY - minY;\n 101→\n 102→ const normalizedPoints = curvePoints.map((p) => ({\n 103→ x: Math.round(((p.x - minX) / curveWidth) * (mooreGridSize - 1)),\n 104→ y: Math.round(((p.y - minY) / curveHeight) * (mooreGridSize - 1)),\n 105→ }));\n 106→\n 107→ return normalizedPoints;\n 108→};\n 109→\n 110→/**\n 111→ * Compute Moore Curve solution in one step (atomic version).\n 112→ * Returns the final tour without intermediate steps.\n 113→ *\n 114→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 115→ * @param {number} mooreGridSize - Size of the Moore grid\n 116→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 117→ */\n 118→export const mooreSolution = (points, mooreGridSize) => {\n 119→ if (points.length === 0) {\n 120→ return { tour: [], curvePoints: [] };\n 121→ }\n 122→\n 123→ // Determine L-system iterations: mooreGridSize = 2^(n+1), so n = log2(mooreGridSize) - 1\n 124→ const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n 125→ const curveSequence = generateMooreCurve(order);\n 126→ const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n 127→\n 128→ const pointsWithCurvePos = points.map((p, idx) => {\n 129→ let minDist = Infinity;\n 130→ let curvePos = 0;\n 131→\n 132→ for (let i = 0; i < curvePoints.length; i++) {\n 133→ const d = distance(p, curvePoints[i]);\n 134→ if (d < minDist) {\n 135→ minDist = d;\n 136→ curvePos = i;\n 137→ }\n 138→ }\n 139→\n 140→ return { idx, curvePos };\n 141→ });\n 142→\n 143→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 144→\n 145→ return {\n 146→ tour: pointsWithCurvePos.map((p) => p.idx),\n 147→ curvePoints,\n 148→ };\n 149→};\n 150→\n 151→export default mooreSolution;\n 152→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Progressive Moore Curve Algorithm for TSP\n 3→ *\n 4→ * Also known as: Space-Filling Curve, Hilbert Curve Variant, Fractal Ordering\n 5→ *\n 6→ * This algorithm provides step-by-step visualization of using a Moore curve\n 7→ * (a variant of the Hilbert curve) to order points:\n 8→ * 1. Generate a Moore curve that fills the grid space\n 9→ * 2. Map each point to its nearest position on the curve\n 10→ * 3. Sort points by their position along the curve\n 11→ * 4. Connect points in curve-order to form a tour\n 12→ *\n 13→ * Time Complexity: O(n log n)\n 14→ * Space Complexity: O(n)\n 15→ */\n 16→\n 17→import { distance } from '../../utils.js';\n 18→import {\n 19→ generateMooreCurve,\n 20→ mooreCurveToPoints,\n 21→} from '../../atomic/solution/moore.js';\n 22→\n 23→// Re-export atomic functions for backward compatibility\n 24→export {\n 25→ mooreSolution,\n 26→ generateMooreCurve,\n 27→ mooreCurveToPoints,\n 28→} from '../../atomic/solution/moore.js';\n 29→\n 30→/**\n 31→ * Generate step-by-step solution using Moore Curve algorithm\n 32→ *\n 33→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 34→ * @param {number} mooreGridSize - Size of the Moore grid\n 35→ * @returns {Array<Object>} Array of steps for visualization\n 36→ */\n 37→export const mooreAlgorithmSteps = (points, mooreGridSize) => {\n 38→ if (points.length === 0) {\n 39→ return [];\n 40→ }\n 41→\n 42→ // Determine L-system iterations based on Moore grid size\n 43→ // generateMooreCurve(n) fills a 2^(n+1) x 2^(n+1) grid\n 44→ // So for mooreGridSize = 2^k, iterations = k - 1\n 45→ const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n 46→ const curveSequence = generateMooreCurve(order);\n 47→ // Generate curve points using the Moore grid size for perfect alignment\n 48→ const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n 49→ const totalCurveLength = curvePoints.length;\n 50→\n 51→ // Points are already on Moore grid coordinates, no scaling needed\n 52→ // Map each point to its position along the curve\n 53→ const pointsWithCurvePos = points.map((p, idx) => {\n 54→ let minDist = Infinity;\n 55→ let curvePos = 0;\n 56→\n 57→ // Points are already in Moore grid coordinates\n 58→ for (let i = 0; i < curvePoints.length; i++) {\n 59→ const d = distance(p, curvePoints[i]);\n 60→ if (d < minDist) {\n 61→ minDist = d;\n 62→ curvePos = i;\n 63→ }\n 64→ }\n 65→\n 66→ return { ...p, idx, curvePos };\n 67→ });\n 68→\n 69→ // Sort points by their position along the curve\n 70→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 71→\n 72→ // Generate steps\n 73→ const steps = [];\n 74→ const tour = [];\n 75→\n 76→ // First step: show the Moore curve\n 77→ steps.push({\n 78→ type: 'curve',\n 79→ curvePoints,\n 80→ mooreGridSize,\n 81→ curveProgress: 0,\n 82→ tour: [],\n 83→ description: `Moore curve generated (order ${order}, ${mooreGridSize}×${mooreGridSize} grid)`,\n 84→ });\n 85→\n 86→ for (let i = 0; i < pointsWithCurvePos.length; i++) {\n 87→ tour.push(pointsWithCurvePos[i].idx);\n 88→ // Calculate progress along the curve as a percentage\n 89→ const curveProgress = (\n 90→ (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 91→ 100\n 92→ ).toFixed(1);\n 93→\n 94→ steps.push({\n 95→ type: 'visit',\n 96→ curvePoints,\n 97→ mooreGridSize,\n 98→ curvePosition: pointsWithCurvePos[i].curvePos,\n 99→ curveProgress: parseFloat(curveProgress),\n 100→ tour: [...tour],\n 101→ description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n 102→ });\n 103→ }\n 104→\n 105→ return steps;\n 106→};\n 107→\n 108→export default mooreAlgorithmSteps;\n 109→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * TSP Algorithms Library\n 3→ *\n 4→ * This module provides two types of TSP algorithms:\n 5→ *\n 6→ * 1. Progressive (step-by-step) - Returns arrays of steps for visualization/animation\n 7→ * 2. Atomic (all-at-once) - Returns final result directly\n 8→ *\n 9→ * Each type includes:\n 10→ * - Solutions: Initial tour construction algorithms (Sonar, Moore, Gosper, Peano, Sierpiński, Comb, SAW, Koch, Space-Filling Tree, Brute-Force)\n 11→ * - Optimizations: Generic tour improvement algorithms (2-opt, 3-opt, k-opt, LK, LKH, Zigzag)\n 12→ *\n 13→ * Additionally, verification algorithms prove tour optimality:\n 14→ * - Lower bound (1-tree): Mathematical proof when upper bound = lower bound\n 15→ *\n 16→ * Available solution algorithms:\n 17→ * - Sonar (Radial Sweep): Sorts points by polar angle from centroid\n 18→ * - Moore Curve: Orders points along a Moore space-filling curve (2^n grid)\n 19→ * - Gosper Curve: Orders points along a hexagonal space-filling curve (60° turns)\n 20→ * - Peano Curve: Orders points along a Peano space-filling curve (3^n grid)\n 21→ * - Sierpiński Curve: Orders points along a Sierpiński space-filling curve\n 22→ * - Comb (Serpentine Scan): Visits points row by row in alternating directions\n 23→ * - Self-Avoiding Walk (SAW): Nearest-neighbor walk that never revisits a point\n 24→ * - Koch Snowflake: Orders points along a Koch snowflake fractal curve\n 25→ * - Space-Filling Tree: Orders points via DFS traversal of a recursive quadtree\n 26→ * - Brute-Force: Exhaustive search for the true optimal tour (small instances)\n 27→ *\n 28→ * Available optimizations (generic, work with any tour):\n 29→ * - 2-opt: Standard segment reversal optimization\n 30→ * - 3-opt: Three-edge exchange optimization\n 31→ * - k-opt: Generalized k-edge exchange (combines 2-opt and 3-opt)\n 32→ * - Lin-Kernighan: Variable-depth edge exchange heuristic\n 33→ * - Lin-Kernighan-Helsgaun: LK with perturbation for escaping local optima\n 34→ * - Zigzag: Adjacent pair swapping optimization\n 35→ * - Combined: Alternates zigzag and 2-opt until no improvement\n 36→ *\n 37→ * @example\n 38→ * // Progressive (for visualization)\n 39→ * import { sonarAlgorithmSteps, sawAlgorithmSteps, kochAlgorithmSteps, twoOptSteps, bruteForceAlgorithmSteps } from './algorithms';\n 40→ * const solutionSteps = sonarAlgorithmSteps(points);\n 41→ * const kochSteps = kochAlgorithmSteps(points, mooreGridSize);\n 42→ * const optSteps = twoOptSteps(points, tour);\n 43→ * const bruteSteps = bruteForceAlgorithmSteps(points);\n 44→ *\n 45→ * @example\n 46→ * // Atomic (for direct computation)\n 47→ * import { sonarSolution, sawSolution, bruteForceSolution } from './algorithms/atomic';\n 48→ * import { twoOpt } from './algorithms/atomic';\n 49→ * const { tour } = sonarSolution(points);\n 50→ * const optimal = bruteForceSolution(points);\n 51→ *\n 52→ * @example\n 53→ * // Verification (prove optimality)\n 54→ * import { verifyOptimality } from './algorithms/verification';\n 55→ * const result = verifyOptimality(tourDistance, points);\n 56→ * if (result.isOptimal) console.log('Tour is proven optimal!');\n 57→ */\n 58→\n 59→// Utility functions\n 60→export * from './utils.js';\n 61→\n 62→// Progressive algorithms (step-by-step)\n 63→export * as progressive from './progressive/index.js';\n 64→\n 65→// Atomic algorithms (all-at-once)\n 66→export * as atomic from './atomic/index.js';\n 67→\n 68→// Verification algorithms (optimality proofs)\n 69→export * as verification from './verification/index.js';\n 70→\n 71→// Export progressive functions at top level for convenience\n 72→export {\n 73→ // Solutions\n 74→ sonarAlgorithmSteps,\n 75→ mooreAlgorithmSteps,\n 76→ generateMooreCurve,\n 77→ mooreCurveToPoints,\n 78→ gosperAlgorithmSteps,\n 79→ generateGosperCurve,\n 80→ gosperCurveToPoints,\n 81→ calculateGosperOrder,\n 82→ peanoAlgorithmSteps,\n 83→ generatePeanoCurve,\n 84→ peanoCurveToPoints,\n 85→ sierpinskiAlgorithmSteps,\n 86→ generateSierpinskiCurve,\n 87→ sierpinskiCurveToPoints,\n 88→ combAlgorithmSteps,\n 89→ sawAlgorithmSteps,\n 90→ sawSolution,\n 91→ kochAlgorithmSteps,\n 92→ generateKochSnowflake,\n 93→ kochCurveToPoints,\n 94→ calculateKochOrder,\n 95→ spaceFillingTreeAlgorithmSteps,\n 96→ bruteForceAlgorithmSteps,\n 97→ bruteForceSolution,\n 98→ calculateOptimalityRatio,\n 99→ BRUTE_FORCE_MAX_POINTS,\n 100→ // Generic optimizations\n 101→ twoOptSteps,\n 102→ threeOptSteps,\n 103→ kOptSteps,\n 104→ zigzagOptSteps,\n 105→ combinedOptSteps,\n 106→ linKernighanSteps,\n 107→ lkHelsgaunSteps,\n 108→ // Legacy aliases for backward compatibility\n 109→ sonarOptimizationSteps,\n 110→ mooreOptimizationSteps,\n 111→} from './progressive/index.js';\n 112→\n 113→// Export verification functions at top level for convenience\n 114→export {\n 115→ oneTreeLowerBound,\n 116→ verifyOptimality,\n 117→ // Backward-compatible aliases\n 118→ bruteForceOptimalTour,\n 119→ bruteForceSteps,\n 120→} from './verification/index.js';\n 121→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * Test file for TSP algorithms\n * Works with Bun test runner\n */\n\nimport { describe, it, expect } from 'bun:test';\nimport {\n calculateMooreGridSize,\n generateRandomPoints,\n distance,\n calculateTotalDistance,\n VALID_GRID_SIZES,\n} from '../lib/algorithms/utils.js';\nimport {\n sonarAlgorithmSteps,\n sonarSolution,\n} from '../lib/algorithms/progressive/solution/sonar.js';\nimport {\n mooreAlgorithmSteps,\n mooreSolution,\n generateMooreCurve,\n mooreCurveToPoints,\n} from '../lib/algorithms/progressive/solution/moore.js';\nimport {\n zigzagOptSteps,\n zigzagOpt,\n // Legacy aliases\n sonarOptimizationSteps,\n sonarOptimization,\n} from '../lib/algorithms/progressive/optimization/zigzag-opt.js';\nimport {\n twoOptSteps,\n twoOpt,\n // Legacy aliases\n mooreOptimizationSteps,\n mooreOptimization,\n} from '../lib/algorithms/progressive/optimization/two-opt.js';\nimport {\n combinedOptSteps,\n combinedOpt,\n} from '../lib/algorithms/progressive/optimization/combined-opt.js';\nimport {\n bruteForceAlgorithmSteps,\n bruteForceSolution,\n calculateOptimalityRatio,\n BRUTE_FORCE_MAX_POINTS,\n} from '../lib/algorithms/progressive/solution/brute-force.js';\nimport {\n oneTreeLowerBound,\n verifyOptimality,\n} from '../lib/algorithms/verification/lower-bound.js';\n\n// Backward-compatible aliases\nconst bruteForceOptimalTour = bruteForceSolution;\nconst bruteForceSteps = bruteForceAlgorithmSteps;\n\n// ============================================================\n// Utility Functions Tests\n// ============================================================\n\ndescribe('VALID_GRID_SIZES', () => {\n it('should contain only powers of 2', () => {\n VALID_GRID_SIZES.forEach((size) => {\n expect(Math.log2(size) % 1).toBe(0);\n });\n });\n\n it('should be sorted in ascending order', () => {\n for (let i = 1; i < VALID_GRID_SIZES.length; i++) {\n expect(VALID_GRID_SIZES[i]).toBeGreaterThan(VALID_GRID_SIZES[i - 1]);\n }\n });\n\n it('should contain expected sizes', () => {\n expect(VALID_GRID_SIZES).toEqual([2, 4, 8, 16, 32, 64, 128]);\n });\n});\n\ndescribe('calculateMooreGridSize', () => {\n it('should return smallest valid size >= input', () => {\n expect(calculateMooreGridSize(2)).toBe(2);\n expect(calculateMooreGridSize(3)).toBe(4);\n expect(calculateMooreGridSize(4)).toBe(4);\n expect(calculateMooreGridSize(5)).toBe(8);\n expect(calculateMooreGridSize(10)).toBe(16);\n expect(calculateMooreGridSize(16)).toBe(16);\n expect(calculateMooreGridSize(20)).toBe(32);\n expect(calculateMooreGridSize(32)).toBe(32);\n expect(calculateMooreGridSize(33)).toBe(64);\n expect(calculateMooreGridSize(64)).toBe(64);\n expect(calculateMooreGridSize(65)).toBe(128);\n expect(calculateMooreGridSize(128)).toBe(128);\n expect(calculateMooreGridSize(129)).toBe(128);\n });\n\n it('should return largest valid size for very large input', () => {\n expect(calculateMooreGridSize(200)).toBe(128);\n });\n\n it('should always return a valid Moore grid size', () => {",
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"content": " 1→/**\n 2→ * Utility functions for TSP algorithms\n 3→ */\n 4→\n 5→/**\n 6→ * Valid grid sizes for Moore curve algorithm.\n 7→ * A Moore curve of order n fills a 2^n x 2^n grid.\n 8→ * These correspond to L-system iteration counts 0..7 (effective orders 1..8).\n 9→ */\n 10→export const VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128];\n 11→\n 12→/**\n 13→ * Valid grid sizes for Peano curve algorithm.\n 14→ * A Peano curve of order n fills a 3^n x 3^n grid.\n 15→ * These correspond to L-system iteration counts 1..4 (orders 1..4).\n 16→ */\n 17→export const VALID_PEANO_GRID_SIZES = [3, 9, 27, 81];\n 18→\n 19→/**\n 20→ * Calculate the Moore grid size based on user-specified grid size\n 21→ * Moore curve fills a grid of size 2^n x 2^n.\n 22→ * Returns the smallest valid Moore grid size >= gridSize.\n 23→ * @param {number} gridSize - User-specified grid size\n 24→ * @returns {number} Moore grid size (power of 2)\n 25→ */\n 26→export const calculateMooreGridSize = (gridSize) => {\n 27→ // Find smallest valid grid size >= gridSize\n 28→ for (const size of VALID_GRID_SIZES) {\n 29→ if (size >= gridSize) {\n 30→ return size;\n 31→ }\n 32→ }\n 33→ return VALID_GRID_SIZES[VALID_GRID_SIZES.length - 1];\n 34→};\n 35→\n 36→/**\n 37→ * Calculate the Peano grid size based on user-specified grid size\n 38→ * Peano curve fills a grid of size 3^n x 3^n.\n 39→ * Returns the smallest valid Peano grid size >= gridSize.\n 40→ * @param {number} gridSize - User-specified grid size\n 41→ * @returns {number} Peano grid size (power of 3)\n 42→ */\n 43→export const calculatePeanoGridSize = (gridSize) => {\n 44→ // Find smallest valid Peano grid size >= gridSize\n 45→ for (const size of VALID_PEANO_GRID_SIZES) {\n 46→ if (size >= gridSize) {\n 47→ return size;\n 48→ }\n 49→ }\n 50→ return VALID_PEANO_GRID_SIZES[VALID_PEANO_GRID_SIZES.length - 1];\n 51→};\n 52→\n 53→/**\n 54→ * Generate random points on the Moore grid\n 55→ * @param {number} mooreGridSize - Size of the Moore grid\n 56→ * @param {number} numPoints - Number of points to generate\n 57→ * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n 58→ */\n 59→export const generateRandomPoints = (mooreGridSize, numPoints) => {\n 60→ const points = [];\n 61→ const usedPositions = new Set();\n 62→ // Points are placed at grid intersections (0 to mooreGridSize-1 inclusive)\n 63→ const maxPoints = Math.min(numPoints, mooreGridSize * mooreGridSize);\n 64→\n 65→ while (points.length < maxPoints) {\n 66→ // Generate integer coordinates aligned to Moore grid vertices [0, mooreGridSize-1]\n 67→ const x = Math.floor(Math.random() * mooreGridSize);\n 68→ const y = Math.floor(Math.random() * mooreGridSize);\n 69→ const key = `${x},${y}`;\n 70→\n 71→ if (!usedPositions.has(key)) {\n 72→ usedPositions.add(key);\n 73→ points.push({ x, y, id: points.length });\n 74→ }\n 75→ }\n 76→ return points;\n 77→};\n 78→\n 79→/**\n 80→ * Calculate Euclidean distance between two points\n 81→ * @param {{x: number, y: number}} p1 - First point\n 82→ * @param {{x: number, y: number}} p2 - Second point\n 83→ * @returns {number} Euclidean distance\n 84→ */\n 85→export const distance = (p1, p2) => Math.hypot(p2.x - p1.x, p2.y - p1.y);\n 86→\n 87→/**\n 88→ * Calculate total tour distance (closed loop)\n 89→ * @param {number[]} tour - Array of point indices forming the tour\n 90→ * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n 91→ * @returns {number} Total distance of the tour\n 92→ */\n 93→export const calculateTotalDistance = (tour, points) => {\n 94→ if (tour.length < 2) {\n 95→ return 0;\n 96→ }\n 97→ let total = 0;\n 98→ for (let i = 0; i < tour.length - 1; i++) {\n 99→ total += distance(points[tour[i]], points[tour[i + 1]]);\n 100→ }\n 101→ // Close the loop\n 102→ total += distance(points[tour[tour.length - 1]], points[tour[0]]);\n 103→ return total;\n 104→};\n 105→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Atomic Peano Curve Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Peano curve (L-system) that fills the grid space\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * The Peano curve fills a 3^n x 3^n grid, complementing the Moore curve\n 13→ * which fills a 2^n x 2^n grid.\n 14→ *\n 15→ * Time Complexity: O(n log n)\n 16→ * Space Complexity: O(n)\n 17→ */\n 18→\n 19→import { distance } from '../../utils.js';\n 20→\n 21→/**\n 22→ * Generate Peano curve using L-system\n 23→ * L-system rules for Peano curve:\n 24→ * Axiom: X\n 25→ * X -> XFYFX+F+YFXFY-F-XFYFX\n 26→ * Y -> YFXFY-F-XFYFX+F+YFXFY\n 27→ *\n 28→ * @param {number} order - Order of the Peano curve (0, 1, 2, ...)\n 29→ * @returns {string} L-system sequence\n 30→ */\n 31→export const generatePeanoCurve = (order) => {\n 32→ let sequence = 'X';\n 33→\n 34→ for (let i = 0; i < order; i++) {\n 35→ let newSequence = '';\n 36→ for (const char of sequence) {\n 37→ if (char === 'X') {\n 38→ newSequence += 'XFYFX+F+YFXFY-F-XFYFX';\n 39→ } else if (char === 'Y') {\n 40→ newSequence += 'YFXFY-F-XFYFX+F+YFXFY';\n 41→ } else {\n 42→ newSequence += char;\n 43→ }\n 44→ }\n 45→ sequence = newSequence;\n 46→ }\n 47→\n 48→ return sequence;\n 49→};\n 50→\n 51→/**\n 52→ * Convert Peano curve L-system sequence to coordinate points\n 53→ *\n 54→ * @param {string} sequence - L-system sequence\n 55→ * @param {number} peanoGridSize - Size of the grid\n 56→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 57→ */\n 58→export const peanoCurveToPoints = (sequence, peanoGridSize) => {\n 59→ const curvePoints = [];\n 60→ let x = 0;\n 61→ let y = 0;\n 62→\n 63→ // Direction: 0=right, 1=up, 2=left, 3=down\n 64→ let direction = 1; // Start going up\n 65→\n 66→ curvePoints.push({ x, y });\n 67→\n 68→ for (const char of sequence) {\n 69→ if (char === 'F') {\n 70→ if (direction === 0) {\n 71→ x += 1;\n 72→ } else if (direction === 1) {\n 73→ y -= 1;\n 74→ } else if (direction === 2) {\n 75→ x -= 1;\n 76→ } else if (direction === 3) {\n 77→ y += 1;\n 78→ }\n 79→\n 80→ curvePoints.push({ x, y });\n 81→ } else if (char === '+') {\n 82→ direction = (direction + 1) % 4;\n 83→ } else if (char === '-') {\n 84→ direction = (direction + 3) % 4;\n 85→ }\n 86→ }\n 87→\n 88→ // Find bounding box of the curve\n 89→ let minX = Infinity,\n 90→ maxX = -Infinity,\n 91→ minY = Infinity,\n 92→ maxY = -Infinity;\n 93→ for (const p of curvePoints) {\n 94→ minX = Math.min(minX, p.x);\n 95→ maxX = Math.max(maxX, p.x);\n 96→ minY = Math.min(minY, p.y);\n 97→ maxY = Math.max(maxY, p.y);\n 98→ }\n 99→\n 100→ const curveWidth = maxX - minX;\n 101→ const curveHeight = maxY - minY;\n 102→\n 103→ // Handle degenerate case where curve has no extent\n 104→ if (curveWidth === 0 && curveHeight === 0) {\n 105→ return curvePoints.map(() => ({ x: 0, y: 0 }));\n 106→ }\n 107→\n 108→ const normalizedPoints = curvePoints.map((p) => ({\n 109→ x:\n 110→ curveWidth > 0\n 111→ ? Math.round(((p.x - minX) / curveWidth) * (peanoGridSize - 1))\n 112→ : 0,\n 113→ y:\n 114→ curveHeight > 0\n 115→ ? Math.round(((p.y - minY) / curveHeight) * (peanoGridSize - 1))\n 116→ : 0,\n 117→ }));\n 118→\n 119→ return normalizedPoints;\n 120→};\n 121→\n 122→/**\n 123→ * Compute Peano Curve solution in one step (atomic version).\n 124→ * Returns the final tour without intermediate steps.\n 125→ *\n 126→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 127→ * @param {number} peanoGridSize - Size of the Peano grid\n 128→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 129→ */\n 130→export const peanoSolution = (points, peanoGridSize) => {\n 131→ if (points.length === 0) {\n 132→ return { tour: [], curvePoints: [] };\n 133→ }\n 134→\n 135→ // Determine L-system iterations: peanoGridSize = 3^n, so n = log3(peanoGridSize)\n 136→ const order = Math.max(1, Math.round(Math.log(peanoGridSize) / Math.log(3)));\n 137→ const curveSequence = generatePeanoCurve(order);\n 138→ const curvePoints = peanoCurveToPoints(curveSequence, peanoGridSize);\n 139→\n 140→ const pointsWithCurvePos = points.map((p, idx) => {\n 141→ let minDist = Infinity;\n 142→ let curvePos = 0;\n 143→\n 144→ for (let i = 0; i < curvePoints.length; i++) {\n 145→ const d = distance(p, curvePoints[i]);\n 146→ if (d < minDist) {\n 147→ minDist = d;\n 148→ curvePos = i;\n 149→ }\n 150→ }\n 151→\n 152→ return { idx, curvePos };\n 153→ });\n 154→\n 155→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 156→\n 157→ return {\n 158→ tour: pointsWithCurvePos.map((p) => p.idx),\n 159→ curvePoints,\n 160→ };\n 161→};\n 162→\n 163→export default peanoSolution;\n 164→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Atomic Gosper Curve Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Gosper curve (L-system) that fills a hexagonal region\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * The Gosper curve (also known as the Peano-Gosper curve or flowsnake) is a\n 13→ * space-filling fractal curve that uses 60-degree turns on a hexagonal grid.\n 14→ *\n 15→ * L-system rules:\n 16→ * - Angle: 60 degrees\n 17→ * - Axiom: A\n 18→ * - A -> A-B--B+A++AA+B-\n 19→ * - B -> +A-BB--B-A++A+B\n 20→ * Where A and B both mean \"move forward\", + means turn left 60°, - means turn right 60°.\n 21→ *\n 22→ * Time Complexity: O(n log n)\n 23→ * Space Complexity: O(n)\n 24→ */\n 25→\n 26→import { distance } from '../../utils.js';\n 27→\n 28→/**\n 29→ * Generate Gosper curve using L-system\n 30→ *\n 31→ * L-system rules for Gosper curve:\n 32→ * Axiom: A\n 33→ * A -> A-B--B+A++AA+B-\n 34→ * B -> +A-BB--B-A++A+B\n 35→ *\n 36→ * @param {number} order - Order of the Gosper curve (number of iterations)\n 37→ * @returns {string} L-system sequence\n 38→ */\n 39→export const generateGosperCurve = (order) => {\n 40→ let sequence = 'A';\n 41→\n 42→ for (let i = 0; i < order; i++) {\n 43→ let newSequence = '';\n 44→ for (const char of sequence) {\n 45→ if (char === 'A') {\n 46→ newSequence += 'A-B--B+A++AA+B-';\n 47→ } else if (char === 'B') {\n 48→ newSequence += '+A-BB--B-A++A+B';\n 49→ } else {\n 50→ newSequence += char;\n 51→ }\n 52→ }\n 53→ sequence = newSequence;\n 54→ }\n 55→\n 56→ return sequence;\n 57→};\n 58→\n 59→/**\n 60→ * Convert Gosper curve L-system sequence to coordinate points.\n 61→ *\n 62→ * Uses turtle graphics with 60-degree angle increments.\n 63→ * Both A and B are treated as forward-drawing commands.\n 64→ *\n 65→ * @param {string} sequence - L-system sequence\n 66→ * @param {number} gridSize - Size of the target grid to normalize into\n 67→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 68→ */\n 69→export const gosperCurveToPoints = (sequence, gridSize) => {\n 70→ const curvePoints = [];\n 71→ let x = 0;\n 72→ let y = 0;\n 73→ // Direction in radians, starting facing right (0 degrees)\n 74→ let angle = 0;\n 75→ const angleStep = Math.PI / 3; // 60 degrees\n 76→\n 77→ curvePoints.push({ x, y });\n 78→\n 79→ for (const char of sequence) {\n 80→ if (char === 'A' || char === 'B') {\n 81→ // Move forward in current direction\n 82→ x += Math.cos(angle);\n 83→ y += Math.sin(angle);\n 84→ curvePoints.push({ x, y });\n 85→ } else if (char === '+') {\n 86→ // Turn left 60 degrees\n 87→ angle += angleStep;\n 88→ } else if (char === '-') {\n 89→ // Turn right 60 degrees\n 90→ angle -= angleStep;\n 91→ }\n 92→ }\n 93→\n 94→ // Find bounding box of the curve\n 95→ let minX = Infinity,\n 96→ maxX = -Infinity,\n 97→ minY = Infinity,\n 98→ maxY = -Infinity;\n 99→ for (const p of curvePoints) {\n 100→ minX = Math.min(minX, p.x);\n 101→ maxX = Math.max(maxX, p.x);\n 102→ minY = Math.min(minY, p.y);\n 103→ maxY = Math.max(maxY, p.y);\n 104→ }\n 105→\n 106→ const curveWidth = maxX - minX;\n 107→ const curveHeight = maxY - minY;\n 108→\n 109→ // Avoid division by zero for degenerate cases\n 110→ const scaleX = curveWidth > 0 ? curveWidth : 1;\n 111→ const scaleY = curveHeight > 0 ? curveHeight : 1;\n 112→\n 113→ // Normalize to [0, gridSize-1] grid coordinates\n 114→ const normalizedPoints = curvePoints.map((p) => ({\n 115→ x: Math.round(((p.x - minX) / scaleX) * (gridSize - 1)),\n 116→ y: Math.round(((p.y - minY) / scaleY) * (gridSize - 1)),\n 117→ }));\n 118→\n 119→ return normalizedPoints;\n 120→};\n 121→\n 122→/**\n 123→ * Determine the appropriate Gosper curve order for a given grid size.\n 124→ *\n 125→ * A Gosper curve of order n has 7^n segments. We choose the order\n 126→ * such that the curve provides sufficient resolution for the grid.\n 127→ *\n 128→ * @param {number} gridSize - The grid size\n 129→ * @returns {number} The appropriate order for the Gosper curve\n 130→ */\n 131→export const calculateGosperOrder = (gridSize) => {\n 132→ // Each order covers roughly sqrt(7)^n ≈ 2.646^n units in each dimension.\n 133→ // For grid sizes: 2->1, 4->2, 8->2, 16->3, 32->3, 64->4, 128->4\n 134→ // We want enough curve points to distinguish all grid positions.\n 135→ if (gridSize <= 2) {\n 136→ return 1;\n 137→ }\n 138→ if (gridSize <= 8) {\n 139→ return 2;\n 140→ }\n 141→ if (gridSize <= 32) {\n 142→ return 3;\n 143→ }\n 144→ if (gridSize <= 128) {\n 145→ return 4;\n 146→ }\n 147→ return 5;\n 148→};\n 149→\n 150→/**\n 151→ * Compute Gosper Curve solution in one step (atomic version).\n 152→ * Returns the final tour without intermediate steps.\n 153→ *\n 154→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 155→ * @param {number} gridSize - Size of the grid\n 156→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 157→ */\n 158→export const gosperSolution = (points, gridSize) => {\n 159→ if (points.length === 0) {\n 160→ return { tour: [], curvePoints: [] };\n 161→ }\n 162→\n 163→ const order = calculateGosperOrder(gridSize);\n 164→ const curveSequence = generateGosperCurve(order);\n 165→ const curvePoints = gosperCurveToPoints(curveSequence, gridSize);\n 166→\n 167→ const pointsWithCurvePos = points.map((p, idx) => {\n 168→ let minDist = Infinity;\n 169→ let curvePos = 0;\n 170→\n 171→ for (let i = 0; i < curvePoints.length; i++) {\n 172→ const d = distance(p, curvePoints[i]);\n 173→ if (d < minDist) {\n 174→ minDist = d;\n 175→ curvePos = i;\n 176→ }\n 177→ }\n 178→\n 179→ return { idx, curvePos };\n 180→ });\n 181→\n 182→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 183→\n 184→ return {\n 185→ tour: pointsWithCurvePos.map((p) => p.idx),\n 186→ curvePoints,\n 187→ };\n 188→};\n 189→\n 190→export default gosperSolution;\n 191→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Atomic Koch Snowflake Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Koch snowflake curve (fractal) that covers the grid space\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * The Koch snowflake starts as an equilateral triangle and recursively\n 13→ * replaces each line segment with a segment containing a triangular bump.\n 14→ * At sufficient depth, the curve visits all regions of the bounded area,\n 15→ * making it useful as a space-filling heuristic for TSP.\n 16→ *\n 17→ * Time Complexity: O(n log n)\n 18→ * Space Complexity: O(n)\n 19→ */\n 20→\n 21→import { distance } from '../../utils.js';\n 22→\n 23→/**\n 24→ * Generate Koch snowflake curve points using recursive subdivision.\n 25→ *\n 26→ * Starting from an equilateral triangle, each edge is subdivided into\n 27→ * four segments forming a triangular bump (the Koch construction).\n 28→ *\n 29→ * @param {number} order - Recursion depth (0 = triangle, higher = more detail)\n 30→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 31→ */\n 32→export const generateKochSnowflake = (order) => {\n 33→ // Start with an equilateral triangle (vertices in clockwise order)\n 34→ // Centered roughly at origin, pointing up\n 35→ const sqrt3over2 = Math.sqrt(3) / 2;\n 36→ const vertices = [\n 37→ { x: 0, y: -1 }, // top\n 38→ { x: sqrt3over2, y: 0.5 }, // bottom-right\n 39→ { x: -sqrt3over2, y: 0.5 }, // bottom-left\n 40→ ];\n 41→\n 42→ /**\n 43→ * Recursively subdivide a line segment using Koch construction.\n 44→ * Each segment A->B becomes A -> P1 -> Peak -> P2 -> B\n 45→ * where P1 is 1/3 along, P2 is 2/3 along, and Peak is the\n 46→ * apex of an equilateral triangle built on the middle third.\n 47→ */\n 48→ const subdivide = (ax, ay, bx, by, depth) => {\n 49→ if (depth === 0) {\n 50→ return [{ x: ax, y: ay }];\n 51→ }\n 52→\n 53→ // Divide segment into thirds\n 54→ const dx = bx - ax;\n 55→ const dy = by - ay;\n 56→\n 57→ const p1x = ax + dx / 3;\n 58→ const p1y = ay + dy / 3;\n 59→ const p2x = ax + (2 * dx) / 3;\n 60→ const p2y = ay + (2 * dy) / 3;\n 61→\n 62→ // Peak of equilateral triangle on middle third\n 63→ // Rotate middle-third vector 60 degrees counterclockwise (outward for clockwise triangle)\n 64→ const peakX = p1x + dx / 6 - (dy * Math.sqrt(3)) / 6;\n 65→ const peakY = p1y + dy / 6 + (dx * Math.sqrt(3)) / 6;\n 66→\n 67→ // Recurse on each of the 4 sub-segments\n 68→ return [\n 69→ ...subdivide(ax, ay, p1x, p1y, depth - 1),\n 70→ ...subdivide(p1x, p1y, peakX, peakY, depth - 1),\n 71→ ...subdivide(peakX, peakY, p2x, p2y, depth - 1),\n 72→ ...subdivide(p2x, p2y, bx, by, depth - 1),\n 73→ ];\n 74→ };\n 75→\n 76→ // Generate curve points for all three edges of the triangle\n 77→ let curvePoints = [];\n 78→ for (let i = 0; i < 3; i++) {\n 79→ const a = vertices[i];\n 80→ const b = vertices[(i + 1) % 3];\n 81→ curvePoints = [...curvePoints, ...subdivide(a.x, a.y, b.x, b.y, order)];\n 82→ }\n 83→\n 84→ return curvePoints;\n 85→};\n 86→\n 87→/**\n 88→ * Normalize Koch snowflake curve points to fit within a grid.\n 89→ *\n 90→ * @param {Array<{x: number, y: number}>} curvePoints - Raw curve points\n 91→ * @param {number} gridSize - Target grid size (points mapped to 0..gridSize-1)\n 92→ * @returns {Array<{x: number, y: number}>} Normalized curve points\n 93→ */\n 94→export const kochCurveToPoints = (curvePoints, gridSize) => {\n 95→ if (curvePoints.length === 0) {\n 96→ return [];\n 97→ }\n 98→\n 99→ // Find bounding box\n 100→ let minX = Infinity,\n 101→ maxX = -Infinity,\n 102→ minY = Infinity,\n 103→ maxY = -Infinity;\n 104→ for (const p of curvePoints) {\n 105→ minX = Math.min(minX, p.x);\n 106→ maxX = Math.max(maxX, p.x);\n 107→ minY = Math.min(minY, p.y);\n 108→ maxY = Math.max(maxY, p.y);\n 109→ }\n 110→\n 111→ const curveWidth = maxX - minX;\n 112→ const curveHeight = maxY - minY;\n 113→\n 114→ // Use the larger dimension to maintain aspect ratio, centering on the grid\n 115→ const maxDim = Math.max(curveWidth, curveHeight);\n 116→\n 117→ const offsetX = (maxDim - curveWidth) / 2;\n 118→ const offsetY = (maxDim - curveHeight) / 2;\n 119→\n 120→ const normalizedPoints = curvePoints.map((p) => ({\n 121→ x: Math.round(((p.x - minX + offsetX) / maxDim) * (gridSize - 1)),\n 122→ y: Math.round(((p.y - minY + offsetY) / maxDim) * (gridSize - 1)),\n 123→ }));\n 124→\n 125→ return normalizedPoints;\n 126→};\n 127→\n 128→/**\n 129→ * Determine the Koch snowflake order based on grid size.\n 130→ * Higher grid sizes need more recursion depth for adequate coverage.\n 131→ *\n 132→ * @param {number} gridSize - Moore grid size\n 133→ * @returns {number} Koch snowflake recursion order\n 134→ */\n 135→export const calculateKochOrder = (gridSize) => {\n 136→ // Each order increases points by 4x (3 sides * 4^order segments)\n 137→ // order 1: 12 points, order 2: 48, order 3: 192, order 4: 768,\n 138→ // order 5: 3072, order 6: 12288, order 7: 49152\n 139→ // We want enough resolution to distinguish grid points\n 140→ if (gridSize <= 4) {\n 141→ return 3;\n 142→ }\n 143→ if (gridSize <= 8) {\n 144→ return 4;\n 145→ }\n 146→ if (gridSize <= 16) {\n 147→ return 5;\n 148→ }\n 149→ if (gridSize <= 32) {\n 150→ return 6;\n 151→ }\n 152→ if (gridSize <= 64) {\n 153→ return 7;\n 154→ }\n 155→ return 8;\n 156→};\n 157→\n 158→/**\n 159→ * Compute Koch Snowflake solution in one step (atomic version).\n 160→ * Returns the final tour without intermediate steps.\n 161→ *\n 162→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 163→ * @param {number} mooreGridSize - Size of the Moore grid\n 164→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 165→ */\n 166→export const kochSolution = (points, mooreGridSize) => {\n 167→ if (points.length === 0) {\n 168→ return { tour: [], curvePoints: [] };\n 169→ }\n 170→\n 171→ const order = calculateKochOrder(mooreGridSize);\n 172→ const rawCurvePoints = generateKochSnowflake(order);\n 173→ const curvePoints = kochCurveToPoints(rawCurvePoints, mooreGridSize);\n 174→\n 175→ const pointsWithCurvePos = points.map((p, idx) => {\n 176→ let minDist = Infinity;\n 177→ let curvePos = 0;\n 178→\n 179→ for (let i = 0; i < curvePoints.length; i++) {\n 180→ const d = distance(p, curvePoints[i]);\n 181→ if (d < minDist) {\n 182→ minDist = d;\n 183→ curvePos = i;\n 184→ }\n 185→ }\n 186→\n 187→ return { idx, curvePos };\n 188→ });\n 189→\n 190→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 191→\n 192→ return {\n 193→ tour: pointsWithCurvePos.map((p) => p.idx),\n 194→ curvePoints,\n 195→ };\n 196→};\n 197→\n 198→export default kochSolution;\n 199→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Progressive Peano Curve Algorithm for TSP\n 3→ *\n 4→ * Also known as: Peano Space-Filling Curve, Peano Ordering\n 5→ *\n 6→ * This algorithm provides step-by-step visualization of using a Peano curve\n 7→ * to order points:\n 8→ * 1. Generate a Peano curve that fills the grid space\n 9→ * 2. Map each point to its nearest position on the curve\n 10→ * 3. Sort points by their position along the curve\n 11→ * 4. Connect points in curve-order to form a tour\n 12→ *\n 13→ * Time Complexity: O(n log n)\n 14→ * Space Complexity: O(n)\n 15→ */\n 16→\n 17→import { distance } from '../../utils.js';\n 18→import {\n 19→ generatePeanoCurve,\n 20→ peanoCurveToPoints,\n 21→} from '../../atomic/solution/peano.js';\n 22→\n 23→// Re-export atomic functions for backward compatibility\n 24→export {\n 25→ peanoSolution,\n 26→ generatePeanoCurve,\n 27→ peanoCurveToPoints,\n 28→} from '../../atomic/solution/peano.js';\n 29→\n 30→/**\n 31→ * Generate step-by-step solution using Peano Curve algorithm\n 32→ *\n 33→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 34→ * @param {number} peanoGridSize - Size of the Peano grid\n 35→ * @returns {Array<Object>} Array of steps for visualization\n 36→ */\n 37→export const peanoAlgorithmSteps = (points, peanoGridSize) => {\n 38→ if (points.length === 0) {\n 39→ return [];\n 40→ }\n 41→\n 42→ // Determine L-system iterations based on Peano grid size\n 43→ // generatePeanoCurve(n) fills a 3^n x 3^n grid\n 44→ const order = Math.max(1, Math.round(Math.log(peanoGridSize) / Math.log(3)));\n 45→ const curveSequence = generatePeanoCurve(order);\n 46→ // Generate curve points using the Peano grid size for alignment\n 47→ const curvePoints = peanoCurveToPoints(curveSequence, peanoGridSize);\n 48→ const totalCurveLength = curvePoints.length;\n 49→\n 50→ // Map each point to its position along the curve\n 51→ const pointsWithCurvePos = points.map((p, idx) => {\n 52→ let minDist = Infinity;\n 53→ let curvePos = 0;\n 54→\n 55→ for (let i = 0; i < curvePoints.length; i++) {\n 56→ const d = distance(p, curvePoints[i]);\n 57→ if (d < minDist) {\n 58→ minDist = d;\n 59→ curvePos = i;\n 60→ }\n 61→ }\n 62→\n 63→ return { ...p, idx, curvePos };\n 64→ });\n 65→\n 66→ // Sort points by their position along the curve\n 67→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 68→\n 69→ // Generate steps\n 70→ const steps = [];\n 71→ const tour = [];\n 72→\n 73→ // First step: show the Peano curve\n 74→ steps.push({\n 75→ type: 'curve',\n 76→ curvePoints,\n 77→ peanoGridSize,\n 78→ curveProgress: 0,\n 79→ tour: [],\n 80→ description: `Peano curve generated (order ${order}, ${peanoGridSize}\\u00D7${peanoGridSize} grid)`,\n 81→ });\n 82→\n 83→ for (let i = 0; i < pointsWithCurvePos.length; i++) {\n 84→ tour.push(pointsWithCurvePos[i].idx);\n 85→ // Calculate progress along the curve as a percentage\n 86→ const curveProgress = (\n 87→ (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 88→ 100\n 89→ ).toFixed(1);\n 90→\n 91→ steps.push({\n 92→ type: 'visit',\n 93→ curvePoints,\n 94→ peanoGridSize,\n 95→ curvePosition: pointsWithCurvePos[i].curvePos,\n 96→ curveProgress: parseFloat(curveProgress),\n 97→ tour: [...tour],\n 98→ description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n 99→ });\n 100→ }\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Progressive Gosper Curve Algorithm for TSP\n 3→ *\n 4→ * Also known as: Flowsnake, Peano-Gosper Curve, Fractal Space-Filling Curve\n 5→ *\n 6→ * This algorithm provides step-by-step visualization of using a Gosper curve\n 7→ * (a hexagonal space-filling fractal curve) to order points:\n 8→ * 1. Generate a Gosper curve that fills a hexagonal region\n 9→ * 2. Map each point to its nearest position on the curve\n 10→ * 3. Sort points by their position along the curve\n 11→ * 4. Connect points in curve-order to form a tour\n 12→ *\n 13→ * Time Complexity: O(n log n)\n 14→ * Space Complexity: O(n)\n 15→ */\n 16→\n 17→import { distance } from '../../utils.js';\n 18→import {\n 19→ generateGosperCurve,\n 20→ gosperCurveToPoints,\n 21→ calculateGosperOrder,\n 22→} from '../../atomic/solution/gosper.js';\n 23→\n 24→// Re-export atomic functions for backward compatibility\n 25→export {\n 26→ gosperSolution,\n 27→ generateGosperCurve,\n 28→ gosperCurveToPoints,\n 29→ calculateGosperOrder,\n 30→} from '../../atomic/solution/gosper.js';\n 31→\n 32→/**\n 33→ * Generate step-by-step solution using Gosper Curve algorithm\n 34→ *\n 35→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 36→ * @param {number} gridSize - Size of the grid\n 37→ * @returns {Array<Object>} Array of steps for visualization\n 38→ */\n 39→export const gosperAlgorithmSteps = (points, gridSize) => {\n 40→ if (points.length === 0) {\n 41→ return [];\n 42→ }\n 43→\n 44→ const order = calculateGosperOrder(gridSize);\n 45→ const curveSequence = generateGosperCurve(order);\n 46→ const curvePoints = gosperCurveToPoints(curveSequence, gridSize);\n 47→ const totalCurveLength = curvePoints.length;\n 48→\n 49→ // Map each point to its position along the curve\n 50→ const pointsWithCurvePos = points.map((p, idx) => {\n 51→ let minDist = Infinity;\n 52→ let curvePos = 0;\n 53→\n 54→ for (let i = 0; i < curvePoints.length; i++) {\n 55→ const d = distance(p, curvePoints[i]);\n 56→ if (d < minDist) {\n 57→ minDist = d;\n 58→ curvePos = i;\n 59→ }\n 60→ }\n 61→\n 62→ return { ...p, idx, curvePos };\n 63→ });\n 64→\n 65→ // Sort points by their position along the curve\n 66→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 67→\n 68→ // Generate steps\n 69→ const steps = [];\n 70→ const tour = [];\n 71→\n 72→ // First step: show the Gosper curve\n 73→ steps.push({\n 74→ type: 'curve',\n 75→ curvePoints,\n 76→ gosperGridSize: gridSize,\n 77→ curveProgress: 0,\n 78→ tour: [],\n 79→ description: `Gosper curve generated (order ${order}, ${gridSize}\\u00D7${gridSize} grid)`,\n 80→ });\n 81→\n 82→ for (let i = 0; i < pointsWithCurvePos.length; i++) {\n 83→ tour.push(pointsWithCurvePos[i].idx);\n 84→ // Calculate progress along the curve as a percentage\n 85→ const curveProgress = (\n 86→ (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 87→ 100\n 88→ ).toFixed(1);\n 89→\n 90→ steps.push({\n 91→ type: 'visit',\n 92→ curvePoints,\n 93→ gosperGridSize: gridSize,\n 94→ curvePosition: pointsWithCurvePos[i].curvePos,\n 95→ curveProgress: parseFloat(curveProgress),\n 96→ tour: [...tour],\n 97→ description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n 98→ });\n 99→ }\n 100→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→# TSP Visual Solver\n 2→\n 3→An interactive visualization tool for the Traveling Salesman Problem (TSP) that demonstrates and compares two space-filling curve-based heuristic algorithms.\n 4→\n 5→![TSP Visual Solver](https://raw.githubusercontent.com/konard/tsp/main/screenshot.png)\n 6→\n 7→## Live Demo\n 8→\n 9→Visit [https://konard.github.io/tsp](https://konard.github.io/tsp) to try the solver.\n 10→\n 11→## Features\n 12→\n 13→- **Interactive Visualization**: Watch algorithms solve TSP step-by-step with animated demonstrations\n 14→- **Two Algorithm Comparison**: Side-by-side comparison of Sonar Visit and Moore Curve algorithms\n 15→- **Grid-Aligned Points**: All points are placed on grid intersections for clean visualization\n 16→- **Progress Tracking**: Real-time progress display (angle for Sonar, percentage for Moore Curve)\n 17→- **Optimization Phase**: Optional 2-opt optimization to improve initial solutions\n 18→- **Responsive Design**: Works on desktop, tablet, and mobile devices\n 19→- **Adjustable Parameters**: Customize grid size, number of points, and animation speed\n 20→\n 21→## Project Structure\n 22→\n 23→```\n 24→./src\n 25→ algorithms/\n 26→ progressive/ # Step-by-step algorithms (for visualization)\n 27→ solution/\n 28→ sonar.js # Sonar (Radial Sweep) algorithm\n 29→ moore.js # Moore Curve algorithm\n 30→ index.js # Solution barrel export\n 31→ optimization/\n 32→ two-opt.js # Generic 2-opt segment reversal\n 33→ zigzag-opt.js # Generic zigzag adjacent swap\n 34→ index.js # Optimization barrel export\n 35→ index.js # Progressive module export\n 36→ atomic/ # All-at-once algorithms (direct computation)\n 37→ solution/\n 38→ sonar.js # Atomic Sonar solution\n 39→ moore.js # Atomic Moore solution\n 40→ index.js\n 41→ optimization/\n 42→ two-opt.js # Atomic 2-opt optimization\n 43→ zigzag-opt.js # Atomic zigzag optimization\n 44→ index.js\n 45→ index.js # Atomic module export\n 46→ verification/ # Optimal tour verification\n 47→ brute-force.js # Brute-force exact solver\n 48→ index.js # Verification barrel export\n 49→ utils.js # Shared utility functions\n 50→ index.js # Main algorithms barrel export\n 51→ ui/\n 52→ components/\n 53→ TSPVisualization.jsx # SVG-based visualization component\n 54→ Controls.jsx # Control panel component\n 55→ Legend.jsx # Color legend components\n 56→ VisualizationPanel.jsx # Complete visualization panel\n 57→ index.js # Components barrel export\n 58→ App.jsx # Main application component\n 59→ styles.css # All CSS styles\n 60→ index.js # UI module export\n 61→index.html # Main HTML entry point\n 62→README.md # This file\n 63→```\n 64→\n 65→## Algorithms\n 66→\n 67→### Sonar Visit Algorithm\n 68→\n 69→Also known as: Radial Sweep, Angular Sort, Polar Angle Sort, Centroid-based Ordering\n 70→\n 71→This algorithm works by:\n 72→\n 73→1. Computing the centroid (center of mass) of all points\n 74→2. Calculating the polar angle of each point relative to the centroid\n 75→3. Sorting points by their polar angle\n 76→4. Connecting points in angular order to form a tour\n 77→\n 78→The visualization shows a \"sweep line\" rotating around the centroid, visiting points as it passes them.\n 79→\n 80→### Moore Curve Algorithm\n 81→\n 82→Also known as: Space-Filling Curve, Hilbert Curve Variant, Fractal Ordering\n 83→\n 84→This algorithm uses a Moore curve (a variant of the Hilbert curve) to order points:\n 85→\n 86→1. Generate a Moore curve that fills the grid space\n 87→2. Map each point to its nearest position on the curve\n 88→3. Sort points by their position along the curve\n 89→4. Connect points in curve-order to form a tour\n 90→\n 91→The Moore curve is a space-filling curve that visits every cell in a grid exactly once while maintaining spatial locality, making it effective for TSP approximations.\n 92→\n 93→### Optimization\n 94→\n 95→Both algorithms support optional optimization phases. Three optimization methods are available:\n 96→\n 97→#### 2-opt (Segment Reversal)\n 98→\n 99→- Picks two non-adjacent edges, removes them, and reconnects by reversing the segment between them\n 100→- Searches all pairs of edges (O(n^2) per pass)\n 101→- Effective at removing crossing edges and untangling large detours\n 102→\n 103→#### ZigZag (Adjacent Pair Swap)\n 104→\n 105→- Examines four consecutive points and swaps the middle two if it reduces distance\n 106→- Scans the tour linearly (O(n) per pass), making it faster per iteration than 2-opt\n 107→- Effective at fine-tuning local ordering, especially in space-filling curve tours\n 108→\n 109→#### Combined (ZigZag + 2-opt)\n 110→\n 111→- Alternates between ZigZag and 2-opt until neither finds improvements\n 112→- Produces the best tour quality at the cost of longer computation time\n 113→\n 114→For a detailed comparison of ZigZag vs 2-opt, see the [case study](docs/case-studies/issue-53/README.md).\n 115→\n 116→## Usage\n 117→\n 118→### Web Application\n 119→\n 120→1. **Set Parameters**:\n 121→ - Grid Size (N): Size of the N×N grid (5-50)\n 122→ - Points (M): Number of random points to generate\n 123→ - Animation Speed: Control visualization pace\n 124→\n 125→2. **Generate Points**: Click \"New Points\" to create random grid-aligned points\n 126→\n 127→3. **Run Algorithms**: Click \"Start\" to watch both algorithms solve the TSP simultaneously\n 128→\n 129→4. **Optimize**: After initial solutions complete, click \"Optimize\" to run 2-opt improvements\n 130→\n 131→### JavaScript Library\n 132→\n 133→The algorithms can be used as a standalone JavaScript library:\n 134→\n 135→```javascript\n 136→// Progressive (step-by-step) - for visualization\n 137→import {\n 138→ sonarAlgorithmSteps,\n 139→ mooreAlgorithmSteps,\n 140→ sonarOptimizationSteps,\n 141→ mooreOptimizationSteps,\n 142→ calculateMooreGridSize,\n 143→ generateRandomPoints,\n 144→ calculateTotalDistance,\n 145→} from './src/algorithms/index.js';\n 146→\n 147→// Generate points\n 148→const gridSize = 10;\n 149→const mooreGridSize = calculateMooreGridSize(gridSize);\n 150→const points = generateRandomPoints(mooreGridSize, 15);\n 151→\n 152→// Get step-by-step solution (for animation)\n 153→const sonarSteps = sonarAlgorithmSteps(points);\n 154→const mooreSteps = mooreAlgorithmSteps(points, mooreGridSize);\n 155→\n 156→// Optimize the tour\n 157→const sonarTour = sonarSteps[sonarSteps.length - 1].tour;\n 158→const optimizationSteps = sonarOptimizationSteps(points, sonarTour);\n 159→\n 160→// Calculate total distance\n 161→const finalTour = optimizationSteps[optimizationSteps.length - 1].tour;\n 162→const distance = calculateTotalDistance(finalTour, points);\n 163→```\n 164→\n 165→```javascript\n 166→// Atomic (all-at-once) - for direct computation\n 167→import { sonarSolution, mooreSolution } from './src/algorithms/atomic/index.js';\n 168→import {\n 169→ sonarOptimization,\n 170→ mooreOptimization,\n 171→} from './src/algorithms/atomic/index.js';\n 172→\n 173→const { tour: sonarTour, centroid } = sonarSolution(points);\n 174→const { tour: mooreTour, curvePoints } = mooreSolution(points, mooreGridSize);\n 175→\n 176→const { tour: optimizedTour, improvement } = sonarOptimization(\n 177→ points,\n 178→ sonarTour\n 179→);\n 180→```\n 181→\n 182→## Technical Details\n 183→\n 184→- Built with React 18 (loaded from CDN for the web app)\n 185→- Uses SVG for high-quality, scalable rendering\n 186→- Modular architecture with clear separation of algorithms and UI\n 187→- Single-file HTML application (no build step required for basic usage)\n 188→- Babel for JSX transpilation in-browser\n 189→\n 190→## Development\n 191→\n 192→To modify the solver, you can:\n 193→\n 194→1. **Edit algorithms**: Modify files in `src/algorithms/` for algorithm changes\n 195→2. **Edit UI**: Modify files in `src/ui/` for interface changes\n 196→3. **Quick prototyping**: Edit `index.html` directly for rapid iteration\n 197→\n 198→### Running Locally\n 199→\n 200→Simply open `index.html` in a browser. The application loads all dependencies from CDN.\n 201→\n 202→### For ES Module Usage\n 203→\n 204→The `src/` directory contains ES modules that can be imported directly in modern JavaScript environments:\n 205→\n 206→```bash\n 207→# With Node.js\n 208→node --experimental-vm-modules your-script.js\n 209→\n 210→# With Deno\n 211→deno run your-script.ts\n 212→\n 213→# With modern bundlers (Vite, esbuild, etc.)\n 214→# Just import directly\n 215→```\n 216→\n 217→## Algorithm Complexity\n 218→\n 219→| Algorithm | Time Complexity | Space Complexity |\n 220→| --------------------- | --------------- | ---------------- |\n 221→| Sonar Visit | O(n log n) | O(n) |\n 222→| Moore Curve | O(n log n) | O(n) |\n 223→| 2-opt Optimization | O(n²) | O(n) |\n 224→| ZigZag Optimization | O(n²) | O(n) |\n 225→| Combined Optimization | O(n³) | O(n) |\n 226→\n 227→Where n is the number of points.\n 228→\n 229→## Performance Benchmarks\n 230→\n 231→Performance tested with Bun runtime on a 128x128 Moore grid (60s time budget, 10 random samples averaged):\n 232→\n 233→### Max Points in 60 Seconds\n 234→\n 235→| Configuration | Max Points | Avg Time | Avg Tour Distance |\n 236→| -------------------- | ---------: | -------: | ----------------: |\n 237→| **Sonar** | 16380 | 7.25ms | 296599.20 |\n 238→| **Moore** | 15650 | 5.01s | 16034.14 |\n 239→| **Sonar + Zigzag** | 10030 | 27.93ms | 135348.73 |\n 240→| **Sonar + 2-opt** | 8060 | 79.96ms | 153571.61 |\n 241→| **Moore + Zigzag** | 1040 | 331.61ms | 3862.26 |\n 242→| **Moore + 2-opt** | 1020 | 575.33ms | 3939.16 |\n 243→| **Sonar + Combined** | 810 | 28.08s | 2920.41 |\n 244→| **Moore + Combined** | 220 | 133.63ms | 1612.12 |\n 245→| **Brute Force** | 10 | 70.15ms | 359.93 |\n 246→\n 247→### Execution Time Growth\n 248→\n 249→![Execution Time Growth](benchmarks/execution-time.svg)\n 250→\n 251→### Tour Quality\n 252→\n 253→![Tour Distance](benchmarks/tour-distance.svg)\n 254→\n 255→**Key findings:**\n 256→\n 257→- **Sonar** solves the most points (16380) within 60 seconds\n 258→- **Sonar** is faster but produces longer tours\n 259→- **Moore** produces significantly better tours, especially for larger problems\n 260→- **ZigZag** outperforms **2-opt** on space-filling curve tours due to its linear-time passes and adjacent-swap strategy that matches the local misordering pattern of these algorithms ([details](docs/case-studies/issue-53/README.md))\n 261→- **Combined** (alternating ZigZag + 2-opt) produces the best tour quality\n 262→\n 263→For detailed benchmark analysis, see [BENCHMARK.md](BENCHMARK.md).\n 264→\n 265→## License\n 266→\n 267→[Unlicense](LICENSE) - Public Domain\n 268→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Atomic Sierpiński Curve Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Sierpiński curve (L-system) that fills the grid space\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * The Sierpiński curve is a space-filling curve that is more symmetrical than\n 13→ * other commonly studied space-filling curves. It has been shown to produce\n 14→ * high-quality TSP heuristic solutions.\n 15→ *\n 16→ * L-system definition (square variant):\n 17→ * Axiom: F+XF+F+XF\n 18→ * Rule: X → XF-F+F-XF+F+XF-F+F-X\n 19→ * Angle: 90°\n 20→ *\n 21→ * Reference: Platzman & Bartholdi, \"Spacefilling curves and the planar\n 22→ * travelling salesman problem\", JACM 36(4):719-737 (1989)\n 23→ *\n 24→ * Time Complexity: O(n log n)\n 25→ * Space Complexity: O(n)\n 26→ */\n 27→\n 28→import { distance } from '../../utils.js';\n 29→\n 30→/**\n 31→ * Generate Sierpiński curve using L-system\n 32→ * L-system rules for Sierpiński curve (square variant):\n 33→ * Axiom: F+XF+F+XF\n 34→ * X -> XF-F+F-XF+F+XF-F+F-X\n 35→ *\n 36→ * @param {number} order - Order of the Sierpiński curve\n 37→ * @returns {string} L-system sequence\n 38→ */\n 39→export const generateSierpinskiCurve = (order) => {\n 40→ let sequence = 'F+XF+F+XF';\n 41→\n 42→ for (let i = 0; i < order; i++) {\n 43→ let newSequence = '';\n 44→ for (const char of sequence) {\n 45→ if (char === 'X') {\n 46→ newSequence += 'XF-F+F-XF+F+XF-F+F-X';\n 47→ } else {\n 48→ newSequence += char;\n 49→ }\n 50→ }\n 51→ sequence = newSequence;\n 52→ }\n 53→\n 54→ return sequence;\n 55→};\n 56→\n 57→/**\n 58→ * Convert Sierpiński curve L-system sequence to coordinate points\n 59→ *\n 60→ * @param {string} sequence - L-system sequence\n 61→ * @param {number} gridSize - Size of the grid\n 62→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 63→ */\n 64→export const sierpinskiCurveToPoints = (sequence, gridSize) => {\n 65→ const stepSize = 1;\n 66→\n 67→ const curvePoints = [];\n 68→ let x = 0;\n 69→ let y = 0;\n 70→\n 71→ // Direction: 0=right, 1=down, 2=left, 3=up\n 72→ let direction = 0;\n 73→\n 74→ curvePoints.push({ x, y });\n 75→\n 76→ for (const char of sequence) {\n 77→ if (char === 'F') {\n 78→ if (direction === 0) {\n 79→ x += stepSize;\n 80→ } else if (direction === 1) {\n 81→ y += stepSize;\n 82→ } else if (direction === 2) {\n 83→ x -= stepSize;\n 84→ } else if (direction === 3) {\n 85→ y -= stepSize;\n 86→ }\n 87→\n 88→ curvePoints.push({ x, y });\n 89→ } else if (char === '+') {\n 90→ direction = (direction + 1) % 4;\n 91→ } else if (char === '-') {\n 92→ direction = (direction + 3) % 4;\n 93→ }\n 94→ }\n 95→\n 96→ // Find bounding box of the curve\n 97→ let minX = Infinity,\n 98→ maxX = -Infinity,\n 99→ minY = Infinity,\n 100→ maxY = -Infinity;\n 101→ for (const p of curvePoints) {\n 102→ minX = Math.min(minX, p.x);\n 103→ maxX = Math.max(maxX, p.x);\n 104→ minY = Math.min(minY, p.y);\n 105→ maxY = Math.max(maxY, p.y);\n 106→ }\n 107→\n 108→ const curveWidth = maxX - minX;\n 109→ const curveHeight = maxY - minY;\n 110→\n 111→ // Avoid division by zero for degenerate cases\n 112→ if (curveWidth === 0 || curveHeight === 0) {\n 113→ return curvePoints.map(() => ({ x: 0, y: 0 }));\n 114→ }\n 115→\n 116→ const normalizedPoints = curvePoints.map((p) => ({\n 117→ x: Math.round(((p.x - minX) / curveWidth) * (gridSize - 1)),\n 118→ y: Math.round(((p.y - minY) / curveHeight) * (gridSize - 1)),\n 119→ }));\n 120→\n 121→ return normalizedPoints;\n 122→};\n 123→\n 124→/**\n 125→ * Compute Sierpiński Curve solution in one step (atomic version).\n 126→ * Returns the final tour without intermediate steps.\n 127→ *\n 128→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 129→ * @param {number} mooreGridSize - Size of the grid (shared grid system)\n 130→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 131→ */\n 132→export const sierpinskiSolution = (points, mooreGridSize) => {\n 133→ if (points.length === 0) {\n 134→ return { tour: [], curvePoints: [] };\n 135→ }\n 136→\n 137→ // Determine L-system iterations: similar to Moore, use log2(gridSize) - 1\n 138→ const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n 139→ const curveSequence = generateSierpinskiCurve(order);\n 140→ const curvePoints = sierpinskiCurveToPoints(curveSequence, mooreGridSize);\n 141→\n 142→ const pointsWithCurvePos = points.map((p, idx) => {\n 143→ let minDist = Infinity;\n 144→ let curvePos = 0;\n 145→\n 146→ for (let i = 0; i < curvePoints.length; i++) {\n 147→ const d = distance(p, curvePoints[i]);\n 148→ if (d < minDist) {\n 149→ minDist = d;\n 150→ curvePos = i;\n 151→ }\n 152→ }\n 153→\n 154→ return { idx, curvePos };\n 155→ });\n 156→\n 157→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 158→\n 159→ return {\n 160→ tour: pointsWithCurvePos.map((p) => p.idx),\n 161→ curvePoints,\n 162→ };\n 163→};\n 164→\n 165→export default sierpinskiSolution;\n 166→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Test file for Sierpiński curve TSP algorithm\n 3→ * Works with Bun test runner\n 4→ */\n 5→\n 6→import { describe, it, expect } from 'bun:test';\n 7→import {\n 8→ sierpinskiAlgorithmSteps,\n 9→ sierpinskiSolution,\n 10→ generateSierpinskiCurve,\n 11→ sierpinskiCurveToPoints,\n 12→} from '../lib/algorithms/progressive/solution/sierpinski.js';\n 13→import {\n 14→ generateRandomPoints,\n 15→ calculateTotalDistance,\n 16→} from '../lib/algorithms/utils.js';\n 17→\n 18→// ============================================================\n 19→// Sierpiński Curve Generation Tests\n 20→// ============================================================\n 21→\n 22→describe('generateSierpinskiCurve', () => {\n 23→ it('should generate valid L-system sequence', () => {\n 24→ const sequence = generateSierpinskiCurve(1);\n 25→ expect(sequence).toContain('F');\n 26→ expect(typeof sequence).toBe('string');\n 27→ });\n 28→\n 29→ it('should expand sequence with higher order', () => {\n 30→ const order1 = generateSierpinskiCurve(1);\n 31→ const order2 = generateSierpinskiCurve(2);\n 32→ expect(order2.length).toBeGreaterThan(order1.length);\n 33→ });\n 34→\n 35→ it('should contain axiom pattern at order 0', () => {\n 36→ const sequence = generateSierpinskiCurve(0);\n 37→ expect(sequence).toBe('F+XF+F+XF');\n 38→ });\n 39→\n 40→ it('should not contain X or Y variables in F-count terms', () => {\n 41→ // The X variable is a non-drawing variable\n 42→ const sequence = generateSierpinskiCurve(2);\n 43→ // X is still present as a variable; it is not drawn\n 44→ expect(sequence).toContain('F');\n 45→ });\n 46→});\n 47→\n 48→describe('sierpinskiCurveToPoints', () => {\n 49→ it('should generate curve points', () => {\n 50→ const sequence = generateSierpinskiCurve(1);\n 51→ const points = sierpinskiCurveToPoints(sequence, 4);\n 52→ expect(points.length).toBeGreaterThan(0);\n 53→ });\n 54→\n 55→ it('should generate points within [0, gridSize-1] bounds', () => {\n 56→ const gridSize = 16;\n 57→ const sequence = generateSierpinskiCurve(3);\n 58→ const points = sierpinskiCurveToPoints(sequence, gridSize);\n 59→ points.forEach((p) => {\n 60→ expect(p.x).toBeGreaterThanOrEqual(0);\n 61→ expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n 62→ expect(p.y).toBeGreaterThanOrEqual(0);\n 63→ expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n 64→ });\n 65→ });\n 66→\n 67→ it('should fill the grid at various orders', () => {\n 68→ for (let order = 0; order <= 3; order++) {\n 69→ const gridSize = Math.pow(2, order + 1);\n 70→ const sequence = generateSierpinskiCurve(order);\n 71→ const points = sierpinskiCurveToPoints(sequence, gridSize);\n 72→\n 73→ // Curve should have more than 1 point\n 74→ expect(points.length).toBeGreaterThan(1);\n 75→\n 76→ // Bounding box should cover (0,0) to (gridSize-1, gridSize-1)\n 77→ let minX = Infinity,\n 78→ maxX = -Infinity,\n 79→ minY = Infinity,\n 80→ maxY = -Infinity;\n 81→ for (const p of points) {\n 82→ minX = Math.min(minX, p.x);\n 83→ maxX = Math.max(maxX, p.x);\n 84→ minY = Math.min(minY, p.y);\n 85→ maxY = Math.max(maxY, p.y);\n 86→ }\n 87→ expect(minX).toBe(0);\n 88→ expect(minY).toBe(0);\n 89→ expect(maxX).toBe(gridSize - 1);\n 90→ expect(maxY).toBe(gridSize - 1);\n 91→ }\n 92→ });\n 93→});\n 94→\n 95→// ============================================================\n 96→// Sierpiński Progressive Algorithm Tests\n 97→// ============================================================\n 98→\n 99→describe('sierpinskiAlgorithmSteps', () => {\n 100→ it('should return empty array for empty points', () => {\n 101→ expect(sierpinskiAlgorithmSteps([], 16)).toEqual([]);\n 102→ });\n 103→\n 104→ it('should generate curve step first', () => {\n 105→ const points = [\n 106→ { x: 0, y: 0, id: 0 },\n 107→ { x: 10, y: 10, id: 1 },\n 108→ ];\n 109→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 110→ expect(steps[0].type).toBe('curve');\n 111→ });\n 112→\n 113→ it('should include all points in final tour', () => {\n 114→ const points = [\n 115→ { x: 0, y: 0, id: 0 },\n 116→ { x: 8, y: 8, id: 1 },\n 117→ { x: 4, y: 12, id: 2 },\n 118→ ];\n 119→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 120→ const finalTour = steps[steps.length - 1].tour;\n 121→ expect(finalTour.length).toBe(points.length);\n 122→ });\n 123→\n 124→ it('should include curve points in each step', () => {\n 125→ const points = [{ x: 5, y: 5, id: 0 }];\n 126→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 127→ steps.forEach((step) => {\n 128→ expect(step.curvePoints !== undefined).toBe(true);\n 129→ expect(step.curvePoints.length).toBeGreaterThan(0);\n 130→ });\n 131→ });\n 132→\n 133→ it('should have correct number of steps (1 curve + n visit)', () => {\n 134→ const points = [\n 135→ { x: 1, y: 1, id: 0 },\n 136→ { x: 5, y: 5, id: 1 },\n 137→ { x: 10, y: 10, id: 2 },\n 138→ ];\n 139→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 140→ // 1 curve step + n visit steps\n 141→ expect(steps.length).toBe(points.length + 1);\n 142→ });\n 143→\n 144→ it('should have visit type for all non-first steps', () => {\n 145→ const points = [\n 146→ { x: 1, y: 1, id: 0 },\n 147→ { x: 5, y: 5, id: 1 },\n 148→ ];\n 149→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 150→ for (let i = 1; i < steps.length; i++) {\n 151→ expect(steps[i].type).toBe('visit');\n 152→ }\n 153→ });\n 154→});\n 155→\n 156→// ============================================================\n 157→// Sierpiński Atomic Solution Tests\n 158→// ============================================================\n 159→\n 160→describe('sierpinskiSolution', () => {\n 161→ it('should return empty tour for empty points', () => {\n 162→ const result = sierpinskiSolution([], 16);\n 163→ expect(result.tour).toEqual([]);\n 164→ expect(result.curvePoints).toEqual([]);\n 165→ });\n 166→\n 167→ it('should return tour with all point indices', () => {\n 168→ const points = [\n 169→ { x: 0, y: 0, id: 0 },\n 170→ { x: 10, y: 0, id: 1 },\n 171→ { x: 5, y: 10, id: 2 },\n 172→ ];\n 173→ const result = sierpinskiSolution(points, 16);\n 174→ expect(result.tour.length).toBe(points.length);\n 175→ expect(new Set(result.tour).size).toBe(points.length);\n 176→ });\n 177→\n 178→ it('should return curve points', () => {\n 179→ const points = [\n 180→ { x: 0, y: 0, id: 0 },\n 181→ { x: 10, y: 0, id: 1 },\n 182→ ];\n 183→ const result = sierpinskiSolution(points, 16);\n 184→ expect(result.curvePoints.length).toBeGreaterThan(0);\n 185→ });\n 186→\n 187→ it('should produce valid tour for random points', () => {\n 188→ const gridSize = 16;\n 189→ const numPoints = 20;\n 190→ const points = generateRandomPoints(gridSize, numPoints);\n 191→ const result = sierpinskiSolution(points, gridSize);\n 192→\n 193→ expect(result.tour.length).toBe(numPoints);\n 194→ expect(new Set(result.tour).size).toBe(numPoints);\n 195→\n 196→ // Calculate distance - should be finite and positive\n 197→ const dist = calculateTotalDistance(result.tour, points);\n 198→ expect(dist).toBeGreaterThan(0);\n 199→ expect(isFinite(dist)).toBe(true);\n 200→ });\n 201→\n 202→ it('should work with different grid sizes', () => {\n 203→ const gridSizes = [4, 8, 16, 32];\n 204→ for (const gridSize of gridSizes) {\n 205→ const points = generateRandomPoints(gridSize, 5);\n 206→ const result = sierpinskiSolution(points, gridSize);\n 207→ expect(result.tour.length).toBe(5);\n 208→ expect(result.curvePoints.length).toBeGreaterThan(0);\n 209→ }\n 210→ });\n 211→});\n 212→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Test file for Koch Snowflake TSP algorithm\n 3→ * Works with Bun test runner\n 4→ */\n 5→\n 6→import { describe, it, expect } from 'bun:test';\n 7→import {\n 8→ kochAlgorithmSteps,\n 9→ kochSolution,\n 10→ generateKochSnowflake,\n 11→ kochCurveToPoints,\n 12→ calculateKochOrder,\n 13→} from '../lib/algorithms/progressive/solution/koch.js';\n 14→\n 15→// ============================================================\n 16→// Koch Snowflake Algorithm Tests\n 17→// ============================================================\n 18→\n 19→describe('generateKochSnowflake', () => {\n 20→ it('should generate curve points for order 0 (triangle)', () => {\n 21→ const points = generateKochSnowflake(0);\n 22→ // Order 0: equilateral triangle = 3 edges, each with 1 segment = 3 points\n 23→ expect(points.length).toBe(3);\n 24→ });\n 25→\n 26→ it('should generate more points at higher orders', () => {\n 27→ const order1 = generateKochSnowflake(1);\n 28→ const order2 = generateKochSnowflake(2);\n 29→ expect(order2.length).toBeGreaterThan(order1.length);\n 30→ });\n 31→\n 32→ it('should generate 3 * 4^order points', () => {\n 33→ // Each edge has 4^order segments, 3 edges total\n 34→ for (let order = 0; order <= 3; order++) {\n 35→ const points = generateKochSnowflake(order);\n 36→ expect(points.length).toBe(3 * Math.pow(4, order));\n 37→ }\n 38→ });\n 39→});\n 40→\n 41→describe('kochCurveToPoints', () => {\n 42→ it('should generate normalized curve points', () => {\n 43→ const rawPoints = generateKochSnowflake(3);\n 44→ const points = kochCurveToPoints(rawPoints, 16);\n 45→ expect(points.length).toBeGreaterThan(0);\n 46→ });\n 47→\n 48→ it('should generate points within [0, gridSize-1] bounds', () => {\n 49→ const gridSize = 16;\n 50→ const rawPoints = generateKochSnowflake(5);\n 51→ const points = kochCurveToPoints(rawPoints, gridSize);\n 52→ points.forEach((p) => {\n 53→ expect(p.x).toBeGreaterThanOrEqual(0);\n 54→ expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n 55→ expect(p.y).toBeGreaterThanOrEqual(0);\n 56→ expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n 57→ });\n 58→ });\n 59→\n 60→ it('should return empty array for empty input', () => {\n 61→ const points = kochCurveToPoints([], 16);\n 62→ expect(points).toEqual([]);\n 63→ });\n 64→});\n 65→\n 66→describe('calculateKochOrder', () => {\n 67→ it('should return higher order for larger grid sizes', () => {\n 68→ const order4 = calculateKochOrder(4);\n 69→ const order16 = calculateKochOrder(16);\n 70→ const order64 = calculateKochOrder(64);\n 71→ expect(order16).toBeGreaterThan(order4);\n 72→ expect(order64).toBeGreaterThan(order16);\n 73→ });\n 74→\n 75→ it('should return at least 3 for smallest grid sizes', () => {\n 76→ expect(calculateKochOrder(2)).toBeGreaterThanOrEqual(3);\n 77→ });\n 78→});\n 79→\n 80→describe('kochAlgorithmSteps', () => {\n 81→ it('should return empty array for empty points', () => {\n 82→ expect(kochAlgorithmSteps([], 16)).toEqual([]);\n 83→ });\n 84→\n 85→ it('should generate curve step first', () => {\n 86→ const points = [\n 87→ { x: 0, y: 0, id: 0 },\n 88→ { x: 10, y: 10, id: 1 },\n 89→ ];\n 90→ const steps = kochAlgorithmSteps(points, 16);\n 91→ expect(steps[0].type).toBe('curve');\n 92→ });\n 93→\n 94→ it('should include all points in final tour', () => {\n 95→ const points = [\n 96→ { x: 0, y: 0, id: 0 },\n 97→ { x: 8, y: 8, id: 1 },\n 98→ { x: 4, y: 12, id: 2 },\n 99→ ];\n 100→ const steps = kochAlgorithmSteps(points, 16);\n 101→ const finalTour = steps[steps.length - 1].tour;\n 102→ expect(finalTour.length).toBe(points.length);\n 103→ });\n 104→\n 105→ it('should include curve points in each step', () => {\n 106→ const points = [{ x: 5, y: 5, id: 0 }];\n 107→ const steps = kochAlgorithmSteps(points, 16);\n 108→ steps.forEach((step) => {\n 109→ expect(step.curvePoints !== undefined).toBe(true);\n 110→ expect(step.curvePoints.length).toBeGreaterThan(0);\n 111→ });\n 112→ });\n 113→});\n 114→\n 115→describe('kochSolution', () => {\n 116→ it('should return empty tour for empty points', () => {\n 117→ const result = kochSolution([], 16);\n 118→ expect(result.tour).toEqual([]);\n 119→ expect(result.curvePoints).toEqual([]);\n 120→ });\n 121→\n 122→ it('should return tour with all point indices', () => {\n 123→ const points = [\n 124→ { x: 0, y: 0, id: 0 },\n 125→ { x: 10, y: 0, id: 1 },\n 126→ { x: 5, y: 10, id: 2 },\n 127→ ];\n 128→ const result = kochSolution(points, 16);\n 129→ expect(result.tour.length).toBe(points.length);\n 130→ expect(new Set(result.tour).size).toBe(points.length);\n 131→ });\n 132→\n 133→ it('should return curve points', () => {\n 134→ const points = [\n 135→ { x: 0, y: 0, id: 0 },\n 136→ { x: 15, y: 15, id: 1 },\n 137→ ];\n 138→ const result = kochSolution(points, 16);\n 139→ expect(result.curvePoints.length).toBeGreaterThan(0);\n 140→ });\n 141→});\n 142→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Progressive Koch Snowflake Algorithm for TSP\n 3→ *\n 4→ * Also known as: Koch Curve, Snowflake Fractal, Koch Fractal Ordering\n 5→ *\n 6→ * This algorithm provides step-by-step visualization of using a Koch snowflake\n 7→ * curve to order points:\n 8→ * 1. Generate a Koch snowflake curve that covers the grid space\n 9→ * 2. Map each point to its nearest position on the curve\n 10→ * 3. Sort points by their position along the curve\n 11→ * 4. Connect points in curve-order to form a tour\n 12→ *\n 13→ * Time Complexity: O(n log n)\n 14→ * Space Complexity: O(n)\n 15→ */\n 16→\n 17→import { distance } from '../../utils.js';\n 18→import {\n 19→ generateKochSnowflake,\n 20→ kochCurveToPoints,\n 21→ calculateKochOrder,\n 22→} from '../../atomic/solution/koch.js';\n 23→\n 24→// Re-export atomic functions for backward compatibility\n 25→export {\n 26→ kochSolution,\n 27→ generateKochSnowflake,\n 28→ kochCurveToPoints,\n 29→ calculateKochOrder,\n 30→} from '../../atomic/solution/koch.js';\n 31→\n 32→/**\n 33→ * Generate step-by-step solution using Koch Snowflake algorithm\n 34→ *\n 35→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 36→ * @param {number} mooreGridSize - Size of the Moore grid\n 37→ * @returns {Array<Object>} Array of steps for visualization\n 38→ */\n 39→export const kochAlgorithmSteps = (points, mooreGridSize) => {\n 40→ if (points.length === 0) {\n 41→ return [];\n 42→ }\n 43→\n 44→ const order = calculateKochOrder(mooreGridSize);\n 45→ const rawCurvePoints = generateKochSnowflake(order);\n 46→ const curvePoints = kochCurveToPoints(rawCurvePoints, mooreGridSize);\n 47→ const totalCurveLength = curvePoints.length;\n 48→\n 49→ // Map each point to its position along the curve\n 50→ const pointsWithCurvePos = points.map((p, idx) => {\n 51→ let minDist = Infinity;\n 52→ let curvePos = 0;\n 53→\n 54→ for (let i = 0; i < curvePoints.length; i++) {\n 55→ const d = distance(p, curvePoints[i]);\n 56→ if (d < minDist) {\n 57→ minDist = d;\n 58→ curvePos = i;\n 59→ }\n 60→ }\n 61→\n 62→ return { ...p, idx, curvePos };\n 63→ });\n 64→\n 65→ // Sort points by their position along the curve\n 66→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 67→\n 68→ // Generate steps\n 69→ const steps = [];\n 70→ const tour = [];\n 71→\n 72→ // First step: show the Koch snowflake curve\n 73→ steps.push({\n 74→ type: 'curve',\n 75→ curvePoints,\n 76→ mooreGridSize,\n 77→ curveProgress: 0,\n 78→ tour: [],\n 79→ description: `Koch snowflake generated (order ${order}, ${mooreGridSize}×${mooreGridSize} grid)`,\n 80→ });\n 81→\n 82→ for (let i = 0; i < pointsWithCurvePos.length; i++) {\n 83→ tour.push(pointsWithCurvePos[i].idx);\n 84→ // Calculate progress along the curve as a percentage\n 85→ const curveProgress = (\n 86→ (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 87→ 100\n 88→ ).toFixed(1);\n 89→\n 90→ steps.push({\n 91→ type: 'visit',\n 92→ curvePoints,\n 93→ mooreGridSize,\n 94→ curvePosition: pointsWithCurvePos[i].curvePos,\n 95→ curveProgress: parseFloat(curveProgress),\n 96→ tour: [...tour],\n 97→ description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n 98→ });\n 99→ }\n 100→\n 101→ return steps;\n 102→};\n 103→\n 104→export default kochAlgorithmSteps;\n 105→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→#!/usr/bin/env node\n 2→\n 3→/* global performance */\n 4→\n 5→/**\n 6→ * TSP Algorithms CLI\n 7→ *\n 8→ * A globally installable command-line interface for running TSP algorithms.\n 9→ * Uses atomic algorithms (all-at-once computation) for benchmark-like performance.\n 10→ *\n 11→ * Supports:\n 12→ * - Algorithm selection: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force\n 13→ * - Optimization: 2-opt, 3-opt, k-opt, lin-kernighan, lkh, zigzag, combined, none\n 14→ * - Random point generation with configurable grid size and point count\n 15→ * - Manual point input via links notation (coordinate pairs as links)\n 16→ * - Verification via lower-bound (1-tree) analysis\n 17→ *\n 18→ * @example\n 19→ * # Random points with sonar algorithm\n 20→ * tsp-algorithms --algorithm sonar --num-points 20 --grid-size 32\n 21→ *\n 22→ * # Moore curve with 2-opt optimization\n 23→ * tsp-algorithms --algorithm moore --optimization 2-opt --num-points 50\n 24→ *\n 25→ * # Lin-Kernighan-Helsgaun optimization\n 26→ * tsp-algorithms --algorithm moore --optimization lkh --num-points 50\n 27→ *\n 28→ * # Manual points via links notation\n 29→ * tsp-algorithms --algorithm sonar --points \"0 0 5 5 10 2 3 8 7 7\"\n 30→ *\n 31→ * # Brute-force with verification\n 32→ * tsp-algorithms --algorithm brute-force --num-points 8 --verify\n 33→ */\n 34→\n 35→import { makeConfig } from 'lino-arguments';\n 36→import {\n 37→ atomic,\n 38→ verification,\n 39→ calculateTotalDistance,\n 40→ generateRandomPoints,\n 41→ calculateMooreGridSize,\n 42→ calculatePeanoGridSize,\n 43→ VALID_GRID_SIZES,\n 44→ VALID_PEANO_GRID_SIZES,\n 45→} from '../lib/index.js';\n 46→\n 47→const {\n 48→ sonarSolution,\n 49→ mooreSolution,\n 50→ gosperSolution,\n 51→ peanoSolution,\n 52→ sierpinskiSolution,\n 53→ combSolution,\n 54→ sawSolution,\n 55→ kochSolution,\n 56→ spaceFillingTreeSolution,\n 57→ bruteForceSolution,\n 58→ twoOpt,\n 59→ threeOpt,\n 60→ kOpt,\n 61→ zigzagOpt,\n 62→ combinedOpt,\n 63→ linKernighan,\n 64→ lkHelsgaun,\n 65→ BRUTE_FORCE_MAX_POINTS,\n 66→} = atomic;\n 67→\n 68→const { verifyOptimality } = verification;\n 69→\n 70→/**\n 71→ * Parse points from a flat coordinate string.\n 72→ * Coordinates are provided as space-separated pairs: \"x1 y1 x2 y2 ...\"\n 73→ *\n 74→ * @param {string} pointsStr - Space-separated coordinate pairs\n 75→ * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n 76→ */\n 77→const parsePoints = (pointsStr) => {\n 78→ const values = pointsStr.trim().split(/\\s+/).map(Number);\n 79→\n 80→ if (values.length < 4 || values.length % 2 !== 0) {\n 81→ throw new Error(\n 82→ 'Points must be space-separated coordinate pairs (at least 2 points): \"x1 y1 x2 y2 ...\"'\n 83→ );\n 84→ }\n 85→\n 86→ if (values.some((v) => isNaN(v))) {\n 87→ throw new Error('All coordinates must be valid numbers');\n 88→ }\n 89→\n 90→ const points = [];\n 91→ for (let i = 0; i < values.length; i += 2) {\n 92→ points.push({ x: values[i], y: values[i + 1], id: points.length });\n 93→ }\n 94→ return points;\n 95→};\n 96→\n 97→/**\n 98→ * Format a tour for display.\n 99→ *\n 100→ * @param {number[]} tour - Array of point indices\n 101→ * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n 102→ * @returns {string} Formatted tour string\n 103→ */\n 104→const formatTour = (tour, points) => {\n 105→ const coordStr = tour\n 106→ .map((idx) => `(${points[idx].x}, ${points[idx].y})`)\n 107→ .join(' → ');\n 108→ return `${tour.join(' → ')}\\n Coordinates: ${coordStr}`;\n 109→};\n 110→\n 111→/**\n 112→ * Run the selected algorithm on the given points.\n 113→ *\n 114→ * @param {string} algorithm - Algorithm name\n 115→ * @param {Array<{x: number, y: number, id: number}>} points - Points\n 116→ * @param {number} gridSize - Grid size (for Moore algorithm)\n 117→ * @returns {{tour: number[], label: string}} Result\n 118→ */\n 119→const runAlgorithm = (algorithm, points, gridSize) => {\n 120→ switch (algorithm) {\n 121→ case 'sonar': {\n 122→ const result = sonarSolution(points);\n 123→ return { tour: result.tour, label: 'Sonar (Radial Sweep)' };\n 124→ }\n 125→ case 'moore': {\n 126→ const mooreGrid = calculateMooreGridSize(gridSize);\n 127→ const result = mooreSolution(points, mooreGrid);\n 128→ return { tour: result.tour, label: `Moore Curve (grid: ${mooreGrid})` };\n 129→ }\n 130→ case 'gosper': {\n 131→ const gosperGrid = calculateMooreGridSize(gridSize);\n 132→ const result = gosperSolution(points, gosperGrid);\n 133→ return {\n 134→ tour: result.tour,\n 135→ label: `Gosper Curve (grid: ${gosperGrid})`,\n 136→ };\n 137→ }\n 138→ case 'peano': {\n 139→ const peanoGrid = calculatePeanoGridSize(gridSize);\n 140→ const result = peanoSolution(points, peanoGrid);\n 141→ return {\n 142→ tour: result.tour,\n 143→ label: `Peano Curve (grid: ${peanoGrid})`,\n 144→ };\n 145→ }\n 146→ case 'sierpinski': {\n 147→ const sierpinskiGrid = calculateMooreGridSize(gridSize);\n 148→ const result = sierpinskiSolution(points, sierpinskiGrid);\n 149→ return {\n 150→ tour: result.tour,\n 151→ label: `Sierpiński Curve (grid: ${sierpinskiGrid})`,\n 152→ };\n 153→ }\n 154→ case 'comb': {\n 155→ const result = combSolution(points);\n 156→ return { tour: result.tour, label: 'Comb (Serpentine Scan)' };\n 157→ }\n 158→ case 'saw': {\n 159→ const result = sawSolution(points);\n 160→ return {\n 161→ tour: result.tour,\n 162→ label: 'Self-Avoiding Walk (Nearest Neighbor)',\n 163→ };\n 164→ }\n 165→ case 'koch': {\n 166→ const mooreGrid = calculateMooreGridSize(gridSize);\n 167→ const result = kochSolution(points, mooreGrid);\n 168→ return {\n 169→ tour: result.tour,\n 170→ label: `Koch Snowflake (grid: ${mooreGrid})`,\n 171→ };\n 172→ }\n 173→ case 'space-filling-tree': {\n 174→ const result = spaceFillingTreeSolution(points);\n 175→ return { tour: result.tour, label: 'Space-Filling Tree (Quadtree DFS)' };\n 176→ }\n 177→ case 'brute-force': {\n 178→ if (points.length > BRUTE_FORCE_MAX_POINTS) {\n 179→ throw new Error(\n 180→ `Brute-force is limited to ${BRUTE_FORCE_MAX_POINTS} points (got ${points.length})`\n 181→ );\n 182→ }\n 183→ const result = bruteForceSolution(points);\n 184→ return {\n 185→ tour: result.tour,\n 186→ label: `Brute-Force (optimal distance: ${result.distance.toFixed(2)})`,\n 187→ };\n 188→ }\n 189→ default:\n 190→ throw new Error(\n 191→ `Unknown algorithm: ${algorithm}. Choose from: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force`\n 192→ );\n 193→ }\n 194→};\n 195→\n 196→/**\n 197→ * Apply the selected optimization to a tour.\n 198→ *\n 199→ * @param {string} optimization - Optimization name\n 200→ * @param {Array<{x: number, y: number}>} points - Points\n 201→ * @param {number[]} tour - Initial tour\n 202→ * @returns {{tour: number[], improvement: number, label: string}} Optimized result\n 203→ */\n 204→const runOptimization = (optimization, points, tour) => {\n 205→ switch (optimization) {\n 206→ case 'none':\n 207→ return { tour, improvement: 0, label: 'None' };\n 208→ case '2-opt': {\n 209→ const result = twoOpt(points, tour);\n 210→ return { ...result, label: '2-opt' };\n 211→ }\n 212→ case '3-opt': {\n 213→ const result = threeOpt(points, tour);\n 214→ return { ...result, label: '3-opt' };\n 215→ }\n 216→ case 'k-opt': {\n 217→ const result = kOpt(points, tour);\n 218→ return { ...result, label: 'k-opt' };\n 219→ }\n 220→ case 'zigzag': {\n 221→ const result = zigzagOpt(points, tour);\n 222→ return { ...result, label: 'Zigzag' };\n 223→ }\n 224→ case 'combined': {\n 225→ const result = combinedOpt(points, tour);\n 226→ return { ...result, label: 'Combined (zigzag + 2-opt)' };\n 227→ }\n 228→ case 'lin-kernighan': {\n 229→ const result = linKernighan(points, tour);\n 230→ return { ...result, label: 'Lin-Kernighan' };\n 231→ }\n 232→ case 'lkh': {\n 233→ const result = lkHelsgaun(points, tour);\n 234→ return { ...result, label: 'Lin-Kernighan-Helsgaun' };\n 235→ }\n 236→ default:\n 237→ throw new Error(\n 238→ `Unknown optimization: ${optimization}. Choose from: none, 2-opt, 3-opt, k-opt, lin-kernighan, lkh, zigzag, combined`\n 239→ );\n 240→ }\n 241→};\n 242→\n 243→/**\n 244→ * Main CLI entry point.\n 245→ * Parses arguments, runs the algorithm, and prints results.\n 246→ */\n 247→// eslint-disable-next-line max-lines-per-function\n 248→export const main = () => {\n 249→ const config = makeConfig({\n 250→ yargs: ({ yargs, getenv }) =>\n 251→ yargs\n 252→ .usage('Usage: $0 [options]')\n 253→ .option('algorithm', {\n 254→ alias: 'a',\n 255→ type: 'string',\n 256→ describe: 'TSP algorithm to use',\n 257→ choices: [\n 258→ 'sonar',\n 259→ 'moore',\n 260→ 'gosper',\n 261→ 'peano',\n 262→ 'sierpinski',\n 263→ 'comb',\n 264→ 'saw',\n 265→ 'koch',\n 266→ 'space-filling-tree',\n 267→ 'brute-force',\n 268→ ],\n 269→ default: getenv('TSP_ALGORITHM', 'sonar'),\n 270→ })\n 271→ .option('optimization', {\n 272→ alias: 'o',\n 273→ type: 'string',\n 274→ describe: 'Optimization to apply after initial solution',\n 275→ choices: [\n 276→ 'none',\n 277→ '2-opt',\n 278→ '3-opt',\n 279→ 'k-opt',\n 280→ 'lin-kernighan',\n 281→ 'lkh',\n 282→ 'zigzag',\n 283→ 'combined',\n 284→ ],\n 285→ default: getenv('TSP_OPTIMIZATION', 'none'),\n 286→ })\n 287→ .option('num-points', {\n 288→ alias: 'n',\n 289→ type: 'number',\n 290→ describe: 'Number of random points to generate',\n 291→ default: getenv('TSP_NUM_POINTS', 10),\n 292→ })\n 293→ .option('grid-size', {\n 294→ alias: 'g',\n 295→ type: 'number',\n 296→ describe: `Grid size for point generation (Moore: ${VALID_GRID_SIZES.join(', ')}; Peano: ${VALID_PEANO_GRID_SIZES.join(', ')})`,\n 297→ default: getenv('TSP_GRID_SIZE', 16),\n 298→ })\n 299→ .option('points', {\n 300→ alias: 'p',\n 301→ type: 'string',\n 302→ describe:\n 303→ 'Manual points as space-separated coordinate pairs: \"x1 y1 x2 y2 ...\"',\n 304→ })\n 305→ .option('verify', {\n 306→ alias: 'v',\n 307→ type: 'boolean',\n 308→ describe: 'Run lower-bound verification on the result',\n 309→ default: false,\n 310→ })\n 311→ .option('json', {\n 312→ alias: 'j',\n 313→ type: 'boolean',\n 314→ describe: 'Output results as JSON',\n 315→ default: false,\n 316→ })\n 317→ .example(\n 318→ '$0 --algorithm sonar --num-points 20',\n 319→ 'Sonar with 20 random points'\n 320→ )\n 321→ .example(\n 322→ '$0 -a moore -o 2-opt -n 50 -g 32',\n 323→ 'Moore + 2-opt, 50 points on 32x32 grid'\n 324→ )\n 325→ .example(\n 326→ '$0 -a moore -o lkh -n 50',\n 327→ 'Moore + LKH optimization, 50 points'\n 328→ )\n 329→ .example(\n 330→ '$0 -a peano -o 2-opt -n 50 -g 27',\n 331→ 'Peano + 2-opt, 50 points on 27x27 grid'\n 332→ )\n 333→ .example(\n 334→ '$0 -a brute-force --points \"0 0 5 5 10 2 3 8\"',\n 335→ 'Brute-force with manual points'\n 336→ )\n 337→ .example(\n 338→ '$0 -a sonar -o combined -n 100 --verify',\n 339→ 'Sonar + combined + verify'\n 340→ )\n 341→ .epilog(\n 342→ 'TSP Algorithms CLI — https://github.com/konard/tsp\\n' +\n 343→ 'Runs atomic (all-at-once) algorithms for benchmark-like performance.'\n 344→ ),\n 345→ });\n 346→\n 347→ const {\n 348→ algorithm,\n 349→ optimization,\n 350→ numPoints,\n 351→ gridSize,\n 352→ points: pointsStr,\n 353→ verify,\n 354→ json: jsonOutput,\n 355→ } = config;\n 356→\n 357→ try {\n 358→ // Generate or parse points\n 359→ let points;\n 360→ if (pointsStr) {\n 361→ points = parsePoints(pointsStr);\n 362→ } else {\n 363→ const mooreGrid = calculateMooreGridSize(gridSize);\n 364→ points = generateRandomPoints(mooreGrid, numPoints);\n 365→ }\n 366→\n 367→ // Measure total execution time\n 368→ const startTime = performance.now();\n 369→\n 370→ // Run algorithm\n 371→ const algoResult = runAlgorithm(algorithm, points, gridSize);\n 372→\n 373→ // Run optimization\n 374→ const optResult = runOptimization(optimization, points, algoResult.tour);\n 375→\n 376→ // Calculate distances\n 377→ const initialDistance = calculateTotalDistance(algoResult.tour, points);\n 378→ const finalDistance = calculateTotalDistance(optResult.tour, points);\n 379→\n 380→ const endTime = performance.now();\n 381→ const timeMs = endTime - startTime;\n 382→\n 383→ // Verification\n 384→ let verificationResult = null;\n 385→ if (verify) {\n 386→ verificationResult = verifyOptimality(finalDistance, points);\n 387→ }\n 388→\n 389→ if (jsonOutput) {\n 390→ const output = {\n 391→ algorithm: algoResult.label,\n 392→ optimization: optResult.label,\n 393→ points: points.length,\n 394→ gridSize,\n 395→ tour: optResult.tour,\n 396→ initialDistance,\n 397→ finalDistance,\n 398→ improvement: optResult.improvement,\n 399→ timeMs: Math.round(timeMs * 100) / 100,\n 400→ };\n 401→ if (verificationResult) {\n 402→ output.verification = verificationResult;\n 403→ }\n 404→ console.log(JSON.stringify(output, null, 2));\n 405→ } else {\n 406→ console.log('TSP Algorithms CLI');\n 407→ console.log('==================\\n');\n 408→ console.log(`Algorithm: ${algoResult.label}`);\n 409→ console.log(`Optimization: ${optResult.label}`);\n 410→ console.log(`Points: ${points.length}`);\n 411→ console.log(`Grid size: ${gridSize}\\n`);\n 412→\n 413→ console.log(`Tour: ${formatTour(optResult.tour, points)}\\n`);\n 414→\n 415→ console.log(`Initial distance: ${initialDistance.toFixed(2)}`);\n 416→ if (optimization !== 'none') {\n 417→ console.log(`Final distance: ${finalDistance.toFixed(2)}`);\n 418→ console.log(`Improvement: ${optResult.improvement.toFixed(2)}`);\n 419→ }\n 420→ console.log(`Time: ${timeMs.toFixed(2)}ms`);\n 421→\n 422→ if (verificationResult) {\n 423→ console.log('\\nVerification (1-tree lower bound):');\n 424→ console.log(\n 425→ ` Lower bound: ${verificationResult.lowerBound.toFixed(2)}`\n 426→ );\n 427→ console.log(\n 428→ ` Gap: ${verificationResult.gapPercent.toFixed(2)}%`\n 429→ );\n 430→ console.log(\n 431→ ` Optimal: ${verificationResult.isOptimal ? 'YES ✓' : 'Not proven'}`\n 432→ );\n 433→ }\n 434→ }\n 435→ } catch (error) {\n 436→ if (jsonOutput) {\n 437→ console.error(JSON.stringify({ error: error.message }));\n 438→ } else {\n 439→ console.error(`Error: ${error.message}`);\n 440→ }\n 441→ process.exitCode = 1;\n 442→ }\n 443→};\n 444→\n 445→main();\n 446→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"text": "Perfect! Now I have all the information I need. Let me compile a comprehensive exploration report.\n\n## Comprehensive Repository Exploration Report\n\nI've completed a thorough exploration of the TSP (Traveling Salesman Problem) solver repository. Here's my detailed analysis:\n\n### 1. DIRECTORY STRUCTURE\n\n```\n/tmp/gh-issue-solver-1770502738798/\n├── src/\n│ ├── lib/\n│ │ └── algorithms/\n│ │ ├── atomic/ # All-at-once algorithms (final result directly)\n│ │ │ ├── solution/ # Algorithm implementations\n│ │ │ │ ├── moore.js, peano.js, gosper.js, koch.js\n│ │ │ │ ├── sierpinski.js, sonar.js, saw.js, comb.js\n│ │ │ │ ├── space-filling-tree.js, brute-force.js\n│ │ │ │ └── index.js (exports)\n│ │ │ └── optimization/ # Improvement algorithms\n│ │ │ ├── 2-opt, 3-opt, k-opt, lin-kernighan, lk-helsgaun\n│ │ │ ├── zigzag-opt, combined-opt\n│ │ │ └── index.js\n│ │ ├── progressive/ # Step-by-step (with visualization steps)\n│ │ │ ├── solution/ # Progressive versions (reuse atomic)\n│ │ │ └── optimization/\n│ │ ├── verification/ # Optimality proofs\n│ │ │ └── lower-bound.js\n│ │ ├── utils.js # Shared utilities\n│ │ └── index.js # Main export barrel\n│ ├── app/\n│ │ └── ui/\n│ │ └── components/ # React UI components\n│ └── cli/\n│ └── index.js # CLI entry point\n├── src/tests/\n│ ├── index.test.js # Main test file\n│ ├── sierpinski-algorithm.test.js\n│ ├── koch-algorithm.test.js\n│ ├── gosper-algorithm.test.js\n│ ├── peano-algorithm.test.js\n│ └── ... (other test files)\n├── experiments/\n│ ├── moore-curve-*.js\n│ └── ... (experimental scripts)\n├── docs/case-studies/\n│ └── (documentation for previous issues)\n├── package.json\n├── bunfig.toml\n├── eslint.config.js\n├── .prettierrc\n└── README.md\n```\n\n### 2. MOORE CURVE IMPLEMENTATION - ALL FILES\n\n**Atomic Moore (Main Implementation):**\n- `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/moore.js` (152 lines)\n\n**Progressive Moore (Step-by-step Wrapper):**\n- `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/progressive/solution/moore.js` (109 lines)\n\n**Key Functions in Moore:**\n- `generateMooreCurve(order)` - L-system generation (axiom: \"LFL+F+LFL\", rules for L and R)\n- `mooreCurveToPoints(sequence, gridSize)` - Convert to coordinates with bounding box normalization\n- `mooreSolution(points, gridSize)` - Maps points to curve, sorts by position, returns tour\n\n**Moore Curve Details:**\n- Uses L-system with quaternary (4-directional) turtle graphics (0=up, 1=right, 2=down, 3=left)\n- Fills 2^n × 2^n grids (VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128])\n- Order calculation: `order = Math.round(Math.log2(gridSize)) - 1`\n- Algorithm: grid size → L-system order → generate curve → find nearest points → sort → return tour\n\n### 3. RELATED SPACE-FILLING CURVE ALGORITHMS\n\nAll follow the same pattern as Moore but with different L-system rules:\n\n**Peano Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/peano.js`\n- Fills 3^n × 3^n grids (VALID_PEANO_GRID_SIZES = [3, 9, 27, 81])\n- L-system: Axiom \"X\", Rules: X → XFYFX+F+YFXFY-F-XFYFX, Y → YFXFY-F-XFYFX+F+YFXFY\n- Order: `Math.round(Math.log(gridSize) / Math.log(3))`\n\n**Sierpinski Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/sierpinski.js`\n- Fills 2^n × 2^n grids (uses Moore grid sizes)\n- L-system: Axiom \"F+XF+F+XF\", Rule: X → XF-F+F-XF+F+XF-F+F-X\n- Order: Same as Moore (`Math.round(Math.log2(gridSize)) - 1`)\n\n**Gosper Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/gosper.js`\n- Hexagonal space-filling curve (60-degree angles)\n- L-system: Axiom \"A\", Rules: A → A-B--B+A++AA+B-, B → +A-BB--B-A++A+B\n- Uses 60° angle increments (π/3 radians) with trigonometric positioning\n- Has custom order calculation: `calculateGosperOrder(gridSize)` based on heuristics\n\n**Koch Snowflake:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/koch.js`\n- Not L-system based; uses recursive subdivision\n- `generateKochSnowflake(order)` - Equilateral triangle with recursive Koch construction\n- `kochCurveToPoints()` - Normalizes to grid\n- Order calculation: `calculateKochOrder(gridSize)` (returns 3-8 depending on grid size)\n\n### 4. TEST STRUCTURE AND PATTERNS\n\n**Test Framework:** Bun test runner\n**Test File Pattern:**\n```javascript\nimport { describe, it, expect } from 'bun:test';\nimport { generateXyzCurve, xyzCurveToPoints, xyzAlgorithmSteps, xyzSolution } from '../lib/algorithms/...';\n```\n\n**Test Categories (from sierpinski-algorithm.test.js as template):**\n\n1. **Generation Tests** (e.g., `generateSierpinskiCurve`)\n - Valid sequence generation\n - Sequence expansion with higher orders\n - Axiom pattern verification\n\n2. **Normalization Tests** (e.g., `sierpinskiCurveToPoints`)\n - Points within [0, gridSize-1] bounds\n - Bounding box coverage (minX=0, maxX=gridSize-1, etc.)\n - Handles degenerate cases (zero-extent curves)\n\n3. **Progressive Algorithm Tests** (e.g., `sierpinskiAlgorithmSteps`)\n - Empty points return empty array\n - First step is 'curve' type\n - All points in final tour\n - Curve points present in each step\n - Correct step count (1 curve + n visits)\n - 'visit' type for non-first steps\n\n4. **Atomic Solution Tests** (e.g., `sierpinskiSolution`)\n - Empty points return empty tour + empty curve\n - All point indices in final tour\n - No duplicates in tour (Set size = points.length)\n - Curve points generated\n - Valid distance (finite, positive)\n - Works with different grid sizes\n\n### 5. CONFIGURATION FILES\n\n**package.json:**\n- Name: `tsp-algorithms`, Version: 0.5.0\n- Main: `src/lib/index.js`\n- CLI bin: `src/cli/index.js`\n- TypeScript definitions: `src/lib/index.d.ts`\n- Test runner: Bun (`bun test`)\n- Linter: ESLint\n- Formatter: Prettier (width: 80, semi: true, single quotes, 2-space tabs)\n\n**prettier.rc:**\n```json\n{\n \"semi\": true,\n \"trailingComma\": \"es5\",\n \"singleQuote\": true,\n \"printWidth\": 80,\n \"tabWidth\": 2,\n \"useTabs\": false,\n \"arrowParens\": \"always\",\n \"endOfLine\": \"lf\"\n}\n```\n\n**Scripts:**\n- `npm test` or `bun test` - Run all tests\n- `npm run lint` - Run ESLint\n- `npm run format:check` - Check Prettier formatting\n- `npm run check` - All checks (lint + format + duplication)\n- `npm run dev` - Development with hot reload\n- `npm run build:app` - Build web app\n\n### 6. CONTRIBUTING GUIDELINES\n\n**Patterns for Adding New Algorithms:**\n\n1. **Create Atomic Version** (`src/lib/algorithms/atomic/solution/xyz.js`):\n ```javascript\n - Export generateXyzCurve(order) // or custom generation\n - Export xyzCurveToPoints(sequence, gridSize) // normalize to grid\n - Export xyzSolution(points, gridSize) // main algorithm\n - Handle empty points edge case\n - Return { tour: [], curvePoints: [] }\n ```\n\n2. **Create Progressive Wrapper** (`src/lib/algorithms/progressive/solution/xyz.js`):\n ```javascript\n - Import atomic functions\n - Re-export atomic functions for compatibility\n - Export xyzAlgorithmSteps(points, gridSize)\n - Generate steps: first 'curve' step, then 'visit' steps\n - Include description, curveProgress, curvePoints in each step\n ```\n\n3. **Update Index Files:**\n - Add exports to `atomic/solution/index.js`\n - Add exports to `progressive/solution/index.js`\n - Add exports to main `index.js` with examples\n\n4. **Create Tests** (`src/tests/xyz-algorithm.test.js`):\n ```javascript\n - Test curve generation (valid sequence, expansion, axiom)\n - Test point normalization (bounds, bounding box)\n - Test progressive algorithm (empty, curve type, tour count)\n - Test atomic solution (empty, indices, distance, grid sizes)\n ```\n\n5. **CLI Integration** (update `src/cli/index.js`):\n - Add algorithm case in `runAlgorithm()`\n - Import solution function\n - Add to algorithm choices in yargs config\n\n### 7. CODE PATTERNS AND CONVENTIONS\n\n**Algorithm Structure (All Algorithms Follow):**\n1. Generate curve (L-system or recursive)\n2. Normalize curve to grid coordinates\n3. Map each point to nearest curve position\n4. Sort points by curve position\n5. Return tour indices in sorted order\n\n**Progressive vs Atomic:**\n- **Atomic:** Returns final result directly: `{ tour: number[], curvePoints: Point[] }`\n- **Progressive:** Returns array of visualization steps with `{ type, curvePoints, tour, description, curveProgress }`\n\n**Shared Utilities** (`src/lib/algorithms/utils.js`):\n- `distance(p1, p2)` - Euclidean distance\n- `calculateTotalDistance(tour, points)` - Closed loop distance\n- `generateRandomPoints(gridSize, numPoints)` - Random point generation\n- `calculateMooreGridSize(gridSize)` - Snap to nearest power of 2\n- `calculatePeanoGridSize(gridSize)` - Snap to nearest power of 3\n- `VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128]`\n- `VALID_PEANO_GRID_SIZES = [3, 9, 27, 81]`\n\n**Type Definitions:**\n```javascript\nPoint: { x: number, y: number, id?: number }\nProgressiveStep: { \n type: 'curve' | 'visit', \n curvePoints: Point[], \n tour: number[], \n description: string, \n curveProgress: number,\n [gridSizeField]: number\n}\nAtomicResult: { tour: number[], curvePoints: Point[] }\n```\n\n### 8. BUILD AND TEST COMMANDS\n\n```bash\n# Install dependencies\nnpm install # or bun install\n\n# Run tests\nnpm test # or bun test\nnpm run test:e2e # E2E tests with Playwright\n\n# Code quality\nnpm run lint # Run ESLint\nnpm run lint:fix # Fix ESLint issues\nnpm run format:check # Check Prettier formatting\nnpm run format # Format all files\nnpm run check:duplication # Check for code duplication\nnpm run check # All checks combined\n\n# Build\nnpm run build # Build app\nnpm run dev # Dev mode with hot reload\n\n# Package management\nnpm run changeset # Create changeset entry\nnpm run changeset:version # Version bump from changeset\nnpm run changeset:publish # Publish to npm\n```\n\n### 9. BRANCH AND ISSUE CONTEXT\n\n**Current Branch:** `issue-65-37a562943dfc`\n**Main Branch:** `main`\n**Issue:** https://github.com/konard/tsp/issues/65\n\nThe branch is prepared for implementing a U-fork fractal algorithm as mentioned in the CLAUDE.md task file. The repository structure and patterns are fully established for adding new space-filling curve algorithms.\n\n### 10. KEY INSIGHTS FOR U-FORK IMPLEMENTATION\n\nBased on this exploration, here's what you'll need to replicate the patterns:\n\n1. **Define the U-fork L-system rules** (axiom, production rules for each variable)\n2. **Implement turtle graphics interpreter** (handle F, +, - commands and angle system)\n3. **Create two files:**\n - `atomic/solution/u-fork.js` with `generateUForkCurve()`, `uForkCurveToPoints()`, `uForkSolution()`\n - `progressive/solution/u-fork.js` with `uForkAlgorithmSteps()` wrapper\n4. **Add comprehensive tests** following the sierpinski/koch test patterns\n5. **Update all index.js files** to export new functions\n6. **Update CLI** with u-fork algorithm option\n7. **Follow code style:** 80-char lines, single quotes, 2-space indentation, trailing commas (ES5)\n\nAll existing implementations provide excellent templates for this work!"
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"prompt": "Explore the repository at /tmp/gh-issue-solver-1770502738798/ thoroughly. I need to understand:\n1. The full directory structure\n2. The existing Moore Curve implementation - all files related to it\n3. The test structure and patterns\n4. Any configuration files (package.json, tsconfig, etc.)\n5. Contributing guidelines\n6. Build/test commands\n\nBe very thorough - find ALL files related to the Moore Curve algorithm and any related algorithms. I need to understand the patterns used so I can replicate them for a U-fork fractal algorithm.",
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"text": "Perfect! Now I have all the information I need. Let me compile a comprehensive exploration report.\n\n## Comprehensive Repository Exploration Report\n\nI've completed a thorough exploration of the TSP (Traveling Salesman Problem) solver repository. Here's my detailed analysis:\n\n### 1. DIRECTORY STRUCTURE\n\n```\n/tmp/gh-issue-solver-1770502738798/\n├── src/\n│ ├── lib/\n│ │ └── algorithms/\n│ │ ├── atomic/ # All-at-once algorithms (final result directly)\n│ │ │ ├── solution/ # Algorithm implementations\n│ │ │ │ ├── moore.js, peano.js, gosper.js, koch.js\n│ │ │ │ ├── sierpinski.js, sonar.js, saw.js, comb.js\n│ │ │ │ ├── space-filling-tree.js, brute-force.js\n│ │ │ │ └── index.js (exports)\n│ │ │ └── optimization/ # Improvement algorithms\n│ │ │ ├── 2-opt, 3-opt, k-opt, lin-kernighan, lk-helsgaun\n│ │ │ ├── zigzag-opt, combined-opt\n│ │ │ └── index.js\n│ │ ├── progressive/ # Step-by-step (with visualization steps)\n│ │ │ ├── solution/ # Progressive versions (reuse atomic)\n│ │ │ └── optimization/\n│ │ ├── verification/ # Optimality proofs\n│ │ │ └── lower-bound.js\n│ │ ├── utils.js # Shared utilities\n│ │ └── index.js # Main export barrel\n│ ├── app/\n│ │ └── ui/\n│ │ └── components/ # React UI components\n│ └── cli/\n│ └── index.js # CLI entry point\n├── src/tests/\n│ ├── index.test.js # Main test file\n│ ├── sierpinski-algorithm.test.js\n│ ├── koch-algorithm.test.js\n│ ├── gosper-algorithm.test.js\n│ ├── peano-algorithm.test.js\n│ └── ... (other test files)\n├── experiments/\n│ ├── moore-curve-*.js\n│ └── ... (experimental scripts)\n├── docs/case-studies/\n│ └── (documentation for previous issues)\n├── package.json\n├── bunfig.toml\n├── eslint.config.js\n├── .prettierrc\n└── README.md\n```\n\n### 2. MOORE CURVE IMPLEMENTATION - ALL FILES\n\n**Atomic Moore (Main Implementation):**\n- `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/moore.js` (152 lines)\n\n**Progressive Moore (Step-by-step Wrapper):**\n- `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/progressive/solution/moore.js` (109 lines)\n\n**Key Functions in Moore:**\n- `generateMooreCurve(order)` - L-system generation (axiom: \"LFL+F+LFL\", rules for L and R)\n- `mooreCurveToPoints(sequence, gridSize)` - Convert to coordinates with bounding box normalization\n- `mooreSolution(points, gridSize)` - Maps points to curve, sorts by position, returns tour\n\n**Moore Curve Details:**\n- Uses L-system with quaternary (4-directional) turtle graphics (0=up, 1=right, 2=down, 3=left)\n- Fills 2^n × 2^n grids (VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128])\n- Order calculation: `order = Math.round(Math.log2(gridSize)) - 1`\n- Algorithm: grid size → L-system order → generate curve → find nearest points → sort → return tour\n\n### 3. RELATED SPACE-FILLING CURVE ALGORITHMS\n\nAll follow the same pattern as Moore but with different L-system rules:\n\n**Peano Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/peano.js`\n- Fills 3^n × 3^n grids (VALID_PEANO_GRID_SIZES = [3, 9, 27, 81])\n- L-system: Axiom \"X\", Rules: X → XFYFX+F+YFXFY-F-XFYFX, Y → YFXFY-F-XFYFX+F+YFXFY\n- Order: `Math.round(Math.log(gridSize) / Math.log(3))`\n\n**Sierpinski Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/sierpinski.js`\n- Fills 2^n × 2^n grids (uses Moore grid sizes)\n- L-system: Axiom \"F+XF+F+XF\", Rule: X → XF-F+F-XF+F+XF-F+F-X\n- Order: Same as Moore (`Math.round(Math.log2(gridSize)) - 1`)\n\n**Gosper Curve:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/gosper.js`\n- Hexagonal space-filling curve (60-degree angles)\n- L-system: Axiom \"A\", Rules: A → A-B--B+A++AA+B-, B → +A-BB--B-A++A+B\n- Uses 60° angle increments (π/3 radians) with trigonometric positioning\n- Has custom order calculation: `calculateGosperOrder(gridSize)` based on heuristics\n\n**Koch Snowflake:**\n- File: `/tmp/gh-issue-solver-1770502738798/src/lib/algorithms/atomic/solution/koch.js`\n- Not L-system based; uses recursive subdivision\n- `generateKochSnowflake(order)` - Equilateral triangle with recursive Koch construction\n- `kochCurveToPoints()` - Normalizes to grid\n- Order calculation: `calculateKochOrder(gridSize)` (returns 3-8 depending on grid size)\n\n### 4. TEST STRUCTURE AND PATTERNS\n\n**Test Framework:** Bun test runner\n**Test File Pattern:**\n```javascript\nimport { describe, it, expect } from 'bun:test';\nimport { generateXyzCurve, xyzCurveToPoints, xyzAlgorithmSteps, xyzSolution } from '../lib/algorithms/...';\n```\n\n**Test Categories (from sierpinski-algorithm.test.js as template):**\n\n1. **Generation Tests** (e.g., `generateSierpinskiCurve`)\n - Valid sequence generation\n - Sequence expansion with higher orders\n - Axiom pattern verification\n\n2. **Normalization Tests** (e.g., `sierpinskiCurveToPoints`)\n - Points within [0, gridSize-1] bounds\n - Bounding box coverage (minX=0, maxX=gridSize-1, etc.)\n - Handles degenerate cases (zero-extent curves)\n\n3. **Progressive Algorithm Tests** (e.g., `sierpinskiAlgorithmSteps`)\n - Empty points return empty array\n - First step is 'curve' type\n - All points in final tour\n - Curve points present in each step\n - Correct step count (1 curve + n visits)\n - 'visit' type for non-first steps\n\n4. **Atomic Solution Tests** (e.g., `sierpinskiSolution`)\n - Empty points return empty tour + empty curve\n - All point indices in final tour\n - No duplicates in tour (Set size = points.length)\n - Curve points generated\n - Valid distance (finite, positive)\n - Works with different grid sizes\n\n### 5. CONFIGURATION FILES\n\n**package.json:**\n- Name: `tsp-algorithms`, Version: 0.5.0\n- Main: `src/lib/index.js`\n- CLI bin: `src/cli/index.js`\n- TypeScript definitions: `src/lib/index.d.ts`\n- Test runner: Bun (`bun test`)\n- Linter: ESLint\n- Formatter: Prettier (width: 80, semi: true, single quotes, 2-space tabs)\n\n**prettier.rc:**\n```json\n{\n \"semi\": true,\n \"trailingComma\": \"es5\",\n \"singleQuote\": true,\n \"printWidth\": 80,\n \"tabWidth\": 2,\n \"useTabs\": false,\n \"arrowParens\": \"always\",\n \"endOfLine\": \"lf\"\n}\n```\n\n**Scripts:**\n- `npm test` or `bun test` - Run all tests\n- `npm run lint` - Run ESLint\n- `npm run format:check` - Check Prettier formatting\n- `npm run check` - All checks (lint + format + duplication)\n- `npm run dev` - Development with hot reload\n- `npm run build:app` - Build web app\n\n### 6. CONTRIBUTING GUIDELINES\n\n**Patterns for Adding New Algorithms:**\n\n1. **Create Atomic Version** (`src/lib/algorithms/atomic/solution/xyz.js`):\n ```javascript\n - Export generateXyzCurve(order) // or custom generation\n - Export xyzCurveToPoints(sequence, gridSize) // normalize to grid\n - Export xyzSolution(points, gridSize) // main algorithm\n - Handle empty points edge case\n - Return { tour: [], curvePoints: [] }\n ```\n\n2. **Create Progressive Wrapper** (`src/lib/algorithms/progressive/solution/xyz.js`):\n ```javascript\n - Import atomic functions\n - Re-export atomic functions for compatibility\n - Export xyzAlgorithmSteps(points, gridSize)\n - Generate steps: first 'curve' step, then 'visit' steps\n - Include description, curveProgress, curvePoints in each step\n ```\n\n3. **Update Index Files:**\n - Add exports to `atomic/solution/index.js`\n - Add exports to `progressive/solution/index.js`\n - Add exports to main `index.js` with examples\n\n4. **Create Tests** (`src/tests/xyz-algorithm.test.js`):\n ```javascript\n - Test curve generation (valid sequence, expansion, axiom)\n - Test point normalization (bounds, bounding box)\n - Test progressive algorithm (empty, curve type, tour count)\n - Test atomic solution (empty, indices, distance, grid sizes)\n ```\n\n5. **CLI Integration** (update `src/cli/index.js`):\n - Add algorithm case in `runAlgorithm()`\n - Import solution function\n - Add to algorithm choices in yargs config\n\n### 7. CODE PATTERNS AND CONVENTIONS\n\n**Algorithm Structure (All Algorithms Follow):**\n1. Generate curve (L-system or recursive)\n2. Normalize curve to grid coordinates\n3. Map each point to nearest curve position\n4. Sort points by curve position\n5. Return tour indices in sorted order\n\n**Progressive vs Atomic:**\n- **Atomic:** Returns final result directly: `{ tour: number[], curvePoints: Point[] }`\n- **Progressive:** Returns array of visualization steps with `{ type, curvePoints, tour, description, curveProgress }`\n\n**Shared Utilities** (`src/lib/algorithms/utils.js`):\n- `distance(p1, p2)` - Euclidean distance\n- `calculateTotalDistance(tour, points)` - Closed loop distance\n- `generateRandomPoints(gridSize, numPoints)` - Random point generation\n- `calculateMooreGridSize(gridSize)` - Snap to nearest power of 2\n- `calculatePeanoGridSize(gridSize)` - Snap to nearest power of 3\n- `VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128]`\n- `VALID_PEANO_GRID_SIZES = [3, 9, 27, 81]`\n\n**Type Definitions:**\n```javascript\nPoint: { x: number, y: number, id?: number }\nProgressiveStep: { \n type: 'curve' | 'visit', \n curvePoints: Point[], \n tour: number[], \n description: string, \n curveProgress: number,\n [gridSizeField]: number\n}\nAtomicResult: { tour: number[], curvePoints: Point[] }\n```\n\n### 8. BUILD AND TEST COMMANDS\n\n```bash\n# Install dependencies\nnpm install # or bun install\n\n# Run tests\nnpm test # or bun test\nnpm run test:e2e # E2E tests with Playwright\n\n# Code quality\nnpm run lint # Run ESLint\nnpm run lint:fix # Fix ESLint issues\nnpm run format:check # Check Prettier formatting\nnpm run format # Format all files\nnpm run check:duplication # Check for code duplication\nnpm run check # All checks combined\n\n# Build\nnpm run build # Build app\nnpm run dev # Dev mode with hot reload\n\n# Package management\nnpm run changeset # Create changeset entry\nnpm run changeset:version # Version bump from changeset\nnpm run changeset:publish # Publish to npm\n```\n\n### 9. BRANCH AND ISSUE CONTEXT\n\n**Current Branch:** `issue-65-37a562943dfc`\n**Main Branch:** `main`\n**Issue:** https://github.com/konard/tsp/issues/65\n\nThe branch is prepared for implementing a U-fork fractal algorithm as mentioned in the CLAUDE.md task file. The repository structure and patterns are fully established for adding new space-filling curve algorithms.\n\n### 10. KEY INSIGHTS FOR U-FORK IMPLEMENTATION\n\nBased on this exploration, here's what you'll need to replicate the patterns:\n\n1. **Define the U-fork L-system rules** (axiom, production rules for each variable)\n2. **Implement turtle graphics interpreter** (handle F, +, - commands and angle system)\n3. **Create two files:**\n - `atomic/solution/u-fork.js` with `generateUForkCurve()`, `uForkCurveToPoints()`, `uForkSolution()`\n - `progressive/solution/u-fork.js` with `uForkAlgorithmSteps()` wrapper\n4. **Add comprehensive tests** following the sierpinski/koch test patterns\n5. **Update all index.js files** to export new functions\n6. **Update CLI** with u-fork algorithm option\n7. **Follow code style:** 80-char lines, single quotes, 2-space indentation, trailing commas (ES5)\n\nAll existing implementations provide excellent templates for this work!"
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"content": " 1→/**\n 2→ * Atomic Moore Curve Solution for TSP\n 3→ *\n 4→ * Computes the final tour directly without intermediate visualization steps.\n 5→ *\n 6→ * Algorithm:\n 7→ * 1. Generate a Moore curve (L-system) that fills the grid space\n 8→ * 2. Map each point to its nearest position on the curve\n 9→ * 3. Sort points by their position along the curve\n 10→ * 4. Return the sorted order as the tour\n 11→ *\n 12→ * Time Complexity: O(n log n)\n 13→ * Space Complexity: O(n)\n 14→ */\n 15→\n 16→import { distance } from '../../utils.js';\n 17→\n 18→/**\n 19→ * Generate Moore curve using L-system\n 20→ * L-system rules for Moore curve:\n 21→ * Axiom: LFL+F+LFL\n 22→ * L -> -RF+LFL+FR-\n 23→ * R -> +LF-RFR-FL+\n 24→ *\n 25→ * @param {number} order - Order of the Moore curve\n 26→ * @returns {string} L-system sequence\n 27→ */\n 28→export const generateMooreCurve = (order) => {\n 29→ let sequence = 'LFL+F+LFL';\n 30→\n 31→ for (let i = 0; i < order; i++) {\n 32→ let newSequence = '';\n 33→ for (const char of sequence) {\n 34→ if (char === 'L') {\n 35→ newSequence += '-RF+LFL+FR-';\n 36→ } else if (char === 'R') {\n 37→ newSequence += '+LF-RFR-FL+';\n 38→ } else {\n 39→ newSequence += char;\n 40→ }\n 41→ }\n 42→ sequence = newSequence;\n 43→ }\n 44→\n 45→ return sequence;\n 46→};\n 47→\n 48→/**\n 49→ * Convert Moore curve L-system sequence to coordinate points\n 50→ *\n 51→ * @param {string} sequence - L-system sequence\n 52→ * @param {number} mooreGridSize - Size of the grid\n 53→ * @returns {Array<{x: number, y: number}>} Array of curve points\n 54→ */\n 55→export const mooreCurveToPoints = (sequence, mooreGridSize) => {\n 56→ const stepSize = 1;\n 57→\n 58→ const curvePoints = [];\n 59→ let x = 0;\n 60→ let y = 0;\n 61→\n 62→ // Direction: 0=up, 1=right, 2=down, 3=left\n 63→ let direction = 0;\n 64→\n 65→ curvePoints.push({ x, y });\n 66→\n 67→ for (const char of sequence) {\n 68→ if (char === 'F') {\n 69→ if (direction === 0) {\n 70→ y -= stepSize;\n 71→ } else if (direction === 1) {\n 72→ x += stepSize;\n 73→ } else if (direction === 2) {\n 74→ y += stepSize;\n 75→ } else if (direction === 3) {\n 76→ x -= stepSize;\n 77→ }\n 78→\n 79→ curvePoints.push({ x, y });\n 80→ } else if (char === '+') {\n 81→ direction = (direction + 1) % 4;\n 82→ } else if (char === '-') {\n 83→ direction = (direction + 3) % 4;\n 84→ }\n 85→ }\n 86→\n 87→ // Find bounding box of the curve\n 88→ let minX = Infinity,\n 89→ maxX = -Infinity,\n 90→ minY = Infinity,\n 91→ maxY = -Infinity;\n 92→ for (const p of curvePoints) {\n 93→ minX = Math.min(minX, p.x);\n 94→ maxX = Math.max(maxX, p.x);\n 95→ minY = Math.min(minY, p.y);\n 96→ maxY = Math.max(maxY, p.y);\n 97→ }\n 98→\n 99→ const curveWidth = maxX - minX;\n 100→ const curveHeight = maxY - minY;\n 101→\n 102→ const normalizedPoints = curvePoints.map((p) => ({\n 103→ x: Math.round(((p.x - minX) / curveWidth) * (mooreGridSize - 1)),\n 104→ y: Math.round(((p.y - minY) / curveHeight) * (mooreGridSize - 1)),\n 105→ }));\n 106→\n 107→ return normalizedPoints;\n 108→};\n 109→\n 110→/**\n 111→ * Compute Moore Curve solution in one step (atomic version).\n 112→ * Returns the final tour without intermediate steps.\n 113→ *\n 114→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 115→ * @param {number} mooreGridSize - Size of the Moore grid\n 116→ * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n 117→ */\n 118→export const mooreSolution = (points, mooreGridSize) => {\n 119→ if (points.length === 0) {\n 120→ return { tour: [], curvePoints: [] };\n 121→ }\n 122→\n 123→ // Determine L-system iterations: mooreGridSize = 2^(n+1), so n = log2(mooreGridSize) - 1\n 124→ const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n 125→ const curveSequence = generateMooreCurve(order);\n 126→ const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n 127→\n 128→ const pointsWithCurvePos = points.map((p, idx) => {\n 129→ let minDist = Infinity;\n 130→ let curvePos = 0;\n 131→\n 132→ for (let i = 0; i < curvePoints.length; i++) {\n 133→ const d = distance(p, curvePoints[i]);\n 134→ if (d < minDist) {\n 135→ minDist = d;\n 136→ curvePos = i;\n 137→ }\n 138→ }\n 139→\n 140→ return { idx, curvePos };\n 141→ });\n 142→\n 143→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 144→\n 145→ return {\n 146→ tour: pointsWithCurvePos.map((p) => p.idx),\n 147→ curvePoints,\n 148→ };\n 149→};\n 150→\n 151→export default mooreSolution;\n 152→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * Atomic Moore Curve Solution for TSP\n *\n * Computes the final tour directly without intermediate visualization steps.\n *\n * Algorithm:\n * 1. Generate a Moore curve (L-system) that fills the grid space\n * 2. Map each point to its nearest position on the curve\n * 3. Sort points by their position along the curve\n * 4. Return the sorted order as the tour\n *\n * Time Complexity: O(n log n)\n * Space Complexity: O(n)\n */\n\nimport { distance } from '../../utils.js';\n\n/**\n * Generate Moore curve using L-system\n * L-system rules for Moore curve:\n * Axiom: LFL+F+LFL\n * L -> -RF+LFL+FR-\n * R -> +LF-RFR-FL+\n *\n * @param {number} order - Order of the Moore curve\n * @returns {string} L-system sequence\n */\nexport const generateMooreCurve = (order) => {\n let sequence = 'LFL+F+LFL';\n\n for (let i = 0; i < order; i++) {\n let newSequence = '';\n for (const char of sequence) {\n if (char === 'L') {\n newSequence += '-RF+LFL+FR-';\n } else if (char === 'R') {\n newSequence += '+LF-RFR-FL+';\n } else {\n newSequence += char;\n }\n }\n sequence = newSequence;\n }\n\n return sequence;\n};\n\n/**\n * Convert Moore curve L-system sequence to coordinate points\n *\n * @param {string} sequence - L-system sequence\n * @param {number} mooreGridSize - Size of the grid\n * @returns {Array<{x: number, y: number}>} Array of curve points\n */\nexport const mooreCurveToPoints = (sequence, mooreGridSize) => {\n const stepSize = 1;\n\n const curvePoints = [];\n let x = 0;\n let y = 0;\n\n // Direction: 0=up, 1=right, 2=down, 3=left\n let direction = 0;\n\n curvePoints.push({ x, y });\n\n for (const char of sequence) {\n if (char === 'F') {\n if (direction === 0) {\n y -= stepSize;\n } else if (direction === 1) {\n x += stepSize;\n } else if (direction === 2) {\n y += stepSize;\n } else if (direction === 3) {\n x -= stepSize;\n }\n\n curvePoints.push({ x, y });\n } else if (char === '+') {\n direction = (direction + 1) % 4;\n } else if (char === '-') {\n direction = (direction + 3) % 4;\n }\n }\n\n // Find bounding box of the curve\n let minX = Infinity,\n maxX = -Infinity,\n minY = Infinity,\n maxY = -Infinity;\n for (const p of curvePoints) {\n minX = Math.min(minX, p.x);\n maxX = Math.max(maxX, p.x);\n minY = Math.min(minY, p.y);\n maxY = Math.max(maxY, p.y);\n }\n\n const curveWidth = maxX - minX;\n const curveHeight = maxY - minY;\n\n const normalizedPoints = curvePoints.map((p) => ({\n x: Math.round(((p.x - minX) / curveWidth) * (mooreGridSize - 1)),\n y: Math.round(((p.y - minY) / curveHeight) * (mooreGridSize - 1)),\n }));\n\n return normalizedPoints;\n};\n\n/**\n * Compute Moore Curve solution in one step (atomic version).\n * Returns the final tour without intermediate steps.\n *\n * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n * @param {number} mooreGridSize - Size of the Moore grid\n * @returns {{tour: number[], curvePoints: Array<{x: number, y: number}>}} Final tour and curve\n */\nexport const mooreSolution = (points, mooreGridSize) => {\n if (points.length === 0) {\n return { tour: [], curvePoints: [] };\n }\n\n // Determine L-system iterations: mooreGridSize = 2^(n+1), so n = log2(mooreGridSize) - 1\n const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n const curveSequence = generateMooreCurve(order);\n const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n\n const pointsWithCurvePos = points.map((p, idx) => {\n let minDist = Infinity;\n let curvePos = 0;\n\n for (let i = 0; i < curvePoints.length; i++) {\n const d = distance(p, curvePoints[i]);\n if (d < minDist) {\n minDist = d;\n curvePos = i;\n }\n }\n\n return { idx, curvePos };\n });\n\n pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n\n return {\n tour: pointsWithCurvePos.map((p) => p.idx),\n curvePoints,\n };\n};\n\nexport default mooreSolution;\n",
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"content": " 1→/**\n 2→ * Progressive Moore Curve Algorithm for TSP\n 3→ *\n 4→ * Also known as: Space-Filling Curve, Hilbert Curve Variant, Fractal Ordering\n 5→ *\n 6→ * This algorithm provides step-by-step visualization of using a Moore curve\n 7→ * (a variant of the Hilbert curve) to order points:\n 8→ * 1. Generate a Moore curve that fills the grid space\n 9→ * 2. Map each point to its nearest position on the curve\n 10→ * 3. Sort points by their position along the curve\n 11→ * 4. Connect points in curve-order to form a tour\n 12→ *\n 13→ * Time Complexity: O(n log n)\n 14→ * Space Complexity: O(n)\n 15→ */\n 16→\n 17→import { distance } from '../../utils.js';\n 18→import {\n 19→ generateMooreCurve,\n 20→ mooreCurveToPoints,\n 21→} from '../../atomic/solution/moore.js';\n 22→\n 23→// Re-export atomic functions for backward compatibility\n 24→export {\n 25→ mooreSolution,\n 26→ generateMooreCurve,\n 27→ mooreCurveToPoints,\n 28→} from '../../atomic/solution/moore.js';\n 29→\n 30→/**\n 31→ * Generate step-by-step solution using Moore Curve algorithm\n 32→ *\n 33→ * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n 34→ * @param {number} mooreGridSize - Size of the Moore grid\n 35→ * @returns {Array<Object>} Array of steps for visualization\n 36→ */\n 37→export const mooreAlgorithmSteps = (points, mooreGridSize) => {\n 38→ if (points.length === 0) {\n 39→ return [];\n 40→ }\n 41→\n 42→ // Determine L-system iterations based on Moore grid size\n 43→ // generateMooreCurve(n) fills a 2^(n+1) x 2^(n+1) grid\n 44→ // So for mooreGridSize = 2^k, iterations = k - 1\n 45→ const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n 46→ const curveSequence = generateMooreCurve(order);\n 47→ // Generate curve points using the Moore grid size for perfect alignment\n 48→ const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n 49→ const totalCurveLength = curvePoints.length;\n 50→\n 51→ // Points are already on Moore grid coordinates, no scaling needed\n 52→ // Map each point to its position along the curve\n 53→ const pointsWithCurvePos = points.map((p, idx) => {\n 54→ let minDist = Infinity;\n 55→ let curvePos = 0;\n 56→\n 57→ // Points are already in Moore grid coordinates\n 58→ for (let i = 0; i < curvePoints.length; i++) {\n 59→ const d = distance(p, curvePoints[i]);\n 60→ if (d < minDist) {\n 61→ minDist = d;\n 62→ curvePos = i;\n 63→ }\n 64→ }\n 65→\n 66→ return { ...p, idx, curvePos };\n 67→ });\n 68→\n 69→ // Sort points by their position along the curve\n 70→ pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n 71→\n 72→ // Generate steps\n 73→ const steps = [];\n 74→ const tour = [];\n 75→\n 76→ // First step: show the Moore curve\n 77→ steps.push({\n 78→ type: 'curve',\n 79→ curvePoints,\n 80→ mooreGridSize,\n 81→ curveProgress: 0,\n 82→ tour: [],\n 83→ description: `Moore curve generated (order ${order}, ${mooreGridSize}×${mooreGridSize} grid)`,\n 84→ });\n 85→\n 86→ for (let i = 0; i < pointsWithCurvePos.length; i++) {\n 87→ tour.push(pointsWithCurvePos[i].idx);\n 88→ // Calculate progress along the curve as a percentage\n 89→ const curveProgress = (\n 90→ (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 91→ 100\n 92→ ).toFixed(1);\n 93→\n 94→ steps.push({\n 95→ type: 'visit',\n 96→ curvePoints,\n 97→ mooreGridSize,\n 98→ curvePosition: pointsWithCurvePos[i].curvePos,\n 99→ curveProgress: parseFloat(curveProgress),\n 100→ tour: [...tour],\n 101→ description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n 102→ });\n 103→ }\n 104→\n 105→ return steps;\n 106→};\n 107→\n 108→export default mooreAlgorithmSteps;\n 109→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * Progressive Moore Curve Algorithm for TSP\n *\n * Also known as: Space-Filling Curve, Hilbert Curve Variant, Fractal Ordering\n *\n * This algorithm provides step-by-step visualization of using a Moore curve\n * (a variant of the Hilbert curve) to order points:\n * 1. Generate a Moore curve that fills the grid space\n * 2. Map each point to its nearest position on the curve\n * 3. Sort points by their position along the curve\n * 4. Connect points in curve-order to form a tour\n *\n * Time Complexity: O(n log n)\n * Space Complexity: O(n)\n */\n\nimport { distance } from '../../utils.js';\nimport {\n generateMooreCurve,\n mooreCurveToPoints,\n} from '../../atomic/solution/moore.js';\n\n// Re-export atomic functions for backward compatibility\nexport {\n mooreSolution,\n generateMooreCurve,\n mooreCurveToPoints,\n} from '../../atomic/solution/moore.js';\n\n/**\n * Generate step-by-step solution using Moore Curve algorithm\n *\n * @param {Array<{x: number, y: number, id: number}>} points - Array of points\n * @param {number} mooreGridSize - Size of the Moore grid\n * @returns {Array<Object>} Array of steps for visualization\n */\nexport const mooreAlgorithmSteps = (points, mooreGridSize) => {\n if (points.length === 0) {\n return [];\n }\n\n // Determine L-system iterations based on Moore grid size\n // generateMooreCurve(n) fills a 2^(n+1) x 2^(n+1) grid\n // So for mooreGridSize = 2^k, iterations = k - 1\n const order = Math.max(0, Math.round(Math.log2(mooreGridSize)) - 1);\n const curveSequence = generateMooreCurve(order);\n // Generate curve points using the Moore grid size for perfect alignment\n const curvePoints = mooreCurveToPoints(curveSequence, mooreGridSize);\n const totalCurveLength = curvePoints.length;\n\n // Points are already on Moore grid coordinates, no scaling needed\n // Map each point to its position along the curve\n const pointsWithCurvePos = points.map((p, idx) => {\n let minDist = Infinity;\n let curvePos = 0;\n\n // Points are already in Moore grid coordinates\n for (let i = 0; i < curvePoints.length; i++) {\n const d = distance(p, curvePoints[i]);\n if (d < minDist) {\n minDist = d;\n curvePos = i;\n }\n }\n\n return { ...p, idx, curvePos };\n });\n\n // Sort points by their position along the curve\n pointsWithCurvePos.sort((a, b) => a.curvePos - b.curvePos);\n\n // Generate steps\n const steps = [];\n const tour = [];\n\n // First step: show the Moore curve\n steps.push({\n type: 'curve',\n curvePoints,\n mooreGridSize,\n curveProgress: 0,\n tour: [],\n description: `Moore curve generated (order ${order}, ${mooreGridSize}×${mooreGridSize} grid)`,\n });\n\n for (let i = 0; i < pointsWithCurvePos.length; i++) {\n tour.push(pointsWithCurvePos[i].idx);\n // Calculate progress along the curve as a percentage\n const curveProgress = (\n (pointsWithCurvePos[i].curvePos / (totalCurveLength - 1)) *\n 100\n ).toFixed(1);\n\n steps.push({\n type: 'visit',\n curvePoints,\n mooreGridSize,\n curvePosition: pointsWithCurvePos[i].curvePos,\n curveProgress: parseFloat(curveProgress),\n tour: [...tour],\n description: `Progress: ${curveProgress}% | Point ${pointsWithCurvePos[i].idx} (${pointsWithCurvePos[i].x}, ${pointsWithCurvePos[i].y})`,\n });\n }\n\n return steps;\n};\n\nexport default mooreAlgorithmSteps;\n",
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"content": " 1→/**\n 2→ * Atomic Solutions - All-at-once TSP algorithms\n 3→ */\n 4→\n 5→export { sonarSolution } from './sonar.js';\n 6→export { mooreSolution } from './moore.js';\n 7→export { gosperSolution } from './gosper.js';\n 8→export { peanoSolution } from './peano.js';\n 9→export { sierpinskiSolution } from './sierpinski.js';\n 10→export { combSolution } from './comb.js';\n 11→export { sawSolution } from './saw.js';\n 12→export { kochSolution } from './koch.js';\n 13→export { spaceFillingTreeSolution } from './space-filling-tree.js';\n 14→export {\n 15→ bruteForceSolution,\n 16→ calculateOptimalityRatio,\n 17→ BRUTE_FORCE_MAX_POINTS,\n 18→} from './brute-force.js';\n 19→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * Progressive Solutions - Step-by-step TSP algorithms\n 3→ */\n 4→\n 5→export { sonarAlgorithmSteps, sonarSolution } from './sonar.js';\n 6→export {\n 7→ mooreAlgorithmSteps,\n 8→ mooreSolution,\n 9→ generateMooreCurve,\n 10→ mooreCurveToPoints,\n 11→} from './moore.js';\n 12→export { combAlgorithmSteps, combSolution } from './comb.js';\n 13→export { sawAlgorithmSteps, sawSolution } from './saw.js';\n 14→export {\n 15→ kochAlgorithmSteps,\n 16→ kochSolution,\n 17→ generateKochSnowflake,\n 18→ kochCurveToPoints,\n 19→ calculateKochOrder,\n 20→} from './koch.js';\n 21→export {\n 22→ spaceFillingTreeAlgorithmSteps,\n 23→ spaceFillingTreeSolution,\n 24→} from './space-filling-tree.js';\n 25→export {\n 26→ sierpinskiAlgorithmSteps,\n 27→ sierpinskiSolution,\n 28→ generateSierpinskiCurve,\n 29→ sierpinskiCurveToPoints,\n 30→} from './sierpinski.js';\n 31→export {\n 32→ peanoAlgorithmSteps,\n 33→ peanoSolution,\n 34→ generatePeanoCurve,\n 35→ peanoCurveToPoints,\n 36→} from './peano.js';\n 37→export {\n 38→ gosperAlgorithmSteps,\n 39→ gosperSolution,\n 40→ generateGosperCurve,\n 41→ gosperCurveToPoints,\n 42→ calculateGosperOrder,\n 43→} from './gosper.js';\n 44→export {\n 45→ bruteForceAlgorithmSteps,\n 46→ bruteForceSolution,\n 47→ calculateOptimalityRatio,\n 48→ BRUTE_FORCE_MAX_POINTS,\n 49→} from './brute-force.js';\n 50→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": " 1→/**\n 2→ * TSP Algorithms Library\n 3→ *\n 4→ * This module provides two types of TSP algorithms:\n 5→ *\n 6→ * 1. Progressive (step-by-step) - Returns arrays of steps for visualization/animation\n 7→ * 2. Atomic (all-at-once) - Returns final result directly\n 8→ *\n 9→ * Each type includes:\n 10→ * - Solutions: Initial tour construction algorithms (Sonar, Moore, Gosper, Peano, Sierpiński, Comb, SAW, Koch, Space-Filling Tree, Brute-Force)\n 11→ * - Optimizations: Generic tour improvement algorithms (2-opt, 3-opt, k-opt, LK, LKH, Zigzag)\n 12→ *\n 13→ * Additionally, verification algorithms prove tour optimality:\n 14→ * - Lower bound (1-tree): Mathematical proof when upper bound = lower bound\n 15→ *\n 16→ * Available solution algorithms:\n 17→ * - Sonar (Radial Sweep): Sorts points by polar angle from centroid\n 18→ * - Moore Curve: Orders points along a Moore space-filling curve (2^n grid)\n 19→ * - Gosper Curve: Orders points along a hexagonal space-filling curve (60° turns)\n 20→ * - Peano Curve: Orders points along a Peano space-filling curve (3^n grid)\n 21→ * - Sierpiński Curve: Orders points along a Sierpiński space-filling curve\n 22→ * - Comb (Serpentine Scan): Visits points row by row in alternating directions\n 23→ * - Self-Avoiding Walk (SAW): Nearest-neighbor walk that never revisits a point\n 24→ * - Koch Snowflake: Orders points along a Koch snowflake fractal curve\n 25→ * - Space-Filling Tree: Orders points via DFS traversal of a recursive quadtree\n 26→ * - Brute-Force: Exhaustive search for the true optimal tour (small instances)\n 27→ *\n 28→ * Available optimizations (generic, work with any tour):\n 29→ * - 2-opt: Standard segment reversal optimization\n 30→ * - 3-opt: Three-edge exchange optimization\n 31→ * - k-opt: Generalized k-edge exchange (combines 2-opt and 3-opt)\n 32→ * - Lin-Kernighan: Variable-depth edge exchange heuristic\n 33→ * - Lin-Kernighan-Helsgaun: LK with perturbation for escaping local optima\n 34→ * - Zigzag: Adjacent pair swapping optimization\n 35→ * - Combined: Alternates zigzag and 2-opt until no improvement\n 36→ *\n 37→ * @example\n 38→ * // Progressive (for visualization)\n 39→ * import { sonarAlgorithmSteps, sawAlgorithmSteps, kochAlgorithmSteps, twoOptSteps, bruteForceAlgorithmSteps } from './algorithms';\n 40→ * const solutionSteps = sonarAlgorithmSteps(points);\n 41→ * const kochSteps = kochAlgorithmSteps(points, mooreGridSize);\n 42→ * const optSteps = twoOptSteps(points, tour);\n 43→ * const bruteSteps = bruteForceAlgorithmSteps(points);\n 44→ *\n 45→ * @example\n 46→ * // Atomic (for direct computation)\n 47→ * import { sonarSolution, sawSolution, bruteForceSolution } from './algorithms/atomic';\n 48→ * import { twoOpt } from './algorithms/atomic';\n 49→ * const { tour } = sonarSolution(points);\n 50→ * const optimal = bruteForceSolution(points);\n 51→ *\n 52→ * @example\n 53→ * // Verification (prove optimality)\n 54→ * import { verifyOptimality } from './algorithms/verification';\n 55→ * const result = verifyOptimality(tourDistance, points);\n 56→ * if (result.isOptimal) console.log('Tour is proven optimal!');\n 57→ */\n 58→\n 59→// Utility functions\n 60→export * from './utils.js';\n 61→\n 62→// Progressive algorithms (step-by-step)\n 63→export * as progressive from './progressive/index.js';\n 64→\n 65→// Atomic algorithms (all-at-once)\n 66→export * as atomic from './atomic/index.js';\n 67→\n 68→// Verification algorithms (optimality proofs)\n 69→export * as verification from './verification/index.js';\n 70→\n 71→// Export progressive functions at top level for convenience\n 72→export {\n 73→ // Solutions\n 74→ sonarAlgorithmSteps,\n 75→ mooreAlgorithmSteps,\n 76→ generateMooreCurve,\n 77→ mooreCurveToPoints,\n 78→ gosperAlgorithmSteps,\n 79→ generateGosperCurve,\n 80→ gosperCurveToPoints,\n 81→ calculateGosperOrder,\n 82→ peanoAlgorithmSteps,\n 83→ generatePeanoCurve,\n 84→ peanoCurveToPoints,\n 85→ sierpinskiAlgorithmSteps,\n 86→ generateSierpinskiCurve,\n 87→ sierpinskiCurveToPoints,\n 88→ combAlgorithmSteps,\n 89→ sawAlgorithmSteps,\n 90→ sawSolution,\n 91→ kochAlgorithmSteps,\n 92→ generateKochSnowflake,\n 93→ kochCurveToPoints,\n 94→ calculateKochOrder,\n 95→ spaceFillingTreeAlgorithmSteps,\n 96→ bruteForceAlgorithmSteps,\n 97→ bruteForceSolution,\n 98→ calculateOptimalityRatio,\n 99→ BRUTE_FORCE_MAX_POINTS,\n 100→ // Generic optimizations\n 101→ twoOptSteps,\n 102→ threeOptSteps,\n 103→ kOptSteps,\n 104→ zigzagOptSteps,\n 105→ combinedOptSteps,\n 106→ linKernighanSteps,\n 107→ lkHelsgaunSteps,\n 108→ // Legacy aliases for backward compatibility\n 109→ sonarOptimizationSteps,\n 110→ mooreOptimizationSteps,\n 111→} from './progressive/index.js';\n 112→\n 113→// Export verification functions at top level for convenience\n 114→export {\n 115→ oneTreeLowerBound,\n 116→ verifyOptimality,\n 117→ // Backward-compatible aliases\n 118→ bruteForceOptimalTour,\n 119→ bruteForceSteps,\n 120→} from './verification/index.js';\n 121→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * TSP Algorithms Library\n *\n * This module provides two types of TSP algorithms:\n *\n * 1. Progressive (step-by-step) - Returns arrays of steps for visualization/animation\n * 2. Atomic (all-at-once) - Returns final result directly\n *\n * Each type includes:\n * - Solutions: Initial tour construction algorithms (Sonar, Moore, Gosper, Peano, Sierpiński, Comb, SAW, Koch, Space-Filling Tree, Brute-Force)\n * - Optimizations: Generic tour improvement algorithms (2-opt, 3-opt, k-opt, LK, LKH, Zigzag)\n *\n * Additionally, verification algorithms prove tour optimality:\n * - Lower bound (1-tree): Mathematical proof when upper bound = lower bound\n *\n * Available solution algorithms:\n * - Sonar (Radial Sweep): Sorts points by polar angle from centroid\n * - Moore Curve: Orders points along a Moore space-filling curve (2^n grid)\n * - Gosper Curve: Orders points along a hexagonal space-filling curve (60° turns)\n * - Peano Curve: Orders points along a Peano space-filling curve (3^n grid)\n * - Sierpiński Curve: Orders points along a Sierpiński space-filling curve\n * - Comb (Serpentine Scan): Visits points row by row in alternating directions\n * - Self-Avoiding Walk (SAW): Nearest-neighbor walk that never revisits a point\n * - Koch Snowflake: Orders points along a Koch snowflake fractal curve\n * - Space-Filling Tree: Orders points via DFS traversal of a recursive quadtree\n * - Brute-Force: Exhaustive search for the true optimal tour (small instances)\n *\n * Available optimizations (generic, work with any tour):\n * - 2-opt: Standard segment reversal optimization\n * - 3-opt: Three-edge exchange optimization\n * - k-opt: Generalized k-edge exchange (combines 2-opt and 3-opt)\n * - Lin-Kernighan: Variable-depth edge exchange heuristic\n * - Lin-Kernighan-Helsgaun: LK with perturbation for escaping local optima\n * - Zigzag: Adjacent pair swapping optimization\n * - Combined: Alternates zigzag and 2-opt until no improvement\n *\n * @example\n * // Progressive (for visualization)\n * import { sonarAlgorithmSteps, sawAlgorithmSteps, kochAlgorithmSteps, twoOptSteps, bruteForceAlgorithmSteps } from './algorithms';\n * const solutionSteps = sonarAlgorithmSteps(points);\n * const kochSteps = kochAlgorithmSteps(points, mooreGridSize);\n * const optSteps = twoOptSteps(points, tour);\n * const bruteSteps = bruteForceAlgorithmSteps(points);\n *\n * @example\n * // Atomic (for direct computation)\n * import { sonarSolution, sawSolution, bruteForceSolution } from './algorithms/atomic';\n * import { twoOpt } from './algorithms/atomic';\n * const { tour } = sonarSolution(points);\n * const optimal = bruteForceSolution(points);\n *\n * @example\n * // Verification (prove optimality)\n * import { verifyOptimality } from './algorithms/verification';\n * const result = verifyOptimality(tourDistance, points);\n * if (result.isOptimal) console.log('Tour is proven optimal!');\n */\n\n// Utility functions\nexport * from './utils.js';\n\n// Progressive algorithms (step-by-step)\nexport * as progressive from './progressive/index.js';\n\n// Atomic algorithms (all-at-once)\nexport * as atomic from './atomic/index.js';\n\n// Verification algorithms (optimality proofs)\nexport * as verification from './verification/index.js';\n\n// Export progressive functions at top level for convenience\nexport {\n // Solutions\n sonarAlgorithmSteps,\n mooreAlgorithmSteps,\n generateMooreCurve,\n mooreCurveToPoints,\n gosperAlgorithmSteps,\n generateGosperCurve,\n gosperCurveToPoints,\n calculateGosperOrder,\n peanoAlgorithmSteps,\n generatePeanoCurve,\n peanoCurveToPoints,\n sierpinskiAlgorithmSteps,\n generateSierpinskiCurve,\n sierpinskiCurveToPoints,\n combAlgorithmSteps,\n sawAlgorithmSteps,\n sawSolution,\n kochAlgorithmSteps,\n generateKochSnowflake,\n kochCurveToPoints,\n calculateKochOrder,\n spaceFillingTreeAlgorithmSteps,\n bruteForceAlgorithmSteps,\n bruteForceSolution,\n calculateOptimalityRatio,\n BRUTE_FORCE_MAX_POINTS,\n // Generic optimizations\n twoOptSteps,\n threeOptSteps,\n kOptSteps,\n zigzagOptSteps,\n combinedOptSteps,\n linKernighanSteps,\n lkHelsgaunSteps,\n // Legacy aliases for backward compatibility\n sonarOptimizationSteps,\n mooreOptimizationSteps,\n} from './progressive/index.js';\n\n// Export verification functions at top level for convenience\nexport {\n oneTreeLowerBound,\n verifyOptimality,\n // Backward-compatible aliases\n bruteForceOptimalTour,\n bruteForceSteps,\n} from './verification/index.js';\n",
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"content": " 1→/**\n 2→ * Utility functions for TSP algorithms\n 3→ */\n 4→\n 5→/**\n 6→ * Valid grid sizes for Moore curve algorithm.\n 7→ * A Moore curve of order n fills a 2^n x 2^n grid.\n 8→ * These correspond to L-system iteration counts 0..7 (effective orders 1..8).\n 9→ */\n 10→export const VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128];\n 11→\n 12→/**\n 13→ * Valid grid sizes for Peano curve algorithm.\n 14→ * A Peano curve of order n fills a 3^n x 3^n grid.\n 15→ * These correspond to L-system iteration counts 1..4 (orders 1..4).\n 16→ */\n 17→export const VALID_PEANO_GRID_SIZES = [3, 9, 27, 81];\n 18→\n 19→/**\n 20→ * Calculate the Moore grid size based on user-specified grid size\n 21→ * Moore curve fills a grid of size 2^n x 2^n.\n 22→ * Returns the smallest valid Moore grid size >= gridSize.\n 23→ * @param {number} gridSize - User-specified grid size\n 24→ * @returns {number} Moore grid size (power of 2)\n 25→ */\n 26→export const calculateMooreGridSize = (gridSize) => {\n 27→ // Find smallest valid grid size >= gridSize\n 28→ for (const size of VALID_GRID_SIZES) {\n 29→ if (size >= gridSize) {\n 30→ return size;\n 31→ }\n 32→ }\n 33→ return VALID_GRID_SIZES[VALID_GRID_SIZES.length - 1];\n 34→};\n 35→\n 36→/**\n 37→ * Calculate the Peano grid size based on user-specified grid size\n 38→ * Peano curve fills a grid of size 3^n x 3^n.\n 39→ * Returns the smallest valid Peano grid size >= gridSize.\n 40→ * @param {number} gridSize - User-specified grid size\n 41→ * @returns {number} Peano grid size (power of 3)\n 42→ */\n 43→export const calculatePeanoGridSize = (gridSize) => {\n 44→ // Find smallest valid Peano grid size >= gridSize\n 45→ for (const size of VALID_PEANO_GRID_SIZES) {\n 46→ if (size >= gridSize) {\n 47→ return size;\n 48→ }\n 49→ }\n 50→ return VALID_PEANO_GRID_SIZES[VALID_PEANO_GRID_SIZES.length - 1];\n 51→};\n 52→\n 53→/**\n 54→ * Generate random points on the Moore grid\n 55→ * @param {number} mooreGridSize - Size of the Moore grid\n 56→ * @param {number} numPoints - Number of points to generate\n 57→ * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n 58→ */\n 59→export const generateRandomPoints = (mooreGridSize, numPoints) => {\n 60→ const points = [];\n 61→ const usedPositions = new Set();\n 62→ // Points are placed at grid intersections (0 to mooreGridSize-1 inclusive)\n 63→ const maxPoints = Math.min(numPoints, mooreGridSize * mooreGridSize);\n 64→\n 65→ while (points.length < maxPoints) {\n 66→ // Generate integer coordinates aligned to Moore grid vertices [0, mooreGridSize-1]\n 67→ const x = Math.floor(Math.random() * mooreGridSize);\n 68→ const y = Math.floor(Math.random() * mooreGridSize);\n 69→ const key = `${x},${y}`;\n 70→\n 71→ if (!usedPositions.has(key)) {\n 72→ usedPositions.add(key);\n 73→ points.push({ x, y, id: points.length });\n 74→ }\n 75→ }\n 76→ return points;\n 77→};\n 78→\n 79→/**\n 80→ * Calculate Euclidean distance between two points\n 81→ * @param {{x: number, y: number}} p1 - First point\n 82→ * @param {{x: number, y: number}} p2 - Second point\n 83→ * @returns {number} Euclidean distance\n 84→ */\n 85→export const distance = (p1, p2) => Math.hypot(p2.x - p1.x, p2.y - p1.y);\n 86→\n 87→/**\n 88→ * Calculate total tour distance (closed loop)\n 89→ * @param {number[]} tour - Array of point indices forming the tour\n 90→ * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n 91→ * @returns {number} Total distance of the tour\n 92→ */\n 93→export const calculateTotalDistance = (tour, points) => {\n 94→ if (tour.length < 2) {\n 95→ return 0;\n 96→ }\n 97→ let total = 0;\n 98→ for (let i = 0; i < tour.length - 1; i++) {\n 99→ total += distance(points[tour[i]], points[tour[i + 1]]);\n 100→ }\n 101→ // Close the loop\n 102→ total += distance(points[tour[tour.length - 1]], points[tour[0]]);\n 103→ return total;\n 104→};\n 105→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * Utility functions for TSP algorithms\n */\n\n/**\n * Valid grid sizes for Moore curve algorithm.\n * A Moore curve of order n fills a 2^n x 2^n grid.\n * These correspond to L-system iteration counts 0..7 (effective orders 1..8).\n */\nexport const VALID_GRID_SIZES = [2, 4, 8, 16, 32, 64, 128];\n\n/**\n * Valid grid sizes for Peano curve algorithm.\n * A Peano curve of order n fills a 3^n x 3^n grid.\n * These correspond to L-system iteration counts 1..4 (orders 1..4).\n */\nexport const VALID_PEANO_GRID_SIZES = [3, 9, 27, 81];\n\n/**\n * Calculate the Moore grid size based on user-specified grid size\n * Moore curve fills a grid of size 2^n x 2^n.\n * Returns the smallest valid Moore grid size >= gridSize.\n * @param {number} gridSize - User-specified grid size\n * @returns {number} Moore grid size (power of 2)\n */\nexport const calculateMooreGridSize = (gridSize) => {\n // Find smallest valid grid size >= gridSize\n for (const size of VALID_GRID_SIZES) {\n if (size >= gridSize) {\n return size;\n }\n }\n return VALID_GRID_SIZES[VALID_GRID_SIZES.length - 1];\n};\n\n/**\n * Calculate the Peano grid size based on user-specified grid size\n * Peano curve fills a grid of size 3^n x 3^n.\n * Returns the smallest valid Peano grid size >= gridSize.\n * @param {number} gridSize - User-specified grid size\n * @returns {number} Peano grid size (power of 3)\n */\nexport const calculatePeanoGridSize = (gridSize) => {\n // Find smallest valid Peano grid size >= gridSize\n for (const size of VALID_PEANO_GRID_SIZES) {\n if (size >= gridSize) {\n return size;\n }\n }\n return VALID_PEANO_GRID_SIZES[VALID_PEANO_GRID_SIZES.length - 1];\n};\n\n/**\n * Generate random points on the Moore grid\n * @param {number} mooreGridSize - Size of the Moore grid\n * @param {number} numPoints - Number of points to generate\n * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n */\nexport const generateRandomPoints = (mooreGridSize, numPoints) => {\n const points = [];\n const usedPositions = new Set();\n // Points are placed at grid intersections (0 to mooreGridSize-1 inclusive)\n const maxPoints = Math.min(numPoints, mooreGridSize * mooreGridSize);\n\n while (points.length < maxPoints) {\n // Generate integer coordinates aligned to Moore grid vertices [0, mooreGridSize-1]\n const x = Math.floor(Math.random() * mooreGridSize);\n const y = Math.floor(Math.random() * mooreGridSize);\n const key = `${x},${y}`;\n\n if (!usedPositions.has(key)) {\n usedPositions.add(key);\n points.push({ x, y, id: points.length });\n }\n }\n return points;\n};\n\n/**\n * Calculate Euclidean distance between two points\n * @param {{x: number, y: number}} p1 - First point\n * @param {{x: number, y: number}} p2 - Second point\n * @returns {number} Euclidean distance\n */\nexport const distance = (p1, p2) => Math.hypot(p2.x - p1.x, p2.y - p1.y);\n\n/**\n * Calculate total tour distance (closed loop)\n * @param {number[]} tour - Array of point indices forming the tour\n * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n * @returns {number} Total distance of the tour\n */\nexport const calculateTotalDistance = (tour, points) => {\n if (tour.length < 2) {\n return 0;\n }\n let total = 0;\n for (let i = 0; i < tour.length - 1; i++) {\n total += distance(points[tour[i]], points[tour[i + 1]]);\n }\n // Close the loop\n total += distance(points[tour[tour.length - 1]], points[tour[0]]);\n return total;\n};\n",
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"content": " 1→/**\n 2→ * Test file for TSP algorithms\n 3→ * Works with Bun test runner\n 4→ */\n 5→\n 6→import { describe, it, expect } from 'bun:test';\n 7→import {\n 8→ calculateMooreGridSize,\n 9→ generateRandomPoints,\n 10→ distance,\n 11→ calculateTotalDistance,\n 12→ VALID_GRID_SIZES,\n 13→} from '../lib/algorithms/utils.js';\n 14→import {\n 15→ sonarAlgorithmSteps,\n 16→ sonarSolution,\n 17→} from '../lib/algorithms/progressive/solution/sonar.js';\n 18→import {\n 19→ mooreAlgorithmSteps,\n 20→ mooreSolution,\n 21→ generateMooreCurve,\n 22→ mooreCurveToPoints,\n 23→} from '../lib/algorithms/progressive/solution/moore.js';\n 24→import {\n 25→ zigzagOptSteps,\n 26→ zigzagOpt,\n 27→ // Legacy aliases\n 28→ sonarOptimizationSteps,\n 29→ sonarOptimization,\n 30→} from '../lib/algorithms/progressive/optimization/zigzag-opt.js';\n 31→import {\n 32→ twoOptSteps,\n 33→ twoOpt,\n 34→ // Legacy aliases\n 35→ mooreOptimizationSteps,\n 36→ mooreOptimization,\n 37→} from '../lib/algorithms/progressive/optimization/two-opt.js';\n 38→import {\n 39→ combinedOptSteps,\n 40→ combinedOpt,\n 41→} from '../lib/algorithms/progressive/optimization/combined-opt.js';\n 42→import {\n 43→ bruteForceAlgorithmSteps,\n 44→ bruteForceSolution,\n 45→ calculateOptimalityRatio,\n 46→ BRUTE_FORCE_MAX_POINTS,\n 47→} from '../lib/algorithms/progressive/solution/brute-force.js';\n 48→import {\n 49→ oneTreeLowerBound,\n 50→ verifyOptimality,\n 51→} from '../lib/algorithms/verification/lower-bound.js';\n 52→\n 53→// Backward-compatible aliases\n 54→const bruteForceOptimalTour = bruteForceSolution;\n 55→const bruteForceSteps = bruteForceAlgorithmSteps;\n 56→\n 57→// ============================================================\n 58→// Utility Functions Tests\n 59→// ============================================================\n 60→\n 61→describe('VALID_GRID_SIZES', () => {\n 62→ it('should contain only powers of 2', () => {\n 63→ VALID_GRID_SIZES.forEach((size) => {\n 64→ expect(Math.log2(size) % 1).toBe(0);\n 65→ });\n 66→ });\n 67→\n 68→ it('should be sorted in ascending order', () => {\n 69→ for (let i = 1; i < VALID_GRID_SIZES.length; i++) {\n 70→ expect(VALID_GRID_SIZES[i]).toBeGreaterThan(VALID_GRID_SIZES[i - 1]);\n 71→ }\n 72→ });\n 73→\n 74→ it('should contain expected sizes', () => {\n 75→ expect(VALID_GRID_SIZES).toEqual([2, 4, 8, 16, 32, 64, 128]);\n 76→ });\n 77→});\n 78→\n 79→describe('calculateMooreGridSize', () => {\n 80→ it('should return smallest valid size >= input', () => {\n 81→ expect(calculateMooreGridSize(2)).toBe(2);\n 82→ expect(calculateMooreGridSize(3)).toBe(4);\n 83→ expect(calculateMooreGridSize(4)).toBe(4);\n 84→ expect(calculateMooreGridSize(5)).toBe(8);\n 85→ expect(calculateMooreGridSize(10)).toBe(16);\n 86→ expect(calculateMooreGridSize(16)).toBe(16);\n 87→ expect(calculateMooreGridSize(20)).toBe(32);\n 88→ expect(calculateMooreGridSize(32)).toBe(32);\n 89→ expect(calculateMooreGridSize(33)).toBe(64);\n 90→ expect(calculateMooreGridSize(64)).toBe(64);\n 91→ expect(calculateMooreGridSize(65)).toBe(128);\n 92→ expect(calculateMooreGridSize(128)).toBe(128);\n 93→ expect(calculateMooreGridSize(129)).toBe(128);\n 94→ });\n 95→\n 96→ it('should return largest valid size for very large input', () => {\n 97→ expect(calculateMooreGridSize(200)).toBe(128);\n 98→ });\n 99→\n 100→ it('should always return a valid Moore grid size', () => {\n 101→ for (let gs = 1; gs <= 100; gs++) {\n 102→ const result = calculateMooreGridSize(gs);\n 103→ expect(VALID_GRID_SIZES).toContain(result);\n 104→ }\n 105→ });\n 106→});\n 107→\n 108→describe('generateRandomPoints', () => {\n 109→ it('should generate the requested number of points', () => {\n 110→ const mooreGridSize = 16;\n 111→ const numPoints = 10;\n 112→ const points = generateRandomPoints(mooreGridSize, numPoints);\n 113→ expect(points.length).toBe(numPoints);\n 114→ });\n 115→\n 116→ it('should generate points within [0, mooreGridSize-1] range', () => {\n 117→ const mooreGridSize = 16;\n 118→ const points = generateRandomPoints(mooreGridSize, 5);\n 119→ points.forEach((point) => {\n 120→ expect(point.x).toBeGreaterThanOrEqual(0);\n 121→ expect(point.x).toBeLessThanOrEqual(mooreGridSize - 1);\n 122→ expect(point.y).toBeGreaterThanOrEqual(0);\n 123→ expect(point.y).toBeLessThanOrEqual(mooreGridSize - 1);\n 124→ });\n 125→ });\n 126→\n 127→ it('should generate unique points', () => {\n 128→ const mooreGridSize = 16;\n 129→ const points = generateRandomPoints(mooreGridSize, 10);\n 130→ const uniqueKeys = new Set(points.map((p) => `${p.x},${p.y}`));\n 131→ expect(uniqueKeys.size).toBe(points.length);\n 132→ });\n 133→\n 134→ it('should assign sequential IDs', () => {\n 135→ const points = generateRandomPoints(16, 5);\n 136→ points.forEach((point, idx) => {\n 137→ expect(point.id).toBe(idx);\n 138→ });\n 139→ });\n 140→});\n 141→\n 142→describe('distance', () => {\n 143→ it('should calculate distance between two points', () => {\n 144→ const p1 = { x: 0, y: 0 };\n 145→ const p2 = { x: 3, y: 4 };\n 146→ expect(distance(p1, p2)).toBe(5);\n 147→ });\n 148→\n 149→ it('should return 0 for same point', () => {\n 150→ const p = { x: 5, y: 5 };\n 151→ expect(distance(p, p)).toBe(0);\n 152→ });\n 153→\n 154→ it('should handle negative coordinates', () => {\n 155→ const p1 = { x: -3, y: -4 };\n 156→ const p2 = { x: 0, y: 0 };\n 157→ expect(distance(p1, p2)).toBe(5);\n 158→ });\n 159→});\n 160→\n 161→describe('calculateTotalDistance', () => {\n 162→ it('should return 0 for empty tour', () => {\n 163→ expect(calculateTotalDistance([], [])).toBe(0);\n 164→ });\n 165→\n 166→ it('should return 0 for single point', () => {\n 167→ const points = [{ x: 0, y: 0 }];\n 168→ expect(calculateTotalDistance([0], points)).toBe(0);\n 169→ });\n 170→\n 171→ it('should calculate correct closed loop distance', () => {\n 172→ const points = [\n 173→ { x: 0, y: 0 },\n 174→ { x: 3, y: 0 },\n 175→ { x: 3, y: 4 },\n 176→ ];\n 177→ const tour = [0, 1, 2];\n 178→ // 0->1: 3, 1->2: 4, 2->0: 5 = 12\n 179→ expect(calculateTotalDistance(tour, points)).toBe(12);\n 180→ });\n 181→});\n 182→\n 183→// ============================================================\n 184→// Sonar Algorithm Tests\n 185→// ============================================================\n 186→\n 187→describe('sonarAlgorithmSteps', () => {\n 188→ it('should return empty array for empty points', () => {\n 189→ expect(sonarAlgorithmSteps([])).toEqual([]);\n 190→ });\n 191→\n 192→ it('should generate steps for all points', () => {\n 193→ const points = [\n 194→ { x: 0, y: 0, id: 0 },\n 195→ { x: 10, y: 0, id: 1 },\n 196→ { x: 5, y: 10, id: 2 },\n 197→ ];\n 198→ const steps = sonarAlgorithmSteps(points);\n 199→ expect(steps.length).toBe(points.length);\n 200→ });\n 201→\n 202→ it('should include all points in final tour', () => {\n 203→ const points = [\n 204→ { x: 0, y: 0, id: 0 },\n 205→ { x: 10, y: 0, id: 1 },\n 206→ { x: 5, y: 10, id: 2 },\n 207→ ];\n 208→ const steps = sonarAlgorithmSteps(points);\n 209→ const finalTour = steps[steps.length - 1].tour;\n 210→ expect(finalTour.length).toBe(points.length);\n 211→ expect(new Set(finalTour).size).toBe(points.length);\n 212→ });\n 213→\n 214→ it('should have sweep type for all steps', () => {\n 215→ const points = [\n 216→ { x: 0, y: 0, id: 0 },\n 217→ { x: 10, y: 0, id: 1 },\n 218→ ];\n 219→ const steps = sonarAlgorithmSteps(points);\n 220→ steps.forEach((step) => {\n 221→ expect(step.type).toBe('sweep');\n 222→ });\n 223→ });\n 224→\n 225→ it('should include centroid in each step', () => {\n 226→ const points = [\n 227→ { x: 0, y: 0, id: 0 },\n 228→ { x: 6, y: 0, id: 1 },\n 229→ { x: 3, y: 6, id: 2 },\n 230→ ];\n 231→ const steps = sonarAlgorithmSteps(points);\n 232→ const expectedCentroid = { x: 3, y: 2 };\n 233→ steps.forEach((step) => {\n 234→ expect(step.centroid.x).toBe(expectedCentroid.x);\n 235→ expect(step.centroid.y).toBe(expectedCentroid.y);\n 236→ });\n 237→ });\n 238→});\n 239→\n 240→describe('sonarSolution', () => {\n 241→ it('should return empty tour for empty points', () => {\n 242→ const result = sonarSolution([]);\n 243→ expect(result.tour).toEqual([]);\n 244→ expect(result.centroid).toBe(null);\n 245→ });\n 246→\n 247→ it('should return tour with all point indices', () => {\n 248→ const points = [\n 249→ { x: 0, y: 0, id: 0 },\n 250→ { x: 10, y: 0, id: 1 },\n 251→ { x: 5, y: 10, id: 2 },\n 252→ ];\n 253→ const result = sonarSolution(points);\n 254→ expect(result.tour.length).toBe(points.length);\n 255→ expect(new Set(result.tour).size).toBe(points.length);\n 256→ });\n 257→});\n 258→\n 259→// ============================================================\n 260→// Moore Curve Algorithm Tests\n 261→// ============================================================\n 262→\n 263→describe('generateMooreCurve', () => {\n 264→ it('should generate valid L-system sequence', () => {\n 265→ const sequence = generateMooreCurve(1);\n 266→ expect(sequence).toContain('F');\n 267→ expect(typeof sequence).toBe('string');\n 268→ });\n 269→\n 270→ it('should expand sequence with higher order', () => {\n 271→ const order1 = generateMooreCurve(1);\n 272→ const order2 = generateMooreCurve(2);\n 273→ expect(order2.length).toBeGreaterThan(order1.length);\n 274→ });\n 275→});\n 276→\n 277→describe('mooreCurveToPoints', () => {\n 278→ it('should generate curve points', () => {\n 279→ const sequence = generateMooreCurve(1);\n 280→ const points = mooreCurveToPoints(sequence, 4);\n 281→ expect(points.length).toBeGreaterThan(0);\n 282→ });\n 283→\n 284→ it('should generate points within [0, gridSize-1] bounds', () => {\n 285→ const mooreGridSize = 16;\n 286→ // For mooreGridSize=16, iterations = log2(16) - 1 = 3\n 287→ const sequence = generateMooreCurve(3);\n 288→ const points = mooreCurveToPoints(sequence, mooreGridSize);\n 289→ points.forEach((p) => {\n 290→ expect(p.x).toBeGreaterThanOrEqual(0);\n 291→ expect(p.x).toBeLessThanOrEqual(mooreGridSize - 1);\n 292→ expect(p.y).toBeGreaterThanOrEqual(0);\n 293→ expect(p.y).toBeLessThanOrEqual(mooreGridSize - 1);\n 294→ });\n 295→ });\n 296→});\n 297→\n 298→describe('mooreAlgorithmSteps', () => {\n 299→ it('should return empty array for empty points', () => {\n 300→ expect(mooreAlgorithmSteps([], 16)).toEqual([]);\n 301→ });\n 302→\n 303→ it('should generate curve step first', () => {\n 304→ const points = [\n 305→ { x: 0, y: 0, id: 0 },\n 306→ { x: 10, y: 10, id: 1 },\n 307→ ];\n 308→ const steps = mooreAlgorithmSteps(points, 16);\n 309→ expect(steps[0].type).toBe('curve');\n 310→ });\n 311→\n 312→ it('should include all points in final tour', () => {\n 313→ const points = [\n 314→ { x: 0, y: 0, id: 0 },\n 315→ { x: 8, y: 8, id: 1 },\n 316→ { x: 4, y: 12, id: 2 },\n 317→ ];\n 318→ const steps = mooreAlgorithmSteps(points, 16);\n 319→ const finalTour = steps[steps.length - 1].tour;\n 320→ expect(finalTour.length).toBe(points.length);\n 321→ });\n 322→\n 323→ it('should include curve points in each step', () => {\n 324→ const points = [{ x: 5, y: 5, id: 0 }];\n 325→ const steps = mooreAlgorithmSteps(points, 16);\n 326→ steps.forEach((step) => {\n 327→ expect(step.curvePoints !== undefined).toBe(true);\n 328→ expect(step.curvePoints.length).toBeGreaterThan(0);\n 329→ });\n 330→ });\n 331→});\n 332→\n 333→describe('mooreSolution', () => {\n 334→ it('should return empty tour for empty points', () => {\n 335→ const result = mooreSolution([], 16);\n 336→ expect(result.tour).toEqual([]);\n 337→ expect(result.curvePoints).toEqual([]);\n 338→ });\n 339→\n 340→ it('should return tour with all point indices', () => {\n 341→ const points = [\n 342→ { x: 0, y: 0, id: 0 },\n 343→ { x: 10, y: 0, id: 1 },\n 344→ { x: 5, y: 10, id: 2 },\n 345→ ];\n 346→ const result = mooreSolution(points, 16);\n 347→ expect(result.tour.length).toBe(points.length);\n 348→ expect(new Set(result.tour).size).toBe(points.length);\n 349→ });\n 350→});\n 351→\n 352→// ============================================================\n 353→// Optimization Tests\n 354→// ============================================================\n 355→\n 356→describe('sonarOptimizationSteps', () => {\n 357→ it('should return empty array for tour with less than 4 points', () => {\n 358→ const points = [\n 359→ { x: 0, y: 0 },\n 360→ { x: 1, y: 1 },\n 361→ { x: 2, y: 2 },\n 362→ ];\n 363→ const tour = [0, 1, 2];\n 364→ expect(sonarOptimizationSteps(points, tour)).toEqual([]);\n 365→ });\n 366→\n 367→ it('should return optimization steps for valid tour', () => {\n 368→ const points = [\n 369→ { x: 0, y: 0 },\n 370→ { x: 10, y: 0 },\n 371→ { x: 10, y: 10 },\n 372→ { x: 0, y: 10 },\n 373→ ];\n 374→ // Create a non-optimal tour\n 375→ const tour = [0, 2, 1, 3];\n 376→ const steps = sonarOptimizationSteps(points, tour);\n 377→ // May or may not have improvements depending on tour\n 378→ expect(Array.isArray(steps)).toBe(true);\n 379→ });\n 380→\n 381→ it('should have optimize type for all steps', () => {\n 382→ const points = [\n 383→ { x: 0, y: 0 },\n 384→ { x: 10, y: 0 },\n 385→ { x: 10, y: 10 },\n 386→ { x: 0, y: 10 },\n 387→ { x: 5, y: 5 },\n 388→ ];\n 389→ const tour = [0, 2, 4, 1, 3];\n 390→ const steps = sonarOptimizationSteps(points, tour);\n 391→ steps.forEach((step) => {\n 392→ expect(step.type).toBe('optimize');\n 393→ });\n 394→ });\n 395→});\n 396→\n 397→describe('sonarOptimization', () => {\n 398→ it('should return original tour for small inputs', () => {\n 399→ const points = [\n 400→ { x: 0, y: 0 },\n 401→ { x: 1, y: 1 },\n 402→ ];\n 403→ const tour = [0, 1];\n 404→ const result = sonarOptimization(points, tour);\n 405→ expect(result.tour).toEqual(tour);\n 406→ expect(result.improvement).toBe(0);\n 407→ });\n 408→});\n 409→\n 410→describe('mooreOptimizationSteps', () => {\n 411→ it('should return empty array for tour with less than 4 points', () => {\n 412→ const points = [\n 413→ { x: 0, y: 0 },\n 414→ { x: 1, y: 1 },\n 415→ { x: 2, y: 2 },\n 416→ ];\n 417→ const tour = [0, 1, 2];\n 418→ expect(mooreOptimizationSteps(points, tour)).toEqual([]);\n 419→ });\n 420→\n 421→ it('should have optimize type for all steps', () => {\n 422→ const points = [\n 423→ { x: 0, y: 0 },\n 424→ { x: 10, y: 0 },\n 425→ { x: 10, y: 10 },\n 426→ { x: 0, y: 10 },\n 427→ { x: 5, y: 5 },\n 428→ ];\n 429→ const tour = [0, 2, 4, 1, 3];\n 430→ const steps = mooreOptimizationSteps(points, tour);\n 431→ steps.forEach((step) => {\n 432→ expect(step.type).toBe('optimize');\n 433→ });\n 434→ });\n 435→});\n 436→\n 437→describe('mooreOptimization', () => {\n 438→ it('should return original tour for small inputs', () => {\n 439→ const points = [\n 440→ { x: 0, y: 0 },\n 441→ { x: 1, y: 1 },\n 442→ ];\n 443→ const tour = [0, 1];\n 444→ const result = mooreOptimization(points, tour);\n 445→ expect(result.tour).toEqual(tour);\n 446→ expect(result.improvement).toBe(0);\n 447→ });\n 448→\n 449→ it('should not increase tour distance', () => {\n 450→ const points = [\n 451→ { x: 0, y: 0 },\n 452→ { x: 10, y: 0 },\n 453→ { x: 10, y: 10 },\n 454→ { x: 0, y: 10 },\n 455→ { x: 5, y: 5 },\n 456→ ];\n 457→ const tour = [0, 2, 4, 1, 3];\n 458→ const originalDistance = calculateTotalDistance(tour, points);\n 459→ const result = mooreOptimization(points, tour);\n 460→ const newDistance = calculateTotalDistance(result.tour, points);\n 461→ expect(newDistance).toBeLessThanOrEqual(originalDistance);\n 462→ });\n 463→});\n 464→\n 465→// ============================================================\n 466→// Generic Optimization Tests (new generic API)\n 467→// ============================================================\n 468→\n 469→describe('twoOptSteps (generic 2-opt)', () => {\n 470→ it('should return empty array for tour with less than 4 points', () => {\n 471→ const points = [\n 472→ { x: 0, y: 0 },\n 473→ { x: 1, y: 1 },\n 474→ { x: 2, y: 2 },\n 475→ ];\n 476→ const tour = [0, 1, 2];\n 477→ expect(twoOptSteps(points, tour)).toEqual([]);\n 478→ });\n 479→\n 480→ it('should work on any tour regardless of source algorithm', () => {\n 481→ const points = [\n 482→ { x: 0, y: 0 },\n 483→ { x: 10, y: 0 },\n 484→ { x: 10, y: 10 },\n 485→ { x: 0, y: 10 },\n 486→ { x: 5, y: 5 },\n 487→ ];\n 488→ // This is not from any specific algorithm\n 489→ const randomTour = [0, 2, 4, 1, 3];\n 490→ const steps = twoOptSteps(points, randomTour);\n 491→ expect(Array.isArray(steps)).toBe(true);\n 492→ });\n 493→\n 494→ it('should respect maxIterations option', () => {\n 495→ const points = [\n 496→ { x: 0, y: 0 },\n 497→ { x: 10, y: 0 },\n 498→ { x: 10, y: 10 },\n 499→ { x: 0, y: 10 },\n 500→ ];\n 501→ const tour = [0, 1, 2, 3];\n 502→ const steps = twoOptSteps(points, tour, { maxIterations: 1 });\n 503→ // Should still return valid array\n 504→ expect(Array.isArray(steps)).toBe(true);\n 505→ });\n 506→});\n 507→\n 508→describe('twoOpt (generic atomic 2-opt)', () => {\n 509→ it('should return original tour for small inputs', () => {\n 510→ const points = [\n 511→ { x: 0, y: 0 },\n 512→ { x: 1, y: 1 },\n 513→ ];\n 514→ const tour = [0, 1];\n 515→ const result = twoOpt(points, tour);\n 516→ expect(result.tour).toEqual(tour);\n 517→ expect(result.improvement).toBe(0);\n 518→ });\n 519→\n 520→ it('should never increase tour distance', () => {\n 521→ const points = [\n 522→ { x: 0, y: 0 },\n 523→ { x: 10, y: 0 },\n 524→ { x: 10, y: 10 },\n 525→ { x: 0, y: 10 },\n 526→ { x: 5, y: 5 },\n 527→ ];\n 528→ const tour = [0, 2, 4, 1, 3];\n 529→ const originalDistance = calculateTotalDistance(tour, points);\n 530→ const result = twoOpt(points, tour);\n 531→ const newDistance = calculateTotalDistance(result.tour, points);\n 532→ expect(newDistance).toBeLessThanOrEqual(originalDistance);\n 533→ });\n 534→});\n 535→\n 536→describe('zigzagOptSteps (generic zigzag)', () => {\n 537→ it('should return empty array for tour with less than 4 points', () => {\n 538→ const points = [\n 539→ { x: 0, y: 0 },\n 540→ { x: 1, y: 1 },\n 541→ { x: 2, y: 2 },\n 542→ ];\n 543→ const tour = [0, 1, 2];\n 544→ expect(zigzagOptSteps(points, tour)).toEqual([]);\n 545→ });\n 546→\n 547→ it('should work on any tour regardless of source algorithm', () => {\n 548→ const points = [\n 549→ { x: 0, y: 0 },\n 550→ { x: 10, y: 0 },\n 551→ { x: 10, y: 10 },\n 552→ { x: 0, y: 10 },\n 553→ { x: 5, y: 5 },\n 554→ ];\n 555→ // This is not from any specific algorithm\n 556→ const randomTour = [0, 2, 4, 1, 3];\n 557→ const steps = zigzagOptSteps(points, randomTour);\n 558→ expect(Array.isArray(steps)).toBe(true);\n 559→ });\n 560→});\n 561→\n 562→describe('zigzagOpt (generic atomic zigzag)', () => {\n 563→ it('should return original tour for small inputs', () => {\n 564→ const points = [\n 565→ { x: 0, y: 0 },\n 566→ { x: 1, y: 1 },\n 567→ ];\n 568→ const tour = [0, 1];\n 569→ const result = zigzagOpt(points, tour);\n 570→ expect(result.tour).toEqual(tour);\n 571→ expect(result.improvement).toBe(0);\n 572→ });\n 573→\n 574→ it('should never increase tour distance', () => {\n 575→ const points = [\n 576→ { x: 0, y: 0 },\n 577→ { x: 10, y: 0 },\n 578→ { x: 10, y: 10 },\n 579→ { x: 0, y: 10 },\n 580→ { x: 5, y: 5 },\n 581→ ];\n 582→ const tour = [0, 2, 4, 1, 3];\n 583→ const originalDistance = calculateTotalDistance(tour, points);\n 584→ const result = zigzagOpt(points, tour);\n 585→ const newDistance = calculateTotalDistance(result.tour, points);\n 586→ expect(newDistance).toBeLessThanOrEqual(originalDistance);\n 587→ });\n 588→});\n 589→\n 590→describe('combined optimization (zigzag + 2-opt)', () => {\n 591→ const fivePoints = [\n 592→ { x: 0, y: 0 },\n 593→ { x: 10, y: 0 },\n 594→ { x: 10, y: 10 },\n 595→ { x: 0, y: 10 },\n 596→ { x: 5, y: 5 },\n 597→ ];\n 598→\n 599→ it('steps: should return empty array for tour with less than 4 points', () => {\n 600→ const points = [\n 601→ { x: 0, y: 0 },\n 602→ { x: 1, y: 1 },\n 603→ { x: 2, y: 2 },\n 604→ ];\n 605→ expect(combinedOptSteps(points, [0, 1, 2])).toEqual([]);\n 606→ });\n 607→\n 608→ it('steps: should work on any tour and have optimize type', () => {\n 609→ const steps = combinedOptSteps(fivePoints, [0, 2, 4, 1, 3]);\n 610→ expect(Array.isArray(steps)).toBe(true);\n 611→ steps.forEach((step) => expect(step.type).toBe('optimize'));\n 612→ });\n 613→\n 614→ it('atomic: should return original tour for small inputs', () => {\n 615→ const result = combinedOpt(\n 616→ [\n 617→ { x: 0, y: 0 },\n 618→ { x: 1, y: 1 },\n 619→ ],\n 620→ [0, 1]\n 621→ );\n 622→ expect(result.tour).toEqual([0, 1]);\n 623→ expect(result.improvement).toBe(0);\n 624→ });\n 625→\n 626→ it('atomic: should never increase tour distance', () => {\n 627→ const tour = [0, 2, 4, 1, 3];\n 628→ const originalDistance = calculateTotalDistance(tour, fivePoints);\n 629→ const result = combinedOpt(fivePoints, tour);\n 630→ expect(calculateTotalDistance(result.tour, fivePoints)).toBeLessThanOrEqual(\n 631→ originalDistance\n 632→ );\n 633→ });\n 634→\n 635→ it('atomic: should return zero improvement for optimal square tour', () => {\n 636→ const square = [\n 637→ { x: 0, y: 0 },\n 638→ { x: 10, y: 0 },\n 639→ { x: 10, y: 10 },\n 640→ { x: 0, y: 10 },\n 641→ ];\n 642→ expect(combinedOpt(square, [0, 1, 2, 3]).improvement).toBe(0);\n 643→ });\n 644→});\n 645→\n 646→// ============================================================\n 647→// Already-Optimal Tour Tests (Issue #15)\n 648→// ============================================================\n 649→\n 650→describe('optimization with already-optimal tours (issue #15)', () => {\n 651→ // 3 points forming a triangle — already optimal, no improvements possible\n 652→ const trianglePoints = [\n 653→ { x: 0, y: 0 },\n 654→ { x: 5, y: 0 },\n 655→ { x: 2, y: 4 },\n 656→ ];\n 657→ const triangleTour = [0, 1, 2];\n 658→\n 659→ // 4 points forming a square in optimal order\n 660→ const squarePoints = [\n 661→ { x: 0, y: 0 },\n 662→ { x: 10, y: 0 },\n 663→ { x: 10, y: 10 },\n 664→ { x: 0, y: 10 },\n 665→ ];\n 666→ const squareOptimalTour = [0, 1, 2, 3];\n 667→\n 668→ describe('twoOptSteps with already-optimal tours', () => {\n 669→ it('should return empty array for 3-point triangle tour (< 4 points)', () => {\n 670→ const steps = twoOptSteps(trianglePoints, triangleTour);\n 671→ expect(steps).toEqual([]);\n 672→ });\n 673→\n 674→ it('should return empty array for optimal square tour', () => {\n 675→ const steps = twoOptSteps(squarePoints, squareOptimalTour);\n 676→ expect(steps).toEqual([]);\n 677→ });\n 678→\n 679→ it('should preserve tour distance when no improvement found', () => {\n 680→ const originalDistance = calculateTotalDistance(\n 681→ squareOptimalTour,\n 682→ squarePoints\n 683→ );\n 684→ const result = twoOpt(squarePoints, squareOptimalTour);\n 685→ const newDistance = calculateTotalDistance(result.tour, squarePoints);\n 686→ expect(newDistance).toBe(originalDistance);\n 687→ expect(result.improvement).toBe(0);\n 688→ });\n 689→ });\n 690→\n 691→ describe('zigzagOptSteps with already-optimal tours', () => {\n 692→ it('should return empty array for 3-point triangle tour (< 4 points)', () => {\n 693→ const steps = zigzagOptSteps(trianglePoints, triangleTour);\n 694→ expect(steps).toEqual([]);\n 695→ });\n 696→\n 697→ it('should return empty array for optimal square tour', () => {\n 698→ const steps = zigzagOptSteps(squarePoints, squareOptimalTour);\n 699→ expect(steps).toEqual([]);\n 700→ });\n 701→\n 702→ it('should preserve tour distance when no improvement found', () => {\n 703→ const originalDistance = calculateTotalDistance(\n 704→ squareOptimalTour,\n 705→ squarePoints\n 706→ );\n 707→ const result = zigzagOpt(squarePoints, squareOptimalTour);\n 708→ const newDistance = calculateTotalDistance(result.tour, squarePoints);\n 709→ expect(newDistance).toBe(originalDistance);\n 710→ expect(result.improvement).toBe(0);\n 711→ });\n 712→ });\n 713→\n 714→ describe('full pipeline: solution then optimization on 3-point tour', () => {\n 715→ const points = [\n 716→ { x: 0, y: 0, id: 0 },\n 717→ { x: 5, y: 0, id: 1 },\n 718→ { x: 2, y: 4, id: 2 },\n 719→ ];\n 720→\n 721→ it('should produce valid sonar solution then empty optimization', () => {\n 722→ const steps = sonarAlgorithmSteps(points);\n 723→ expect(steps.length).toBe(points.length);\n 724→ const finalTour = steps[steps.length - 1].tour;\n 725→ expect(finalTour.length).toBe(points.length);\n 726→\n 727→ const optSteps = zigzagOptSteps(points, finalTour);\n 728→ expect(optSteps).toEqual([]);\n 729→\n 730→ // Distance should still be computable from the solution tour\n 731→ const dist = calculateTotalDistance(finalTour, points);\n 732→ expect(dist).toBeGreaterThan(0);\n 733→ });\n 734→\n 735→ it('should produce valid moore solution then empty optimization', () => {\n 736→ const steps = mooreAlgorithmSteps(points, 16);\n 737→ expect(steps.length).toBeGreaterThan(0);\n 738→ const finalTour = steps[steps.length - 1].tour;\n 739→ expect(finalTour.length).toBe(points.length);\n 740→\n 741→ const optSteps = twoOptSteps(points, finalTour);\n 742→ expect(optSteps).toEqual([]);\n 743→\n 744→ // Distance should still be computable from the solution tour\n 745→ const dist = calculateTotalDistance(finalTour, points);\n 746→ expect(dist).toBeGreaterThan(0);\n 747→ });\n 748→ });\n 749→});\n 750→\n 751→// ============================================================\n 752→// Moore Curve Point-by-Point Verification Tests\n 753→// ============================================================\n 754→\n 755→/**\n 756→ * Helper: generate Moore curve points for a given grid size.\n 757→ * Grid size must be a power of 2 (2, 4, 8, 16, 32, 64).\n 758→ * Returns normalized points in [0, gridSize-1] range.\n 759→ */\n 760→function getMooreCurvePoints(gridSize) {\n 761→ const iterations = Math.round(Math.log2(gridSize)) - 1;\n 762→ const sequence = generateMooreCurve(iterations);\n 763→ return mooreCurveToPoints(sequence, gridSize);\n 764→}\n 765→\n 766→describe('Moore curve point-by-point verification (2x2)', () => {\n 767→ const gridSize = 2;\n 768→ const points = getMooreCurvePoints(gridSize);\n 769→\n 770→ it('should have exactly 4 vertices', () => {\n 771→ expect(points.length).toBe(4);\n 772→ });\n 773→\n 774→ it('should visit all 4 grid cells', () => {\n 775→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 776→ expect(unique.size).toBe(4);\n 777→ expect(unique.has('0,0')).toBe(true);\n 778→ expect(unique.has('0,1')).toBe(true);\n 779→ expect(unique.has('1,0')).toBe(true);\n 780→ expect(unique.has('1,1')).toBe(true);\n 781→ });\n 782→\n 783→ it('should follow the exact path: (0,1) -> (0,0) -> (1,0) -> (1,1)', () => {\n 784→ expect(points[0]).toEqual({ x: 0, y: 1 });\n 785→ expect(points[1]).toEqual({ x: 0, y: 0 });\n 786→ expect(points[2]).toEqual({ x: 1, y: 0 });\n 787→ expect(points[3]).toEqual({ x: 1, y: 1 });\n 788→ });\n 789→\n 790→ it('should have all consecutive points adjacent (distance 1)', () => {\n 791→ for (let i = 0; i < points.length - 1; i++) {\n 792→ const d = distance(points[i], points[i + 1]);\n 793→ expect(d).toBe(1);\n 794→ }\n 795→ });\n 796→\n 797→ it('should form a closed loop (first and last points are adjacent)', () => {\n 798→ const d = distance(points[0], points[points.length - 1]);\n 799→ expect(d).toBe(1);\n 800→ });\n 801→});\n 802→\n 803→describe('Moore curve point-by-point verification (4x4)', () => {\n 804→ const gridSize = 4;\n 805→ const points = getMooreCurvePoints(gridSize);\n 806→\n 807→ it('should have exactly 16 vertices', () => {\n 808→ expect(points.length).toBe(16);\n 809→ });\n 810→\n 811→ it('should visit all 16 grid cells', () => {\n 812→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 813→ expect(unique.size).toBe(16);\n 814→ for (let x = 0; x < 4; x++) {\n 815→ for (let y = 0; y < 4; y++) {\n 816→ expect(unique.has(`${x},${y}`)).toBe(true);\n 817→ }\n 818→ }\n 819→ });\n 820→\n 821→ it('should follow the exact 4x4 Moore curve path', () => {\n 822→ const expectedPath = [\n 823→ { x: 1, y: 3 },\n 824→ { x: 0, y: 3 },\n 825→ { x: 0, y: 2 },\n 826→ { x: 1, y: 2 },\n 827→ { x: 1, y: 1 },\n 828→ { x: 0, y: 1 },\n 829→ { x: 0, y: 0 },\n 830→ { x: 1, y: 0 },\n 831→ { x: 2, y: 0 },\n 832→ { x: 3, y: 0 },\n 833→ { x: 3, y: 1 },\n 834→ { x: 2, y: 1 },\n 835→ { x: 2, y: 2 },\n 836→ { x: 3, y: 2 },\n 837→ { x: 3, y: 3 },\n 838→ { x: 2, y: 3 },\n 839→ ];\n 840→ expect(points).toEqual(expectedPath);\n 841→ });\n 842→\n 843→ it('should have all consecutive points adjacent (distance 1)', () => {\n 844→ for (let i = 0; i < points.length - 1; i++) {\n 845→ const d = distance(points[i], points[i + 1]);\n 846→ expect(d).toBe(1);\n 847→ }\n 848→ });\n 849→\n 850→ it('should form a closed loop', () => {\n 851→ const d = distance(points[0], points[points.length - 1]);\n 852→ expect(d).toBe(1);\n 853→ });\n 854→});\n 855→\n 856→describe('Moore curve verification (8x8)', () => {\n 857→ const gridSize = 8;\n 858→ const points = getMooreCurvePoints(gridSize);\n 859→\n 860→ it('should have exactly 64 vertices', () => {\n 861→ expect(points.length).toBe(64);\n 862→ });\n 863→\n 864→ it('should visit all 64 grid cells', () => {\n 865→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 866→ expect(unique.size).toBe(64);\n 867→ for (let x = 0; x < 8; x++) {\n 868→ for (let y = 0; y < 8; y++) {\n 869→ expect(unique.has(`${x},${y}`)).toBe(true);\n 870→ }\n 871→ }\n 872→ });\n 873→\n 874→ it('should have all consecutive points adjacent (distance 1)', () => {\n 875→ for (let i = 0; i < points.length - 1; i++) {\n 876→ const d = distance(points[i], points[i + 1]);\n 877→ expect(d).toBe(1);\n 878→ }\n 879→ });\n 880→\n 881→ it('should form a closed loop', () => {\n 882→ const d = distance(points[0], points[points.length - 1]);\n 883→ expect(d).toBe(1);\n 884→ });\n 885→\n 886→ it('should have no cross-shaped gap (center row and column covered)', () => {\n 887→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 888→ // Center of 8x8 grid is at rows/columns 3 and 4\n 889→ for (let i = 0; i < 8; i++) {\n 890→ expect(unique.has(`${i},3`)).toBe(true);\n 891→ expect(unique.has(`${i},4`)).toBe(true);\n 892→ expect(unique.has(`3,${i}`)).toBe(true);\n 893→ expect(unique.has(`4,${i}`)).toBe(true);\n 894→ }\n 895→ });\n 896→\n 897→ it('should have all points within [0, 7] range', () => {\n 898→ points.forEach((p) => {\n 899→ expect(p.x).toBeGreaterThanOrEqual(0);\n 900→ expect(p.x).toBeLessThanOrEqual(7);\n 901→ expect(p.y).toBeGreaterThanOrEqual(0);\n 902→ expect(p.y).toBeLessThanOrEqual(7);\n 903→ });\n 904→ });\n 905→});\n 906→\n 907→describe('Moore curve verification (16x16)', () => {\n 908→ const gridSize = 16;\n 909→ const points = getMooreCurvePoints(gridSize);\n 910→\n 911→ it('should have exactly 256 vertices', () => {\n 912→ expect(points.length).toBe(256);\n 913→ });\n 914→\n 915→ it('should visit all 256 grid cells', () => {\n 916→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 917→ expect(unique.size).toBe(256);\n 918→ for (let x = 0; x < 16; x++) {\n 919→ for (let y = 0; y < 16; y++) {\n 920→ expect(unique.has(`${x},${y}`)).toBe(true);\n 921→ }\n 922→ }\n 923→ });\n 924→\n 925→ it('should have all consecutive points adjacent (distance 1)', () => {\n 926→ for (let i = 0; i < points.length - 1; i++) {\n 927→ const d = distance(points[i], points[i + 1]);\n 928→ expect(d).toBe(1);\n 929→ }\n 930→ });\n 931→\n 932→ it('should form a closed loop', () => {\n 933→ const d = distance(points[0], points[points.length - 1]);\n 934→ expect(d).toBe(1);\n 935→ });\n 936→\n 937→ it('should have no cross-shaped gap (center covered)', () => {\n 938→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 939→ for (let i = 0; i < 16; i++) {\n 940→ expect(unique.has(`${i},7`)).toBe(true);\n 941→ expect(unique.has(`${i},8`)).toBe(true);\n 942→ expect(unique.has(`7,${i}`)).toBe(true);\n 943→ expect(unique.has(`8,${i}`)).toBe(true);\n 944→ }\n 945→ });\n 946→});\n 947→\n 948→describe('Moore curve verification (32x32)', () => {\n 949→ const gridSize = 32;\n 950→ const points = getMooreCurvePoints(gridSize);\n 951→\n 952→ it('should have exactly 1024 vertices', () => {\n 953→ expect(points.length).toBe(1024);\n 954→ });\n 955→\n 956→ it('should visit all 1024 grid cells', () => {\n 957→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 958→ expect(unique.size).toBe(1024);\n 959→ for (let x = 0; x < 32; x++) {\n 960→ for (let y = 0; y < 32; y++) {\n 961→ expect(unique.has(`${x},${y}`)).toBe(true);\n 962→ }\n 963→ }\n 964→ });\n 965→\n 966→ it('should have all consecutive points adjacent (distance 1)', () => {\n 967→ for (let i = 0; i < points.length - 1; i++) {\n 968→ const d = distance(points[i], points[i + 1]);\n 969→ expect(d).toBe(1);\n 970→ }\n 971→ });\n 972→\n 973→ it('should form a closed loop', () => {\n 974→ const d = distance(points[0], points[points.length - 1]);\n 975→ expect(d).toBe(1);\n 976→ });\n 977→\n 978→ it('should have no cross-shaped gap (center covered)', () => {\n 979→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 980→ for (let i = 0; i < 32; i++) {\n 981→ expect(unique.has(`${i},15`)).toBe(true);\n 982→ expect(unique.has(`${i},16`)).toBe(true);\n 983→ expect(unique.has(`15,${i}`)).toBe(true);\n 984→ expect(unique.has(`16,${i}`)).toBe(true);\n 985→ }\n 986→ });\n 987→});\n 988→\n 989→// ============================================================\n 990→// Moore Curve Edge Verification Tests\n 991→// ============================================================\n 992→\n 993→describe('Moore curve edge verification (2x2)', () => {\n 994→ const points = getMooreCurvePoints(2);\n 995→\n 996→ it('should have correct edges', () => {\n 997→ // Path: (0,1) -> (0,0) -> (1,0) -> (1,1)\n 998→ const edges = [];\n 999→ for (let i = 0; i < points.length - 1; i++) {\n 1000→ edges.push(\n 1001→ `(${points[i].x},${points[i].y})->(${points[i + 1].x},${points[i + 1].y})`\n 1002→ );\n 1003→ }\n 1004→ expect(edges).toEqual(['(0,1)->(0,0)', '(0,0)->(1,0)', '(1,0)->(1,1)']);\n 1005→ });\n 1006→\n 1007→ it('should have closing edge back to start', () => {\n 1008→ const last = points[points.length - 1];\n 1009→ const first = points[0];\n 1010→ const d = distance(last, first);\n 1011→ expect(d).toBe(1);\n 1012→ // (1,1) -> (0,1) closing edge\n 1013→ expect(last).toEqual({ x: 1, y: 1 });\n 1014→ expect(first).toEqual({ x: 0, y: 1 });\n 1015→ });\n 1016→});\n 1017→\n 1018→describe('Moore curve edge verification (4x4)', () => {\n 1019→ const points = getMooreCurvePoints(4);\n 1020→\n 1021→ it('should have exactly 15 edges (16 vertices - 1)', () => {\n 1022→ const edges = [];\n 1023→ for (let i = 0; i < points.length - 1; i++) {\n 1024→ edges.push({\n 1025→ from: points[i],\n 1026→ to: points[i + 1],\n 1027→ });\n 1028→ }\n 1029→ expect(edges.length).toBe(15);\n 1030→ });\n 1031→\n 1032→ it('should have all edges with length 1 (horizontal or vertical)', () => {\n 1033→ for (let i = 0; i < points.length - 1; i++) {\n 1034→ const dx = Math.abs(points[i + 1].x - points[i].x);\n 1035→ const dy = Math.abs(points[i + 1].y - points[i].y);\n 1036→ // Each edge must be exactly 1 unit horizontal or vertical\n 1037→ expect((dx === 1 && dy === 0) || (dx === 0 && dy === 1)).toBe(true);\n 1038→ }\n 1039→ });\n 1040→\n 1041→ it('should have no self-intersecting edges', () => {\n 1042→ // All edges connect distinct vertices, and all vertices are unique\n 1043→ const visited = new Set();\n 1044→ for (const p of points) {\n 1045→ const key = `${p.x},${p.y}`;\n 1046→ expect(visited.has(key)).toBe(false);\n 1047→ visited.add(key);\n 1048→ }\n 1049→ });\n 1050→});\n 1051→\n 1052→// ============================================================\n 1053→// Moore Curve Properties Tests (all valid sizes)\n 1054→// ============================================================\n 1055→\n 1056→describe('Moore curve properties for all valid grid sizes', () => {\n 1057→ // Test for 2, 4, 8, 16, 32 (skip 64 to keep test fast)\n 1058→ const testSizes = [2, 4, 8, 16, 32];\n 1059→\n 1060→ testSizes.forEach((gridSize) => {\n 1061→ describe(`grid size ${gridSize}x${gridSize}`, () => {\n 1062→ const points = getMooreCurvePoints(gridSize);\n 1063→\n 1064→ it(`should have exactly ${gridSize * gridSize} vertices`, () => {\n 1065→ expect(points.length).toBe(gridSize * gridSize);\n 1066→ });\n 1067→\n 1068→ it('should visit every grid cell exactly once', () => {\n 1069→ const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n 1070→ expect(unique.size).toBe(gridSize * gridSize);\n 1071→ });\n 1072→\n 1073→ it('should have all points within [0, gridSize-1]', () => {\n 1074→ points.forEach((p) => {\n 1075→ expect(p.x).toBeGreaterThanOrEqual(0);\n 1076→ expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n 1077→ expect(p.y).toBeGreaterThanOrEqual(0);\n 1078→ expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n 1079→ });\n 1080→ });\n 1081→\n 1082→ it('should have all consecutive points adjacent (distance 1)', () => {\n 1083→ for (let i = 0; i < points.length - 1; i++) {\n 1084→ const d = distance(points[i], points[i + 1]);\n 1085→ expect(d).toBe(1);\n 1086→ }\n 1087→ });\n 1088→\n 1089→ it('should form a closed loop', () => {\n 1090→ const d = distance(points[0], points[points.length - 1]);\n 1091→ expect(d).toBe(1);\n 1092→ });\n 1093→ });\n 1094→ });\n 1095→});\n 1096→\n 1097→// ============================================================\n 1098→// Brute-Force Verification Tests\n 1099→// ============================================================\n 1100→\n 1101→describe('BRUTE_FORCE_MAX_POINTS', () => {\n 1102→ it('should be a reasonable positive integer', () => {\n 1103→ expect(BRUTE_FORCE_MAX_POINTS).toBeGreaterThanOrEqual(8);\n 1104→ expect(BRUTE_FORCE_MAX_POINTS).toBeLessThanOrEqual(20);\n 1105→ });\n 1106→});\n 1107→\n 1108→describe('bruteForceOptimalTour', () => {\n 1109→ it('should return empty tour for empty points', () => {\n 1110→ const result = bruteForceOptimalTour([]);\n 1111→ expect(result.tour).toEqual([]);\n 1112→ expect(result.distance).toBe(0);\n 1113→ });\n 1114→\n 1115→ it('should return single-point tour', () => {\n 1116→ const result = bruteForceOptimalTour([{ x: 5, y: 5 }]);\n 1117→ expect(result.tour).toEqual([0]);\n 1118→ expect(result.distance).toBe(0);\n 1119→ });\n 1120→\n 1121→ it('should return correct tour for 2 points', () => {\n 1122→ const points = [\n 1123→ { x: 0, y: 0 },\n 1124→ { x: 3, y: 4 },\n 1125→ ];\n 1126→ const result = bruteForceOptimalTour(points);\n 1127→ expect(result.tour).toEqual([0, 1]);\n 1128→ expect(result.distance).toBe(10); // 2 * 5 = 10 (closed loop)\n 1129→ });\n 1130→\n 1131→ it('should return correct optimal tour for a square', () => {\n 1132→ const points = [\n 1133→ { x: 0, y: 0 },\n 1134→ { x: 10, y: 0 },\n 1135→ { x: 10, y: 10 },\n 1136→ { x: 0, y: 10 },\n 1137→ ];\n 1138→ const result = bruteForceOptimalTour(points);\n 1139→ // Optimal tour for a square has distance 40\n 1140→ expect(result.distance).toBe(40);\n 1141→ expect(result.tour.length).toBe(4);\n 1142→ expect(new Set(result.tour).size).toBe(4);\n 1143→ });\n 1144→\n 1145→ it('should return optimal tour for a triangle', () => {\n 1146→ const points = [\n 1147→ { x: 0, y: 0 },\n 1148→ { x: 3, y: 0 },\n 1149→ { x: 0, y: 4 },\n 1150→ ];\n 1151→ const result = bruteForceOptimalTour(points);\n 1152→ // Triangle: 3 + 4 + 5 = 12\n 1153→ expect(result.distance).toBe(12);\n 1154→ expect(result.tour.length).toBe(3);\n 1155→ });\n 1156→\n 1157→ it('should return null for too many points', () => {\n 1158→ const points = Array.from(\n 1159→ { length: BRUTE_FORCE_MAX_POINTS + 1 },\n 1160→ (_, i) => ({\n 1161→ x: i,\n 1162→ y: 0,\n 1163→ })\n 1164→ );\n 1165→ const result = bruteForceOptimalTour(points);\n 1166→ expect(result).toBeNull();\n 1167→ });\n 1168→\n 1169→ it('should find optimal tour shorter than or equal to sonar for 5 points', () => {\n 1170→ const points = [\n 1171→ { x: 0, y: 0, id: 0 },\n 1172→ { x: 10, y: 0, id: 1 },\n 1173→ { x: 10, y: 10, id: 2 },\n 1174→ { x: 0, y: 10, id: 3 },\n 1175→ { x: 5, y: 5, id: 4 },\n 1176→ ];\n 1177→ const optResult = bruteForceOptimalTour(points);\n 1178→ const sonarResult = sonarSolution(points);\n 1179→ const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n 1180→ expect(optResult.distance).toBeLessThanOrEqual(sonarDist + 0.001);\n 1181→ });\n 1182→\n 1183→ it('should find optimal tour shorter than or equal to moore for 5 points', () => {\n 1184→ const points = [\n 1185→ { x: 0, y: 0, id: 0 },\n 1186→ { x: 8, y: 0, id: 1 },\n 1187→ { x: 8, y: 8, id: 2 },\n 1188→ { x: 0, y: 8, id: 3 },\n 1189→ { x: 4, y: 4, id: 4 },\n 1190→ ];\n 1191→ const optResult = bruteForceOptimalTour(points);\n 1192→ const mooreResult = mooreSolution(points, 16);\n 1193→ const mooreDist = calculateTotalDistance(mooreResult.tour, points);\n 1194→ expect(optResult.distance).toBeLessThanOrEqual(mooreDist + 0.001);\n 1195→ });\n 1196→\n 1197→ it('should handle collinear points correctly', () => {\n 1198→ const points = [\n 1199→ { x: 0, y: 0 },\n 1200→ { x: 1, y: 0 },\n 1201→ { x: 2, y: 0 },\n 1202→ { x: 3, y: 0 },\n 1203→ ];\n 1204→ const result = bruteForceOptimalTour(points);\n 1205→ // Optimal for collinear: 0->1->2->3->0 = 1+1+1+3 = 6\n 1206→ expect(result.distance).toBe(6);\n 1207→ });\n 1208→\n 1209→ it('should always start tour from point 0', () => {\n 1210→ const points = [\n 1211→ { x: 0, y: 0 },\n 1212→ { x: 5, y: 0 },\n 1213→ { x: 5, y: 5 },\n 1214→ { x: 0, y: 5 },\n 1215→ ];\n 1216→ const result = bruteForceOptimalTour(points);\n 1217→ expect(result.tour[0]).toBe(0);\n 1218→ });\n 1219→});\n 1220→\n 1221→describe('bruteForceAlgorithmSteps', () => {\n 1222→ it('should return steps for small point sets', () => {\n 1223→ const points = [\n 1224→ { x: 0, y: 0 },\n 1225→ { x: 3, y: 4 },\n 1226→ ];\n 1227→ const steps = bruteForceSteps(points);\n 1228→ expect(steps.length).toBeGreaterThanOrEqual(1);\n 1229→ expect(steps[0].type).toBe('solution');\n 1230→ expect(steps[steps.length - 1].tour.length).toBe(2);\n 1231→ });\n 1232→\n 1233→ it('should report infeasible for too many points', () => {\n 1234→ const points = Array.from(\n 1235→ { length: BRUTE_FORCE_MAX_POINTS + 1 },\n 1236→ (_, i) => ({\n 1237→ x: i,\n 1238→ y: 0,\n 1239→ })\n 1240→ );\n 1241→ const steps = bruteForceSteps(points);\n 1242→ expect(steps.length).toBe(1);\n 1243→ expect(steps[0].feasible).toBe(false);\n 1244→ expect(steps[0].description).toContain('Too many points');\n 1245→ });\n 1246→\n 1247→ it('should include distance in last step description', () => {\n 1248→ const points = [\n 1249→ { x: 0, y: 0 },\n 1250→ { x: 3, y: 4 },\n 1251→ ];\n 1252→ const steps = bruteForceSteps(points);\n 1253→ const lastStep = steps[steps.length - 1];\n 1254→ expect(lastStep.description).toContain('10.00');\n 1255→ });\n 1256→\n 1257→ it('should show progressive improvements for larger sets', () => {\n 1258→ const points = [\n 1259→ { x: 0, y: 0 },\n 1260→ { x: 3, y: 0 },\n 1261→ { x: 3, y: 4 },\n 1262→ { x: 0, y: 4 },\n 1263→ { x: 1, y: 2 },\n 1264→ ];\n 1265→ const steps = bruteForceSteps(points);\n 1266→ // Should have more than one step: initial + improvements + final\n 1267→ expect(steps.length).toBeGreaterThan(1);\n 1268→ // All steps should be type 'solution'\n 1269→ steps.forEach((step) => expect(step.type).toBe('solution'));\n 1270→ });\n 1271→\n 1272→ it('should return empty array for empty points', () => {\n 1273→ const steps = bruteForceSteps([]);\n 1274→ expect(steps).toEqual([]);\n 1275→ });\n 1276→\n 1277→ it('should return trivial tour for single point', () => {\n 1278→ const points = [{ x: 5, y: 3 }];\n 1279→ const steps = bruteForceSteps(points);\n 1280→ expect(steps.length).toBe(1);\n 1281→ expect(steps[0].type).toBe('solution');\n 1282→ expect(steps[0].tour).toEqual([0]);\n 1283→ expect(steps[0].description).toContain('trivial');\n 1284→ });\n 1285→});\n 1286→\n 1287→// ============================================================\n 1288→// Lower-Bound Verification Tests\n 1289→// ============================================================\n 1290→\n 1291→describe('oneTreeLowerBound', () => {\n 1292→ it('should return 0 for empty or single point', () => {\n 1293→ expect(oneTreeLowerBound([]).lowerBound).toBe(0);\n 1294→ expect(oneTreeLowerBound([{ x: 0, y: 0 }]).lowerBound).toBe(0);\n 1295→ });\n 1296→\n 1297→ it('should return exact distance for 2 points', () => {\n 1298→ const points = [\n 1299→ { x: 0, y: 0 },\n 1300→ { x: 3, y: 4 },\n 1301→ ];\n 1302→ const result = oneTreeLowerBound(points);\n 1303→ expect(result.lowerBound).toBe(10); // 2 * 5\n 1304→ expect(result.method).toBe('1-tree');\n 1305→ });\n 1306→\n 1307→ it('should provide a valid lower bound for a square', () => {\n 1308→ const points = [\n 1309→ { x: 0, y: 0 },\n 1310→ { x: 10, y: 0 },\n 1311→ { x: 10, y: 10 },\n 1312→ { x: 0, y: 10 },\n 1313→ ];\n 1314→ const result = oneTreeLowerBound(points);\n 1315→ // The optimal tour for a square is 40\n 1316→ // The lower bound should be <= 40\n 1317→ expect(result.lowerBound).toBeLessThanOrEqual(40 + 0.001);\n 1318→ expect(result.lowerBound).toBeGreaterThan(0);\n 1319→ });\n 1320→\n 1321→ it('should be less than or equal to brute-force optimal for small instances', () => {\n 1322→ const points = [\n 1323→ { x: 0, y: 0, id: 0 },\n 1324→ { x: 5, y: 0, id: 1 },\n 1325→ { x: 5, y: 5, id: 2 },\n 1326→ { x: 0, y: 5, id: 3 },\n 1327→ { x: 2, y: 3, id: 4 },\n 1328→ ];\n 1329→ const lbResult = oneTreeLowerBound(points);\n 1330→ const bfResult = bruteForceSolution(points);\n 1331→ expect(lbResult.lowerBound).toBeLessThanOrEqual(bfResult.distance + 0.001);\n 1332→ });\n 1333→});\n 1334→\n 1335→describe('verifyOptimality', () => {\n 1336→ it('should verify optimal tour for simple cases', () => {\n 1337→ const points = [\n 1338→ { x: 0, y: 0 },\n 1339→ { x: 3, y: 4 },\n 1340→ ];\n 1341→ // Optimal distance is 10\n 1342→ const result = verifyOptimality(10, points);\n 1343→ expect(result.isOptimal).toBe(true);\n 1344→ expect(result.gap).toBeCloseTo(0);\n 1345→ });\n 1346→\n 1347→ it('should detect suboptimal tour', () => {\n 1348→ const points = [\n 1349→ { x: 0, y: 0 },\n 1350→ { x: 10, y: 0 },\n 1351→ { x: 10, y: 10 },\n 1352→ { x: 0, y: 10 },\n 1353→ ];\n 1354→ // Tour distance much larger than optimal\n 1355→ const result = verifyOptimality(100, points);\n 1356→ expect(result.isOptimal).toBe(false);\n 1357→ expect(result.gapPercent).toBeGreaterThan(0);\n 1358→ });\n 1359→\n 1360→ it('should report method as 1-tree', () => {\n 1361→ const points = [\n 1362→ { x: 0, y: 0 },\n 1363→ { x: 1, y: 0 },\n 1364→ ];\n 1365→ const result = verifyOptimality(2, points);\n 1366→ expect(result.method).toBe('1-tree');\n 1367→ });\n 1368→});\n 1369→\n 1370→describe('calculateOptimalityRatio', () => {\n 1371→ it('should return 1.0 for optimal tour', () => {\n 1372→ expect(calculateOptimalityRatio(10, 10)).toBe(1.0);\n 1373→ });\n 1374→\n 1375→ it('should return correct ratio for suboptimal tour', () => {\n 1376→ expect(calculateOptimalityRatio(15, 10)).toBe(1.5);\n 1377→ });\n 1378→\n 1379→ it('should return 1.0 when both distances are 0', () => {\n 1380→ expect(calculateOptimalityRatio(0, 0)).toBe(1.0);\n 1381→ });\n 1382→\n 1383→ it('should return Infinity when optimal is 0 but tour is not', () => {\n 1384→ expect(calculateOptimalityRatio(5, 0)).toBe(Infinity);\n 1385→ });\n 1386→});\n 1387→\n 1388→// ============================================================\n 1389→// Integration: Verification with Algorithm Pipelines (Issue #31)\n 1390→// ============================================================\n 1391→\n 1392→describe('verification integration with algorithm pipelines', () => {\n 1393→ const points = [\n 1394→ { x: 0, y: 0, id: 0 },\n 1395→ { x: 5, y: 0, id: 1 },\n 1396→ { x: 5, y: 5, id: 2 },\n 1397→ { x: 0, y: 5, id: 3 },\n 1398→ ];\n 1399→\n 1400→ it('should verify sonar tour is not falsely claimed optimal', () => {\n 1401→ const sonarResult = sonarSolution(points);\n 1402→ const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n 1403→ const optimal = bruteForceOptimalTour(points);\n 1404→\n 1405→ // The sonar tour may or may not be optimal, but we can verify the claim\n 1406→ if (Math.abs(sonarDist - optimal.distance) < 0.001) {\n 1407→ expect(calculateOptimalityRatio(sonarDist, optimal.distance)).toBeCloseTo(\n 1408→ 1.0\n 1409→ );\n 1410→ } else {\n 1411→ expect(sonarDist).toBeGreaterThan(optimal.distance);\n 1412→ }\n 1413→ });\n 1414→\n 1415→ it('should verify moore tour is not falsely claimed optimal', () => {\n 1416→ const mooreResult = mooreSolution(points, 16);\n 1417→ const mooreDist = calculateTotalDistance(mooreResult.tour, points);\n 1418→ const optimal = bruteForceOptimalTour(points);\n 1419→\n 1420→ if (Math.abs(mooreDist - optimal.distance) < 0.001) {\n 1421→ expect(calculateOptimalityRatio(mooreDist, optimal.distance)).toBeCloseTo(\n 1422→ 1.0\n 1423→ );\n 1424→ } else {\n 1425→ expect(mooreDist).toBeGreaterThan(optimal.distance);\n 1426→ }\n 1427→ });\n 1428→\n 1429→ it('should verify optimized tour is at least as good as unoptimized', () => {\n 1430→ const sonarResult = sonarSolution(points);\n 1431→ const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n 1432→ const optimized = twoOpt(points, sonarResult.tour);\n 1433→ const optDist = calculateTotalDistance(optimized.tour, points);\n 1434→ const optimal = bruteForceOptimalTour(points);\n 1435→\n 1436→ expect(optDist).toBeLessThanOrEqual(sonarDist + 0.001);\n 1437→ expect(optimal.distance).toBeLessThanOrEqual(optDist + 0.001);\n 1438→ });\n 1439→\n 1440→ it('should compute meaningful optimality ratio for heuristic results', () => {\n 1441→ const points8 = [\n 1442→ { x: 0, y: 0, id: 0 },\n 1443→ { x: 4, y: 0, id: 1 },\n 1444→ { x: 8, y: 0, id: 2 },\n 1445→ { x: 8, y: 4, id: 3 },\n 1446→ { x: 8, y: 8, id: 4 },\n 1447→ { x: 4, y: 8, id: 5 },\n 1448→ { x: 0, y: 8, id: 6 },\n 1449→ { x: 0, y: 4, id: 7 },\n 1450→ ];\n 1451→ const optimal = bruteForceOptimalTour(points8);\n 1452→ const sonarResult = sonarSolution(points8);\n 1453→ const sonarDist = calculateTotalDistance(sonarResult.tour, points8);\n 1454→ const ratio = calculateOptimalityRatio(sonarDist, optimal.distance);\n 1455→\n 1456→ // Ratio should be >= 1.0 (optimal or worse)\n 1457→ expect(ratio).toBeGreaterThanOrEqual(1.0);\n 1458→ // And for these simple cases, not too far from optimal\n 1459→ expect(ratio).toBeLessThanOrEqual(2.0);\n 1460→ });\n 1461→});\n 1462→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
}
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"content": "/**\n * Test file for TSP algorithms\n * Works with Bun test runner\n */\n\nimport { describe, it, expect } from 'bun:test';\nimport {\n calculateMooreGridSize,\n generateRandomPoints,\n distance,\n calculateTotalDistance,\n VALID_GRID_SIZES,\n} from '../lib/algorithms/utils.js';\nimport {\n sonarAlgorithmSteps,\n sonarSolution,\n} from '../lib/algorithms/progressive/solution/sonar.js';\nimport {\n mooreAlgorithmSteps,\n mooreSolution,\n generateMooreCurve,\n mooreCurveToPoints,\n} from '../lib/algorithms/progressive/solution/moore.js';\nimport {\n zigzagOptSteps,\n zigzagOpt,\n // Legacy aliases\n sonarOptimizationSteps,\n sonarOptimization,\n} from '../lib/algorithms/progressive/optimization/zigzag-opt.js';\nimport {\n twoOptSteps,\n twoOpt,\n // Legacy aliases\n mooreOptimizationSteps,\n mooreOptimization,\n} from '../lib/algorithms/progressive/optimization/two-opt.js';\nimport {\n combinedOptSteps,\n combinedOpt,\n} from '../lib/algorithms/progressive/optimization/combined-opt.js';\nimport {\n bruteForceAlgorithmSteps,\n bruteForceSolution,\n calculateOptimalityRatio,\n BRUTE_FORCE_MAX_POINTS,\n} from '../lib/algorithms/progressive/solution/brute-force.js';\nimport {\n oneTreeLowerBound,\n verifyOptimality,\n} from '../lib/algorithms/verification/lower-bound.js';\n\n// Backward-compatible aliases\nconst bruteForceOptimalTour = bruteForceSolution;\nconst bruteForceSteps = bruteForceAlgorithmSteps;\n\n// ============================================================\n// Utility Functions Tests\n// ============================================================\n\ndescribe('VALID_GRID_SIZES', () => {\n it('should contain only powers of 2', () => {\n VALID_GRID_SIZES.forEach((size) => {\n expect(Math.log2(size) % 1).toBe(0);\n });\n });\n\n it('should be sorted in ascending order', () => {\n for (let i = 1; i < VALID_GRID_SIZES.length; i++) {\n expect(VALID_GRID_SIZES[i]).toBeGreaterThan(VALID_GRID_SIZES[i - 1]);\n }\n });\n\n it('should contain expected sizes', () => {\n expect(VALID_GRID_SIZES).toEqual([2, 4, 8, 16, 32, 64, 128]);\n });\n});\n\ndescribe('calculateMooreGridSize', () => {\n it('should return smallest valid size >= input', () => {\n expect(calculateMooreGridSize(2)).toBe(2);\n expect(calculateMooreGridSize(3)).toBe(4);\n expect(calculateMooreGridSize(4)).toBe(4);\n expect(calculateMooreGridSize(5)).toBe(8);\n expect(calculateMooreGridSize(10)).toBe(16);\n expect(calculateMooreGridSize(16)).toBe(16);\n expect(calculateMooreGridSize(20)).toBe(32);\n expect(calculateMooreGridSize(32)).toBe(32);\n expect(calculateMooreGridSize(33)).toBe(64);\n expect(calculateMooreGridSize(64)).toBe(64);\n expect(calculateMooreGridSize(65)).toBe(128);\n expect(calculateMooreGridSize(128)).toBe(128);\n expect(calculateMooreGridSize(129)).toBe(128);\n });\n\n it('should return largest valid size for very large input', () => {\n expect(calculateMooreGridSize(200)).toBe(128);\n });\n\n it('should always return a valid Moore grid size', () => {\n for (let gs = 1; gs <= 100; gs++) {\n const result = calculateMooreGridSize(gs);\n expect(VALID_GRID_SIZES).toContain(result);\n }\n });\n});\n\ndescribe('generateRandomPoints', () => {\n it('should generate the requested number of points', () => {\n const mooreGridSize = 16;\n const numPoints = 10;\n const points = generateRandomPoints(mooreGridSize, numPoints);\n expect(points.length).toBe(numPoints);\n });\n\n it('should generate points within [0, mooreGridSize-1] range', () => {\n const mooreGridSize = 16;\n const points = generateRandomPoints(mooreGridSize, 5);\n points.forEach((point) => {\n expect(point.x).toBeGreaterThanOrEqual(0);\n expect(point.x).toBeLessThanOrEqual(mooreGridSize - 1);\n expect(point.y).toBeGreaterThanOrEqual(0);\n expect(point.y).toBeLessThanOrEqual(mooreGridSize - 1);\n });\n });\n\n it('should generate unique points', () => {\n const mooreGridSize = 16;\n const points = generateRandomPoints(mooreGridSize, 10);\n const uniqueKeys = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(uniqueKeys.size).toBe(points.length);\n });\n\n it('should assign sequential IDs', () => {\n const points = generateRandomPoints(16, 5);\n points.forEach((point, idx) => {\n expect(point.id).toBe(idx);\n });\n });\n});\n\ndescribe('distance', () => {\n it('should calculate distance between two points', () => {\n const p1 = { x: 0, y: 0 };\n const p2 = { x: 3, y: 4 };\n expect(distance(p1, p2)).toBe(5);\n });\n\n it('should return 0 for same point', () => {\n const p = { x: 5, y: 5 };\n expect(distance(p, p)).toBe(0);\n });\n\n it('should handle negative coordinates', () => {\n const p1 = { x: -3, y: -4 };\n const p2 = { x: 0, y: 0 };\n expect(distance(p1, p2)).toBe(5);\n });\n});\n\ndescribe('calculateTotalDistance', () => {\n it('should return 0 for empty tour', () => {\n expect(calculateTotalDistance([], [])).toBe(0);\n });\n\n it('should return 0 for single point', () => {\n const points = [{ x: 0, y: 0 }];\n expect(calculateTotalDistance([0], points)).toBe(0);\n });\n\n it('should calculate correct closed loop distance', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 0 },\n { x: 3, y: 4 },\n ];\n const tour = [0, 1, 2];\n // 0->1: 3, 1->2: 4, 2->0: 5 = 12\n expect(calculateTotalDistance(tour, points)).toBe(12);\n });\n});\n\n// ============================================================\n// Sonar Algorithm Tests\n// ============================================================\n\ndescribe('sonarAlgorithmSteps', () => {\n it('should return empty array for empty points', () => {\n expect(sonarAlgorithmSteps([])).toEqual([]);\n });\n\n it('should generate steps for all points', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 5, y: 10, id: 2 },\n ];\n const steps = sonarAlgorithmSteps(points);\n expect(steps.length).toBe(points.length);\n });\n\n it('should include all points in final tour', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 5, y: 10, id: 2 },\n ];\n const steps = sonarAlgorithmSteps(points);\n const finalTour = steps[steps.length - 1].tour;\n expect(finalTour.length).toBe(points.length);\n expect(new Set(finalTour).size).toBe(points.length);\n });\n\n it('should have sweep type for all steps', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n ];\n const steps = sonarAlgorithmSteps(points);\n steps.forEach((step) => {\n expect(step.type).toBe('sweep');\n });\n });\n\n it('should include centroid in each step', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 6, y: 0, id: 1 },\n { x: 3, y: 6, id: 2 },\n ];\n const steps = sonarAlgorithmSteps(points);\n const expectedCentroid = { x: 3, y: 2 };\n steps.forEach((step) => {\n expect(step.centroid.x).toBe(expectedCentroid.x);\n expect(step.centroid.y).toBe(expectedCentroid.y);\n });\n });\n});\n\ndescribe('sonarSolution', () => {\n it('should return empty tour for empty points', () => {\n const result = sonarSolution([]);\n expect(result.tour).toEqual([]);\n expect(result.centroid).toBe(null);\n });\n\n it('should return tour with all point indices', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 5, y: 10, id: 2 },\n ];\n const result = sonarSolution(points);\n expect(result.tour.length).toBe(points.length);\n expect(new Set(result.tour).size).toBe(points.length);\n });\n});\n\n// ============================================================\n// Moore Curve Algorithm Tests\n// ============================================================\n\ndescribe('generateMooreCurve', () => {\n it('should generate valid L-system sequence', () => {\n const sequence = generateMooreCurve(1);\n expect(sequence).toContain('F');\n expect(typeof sequence).toBe('string');\n });\n\n it('should expand sequence with higher order', () => {\n const order1 = generateMooreCurve(1);\n const order2 = generateMooreCurve(2);\n expect(order2.length).toBeGreaterThan(order1.length);\n });\n});\n\ndescribe('mooreCurveToPoints', () => {\n it('should generate curve points', () => {\n const sequence = generateMooreCurve(1);\n const points = mooreCurveToPoints(sequence, 4);\n expect(points.length).toBeGreaterThan(0);\n });\n\n it('should generate points within [0, gridSize-1] bounds', () => {\n const mooreGridSize = 16;\n // For mooreGridSize=16, iterations = log2(16) - 1 = 3\n const sequence = generateMooreCurve(3);\n const points = mooreCurveToPoints(sequence, mooreGridSize);\n points.forEach((p) => {\n expect(p.x).toBeGreaterThanOrEqual(0);\n expect(p.x).toBeLessThanOrEqual(mooreGridSize - 1);\n expect(p.y).toBeGreaterThanOrEqual(0);\n expect(p.y).toBeLessThanOrEqual(mooreGridSize - 1);\n });\n });\n});\n\ndescribe('mooreAlgorithmSteps', () => {\n it('should return empty array for empty points', () => {\n expect(mooreAlgorithmSteps([], 16)).toEqual([]);\n });\n\n it('should generate curve step first', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 10, id: 1 },\n ];\n const steps = mooreAlgorithmSteps(points, 16);\n expect(steps[0].type).toBe('curve');\n });\n\n it('should include all points in final tour', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 8, y: 8, id: 1 },\n { x: 4, y: 12, id: 2 },\n ];\n const steps = mooreAlgorithmSteps(points, 16);\n const finalTour = steps[steps.length - 1].tour;\n expect(finalTour.length).toBe(points.length);\n });\n\n it('should include curve points in each step', () => {\n const points = [{ x: 5, y: 5, id: 0 }];\n const steps = mooreAlgorithmSteps(points, 16);\n steps.forEach((step) => {\n expect(step.curvePoints !== undefined).toBe(true);\n expect(step.curvePoints.length).toBeGreaterThan(0);\n });\n });\n});\n\ndescribe('mooreSolution', () => {\n it('should return empty tour for empty points', () => {\n const result = mooreSolution([], 16);\n expect(result.tour).toEqual([]);\n expect(result.curvePoints).toEqual([]);\n });\n\n it('should return tour with all point indices', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 5, y: 10, id: 2 },\n ];\n const result = mooreSolution(points, 16);\n expect(result.tour.length).toBe(points.length);\n expect(new Set(result.tour).size).toBe(points.length);\n });\n});\n\n// ============================================================\n// Optimization Tests\n// ============================================================\n\ndescribe('sonarOptimizationSteps', () => {\n it('should return empty array for tour with less than 4 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n { x: 2, y: 2 },\n ];\n const tour = [0, 1, 2];\n expect(sonarOptimizationSteps(points, tour)).toEqual([]);\n });\n\n it('should return optimization steps for valid tour', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n // Create a non-optimal tour\n const tour = [0, 2, 1, 3];\n const steps = sonarOptimizationSteps(points, tour);\n // May or may not have improvements depending on tour\n expect(Array.isArray(steps)).toBe(true);\n });\n\n it('should have optimize type for all steps', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n const tour = [0, 2, 4, 1, 3];\n const steps = sonarOptimizationSteps(points, tour);\n steps.forEach((step) => {\n expect(step.type).toBe('optimize');\n });\n });\n});\n\ndescribe('sonarOptimization', () => {\n it('should return original tour for small inputs', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n ];\n const tour = [0, 1];\n const result = sonarOptimization(points, tour);\n expect(result.tour).toEqual(tour);\n expect(result.improvement).toBe(0);\n });\n});\n\ndescribe('mooreOptimizationSteps', () => {\n it('should return empty array for tour with less than 4 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n { x: 2, y: 2 },\n ];\n const tour = [0, 1, 2];\n expect(mooreOptimizationSteps(points, tour)).toEqual([]);\n });\n\n it('should have optimize type for all steps', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n const tour = [0, 2, 4, 1, 3];\n const steps = mooreOptimizationSteps(points, tour);\n steps.forEach((step) => {\n expect(step.type).toBe('optimize');\n });\n });\n});\n\ndescribe('mooreOptimization', () => {\n it('should return original tour for small inputs', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n ];\n const tour = [0, 1];\n const result = mooreOptimization(points, tour);\n expect(result.tour).toEqual(tour);\n expect(result.improvement).toBe(0);\n });\n\n it('should not increase tour distance', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n const tour = [0, 2, 4, 1, 3];\n const originalDistance = calculateTotalDistance(tour, points);\n const result = mooreOptimization(points, tour);\n const newDistance = calculateTotalDistance(result.tour, points);\n expect(newDistance).toBeLessThanOrEqual(originalDistance);\n });\n});\n\n// ============================================================\n// Generic Optimization Tests (new generic API)\n// ============================================================\n\ndescribe('twoOptSteps (generic 2-opt)', () => {\n it('should return empty array for tour with less than 4 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n { x: 2, y: 2 },\n ];\n const tour = [0, 1, 2];\n expect(twoOptSteps(points, tour)).toEqual([]);\n });\n\n it('should work on any tour regardless of source algorithm', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n // This is not from any specific algorithm\n const randomTour = [0, 2, 4, 1, 3];\n const steps = twoOptSteps(points, randomTour);\n expect(Array.isArray(steps)).toBe(true);\n });\n\n it('should respect maxIterations option', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n const tour = [0, 1, 2, 3];\n const steps = twoOptSteps(points, tour, { maxIterations: 1 });\n // Should still return valid array\n expect(Array.isArray(steps)).toBe(true);\n });\n});\n\ndescribe('twoOpt (generic atomic 2-opt)', () => {\n it('should return original tour for small inputs', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n ];\n const tour = [0, 1];\n const result = twoOpt(points, tour);\n expect(result.tour).toEqual(tour);\n expect(result.improvement).toBe(0);\n });\n\n it('should never increase tour distance', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n const tour = [0, 2, 4, 1, 3];\n const originalDistance = calculateTotalDistance(tour, points);\n const result = twoOpt(points, tour);\n const newDistance = calculateTotalDistance(result.tour, points);\n expect(newDistance).toBeLessThanOrEqual(originalDistance);\n });\n});\n\ndescribe('zigzagOptSteps (generic zigzag)', () => {\n it('should return empty array for tour with less than 4 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n { x: 2, y: 2 },\n ];\n const tour = [0, 1, 2];\n expect(zigzagOptSteps(points, tour)).toEqual([]);\n });\n\n it('should work on any tour regardless of source algorithm', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n // This is not from any specific algorithm\n const randomTour = [0, 2, 4, 1, 3];\n const steps = zigzagOptSteps(points, randomTour);\n expect(Array.isArray(steps)).toBe(true);\n });\n});\n\ndescribe('zigzagOpt (generic atomic zigzag)', () => {\n it('should return original tour for small inputs', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n ];\n const tour = [0, 1];\n const result = zigzagOpt(points, tour);\n expect(result.tour).toEqual(tour);\n expect(result.improvement).toBe(0);\n });\n\n it('should never increase tour distance', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n const tour = [0, 2, 4, 1, 3];\n const originalDistance = calculateTotalDistance(tour, points);\n const result = zigzagOpt(points, tour);\n const newDistance = calculateTotalDistance(result.tour, points);\n expect(newDistance).toBeLessThanOrEqual(originalDistance);\n });\n});\n\ndescribe('combined optimization (zigzag + 2-opt)', () => {\n const fivePoints = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n { x: 5, y: 5 },\n ];\n\n it('steps: should return empty array for tour with less than 4 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n { x: 2, y: 2 },\n ];\n expect(combinedOptSteps(points, [0, 1, 2])).toEqual([]);\n });\n\n it('steps: should work on any tour and have optimize type', () => {\n const steps = combinedOptSteps(fivePoints, [0, 2, 4, 1, 3]);\n expect(Array.isArray(steps)).toBe(true);\n steps.forEach((step) => expect(step.type).toBe('optimize'));\n });\n\n it('atomic: should return original tour for small inputs', () => {\n const result = combinedOpt(\n [\n { x: 0, y: 0 },\n { x: 1, y: 1 },\n ],\n [0, 1]\n );\n expect(result.tour).toEqual([0, 1]);\n expect(result.improvement).toBe(0);\n });\n\n it('atomic: should never increase tour distance', () => {\n const tour = [0, 2, 4, 1, 3];\n const originalDistance = calculateTotalDistance(tour, fivePoints);\n const result = combinedOpt(fivePoints, tour);\n expect(calculateTotalDistance(result.tour, fivePoints)).toBeLessThanOrEqual(\n originalDistance\n );\n });\n\n it('atomic: should return zero improvement for optimal square tour', () => {\n const square = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n expect(combinedOpt(square, [0, 1, 2, 3]).improvement).toBe(0);\n });\n});\n\n// ============================================================\n// Already-Optimal Tour Tests (Issue #15)\n// ============================================================\n\ndescribe('optimization with already-optimal tours (issue #15)', () => {\n // 3 points forming a triangle — already optimal, no improvements possible\n const trianglePoints = [\n { x: 0, y: 0 },\n { x: 5, y: 0 },\n { x: 2, y: 4 },\n ];\n const triangleTour = [0, 1, 2];\n\n // 4 points forming a square in optimal order\n const squarePoints = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n const squareOptimalTour = [0, 1, 2, 3];\n\n describe('twoOptSteps with already-optimal tours', () => {\n it('should return empty array for 3-point triangle tour (< 4 points)', () => {\n const steps = twoOptSteps(trianglePoints, triangleTour);\n expect(steps).toEqual([]);\n });\n\n it('should return empty array for optimal square tour', () => {\n const steps = twoOptSteps(squarePoints, squareOptimalTour);\n expect(steps).toEqual([]);\n });\n\n it('should preserve tour distance when no improvement found', () => {\n const originalDistance = calculateTotalDistance(\n squareOptimalTour,\n squarePoints\n );\n const result = twoOpt(squarePoints, squareOptimalTour);\n const newDistance = calculateTotalDistance(result.tour, squarePoints);\n expect(newDistance).toBe(originalDistance);\n expect(result.improvement).toBe(0);\n });\n });\n\n describe('zigzagOptSteps with already-optimal tours', () => {\n it('should return empty array for 3-point triangle tour (< 4 points)', () => {\n const steps = zigzagOptSteps(trianglePoints, triangleTour);\n expect(steps).toEqual([]);\n });\n\n it('should return empty array for optimal square tour', () => {\n const steps = zigzagOptSteps(squarePoints, squareOptimalTour);\n expect(steps).toEqual([]);\n });\n\n it('should preserve tour distance when no improvement found', () => {\n const originalDistance = calculateTotalDistance(\n squareOptimalTour,\n squarePoints\n );\n const result = zigzagOpt(squarePoints, squareOptimalTour);\n const newDistance = calculateTotalDistance(result.tour, squarePoints);\n expect(newDistance).toBe(originalDistance);\n expect(result.improvement).toBe(0);\n });\n });\n\n describe('full pipeline: solution then optimization on 3-point tour', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 5, y: 0, id: 1 },\n { x: 2, y: 4, id: 2 },\n ];\n\n it('should produce valid sonar solution then empty optimization', () => {\n const steps = sonarAlgorithmSteps(points);\n expect(steps.length).toBe(points.length);\n const finalTour = steps[steps.length - 1].tour;\n expect(finalTour.length).toBe(points.length);\n\n const optSteps = zigzagOptSteps(points, finalTour);\n expect(optSteps).toEqual([]);\n\n // Distance should still be computable from the solution tour\n const dist = calculateTotalDistance(finalTour, points);\n expect(dist).toBeGreaterThan(0);\n });\n\n it('should produce valid moore solution then empty optimization', () => {\n const steps = mooreAlgorithmSteps(points, 16);\n expect(steps.length).toBeGreaterThan(0);\n const finalTour = steps[steps.length - 1].tour;\n expect(finalTour.length).toBe(points.length);\n\n const optSteps = twoOptSteps(points, finalTour);\n expect(optSteps).toEqual([]);\n\n // Distance should still be computable from the solution tour\n const dist = calculateTotalDistance(finalTour, points);\n expect(dist).toBeGreaterThan(0);\n });\n });\n});\n\n// ============================================================\n// Moore Curve Point-by-Point Verification Tests\n// ============================================================\n\n/**\n * Helper: generate Moore curve points for a given grid size.\n * Grid size must be a power of 2 (2, 4, 8, 16, 32, 64).\n * Returns normalized points in [0, gridSize-1] range.\n */\nfunction getMooreCurvePoints(gridSize) {\n const iterations = Math.round(Math.log2(gridSize)) - 1;\n const sequence = generateMooreCurve(iterations);\n return mooreCurveToPoints(sequence, gridSize);\n}\n\ndescribe('Moore curve point-by-point verification (2x2)', () => {\n const gridSize = 2;\n const points = getMooreCurvePoints(gridSize);\n\n it('should have exactly 4 vertices', () => {\n expect(points.length).toBe(4);\n });\n\n it('should visit all 4 grid cells', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(4);\n expect(unique.has('0,0')).toBe(true);\n expect(unique.has('0,1')).toBe(true);\n expect(unique.has('1,0')).toBe(true);\n expect(unique.has('1,1')).toBe(true);\n });\n\n it('should follow the exact path: (0,1) -> (0,0) -> (1,0) -> (1,1)', () => {\n expect(points[0]).toEqual({ x: 0, y: 1 });\n expect(points[1]).toEqual({ x: 0, y: 0 });\n expect(points[2]).toEqual({ x: 1, y: 0 });\n expect(points[3]).toEqual({ x: 1, y: 1 });\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop (first and last points are adjacent)', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n});\n\ndescribe('Moore curve point-by-point verification (4x4)', () => {\n const gridSize = 4;\n const points = getMooreCurvePoints(gridSize);\n\n it('should have exactly 16 vertices', () => {\n expect(points.length).toBe(16);\n });\n\n it('should visit all 16 grid cells', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(16);\n for (let x = 0; x < 4; x++) {\n for (let y = 0; y < 4; y++) {\n expect(unique.has(`${x},${y}`)).toBe(true);\n }\n }\n });\n\n it('should follow the exact 4x4 Moore curve path', () => {\n const expectedPath = [\n { x: 1, y: 3 },\n { x: 0, y: 3 },\n { x: 0, y: 2 },\n { x: 1, y: 2 },\n { x: 1, y: 1 },\n { x: 0, y: 1 },\n { x: 0, y: 0 },\n { x: 1, y: 0 },\n { x: 2, y: 0 },\n { x: 3, y: 0 },\n { x: 3, y: 1 },\n { x: 2, y: 1 },\n { x: 2, y: 2 },\n { x: 3, y: 2 },\n { x: 3, y: 3 },\n { x: 2, y: 3 },\n ];\n expect(points).toEqual(expectedPath);\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n});\n\ndescribe('Moore curve verification (8x8)', () => {\n const gridSize = 8;\n const points = getMooreCurvePoints(gridSize);\n\n it('should have exactly 64 vertices', () => {\n expect(points.length).toBe(64);\n });\n\n it('should visit all 64 grid cells', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(64);\n for (let x = 0; x < 8; x++) {\n for (let y = 0; y < 8; y++) {\n expect(unique.has(`${x},${y}`)).toBe(true);\n }\n }\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n\n it('should have no cross-shaped gap (center row and column covered)', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n // Center of 8x8 grid is at rows/columns 3 and 4\n for (let i = 0; i < 8; i++) {\n expect(unique.has(`${i},3`)).toBe(true);\n expect(unique.has(`${i},4`)).toBe(true);\n expect(unique.has(`3,${i}`)).toBe(true);\n expect(unique.has(`4,${i}`)).toBe(true);\n }\n });\n\n it('should have all points within [0, 7] range', () => {\n points.forEach((p) => {\n expect(p.x).toBeGreaterThanOrEqual(0);\n expect(p.x).toBeLessThanOrEqual(7);\n expect(p.y).toBeGreaterThanOrEqual(0);\n expect(p.y).toBeLessThanOrEqual(7);\n });\n });\n});\n\ndescribe('Moore curve verification (16x16)', () => {\n const gridSize = 16;\n const points = getMooreCurvePoints(gridSize);\n\n it('should have exactly 256 vertices', () => {\n expect(points.length).toBe(256);\n });\n\n it('should visit all 256 grid cells', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(256);\n for (let x = 0; x < 16; x++) {\n for (let y = 0; y < 16; y++) {\n expect(unique.has(`${x},${y}`)).toBe(true);\n }\n }\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n\n it('should have no cross-shaped gap (center covered)', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n for (let i = 0; i < 16; i++) {\n expect(unique.has(`${i},7`)).toBe(true);\n expect(unique.has(`${i},8`)).toBe(true);\n expect(unique.has(`7,${i}`)).toBe(true);\n expect(unique.has(`8,${i}`)).toBe(true);\n }\n });\n});\n\ndescribe('Moore curve verification (32x32)', () => {\n const gridSize = 32;\n const points = getMooreCurvePoints(gridSize);\n\n it('should have exactly 1024 vertices', () => {\n expect(points.length).toBe(1024);\n });\n\n it('should visit all 1024 grid cells', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(1024);\n for (let x = 0; x < 32; x++) {\n for (let y = 0; y < 32; y++) {\n expect(unique.has(`${x},${y}`)).toBe(true);\n }\n }\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n\n it('should have no cross-shaped gap (center covered)', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n for (let i = 0; i < 32; i++) {\n expect(unique.has(`${i},15`)).toBe(true);\n expect(unique.has(`${i},16`)).toBe(true);\n expect(unique.has(`15,${i}`)).toBe(true);\n expect(unique.has(`16,${i}`)).toBe(true);\n }\n });\n});\n\n// ============================================================\n// Moore Curve Edge Verification Tests\n// ============================================================\n\ndescribe('Moore curve edge verification (2x2)', () => {\n const points = getMooreCurvePoints(2);\n\n it('should have correct edges', () => {\n // Path: (0,1) -> (0,0) -> (1,0) -> (1,1)\n const edges = [];\n for (let i = 0; i < points.length - 1; i++) {\n edges.push(\n `(${points[i].x},${points[i].y})->(${points[i + 1].x},${points[i + 1].y})`\n );\n }\n expect(edges).toEqual(['(0,1)->(0,0)', '(0,0)->(1,0)', '(1,0)->(1,1)']);\n });\n\n it('should have closing edge back to start', () => {\n const last = points[points.length - 1];\n const first = points[0];\n const d = distance(last, first);\n expect(d).toBe(1);\n // (1,1) -> (0,1) closing edge\n expect(last).toEqual({ x: 1, y: 1 });\n expect(first).toEqual({ x: 0, y: 1 });\n });\n});\n\ndescribe('Moore curve edge verification (4x4)', () => {\n const points = getMooreCurvePoints(4);\n\n it('should have exactly 15 edges (16 vertices - 1)', () => {\n const edges = [];\n for (let i = 0; i < points.length - 1; i++) {\n edges.push({\n from: points[i],\n to: points[i + 1],\n });\n }\n expect(edges.length).toBe(15);\n });\n\n it('should have all edges with length 1 (horizontal or vertical)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const dx = Math.abs(points[i + 1].x - points[i].x);\n const dy = Math.abs(points[i + 1].y - points[i].y);\n // Each edge must be exactly 1 unit horizontal or vertical\n expect((dx === 1 && dy === 0) || (dx === 0 && dy === 1)).toBe(true);\n }\n });\n\n it('should have no self-intersecting edges', () => {\n // All edges connect distinct vertices, and all vertices are unique\n const visited = new Set();\n for (const p of points) {\n const key = `${p.x},${p.y}`;\n expect(visited.has(key)).toBe(false);\n visited.add(key);\n }\n });\n});\n\n// ============================================================\n// Moore Curve Properties Tests (all valid sizes)\n// ============================================================\n\ndescribe('Moore curve properties for all valid grid sizes', () => {\n // Test for 2, 4, 8, 16, 32 (skip 64 to keep test fast)\n const testSizes = [2, 4, 8, 16, 32];\n\n testSizes.forEach((gridSize) => {\n describe(`grid size ${gridSize}x${gridSize}`, () => {\n const points = getMooreCurvePoints(gridSize);\n\n it(`should have exactly ${gridSize * gridSize} vertices`, () => {\n expect(points.length).toBe(gridSize * gridSize);\n });\n\n it('should visit every grid cell exactly once', () => {\n const unique = new Set(points.map((p) => `${p.x},${p.y}`));\n expect(unique.size).toBe(gridSize * gridSize);\n });\n\n it('should have all points within [0, gridSize-1]', () => {\n points.forEach((p) => {\n expect(p.x).toBeGreaterThanOrEqual(0);\n expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n expect(p.y).toBeGreaterThanOrEqual(0);\n expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n });\n });\n\n it('should have all consecutive points adjacent (distance 1)', () => {\n for (let i = 0; i < points.length - 1; i++) {\n const d = distance(points[i], points[i + 1]);\n expect(d).toBe(1);\n }\n });\n\n it('should form a closed loop', () => {\n const d = distance(points[0], points[points.length - 1]);\n expect(d).toBe(1);\n });\n });\n });\n});\n\n// ============================================================\n// Brute-Force Verification Tests\n// ============================================================\n\ndescribe('BRUTE_FORCE_MAX_POINTS', () => {\n it('should be a reasonable positive integer', () => {\n expect(BRUTE_FORCE_MAX_POINTS).toBeGreaterThanOrEqual(8);\n expect(BRUTE_FORCE_MAX_POINTS).toBeLessThanOrEqual(20);\n });\n});\n\ndescribe('bruteForceOptimalTour', () => {\n it('should return empty tour for empty points', () => {\n const result = bruteForceOptimalTour([]);\n expect(result.tour).toEqual([]);\n expect(result.distance).toBe(0);\n });\n\n it('should return single-point tour', () => {\n const result = bruteForceOptimalTour([{ x: 5, y: 5 }]);\n expect(result.tour).toEqual([0]);\n expect(result.distance).toBe(0);\n });\n\n it('should return correct tour for 2 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 4 },\n ];\n const result = bruteForceOptimalTour(points);\n expect(result.tour).toEqual([0, 1]);\n expect(result.distance).toBe(10); // 2 * 5 = 10 (closed loop)\n });\n\n it('should return correct optimal tour for a square', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n const result = bruteForceOptimalTour(points);\n // Optimal tour for a square has distance 40\n expect(result.distance).toBe(40);\n expect(result.tour.length).toBe(4);\n expect(new Set(result.tour).size).toBe(4);\n });\n\n it('should return optimal tour for a triangle', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 0 },\n { x: 0, y: 4 },\n ];\n const result = bruteForceOptimalTour(points);\n // Triangle: 3 + 4 + 5 = 12\n expect(result.distance).toBe(12);\n expect(result.tour.length).toBe(3);\n });\n\n it('should return null for too many points', () => {\n const points = Array.from(\n { length: BRUTE_FORCE_MAX_POINTS + 1 },\n (_, i) => ({\n x: i,\n y: 0,\n })\n );\n const result = bruteForceOptimalTour(points);\n expect(result).toBeNull();\n });\n\n it('should find optimal tour shorter than or equal to sonar for 5 points', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 10, y: 10, id: 2 },\n { x: 0, y: 10, id: 3 },\n { x: 5, y: 5, id: 4 },\n ];\n const optResult = bruteForceOptimalTour(points);\n const sonarResult = sonarSolution(points);\n const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n expect(optResult.distance).toBeLessThanOrEqual(sonarDist + 0.001);\n });\n\n it('should find optimal tour shorter than or equal to moore for 5 points', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 8, y: 0, id: 1 },\n { x: 8, y: 8, id: 2 },\n { x: 0, y: 8, id: 3 },\n { x: 4, y: 4, id: 4 },\n ];\n const optResult = bruteForceOptimalTour(points);\n const mooreResult = mooreSolution(points, 16);\n const mooreDist = calculateTotalDistance(mooreResult.tour, points);\n expect(optResult.distance).toBeLessThanOrEqual(mooreDist + 0.001);\n });\n\n it('should handle collinear points correctly', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 0 },\n { x: 2, y: 0 },\n { x: 3, y: 0 },\n ];\n const result = bruteForceOptimalTour(points);\n // Optimal for collinear: 0->1->2->3->0 = 1+1+1+3 = 6\n expect(result.distance).toBe(6);\n });\n\n it('should always start tour from point 0', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 5, y: 0 },\n { x: 5, y: 5 },\n { x: 0, y: 5 },\n ];\n const result = bruteForceOptimalTour(points);\n expect(result.tour[0]).toBe(0);\n });\n});\n\ndescribe('bruteForceAlgorithmSteps', () => {\n it('should return steps for small point sets', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 4 },\n ];\n const steps = bruteForceSteps(points);\n expect(steps.length).toBeGreaterThanOrEqual(1);\n expect(steps[0].type).toBe('solution');\n expect(steps[steps.length - 1].tour.length).toBe(2);\n });\n\n it('should report infeasible for too many points', () => {\n const points = Array.from(\n { length: BRUTE_FORCE_MAX_POINTS + 1 },\n (_, i) => ({\n x: i,\n y: 0,\n })\n );\n const steps = bruteForceSteps(points);\n expect(steps.length).toBe(1);\n expect(steps[0].feasible).toBe(false);\n expect(steps[0].description).toContain('Too many points');\n });\n\n it('should include distance in last step description', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 4 },\n ];\n const steps = bruteForceSteps(points);\n const lastStep = steps[steps.length - 1];\n expect(lastStep.description).toContain('10.00');\n });\n\n it('should show progressive improvements for larger sets', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 0 },\n { x: 3, y: 4 },\n { x: 0, y: 4 },\n { x: 1, y: 2 },\n ];\n const steps = bruteForceSteps(points);\n // Should have more than one step: initial + improvements + final\n expect(steps.length).toBeGreaterThan(1);\n // All steps should be type 'solution'\n steps.forEach((step) => expect(step.type).toBe('solution'));\n });\n\n it('should return empty array for empty points', () => {\n const steps = bruteForceSteps([]);\n expect(steps).toEqual([]);\n });\n\n it('should return trivial tour for single point', () => {\n const points = [{ x: 5, y: 3 }];\n const steps = bruteForceSteps(points);\n expect(steps.length).toBe(1);\n expect(steps[0].type).toBe('solution');\n expect(steps[0].tour).toEqual([0]);\n expect(steps[0].description).toContain('trivial');\n });\n});\n\n// ============================================================\n// Lower-Bound Verification Tests\n// ============================================================\n\ndescribe('oneTreeLowerBound', () => {\n it('should return 0 for empty or single point', () => {\n expect(oneTreeLowerBound([]).lowerBound).toBe(0);\n expect(oneTreeLowerBound([{ x: 0, y: 0 }]).lowerBound).toBe(0);\n });\n\n it('should return exact distance for 2 points', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 4 },\n ];\n const result = oneTreeLowerBound(points);\n expect(result.lowerBound).toBe(10); // 2 * 5\n expect(result.method).toBe('1-tree');\n });\n\n it('should provide a valid lower bound for a square', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n const result = oneTreeLowerBound(points);\n // The optimal tour for a square is 40\n // The lower bound should be <= 40\n expect(result.lowerBound).toBeLessThanOrEqual(40 + 0.001);\n expect(result.lowerBound).toBeGreaterThan(0);\n });\n\n it('should be less than or equal to brute-force optimal for small instances', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 5, y: 0, id: 1 },\n { x: 5, y: 5, id: 2 },\n { x: 0, y: 5, id: 3 },\n { x: 2, y: 3, id: 4 },\n ];\n const lbResult = oneTreeLowerBound(points);\n const bfResult = bruteForceSolution(points);\n expect(lbResult.lowerBound).toBeLessThanOrEqual(bfResult.distance + 0.001);\n });\n});\n\ndescribe('verifyOptimality', () => {\n it('should verify optimal tour for simple cases', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 3, y: 4 },\n ];\n // Optimal distance is 10\n const result = verifyOptimality(10, points);\n expect(result.isOptimal).toBe(true);\n expect(result.gap).toBeCloseTo(0);\n });\n\n it('should detect suboptimal tour', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 10, y: 0 },\n { x: 10, y: 10 },\n { x: 0, y: 10 },\n ];\n // Tour distance much larger than optimal\n const result = verifyOptimality(100, points);\n expect(result.isOptimal).toBe(false);\n expect(result.gapPercent).toBeGreaterThan(0);\n });\n\n it('should report method as 1-tree', () => {\n const points = [\n { x: 0, y: 0 },\n { x: 1, y: 0 },\n ];\n const result = verifyOptimality(2, points);\n expect(result.method).toBe('1-tree');\n });\n});\n\ndescribe('calculateOptimalityRatio', () => {\n it('should return 1.0 for optimal tour', () => {\n expect(calculateOptimalityRatio(10, 10)).toBe(1.0);\n });\n\n it('should return correct ratio for suboptimal tour', () => {\n expect(calculateOptimalityRatio(15, 10)).toBe(1.5);\n });\n\n it('should return 1.0 when both distances are 0', () => {\n expect(calculateOptimalityRatio(0, 0)).toBe(1.0);\n });\n\n it('should return Infinity when optimal is 0 but tour is not', () => {\n expect(calculateOptimalityRatio(5, 0)).toBe(Infinity);\n });\n});\n\n// ============================================================\n// Integration: Verification with Algorithm Pipelines (Issue #31)\n// ============================================================\n\ndescribe('verification integration with algorithm pipelines', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 5, y: 0, id: 1 },\n { x: 5, y: 5, id: 2 },\n { x: 0, y: 5, id: 3 },\n ];\n\n it('should verify sonar tour is not falsely claimed optimal', () => {\n const sonarResult = sonarSolution(points);\n const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n const optimal = bruteForceOptimalTour(points);\n\n // The sonar tour may or may not be optimal, but we can verify the claim\n if (Math.abs(sonarDist - optimal.distance) < 0.001) {\n expect(calculateOptimalityRatio(sonarDist, optimal.distance)).toBeCloseTo(\n 1.0\n );\n } else {\n expect(sonarDist).toBeGreaterThan(optimal.distance);\n }\n });\n\n it('should verify moore tour is not falsely claimed optimal', () => {\n const mooreResult = mooreSolution(points, 16);\n const mooreDist = calculateTotalDistance(mooreResult.tour, points);\n const optimal = bruteForceOptimalTour(points);\n\n if (Math.abs(mooreDist - optimal.distance) < 0.001) {\n expect(calculateOptimalityRatio(mooreDist, optimal.distance)).toBeCloseTo(\n 1.0\n );\n } else {\n expect(mooreDist).toBeGreaterThan(optimal.distance);\n }\n });\n\n it('should verify optimized tour is at least as good as unoptimized', () => {\n const sonarResult = sonarSolution(points);\n const sonarDist = calculateTotalDistance(sonarResult.tour, points);\n const optimized = twoOpt(points, sonarResult.tour);\n const optDist = calculateTotalDistance(optimized.tour, points);\n const optimal = bruteForceOptimalTour(points);\n\n expect(optDist).toBeLessThanOrEqual(sonarDist + 0.001);\n expect(optimal.distance).toBeLessThanOrEqual(optDist + 0.001);\n });\n\n it('should compute meaningful optimality ratio for heuristic results', () => {\n const points8 = [\n { x: 0, y: 0, id: 0 },\n { x: 4, y: 0, id: 1 },\n { x: 8, y: 0, id: 2 },\n { x: 8, y: 4, id: 3 },\n { x: 8, y: 8, id: 4 },\n { x: 4, y: 8, id: 5 },\n { x: 0, y: 8, id: 6 },\n { x: 0, y: 4, id: 7 },\n ];\n const optimal = bruteForceOptimalTour(points8);\n const sonarResult = sonarSolution(points8);\n const sonarDist = calculateTotalDistance(sonarResult.tour, points8);\n const ratio = calculateOptimalityRatio(sonarDist, optimal.distance);\n\n // Ratio should be >= 1.0 (optimal or worse)\n expect(ratio).toBeGreaterThanOrEqual(1.0);\n // And for these simple cases, not too far from optimal\n expect(ratio).toBeLessThanOrEqual(2.0);\n });\n});\n",
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"text": "Now let me look at the existing test files for other algorithms to understand the pattern, and also look at a specific algorithm test and the CLI."
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"content": " 1→/**\n 2→ * Test file for Sierpiński curve TSP algorithm\n 3→ * Works with Bun test runner\n 4→ */\n 5→\n 6→import { describe, it, expect } from 'bun:test';\n 7→import {\n 8→ sierpinskiAlgorithmSteps,\n 9→ sierpinskiSolution,\n 10→ generateSierpinskiCurve,\n 11→ sierpinskiCurveToPoints,\n 12→} from '../lib/algorithms/progressive/solution/sierpinski.js';\n 13→import {\n 14→ generateRandomPoints,\n 15→ calculateTotalDistance,\n 16→} from '../lib/algorithms/utils.js';\n 17→\n 18→// ============================================================\n 19→// Sierpiński Curve Generation Tests\n 20→// ============================================================\n 21→\n 22→describe('generateSierpinskiCurve', () => {\n 23→ it('should generate valid L-system sequence', () => {\n 24→ const sequence = generateSierpinskiCurve(1);\n 25→ expect(sequence).toContain('F');\n 26→ expect(typeof sequence).toBe('string');\n 27→ });\n 28→\n 29→ it('should expand sequence with higher order', () => {\n 30→ const order1 = generateSierpinskiCurve(1);\n 31→ const order2 = generateSierpinskiCurve(2);\n 32→ expect(order2.length).toBeGreaterThan(order1.length);\n 33→ });\n 34→\n 35→ it('should contain axiom pattern at order 0', () => {\n 36→ const sequence = generateSierpinskiCurve(0);\n 37→ expect(sequence).toBe('F+XF+F+XF');\n 38→ });\n 39→\n 40→ it('should not contain X or Y variables in F-count terms', () => {\n 41→ // The X variable is a non-drawing variable\n 42→ const sequence = generateSierpinskiCurve(2);\n 43→ // X is still present as a variable; it is not drawn\n 44→ expect(sequence).toContain('F');\n 45→ });\n 46→});\n 47→\n 48→describe('sierpinskiCurveToPoints', () => {\n 49→ it('should generate curve points', () => {\n 50→ const sequence = generateSierpinskiCurve(1);\n 51→ const points = sierpinskiCurveToPoints(sequence, 4);\n 52→ expect(points.length).toBeGreaterThan(0);\n 53→ });\n 54→\n 55→ it('should generate points within [0, gridSize-1] bounds', () => {\n 56→ const gridSize = 16;\n 57→ const sequence = generateSierpinskiCurve(3);\n 58→ const points = sierpinskiCurveToPoints(sequence, gridSize);\n 59→ points.forEach((p) => {\n 60→ expect(p.x).toBeGreaterThanOrEqual(0);\n 61→ expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n 62→ expect(p.y).toBeGreaterThanOrEqual(0);\n 63→ expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n 64→ });\n 65→ });\n 66→\n 67→ it('should fill the grid at various orders', () => {\n 68→ for (let order = 0; order <= 3; order++) {\n 69→ const gridSize = Math.pow(2, order + 1);\n 70→ const sequence = generateSierpinskiCurve(order);\n 71→ const points = sierpinskiCurveToPoints(sequence, gridSize);\n 72→\n 73→ // Curve should have more than 1 point\n 74→ expect(points.length).toBeGreaterThan(1);\n 75→\n 76→ // Bounding box should cover (0,0) to (gridSize-1, gridSize-1)\n 77→ let minX = Infinity,\n 78→ maxX = -Infinity,\n 79→ minY = Infinity,\n 80→ maxY = -Infinity;\n 81→ for (const p of points) {\n 82→ minX = Math.min(minX, p.x);\n 83→ maxX = Math.max(maxX, p.x);\n 84→ minY = Math.min(minY, p.y);\n 85→ maxY = Math.max(maxY, p.y);\n 86→ }\n 87→ expect(minX).toBe(0);\n 88→ expect(minY).toBe(0);\n 89→ expect(maxX).toBe(gridSize - 1);\n 90→ expect(maxY).toBe(gridSize - 1);\n 91→ }\n 92→ });\n 93→});\n 94→\n 95→// ============================================================\n 96→// Sierpiński Progressive Algorithm Tests\n 97→// ============================================================\n 98→\n 99→describe('sierpinskiAlgorithmSteps', () => {\n 100→ it('should return empty array for empty points', () => {\n 101→ expect(sierpinskiAlgorithmSteps([], 16)).toEqual([]);\n 102→ });\n 103→\n 104→ it('should generate curve step first', () => {\n 105→ const points = [\n 106→ { x: 0, y: 0, id: 0 },\n 107→ { x: 10, y: 10, id: 1 },\n 108→ ];\n 109→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 110→ expect(steps[0].type).toBe('curve');\n 111→ });\n 112→\n 113→ it('should include all points in final tour', () => {\n 114→ const points = [\n 115→ { x: 0, y: 0, id: 0 },\n 116→ { x: 8, y: 8, id: 1 },\n 117→ { x: 4, y: 12, id: 2 },\n 118→ ];\n 119→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 120→ const finalTour = steps[steps.length - 1].tour;\n 121→ expect(finalTour.length).toBe(points.length);\n 122→ });\n 123→\n 124→ it('should include curve points in each step', () => {\n 125→ const points = [{ x: 5, y: 5, id: 0 }];\n 126→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 127→ steps.forEach((step) => {\n 128→ expect(step.curvePoints !== undefined).toBe(true);\n 129→ expect(step.curvePoints.length).toBeGreaterThan(0);\n 130→ });\n 131→ });\n 132→\n 133→ it('should have correct number of steps (1 curve + n visit)', () => {\n 134→ const points = [\n 135→ { x: 1, y: 1, id: 0 },\n 136→ { x: 5, y: 5, id: 1 },\n 137→ { x: 10, y: 10, id: 2 },\n 138→ ];\n 139→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 140→ // 1 curve step + n visit steps\n 141→ expect(steps.length).toBe(points.length + 1);\n 142→ });\n 143→\n 144→ it('should have visit type for all non-first steps', () => {\n 145→ const points = [\n 146→ { x: 1, y: 1, id: 0 },\n 147→ { x: 5, y: 5, id: 1 },\n 148→ ];\n 149→ const steps = sierpinskiAlgorithmSteps(points, 16);\n 150→ for (let i = 1; i < steps.length; i++) {\n 151→ expect(steps[i].type).toBe('visit');\n 152→ }\n 153→ });\n 154→});\n 155→\n 156→// ============================================================\n 157→// Sierpiński Atomic Solution Tests\n 158→// ============================================================\n 159→\n 160→describe('sierpinskiSolution', () => {\n 161→ it('should return empty tour for empty points', () => {\n 162→ const result = sierpinskiSolution([], 16);\n 163→ expect(result.tour).toEqual([]);\n 164→ expect(result.curvePoints).toEqual([]);\n 165→ });\n 166→\n 167→ it('should return tour with all point indices', () => {\n 168→ const points = [\n 169→ { x: 0, y: 0, id: 0 },\n 170→ { x: 10, y: 0, id: 1 },\n 171→ { x: 5, y: 10, id: 2 },\n 172→ ];\n 173→ const result = sierpinskiSolution(points, 16);\n 174→ expect(result.tour.length).toBe(points.length);\n 175→ expect(new Set(result.tour).size).toBe(points.length);\n 176→ });\n 177→\n 178→ it('should return curve points', () => {\n 179→ const points = [\n 180→ { x: 0, y: 0, id: 0 },\n 181→ { x: 10, y: 0, id: 1 },\n 182→ ];\n 183→ const result = sierpinskiSolution(points, 16);\n 184→ expect(result.curvePoints.length).toBeGreaterThan(0);\n 185→ });\n 186→\n 187→ it('should produce valid tour for random points', () => {\n 188→ const gridSize = 16;\n 189→ const numPoints = 20;\n 190→ const points = generateRandomPoints(gridSize, numPoints);\n 191→ const result = sierpinskiSolution(points, gridSize);\n 192→\n 193→ expect(result.tour.length).toBe(numPoints);\n 194→ expect(new Set(result.tour).size).toBe(numPoints);\n 195→\n 196→ // Calculate distance - should be finite and positive\n 197→ const dist = calculateTotalDistance(result.tour, points);\n 198→ expect(dist).toBeGreaterThan(0);\n 199→ expect(isFinite(dist)).toBe(true);\n 200→ });\n 201→\n 202→ it('should work with different grid sizes', () => {\n 203→ const gridSizes = [4, 8, 16, 32];\n 204→ for (const gridSize of gridSizes) {\n 205→ const points = generateRandomPoints(gridSize, 5);\n 206→ const result = sierpinskiSolution(points, gridSize);\n 207→ expect(result.tour.length).toBe(5);\n 208→ expect(result.curvePoints.length).toBeGreaterThan(0);\n 209→ }\n 210→ });\n 211→});\n 212→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "/**\n * Test file for Sierpiński curve TSP algorithm\n * Works with Bun test runner\n */\n\nimport { describe, it, expect } from 'bun:test';\nimport {\n sierpinskiAlgorithmSteps,\n sierpinskiSolution,\n generateSierpinskiCurve,\n sierpinskiCurveToPoints,\n} from '../lib/algorithms/progressive/solution/sierpinski.js';\nimport {\n generateRandomPoints,\n calculateTotalDistance,\n} from '../lib/algorithms/utils.js';\n\n// ============================================================\n// Sierpiński Curve Generation Tests\n// ============================================================\n\ndescribe('generateSierpinskiCurve', () => {\n it('should generate valid L-system sequence', () => {\n const sequence = generateSierpinskiCurve(1);\n expect(sequence).toContain('F');\n expect(typeof sequence).toBe('string');\n });\n\n it('should expand sequence with higher order', () => {\n const order1 = generateSierpinskiCurve(1);\n const order2 = generateSierpinskiCurve(2);\n expect(order2.length).toBeGreaterThan(order1.length);\n });\n\n it('should contain axiom pattern at order 0', () => {\n const sequence = generateSierpinskiCurve(0);\n expect(sequence).toBe('F+XF+F+XF');\n });\n\n it('should not contain X or Y variables in F-count terms', () => {\n // The X variable is a non-drawing variable\n const sequence = generateSierpinskiCurve(2);\n // X is still present as a variable; it is not drawn\n expect(sequence).toContain('F');\n });\n});\n\ndescribe('sierpinskiCurveToPoints', () => {\n it('should generate curve points', () => {\n const sequence = generateSierpinskiCurve(1);\n const points = sierpinskiCurveToPoints(sequence, 4);\n expect(points.length).toBeGreaterThan(0);\n });\n\n it('should generate points within [0, gridSize-1] bounds', () => {\n const gridSize = 16;\n const sequence = generateSierpinskiCurve(3);\n const points = sierpinskiCurveToPoints(sequence, gridSize);\n points.forEach((p) => {\n expect(p.x).toBeGreaterThanOrEqual(0);\n expect(p.x).toBeLessThanOrEqual(gridSize - 1);\n expect(p.y).toBeGreaterThanOrEqual(0);\n expect(p.y).toBeLessThanOrEqual(gridSize - 1);\n });\n });\n\n it('should fill the grid at various orders', () => {\n for (let order = 0; order <= 3; order++) {\n const gridSize = Math.pow(2, order + 1);\n const sequence = generateSierpinskiCurve(order);\n const points = sierpinskiCurveToPoints(sequence, gridSize);\n\n // Curve should have more than 1 point\n expect(points.length).toBeGreaterThan(1);\n\n // Bounding box should cover (0,0) to (gridSize-1, gridSize-1)\n let minX = Infinity,\n maxX = -Infinity,\n minY = Infinity,\n maxY = -Infinity;\n for (const p of points) {\n minX = Math.min(minX, p.x);\n maxX = Math.max(maxX, p.x);\n minY = Math.min(minY, p.y);\n maxY = Math.max(maxY, p.y);\n }\n expect(minX).toBe(0);\n expect(minY).toBe(0);\n expect(maxX).toBe(gridSize - 1);\n expect(maxY).toBe(gridSize - 1);\n }\n });\n});\n\n// ============================================================\n// Sierpiński Progressive Algorithm Tests\n// ============================================================\n\ndescribe('sierpinskiAlgorithmSteps', () => {\n it('should return empty array for empty points', () => {\n expect(sierpinskiAlgorithmSteps([], 16)).toEqual([]);\n });\n\n it('should generate curve step first', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 10, id: 1 },\n ];\n const steps = sierpinskiAlgorithmSteps(points, 16);\n expect(steps[0].type).toBe('curve');\n });\n\n it('should include all points in final tour', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 8, y: 8, id: 1 },\n { x: 4, y: 12, id: 2 },\n ];\n const steps = sierpinskiAlgorithmSteps(points, 16);\n const finalTour = steps[steps.length - 1].tour;\n expect(finalTour.length).toBe(points.length);\n });\n\n it('should include curve points in each step', () => {\n const points = [{ x: 5, y: 5, id: 0 }];\n const steps = sierpinskiAlgorithmSteps(points, 16);\n steps.forEach((step) => {\n expect(step.curvePoints !== undefined).toBe(true);\n expect(step.curvePoints.length).toBeGreaterThan(0);\n });\n });\n\n it('should have correct number of steps (1 curve + n visit)', () => {\n const points = [\n { x: 1, y: 1, id: 0 },\n { x: 5, y: 5, id: 1 },\n { x: 10, y: 10, id: 2 },\n ];\n const steps = sierpinskiAlgorithmSteps(points, 16);\n // 1 curve step + n visit steps\n expect(steps.length).toBe(points.length + 1);\n });\n\n it('should have visit type for all non-first steps', () => {\n const points = [\n { x: 1, y: 1, id: 0 },\n { x: 5, y: 5, id: 1 },\n ];\n const steps = sierpinskiAlgorithmSteps(points, 16);\n for (let i = 1; i < steps.length; i++) {\n expect(steps[i].type).toBe('visit');\n }\n });\n});\n\n// ============================================================\n// Sierpiński Atomic Solution Tests\n// ============================================================\n\ndescribe('sierpinskiSolution', () => {\n it('should return empty tour for empty points', () => {\n const result = sierpinskiSolution([], 16);\n expect(result.tour).toEqual([]);\n expect(result.curvePoints).toEqual([]);\n });\n\n it('should return tour with all point indices', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n { x: 5, y: 10, id: 2 },\n ];\n const result = sierpinskiSolution(points, 16);\n expect(result.tour.length).toBe(points.length);\n expect(new Set(result.tour).size).toBe(points.length);\n });\n\n it('should return curve points', () => {\n const points = [\n { x: 0, y: 0, id: 0 },\n { x: 10, y: 0, id: 1 },\n ];\n const result = sierpinskiSolution(points, 16);\n expect(result.curvePoints.length).toBeGreaterThan(0);\n });\n\n it('should produce valid tour for random points', () => {\n const gridSize = 16;\n const numPoints = 20;\n const points = generateRandomPoints(gridSize, numPoints);\n const result = sierpinskiSolution(points, gridSize);\n\n expect(result.tour.length).toBe(numPoints);\n expect(new Set(result.tour).size).toBe(numPoints);\n\n // Calculate distance - should be finite and positive\n const dist = calculateTotalDistance(result.tour, points);\n expect(dist).toBeGreaterThan(0);\n expect(isFinite(dist)).toBe(true);\n });\n\n it('should work with different grid sizes', () => {\n const gridSizes = [4, 8, 16, 32];\n for (const gridSize of gridSizes) {\n const points = generateRandomPoints(gridSize, 5);\n const result = sierpinskiSolution(points, gridSize);\n expect(result.tour.length).toBe(5);\n expect(result.curvePoints.length).toBeGreaterThan(0);\n }\n });\n});\n",
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"content": " 1→#!/usr/bin/env node\n 2→\n 3→/* global performance */\n 4→\n 5→/**\n 6→ * TSP Algorithms CLI\n 7→ *\n 8→ * A globally installable command-line interface for running TSP algorithms.\n 9→ * Uses atomic algorithms (all-at-once computation) for benchmark-like performance.\n 10→ *\n 11→ * Supports:\n 12→ * - Algorithm selection: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force\n 13→ * - Optimization: 2-opt, 3-opt, k-opt, lin-kernighan, lkh, zigzag, combined, none\n 14→ * - Random point generation with configurable grid size and point count\n 15→ * - Manual point input via links notation (coordinate pairs as links)\n 16→ * - Verification via lower-bound (1-tree) analysis\n 17→ *\n 18→ * @example\n 19→ * # Random points with sonar algorithm\n 20→ * tsp-algorithms --algorithm sonar --num-points 20 --grid-size 32\n 21→ *\n 22→ * # Moore curve with 2-opt optimization\n 23→ * tsp-algorithms --algorithm moore --optimization 2-opt --num-points 50\n 24→ *\n 25→ * # Lin-Kernighan-Helsgaun optimization\n 26→ * tsp-algorithms --algorithm moore --optimization lkh --num-points 50\n 27→ *\n 28→ * # Manual points via links notation\n 29→ * tsp-algorithms --algorithm sonar --points \"0 0 5 5 10 2 3 8 7 7\"\n 30→ *\n 31→ * # Brute-force with verification\n 32→ * tsp-algorithms --algorithm brute-force --num-points 8 --verify\n 33→ */\n 34→\n 35→import { makeConfig } from 'lino-arguments';\n 36→import {\n 37→ atomic,\n 38→ verification,\n 39→ calculateTotalDistance,\n 40→ generateRandomPoints,\n 41→ calculateMooreGridSize,\n 42→ calculatePeanoGridSize,\n 43→ VALID_GRID_SIZES,\n 44→ VALID_PEANO_GRID_SIZES,\n 45→} from '../lib/index.js';\n 46→\n 47→const {\n 48→ sonarSolution,\n 49→ mooreSolution,\n 50→ gosperSolution,\n 51→ peanoSolution,\n 52→ sierpinskiSolution,\n 53→ combSolution,\n 54→ sawSolution,\n 55→ kochSolution,\n 56→ spaceFillingTreeSolution,\n 57→ bruteForceSolution,\n 58→ twoOpt,\n 59→ threeOpt,\n 60→ kOpt,\n 61→ zigzagOpt,\n 62→ combinedOpt,\n 63→ linKernighan,\n 64→ lkHelsgaun,\n 65→ BRUTE_FORCE_MAX_POINTS,\n 66→} = atomic;\n 67→\n 68→const { verifyOptimality } = verification;\n 69→\n 70→/**\n 71→ * Parse points from a flat coordinate string.\n 72→ * Coordinates are provided as space-separated pairs: \"x1 y1 x2 y2 ...\"\n 73→ *\n 74→ * @param {string} pointsStr - Space-separated coordinate pairs\n 75→ * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n 76→ */\n 77→const parsePoints = (pointsStr) => {\n 78→ const values = pointsStr.trim().split(/\\s+/).map(Number);\n 79→\n 80→ if (values.length < 4 || values.length % 2 !== 0) {\n 81→ throw new Error(\n 82→ 'Points must be space-separated coordinate pairs (at least 2 points): \"x1 y1 x2 y2 ...\"'\n 83→ );\n 84→ }\n 85→\n 86→ if (values.some((v) => isNaN(v))) {\n 87→ throw new Error('All coordinates must be valid numbers');\n 88→ }\n 89→\n 90→ const points = [];\n 91→ for (let i = 0; i < values.length; i += 2) {\n 92→ points.push({ x: values[i], y: values[i + 1], id: points.length });\n 93→ }\n 94→ return points;\n 95→};\n 96→\n 97→/**\n 98→ * Format a tour for display.\n 99→ *\n 100→ * @param {number[]} tour - Array of point indices\n 101→ * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n 102→ * @returns {string} Formatted tour string\n 103→ */\n 104→const formatTour = (tour, points) => {\n 105→ const coordStr = tour\n 106→ .map((idx) => `(${points[idx].x}, ${points[idx].y})`)\n 107→ .join(' → ');\n 108→ return `${tour.join(' → ')}\\n Coordinates: ${coordStr}`;\n 109→};\n 110→\n 111→/**\n 112→ * Run the selected algorithm on the given points.\n 113→ *\n 114→ * @param {string} algorithm - Algorithm name\n 115→ * @param {Array<{x: number, y: number, id: number}>} points - Points\n 116→ * @param {number} gridSize - Grid size (for Moore algorithm)\n 117→ * @returns {{tour: number[], label: string}} Result\n 118→ */\n 119→const runAlgorithm = (algorithm, points, gridSize) => {\n 120→ switch (algorithm) {\n 121→ case 'sonar': {\n 122→ const result = sonarSolution(points);\n 123→ return { tour: result.tour, label: 'Sonar (Radial Sweep)' };\n 124→ }\n 125→ case 'moore': {\n 126→ const mooreGrid = calculateMooreGridSize(gridSize);\n 127→ const result = mooreSolution(points, mooreGrid);\n 128→ return { tour: result.tour, label: `Moore Curve (grid: ${mooreGrid})` };\n 129→ }\n 130→ case 'gosper': {\n 131→ const gosperGrid = calculateMooreGridSize(gridSize);\n 132→ const result = gosperSolution(points, gosperGrid);\n 133→ return {\n 134→ tour: result.tour,\n 135→ label: `Gosper Curve (grid: ${gosperGrid})`,\n 136→ };\n 137→ }\n 138→ case 'peano': {\n 139→ const peanoGrid = calculatePeanoGridSize(gridSize);\n 140→ const result = peanoSolution(points, peanoGrid);\n 141→ return {\n 142→ tour: result.tour,\n 143→ label: `Peano Curve (grid: ${peanoGrid})`,\n 144→ };\n 145→ }\n 146→ case 'sierpinski': {\n 147→ const sierpinskiGrid = calculateMooreGridSize(gridSize);\n 148→ const result = sierpinskiSolution(points, sierpinskiGrid);\n 149→ return {\n 150→ tour: result.tour,\n 151→ label: `Sierpiński Curve (grid: ${sierpinskiGrid})`,\n 152→ };\n 153→ }\n 154→ case 'comb': {\n 155→ const result = combSolution(points);\n 156→ return { tour: result.tour, label: 'Comb (Serpentine Scan)' };\n 157→ }\n 158→ case 'saw': {\n 159→ const result = sawSolution(points);\n 160→ return {\n 161→ tour: result.tour,\n 162→ label: 'Self-Avoiding Walk (Nearest Neighbor)',\n 163→ };\n 164→ }\n 165→ case 'koch': {\n 166→ const mooreGrid = calculateMooreGridSize(gridSize);\n 167→ const result = kochSolution(points, mooreGrid);\n 168→ return {\n 169→ tour: result.tour,\n 170→ label: `Koch Snowflake (grid: ${mooreGrid})`,\n 171→ };\n 172→ }\n 173→ case 'space-filling-tree': {\n 174→ const result = spaceFillingTreeSolution(points);\n 175→ return { tour: result.tour, label: 'Space-Filling Tree (Quadtree DFS)' };\n 176→ }\n 177→ case 'brute-force': {\n 178→ if (points.length > BRUTE_FORCE_MAX_POINTS) {\n 179→ throw new Error(\n 180→ `Brute-force is limited to ${BRUTE_FORCE_MAX_POINTS} points (got ${points.length})`\n 181→ );\n 182→ }\n 183→ const result = bruteForceSolution(points);\n 184→ return {\n 185→ tour: result.tour,\n 186→ label: `Brute-Force (optimal distance: ${result.distance.toFixed(2)})`,\n 187→ };\n 188→ }\n 189→ default:\n 190→ throw new Error(\n 191→ `Unknown algorithm: ${algorithm}. Choose from: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force`\n 192→ );\n 193→ }\n 194→};\n 195→\n 196→/**\n 197→ * Apply the selected optimization to a tour.\n 198→ *\n 199→ * @param {string} optimization - Optimization name\n 200→ * @param {Array<{x: number, y: number}>} points - Points\n 201→ * @param {number[]} tour - Initial tour\n 202→ * @returns {{tour: number[], improvement: number, label: string}} Optimized result\n 203→ */\n 204→const runOptimization = (optimization, points, tour) => {\n 205→ switch (optimization) {\n 206→ case 'none':\n 207→ return { tour, improvement: 0, label: 'None' };\n 208→ case '2-opt': {\n 209→ const result = twoOpt(points, tour);\n 210→ return { ...result, label: '2-opt' };\n 211→ }\n 212→ case '3-opt': {\n 213→ const result = threeOpt(points, tour);\n 214→ return { ...result, label: '3-opt' };\n 215→ }\n 216→ case 'k-opt': {\n 217→ const result = kOpt(points, tour);\n 218→ return { ...result, label: 'k-opt' };\n 219→ }\n 220→ case 'zigzag': {\n 221→ const result = zigzagOpt(points, tour);\n 222→ return { ...result, label: 'Zigzag' };\n 223→ }\n 224→ case 'combined': {\n 225→ const result = combinedOpt(points, tour);\n 226→ return { ...result, label: 'Combined (zigzag + 2-opt)' };\n 227→ }\n 228→ case 'lin-kernighan': {\n 229→ const result = linKernighan(points, tour);\n 230→ return { ...result, label: 'Lin-Kernighan' };\n 231→ }\n 232→ case 'lkh': {\n 233→ const result = lkHelsgaun(points, tour);\n 234→ return { ...result, label: 'Lin-Kernighan-Helsgaun' };\n 235→ }\n 236→ default:\n 237→ throw new Error(\n 238→ `Unknown optimization: ${optimization}. Choose from: none, 2-opt, 3-opt, k-opt, lin-kernighan, lkh, zigzag, combined`\n 239→ );\n 240→ }\n 241→};\n 242→\n 243→/**\n 244→ * Main CLI entry point.\n 245→ * Parses arguments, runs the algorithm, and prints results.\n 246→ */\n 247→// eslint-disable-next-line max-lines-per-function\n 248→export const main = () => {\n 249→ const config = makeConfig({\n 250→ yargs: ({ yargs, getenv }) =>\n 251→ yargs\n 252→ .usage('Usage: $0 [options]')\n 253→ .option('algorithm', {\n 254→ alias: 'a',\n 255→ type: 'string',\n 256→ describe: 'TSP algorithm to use',\n 257→ choices: [\n 258→ 'sonar',\n 259→ 'moore',\n 260→ 'gosper',\n 261→ 'peano',\n 262→ 'sierpinski',\n 263→ 'comb',\n 264→ 'saw',\n 265→ 'koch',\n 266→ 'space-filling-tree',\n 267→ 'brute-force',\n 268→ ],\n 269→ default: getenv('TSP_ALGORITHM', 'sonar'),\n 270→ })\n 271→ .option('optimization', {\n 272→ alias: 'o',\n 273→ type: 'string',\n 274→ describe: 'Optimization to apply after initial solution',\n 275→ choices: [\n 276→ 'none',\n 277→ '2-opt',\n 278→ '3-opt',\n 279→ 'k-opt',\n 280→ 'lin-kernighan',\n 281→ 'lkh',\n 282→ 'zigzag',\n 283→ 'combined',\n 284→ ],\n 285→ default: getenv('TSP_OPTIMIZATION', 'none'),\n 286→ })\n 287→ .option('num-points', {\n 288→ alias: 'n',\n 289→ type: 'number',\n 290→ describe: 'Number of random points to generate',\n 291→ default: getenv('TSP_NUM_POINTS', 10),\n 292→ })\n 293→ .option('grid-size', {\n 294→ alias: 'g',\n 295→ type: 'number',\n 296→ describe: `Grid size for point generation (Moore: ${VALID_GRID_SIZES.join(', ')}; Peano: ${VALID_PEANO_GRID_SIZES.join(', ')})`,\n 297→ default: getenv('TSP_GRID_SIZE', 16),\n 298→ })\n 299→ .option('points', {\n 300→ alias: 'p',\n 301→ type: 'string',\n 302→ describe:\n 303→ 'Manual points as space-separated coordinate pairs: \"x1 y1 x2 y2 ...\"',\n 304→ })\n 305→ .option('verify', {\n 306→ alias: 'v',\n 307→ type: 'boolean',\n 308→ describe: 'Run lower-bound verification on the result',\n 309→ default: false,\n 310→ })\n 311→ .option('json', {\n 312→ alias: 'j',\n 313→ type: 'boolean',\n 314→ describe: 'Output results as JSON',\n 315→ default: false,\n 316→ })\n 317→ .example(\n 318→ '$0 --algorithm sonar --num-points 20',\n 319→ 'Sonar with 20 random points'\n 320→ )\n 321→ .example(\n 322→ '$0 -a moore -o 2-opt -n 50 -g 32',\n 323→ 'Moore + 2-opt, 50 points on 32x32 grid'\n 324→ )\n 325→ .example(\n 326→ '$0 -a moore -o lkh -n 50',\n 327→ 'Moore + LKH optimization, 50 points'\n 328→ )\n 329→ .example(\n 330→ '$0 -a peano -o 2-opt -n 50 -g 27',\n 331→ 'Peano + 2-opt, 50 points on 27x27 grid'\n 332→ )\n 333→ .example(\n 334→ '$0 -a brute-force --points \"0 0 5 5 10 2 3 8\"',\n 335→ 'Brute-force with manual points'\n 336→ )\n 337→ .example(\n 338→ '$0 -a sonar -o combined -n 100 --verify',\n 339→ 'Sonar + combined + verify'\n 340→ )\n 341→ .epilog(\n 342→ 'TSP Algorithms CLI — https://github.com/konard/tsp\\n' +\n 343→ 'Runs atomic (all-at-once) algorithms for benchmark-like performance.'\n 344→ ),\n 345→ });\n 346→\n 347→ const {\n 348→ algorithm,\n 349→ optimization,\n 350→ numPoints,\n 351→ gridSize,\n 352→ points: pointsStr,\n 353→ verify,\n 354→ json: jsonOutput,\n 355→ } = config;\n 356→\n 357→ try {\n 358→ // Generate or parse points\n 359→ let points;\n 360→ if (pointsStr) {\n 361→ points = parsePoints(pointsStr);\n 362→ } else {\n 363→ const mooreGrid = calculateMooreGridSize(gridSize);\n 364→ points = generateRandomPoints(mooreGrid, numPoints);\n 365→ }\n 366→\n 367→ // Measure total execution time\n 368→ const startTime = performance.now();\n 369→\n 370→ // Run algorithm\n 371→ const algoResult = runAlgorithm(algorithm, points, gridSize);\n 372→\n 373→ // Run optimization\n 374→ const optResult = runOptimization(optimization, points, algoResult.tour);\n 375→\n 376→ // Calculate distances\n 377→ const initialDistance = calculateTotalDistance(algoResult.tour, points);\n 378→ const finalDistance = calculateTotalDistance(optResult.tour, points);\n 379→\n 380→ const endTime = performance.now();\n 381→ const timeMs = endTime - startTime;\n 382→\n 383→ // Verification\n 384→ let verificationResult = null;\n 385→ if (verify) {\n 386→ verificationResult = verifyOptimality(finalDistance, points);\n 387→ }\n 388→\n 389→ if (jsonOutput) {\n 390→ const output = {\n 391→ algorithm: algoResult.label,\n 392→ optimization: optResult.label,\n 393→ points: points.length,\n 394→ gridSize,\n 395→ tour: optResult.tour,\n 396→ initialDistance,\n 397→ finalDistance,\n 398→ improvement: optResult.improvement,\n 399→ timeMs: Math.round(timeMs * 100) / 100,\n 400→ };\n 401→ if (verificationResult) {\n 402→ output.verification = verificationResult;\n 403→ }\n 404→ console.log(JSON.stringify(output, null, 2));\n 405→ } else {\n 406→ console.log('TSP Algorithms CLI');\n 407→ console.log('==================\\n');\n 408→ console.log(`Algorithm: ${algoResult.label}`);\n 409→ console.log(`Optimization: ${optResult.label}`);\n 410→ console.log(`Points: ${points.length}`);\n 411→ console.log(`Grid size: ${gridSize}\\n`);\n 412→\n 413→ console.log(`Tour: ${formatTour(optResult.tour, points)}\\n`);\n 414→\n 415→ console.log(`Initial distance: ${initialDistance.toFixed(2)}`);\n 416→ if (optimization !== 'none') {\n 417→ console.log(`Final distance: ${finalDistance.toFixed(2)}`);\n 418→ console.log(`Improvement: ${optResult.improvement.toFixed(2)}`);\n 419→ }\n 420→ console.log(`Time: ${timeMs.toFixed(2)}ms`);\n 421→\n 422→ if (verificationResult) {\n 423→ console.log('\\nVerification (1-tree lower bound):');\n 424→ console.log(\n 425→ ` Lower bound: ${verificationResult.lowerBound.toFixed(2)}`\n 426→ );\n 427→ console.log(\n 428→ ` Gap: ${verificationResult.gapPercent.toFixed(2)}%`\n 429→ );\n 430→ console.log(\n 431→ ` Optimal: ${verificationResult.isOptimal ? 'YES ✓' : 'Not proven'}`\n 432→ );\n 433→ }\n 434→ }\n 435→ } catch (error) {\n 436→ if (jsonOutput) {\n 437→ console.error(JSON.stringify({ error: error.message }));\n 438→ } else {\n 439→ console.error(`Error: ${error.message}`);\n 440→ }\n 441→ process.exitCode = 1;\n 442→ }\n 443→};\n 444→\n 445→main();\n 446→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"
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"content": "#!/usr/bin/env node\n\n/* global performance */\n\n/**\n * TSP Algorithms CLI\n *\n * A globally installable command-line interface for running TSP algorithms.\n * Uses atomic algorithms (all-at-once computation) for benchmark-like performance.\n *\n * Supports:\n * - Algorithm selection: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force\n * - Optimization: 2-opt, 3-opt, k-opt, lin-kernighan, lkh, zigzag, combined, none\n * - Random point generation with configurable grid size and point count\n * - Manual point input via links notation (coordinate pairs as links)\n * - Verification via lower-bound (1-tree) analysis\n *\n * @example\n * # Random points with sonar algorithm\n * tsp-algorithms --algorithm sonar --num-points 20 --grid-size 32\n *\n * # Moore curve with 2-opt optimization\n * tsp-algorithms --algorithm moore --optimization 2-opt --num-points 50\n *\n * # Lin-Kernighan-Helsgaun optimization\n * tsp-algorithms --algorithm moore --optimization lkh --num-points 50\n *\n * # Manual points via links notation\n * tsp-algorithms --algorithm sonar --points \"0 0 5 5 10 2 3 8 7 7\"\n *\n * # Brute-force with verification\n * tsp-algorithms --algorithm brute-force --num-points 8 --verify\n */\n\nimport { makeConfig } from 'lino-arguments';\nimport {\n atomic,\n verification,\n calculateTotalDistance,\n generateRandomPoints,\n calculateMooreGridSize,\n calculatePeanoGridSize,\n VALID_GRID_SIZES,\n VALID_PEANO_GRID_SIZES,\n} from '../lib/index.js';\n\nconst {\n sonarSolution,\n mooreSolution,\n gosperSolution,\n peanoSolution,\n sierpinskiSolution,\n combSolution,\n sawSolution,\n kochSolution,\n spaceFillingTreeSolution,\n bruteForceSolution,\n twoOpt,\n threeOpt,\n kOpt,\n zigzagOpt,\n combinedOpt,\n linKernighan,\n lkHelsgaun,\n BRUTE_FORCE_MAX_POINTS,\n} = atomic;\n\nconst { verifyOptimality } = verification;\n\n/**\n * Parse points from a flat coordinate string.\n * Coordinates are provided as space-separated pairs: \"x1 y1 x2 y2 ...\"\n *\n * @param {string} pointsStr - Space-separated coordinate pairs\n * @returns {Array<{x: number, y: number, id: number}>} Array of point objects\n */\nconst parsePoints = (pointsStr) => {\n const values = pointsStr.trim().split(/\\s+/).map(Number);\n\n if (values.length < 4 || values.length % 2 !== 0) {\n throw new Error(\n 'Points must be space-separated coordinate pairs (at least 2 points): \"x1 y1 x2 y2 ...\"'\n );\n }\n\n if (values.some((v) => isNaN(v))) {\n throw new Error('All coordinates must be valid numbers');\n }\n\n const points = [];\n for (let i = 0; i < values.length; i += 2) {\n points.push({ x: values[i], y: values[i + 1], id: points.length });\n }\n return points;\n};\n\n/**\n * Format a tour for display.\n *\n * @param {number[]} tour - Array of point indices\n * @param {Array<{x: number, y: number}>} points - Array of point coordinates\n * @returns {string} Formatted tour string\n */\nconst formatTour = (tour, points) => {\n const coordStr = tour\n .map((idx) => `(${points[idx].x}, ${points[idx].y})`)\n .join(' → ');\n return `${tour.join(' → ')}\\n Coordinates: ${coordStr}`;\n};\n\n/**\n * Run the selected algorithm on the given points.\n *\n * @param {string} algorithm - Algorithm name\n * @param {Array<{x: number, y: number, id: number}>} points - Points\n * @param {number} gridSize - Grid size (for Moore algorithm)\n * @returns {{tour: number[], label: string}} Result\n */\nconst runAlgorithm = (algorithm, points, gridSize) => {\n switch (algorithm) {\n case 'sonar': {\n const result = sonarSolution(points);\n return { tour: result.tour, label: 'Sonar (Radial Sweep)' };\n }\n case 'moore': {\n const mooreGrid = calculateMooreGridSize(gridSize);\n const result = mooreSolution(points, mooreGrid);\n return { tour: result.tour, label: `Moore Curve (grid: ${mooreGrid})` };\n }\n case 'gosper': {\n const gosperGrid = calculateMooreGridSize(gridSize);\n const result = gosperSolution(points, gosperGrid);\n return {\n tour: result.tour,\n label: `Gosper Curve (grid: ${gosperGrid})`,\n };\n }\n case 'peano': {\n const peanoGrid = calculatePeanoGridSize(gridSize);\n const result = peanoSolution(points, peanoGrid);\n return {\n tour: result.tour,\n label: `Peano Curve (grid: ${peanoGrid})`,\n };\n }\n case 'sierpinski': {\n const sierpinskiGrid = calculateMooreGridSize(gridSize);\n const result = sierpinskiSolution(points, sierpinskiGrid);\n return {\n tour: result.tour,\n label: `Sierpiński Curve (grid: ${sierpinskiGrid})`,\n };\n }\n case 'comb': {\n const result = combSolution(points);\n return { tour: result.tour, label: 'Comb (Serpentine Scan)' };\n }\n case 'saw': {\n const result = sawSolution(points);\n return {\n tour: result.tour,\n label: 'Self-Avoiding Walk (Nearest Neighbor)',\n };\n }\n case 'koch': {\n const mooreGrid = calculateMooreGridSize(gridSize);\n const result = kochSolution(points, mooreGrid);\n return {\n tour: result.tour,\n label: `Koch Snowflake (grid: ${mooreGrid})`,\n };\n }\n case 'space-filling-tree': {\n const result = spaceFillingTreeSolution(points);\n return { tour: result.tour, label: 'Space-Filling Tree (Quadtree DFS)' };\n }\n case 'brute-force': {\n if (points.length > BRUTE_FORCE_MAX_POINTS) {\n throw new Error(\n `Brute-force is limited to ${BRUTE_FORCE_MAX_POINTS} points (got ${points.length})`\n );\n }\n const result = bruteForceSolution(points);\n return {\n tour: result.tour,\n label: `Brute-Force (optimal distance: ${result.distance.toFixed(2)})`,\n };\n }\n default:\n throw new Error(\n `Unknown algorithm: ${algorithm}. Choose from: sonar, moore, gosper, peano, sierpinski, comb, saw, koch, space-filling-tree, brute-force`\n );\n }\n};\n\n/**\n * Apply the selected optimization to a tour.\n *\n * @param {string} optimization - Optimization name\n * @param {Array<{x: number, y: number}>} points - Points\n * @param {number[]} tour - Initial tour\n * @returns {{tour: number[], improvement: number, label: string}} Optimized result\n */\nconst runOptimization = (optimization, points, tour) => {\n switch (optimization) {\n case 'none':\n return { tour, improvement: 0, label: 'None' };\n case '2-opt': {\n const result = twoOpt(points, tour);\n return { ...result, label: '2-opt' };\n }\n case '3-opt': {\n const result = threeOpt(points, tour);\n return { ...result, label: '3-opt' };\n }\n case 'k-opt': {\n const result = kOpt(points, tour);\n return { ...result, label: 'k-opt' };\n }\n case 'zigzag': {\n const result = zigzagOpt(points, tour);\n return { ...result, label: 'Zigzag' };\n }\n case 'combined': {\n const result = combinedOpt(points, tour);\n return { ...result, label: 'Combined (zigzag + 2-opt)' };\n }\n case 'lin-kernighan': {\n const result = linKernighan(points, tour);\n return { ...result, label: 'Lin-Kernighan' };\n }\n case 'lkh': {\n const result = lkHelsgaun(points, tour);\n return { ...result, label: 'Lin-Kernighan-Helsgaun' };\n }\n default:\n throw new Error(\n `Unknown optimization: ${optimization}. 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"text": "Now let me look at the image from the issue to understand the U-fork fractal pattern. Let me use Playwright to view it."
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"text": "### Ran Playwright code\n```js\nawait page.goto('https://github.com/konard/tsp/issues/65');\n```\n### Page\n- Page URL: https://github.com/konard/tsp/issues/65\n- Page Title: Implement U-fork fractal algorithm · Issue #65 · konard/tsp\n### Snapshot\n```yaml\n- generic [ref=e2]:\n - region:\n - generic:\n - tooltip \"This user is the owner of the tsp repository.\"\n - generic [ref=e3]:\n - link \"Skip to content\" [ref=e4] [cursor=pointer]:\n - /url: \"#start-of-content\"\n - banner [ref=e6]:\n - heading \"Navigation Menu\" [level=2] [ref=e7]\n - generic [ref=e8]:\n - link \"Homepage\" [ref=e10] [cursor=pointer]:\n - /url: /\n - img [ref=e11]\n - generic [ref=e13]:\n - navigation \"Global\" [ref=e16]:\n - list [ref=e17]:\n - listitem [ref=e18]:\n - button \"Platform\" [ref=e20] [cursor=pointer]:\n - text: Platform\n - img [ref=e21]\n - listitem [ref=e23]:\n - button \"Solutions\" [ref=e25] [cursor=pointer]:\n - text: Solutions\n - img [ref=e26]\n - listitem [ref=e28]:\n - 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"text": "### Ran Playwright code\n```js\nawait page.goto('https://github.com/konard/tsp/issues/65');\n```\n### Page\n- Page URL: https://github.com/konard/tsp/issues/65\n- Page Title: Implement U-fork fractal algorithm · Issue #65 · konard/tsp\n### Snapshot\n```yaml\n- generic [ref=e2]:\n - region:\n - generic:\n - tooltip \"This user is the owner of the tsp repository.\"\n - generic [ref=e3]:\n - link \"Skip to content\" [ref=e4] [cursor=pointer]:\n - /url: \"#start-of-content\"\n - banner [ref=e6]:\n - heading \"Navigation Menu\" [level=2] [ref=e7]\n - generic [ref=e8]:\n - link \"Homepage\" [ref=e10] [cursor=pointer]:\n - /url: /\n - img [ref=e11]\n - generic [ref=e13]:\n - navigation \"Global\" [ref=e16]:\n - list [ref=e17]:\n - listitem [ref=e18]:\n - button \"Platform\" [ref=e20] [cursor=pointer]:\n - text: Platform\n - img [ref=e21]\n - listitem [ref=e23]:\n - button \"Solutions\" [ref=e25] [cursor=pointer]:\n - text: Solutions\n - img [ref=e26]\n - listitem [ref=e28]:\n - button \"Resources\" [ref=e30] [cursor=pointer]:\n - text: Resources\n - img [ref=e31]\n - listitem [ref=e33]:\n - button \"Open Source\" [ref=e35] [cursor=pointer]:\n - text: Open Source\n - img [ref=e36]\n - listitem [ref=e38]:\n - button \"Enterprise\" [ref=e40] [cursor=pointer]:\n - text: Enterprise\n - img [ref=e41]\n - listitem [ref=e43]:\n - link \"Pricing\" [ref=e44] [cursor=pointer]:\n - /url: https://github.com/pricing\n - generic [ref=e45]: Pricing\n - generic [ref=e46]:\n - button \"Search or jump to…\" [ref=e49] [cursor=pointer]:\n - img [ref=e51]\n - generic [ref=e53]: Search or jump to...\n - img [ref=e55]\n - link \"Sign in\" [ref=e59] [cursor=pointer]:\n - /url: /login?return_to=https%3A%2F%2Fgithub.com%2Fkonard%2Ftsp%2Fissues%2F65\n - link \"Sign up\" [ref=e60] [cursor=pointer]:\n - /url: /signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E%2Fvoltron%2Fissues_fragments%2Fissue_layout&source=header-repo&source_repo=konard%2Ftsp\n - button \"Appearance settings\" [ref=e63] [cursor=pointer]:\n - img\n - main [ref=e67]:\n - generic [ref=e68]:\n - generic [ref=e69]:\n - generic [ref=e70]:\n - generic [ref=e71]:\n - img [ref=e72]\n - link \"konard\" [ref=e75] [cursor=pointer]:\n - /url: /konard\n - generic [ref=e76]: /\n - strong [ref=e77]:\n - link \"tsp\" [ref=e78] [cursor=pointer]:\n - /url: /konard/tsp\n - generic [ref=e79]: Public\n - generic [ref=e80]:\n - text: generated from\n - link \"link-foundation/js-ai-driven-development-pipeline-template\" [ref=e81] [cursor=pointer]:\n - /url: /link-foundation/js-ai-driven-development-pipeline-template\n - generic [ref=e82]:\n - list:\n - listitem [ref=e83]:\n - link \"You must be signed in to change notification settings\" [ref=e84] [cursor=pointer]:\n - /url: /login?return_to=%2Fkonard%2Ftsp\n - img [ref=e85]\n - text: Notifications\n - listitem [ref=e87]:\n - link \"Fork 0\" [ref=e88] [cursor=pointer]:\n - /url: /login?return_to=%2Fkonard%2Ftsp\n - img [ref=e89]\n - text: Fork\n - generic \"0\" [ref=e91]\n - listitem [ref=e92]:\n - link \"You must be signed in to star a repository\" [ref=e94] [cursor=pointer]:\n - /url: /login?return_to=%2Fkonard%2Ftsp\n - img [ref=e95]\n - text: Star\n - generic \"0 users starred this repository\" [ref=e97]: \"0\"\n - navigation \"Repository\" [ref=e98]:\n - list [ref=e99]:\n - listitem [ref=e100]:\n - link \"Code\" [ref=e101] [cursor=pointer]:\n - /url: /konard/tsp\n - img [ref=e102]\n - generic [ref=e104]: Code\n - listitem [ref=e105]:\n - link \"Issues 5\" [ref=e106] [cursor=pointer]:\n - /url: /konard/tsp/issues\n - img [ref=e107]\n - generic [ref=e110]: Issues\n - generic \"5\" [ref=e111]\n - listitem [ref=e112]:\n - link \"Pull requests 2\" [ref=e113] [cursor=pointer]:\n - /url: /konard/tsp/pulls\n - img [ref=e114]\n - generic [ref=e116]: Pull requests\n - generic \"2\" [ref=e117]\n - listitem [ref=e118]:\n - link \"Actions\" [ref=e119] [cursor=pointer]:\n - /url: /konard/tsp/actions\n - img [ref=e120]\n - generic [ref=e122]: Actions\n - listitem [ref=e123]:\n - link \"Projects\" [ref=e124] [cursor=pointer]:\n - /url: /konard/tsp/projects\n - img [ref=e125]\n - generic [ref=e127]: Projects\n - listitem [ref=e128]:\n - link \"Security\" [ref=e129] [cursor=pointer]:\n - /url: /konard/tsp/security\n - img [ref=e130]\n - generic [ref=e132]: Security\n - listitem [ref=e133]:\n - link \"Insights\" [ref=e134] [cursor=pointer]:\n - /url: /konard/tsp/pulse\n - img [ref=e135]\n - generic [ref=e137]: Insights\n - generic [ref=e145]:\n - region \"Header\" [ref=e147]:\n - generic [ref=e148]:\n - 'heading \"Implement U-fork fractal algorithm #65\" [level=1] [ref=e150]'\n - generic [ref=e152]:\n - link \"New issue\" [ref=e154] [cursor=pointer]:\n - /url: /login?return_to=https://github.com/konard/tsp/issues/65\n - generic [ref=e156]: New issue\n - button \"Copy link\" [ref=e157] [cursor=pointer]:\n - img [ref=e158]\n - generic [ref=e164]:\n - generic [ref=e166]:\n - img \"Issue\" [ref=e167]\n - text: Open\n - 'link \"Draft #68\" [ref=e171] [cursor=pointer]':\n - /url: https://github.com/konard/tsp/pull/68\n - img \"Draft\" [ref=e172]\n - generic [ref=e174]: \"#68\"\n - generic [ref=e176]:\n - generic [ref=e177]:\n - generic [ref=e179]:\n - link \"@konard's profile\" [ref=e180] [cursor=pointer]:\n - /url: https://github.com/konard\n - img \"@konard\" [ref=e181]\n - generic [ref=e182]:\n - heading \"Description\" [level=2] [ref=e183]\n - generic [ref=e185]:\n - generic [ref=e187]:\n - generic [ref=e188]:\n - link \"konard\" [ref=e190] [cursor=pointer]:\n - /url: https://github.com/konard\n - generic [ref=e191]:\n - text: opened\n - link \"on Feb 7, 20265 minutes ago\" [ref=e192] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues/65#issue-3911398184\n - generic \"Feb 7, 2026, 11:16 PM GMT+1\" [ref=e193]: on Feb 7, 20265 minutes ago\n - generic [ref=e195]:\n - generic \"This user is the owner of the tsp repository.\" [ref=e197]: Owner\n - button \"Issue body actions\" [ref=e199] [cursor=pointer]:\n - img [ref=e200]\n - generic [ref=e202]:\n - generic [ref=e204]:\n - paragraph [ref=e205]: \"Reproduce this pattern in tests:\"\n - link \"Image\" [ref=e206] [cursor=pointer]:\n - /url: https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE\n - img \"Image\" [ref=e207]\n - paragraph [ref=e208]: Make sure we implement it similar to all features Moore Curve algorithm has.\n - generic:\n - toolbar \"Reactions\"\n - generic [ref=e210]:\n - generic [ref=e211]:\n - heading \"Activity\" [level=2] [ref=e212]\n - region \"Events\" [ref=e214]:\n - generic [ref=e221]:\n - img [ref=e224]\n - generic [ref=e228]:\n - link \"konard\" [ref=e230] [cursor=pointer]:\n - /url: /konard\n - generic [ref=e231]: konard\n - text: added\n - link \"documentation Improvements or additions to documentation\" [ref=e233] [cursor=pointer]:\n - /url: /konard/tsp/issues?q=state%3Aopen%20label%3A%22documentation%22\n - generic [ref=e235]: documentation\n - generic [ref=e236]: Improvements or additions to documentation\n - link \"enhancement New feature or request\" [ref=e238] [cursor=pointer]:\n - /url: /konard/tsp/issues?q=state%3Aopen%20label%3A%22enhancement%22\n - generic [ref=e240]: enhancement\n - generic [ref=e241]: New feature or request\n - link \"on Feb 7, 20265 minutes ago\" [ref=e242] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues/65#event-22615665402\n - generic \"Feb 7, 2026, 11:16 PM GMT+1\" [ref=e243]: on Feb 7, 20265 minutes ago\n - generic [ref=e250]:\n - img [ref=e253]\n - generic [ref=e257]:\n - link \"konard\" [ref=e259] [cursor=pointer]:\n - /url: /konard\n - generic [ref=e260]: konard\n - text: added a commit that references this issue\n - link \"on Feb 7, 20262 minutes ago\" [ref=e261] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues/65#event-22615714983\n - generic \"Feb 7, 2026, 11:19 PM GMT+1\" [ref=e262]: on Feb 7, 20262 minutes ago\n - generic [ref=e265]:\n - generic [ref=e266]:\n - 'generic \"Initial commit with task details Adding CLAUDE.md with task information for AI processing. This file will be removed when the task is complete. Issue: https://github.com/konard/tsp/issues/65\" [ref=e267]':\n - 'link \"Initial commit with task details Adding CLAUDE.md with task information for AI processing. This file will be removed when the task is complete. Issue: https://github.com/konard/tsp/issues/65\" [ref=e268] [cursor=pointer]':\n - /url: https://github.com/konard/tsp/commit/27bf0a7d90c350e4e666f998eff87aced56a89ca\n - paragraph [ref=e270]: Initial commit with task details\n - button \"Show commit message body\" [ref=e271] [cursor=pointer]:\n - generic [ref=e273]: ...\n - link \"27bf0a7\" [ref=e275] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/commit/27bf0a7d90c350e4e666f998eff87aced56a89ca\n - generic [ref=e279]:\n - img [ref=e282]\n - generic [ref=e284]:\n - generic [ref=e286]:\n - link \"konard\" [ref=e288] [cursor=pointer]:\n - /url: /konard\n - generic [ref=e289]: konard\n - text: linked a pull request that will close this issue\n - link \"on Feb 7, 20262 minutes ago\" [ref=e290] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues/65#event-4679422396\n - generic \"Feb 7, 2026, 11:19 PM GMT+1\" [ref=e291]: on Feb 7, 20262 minutes ago\n - region \"Issues mentioned\" [ref=e294]:\n - list [ref=e295]:\n - listitem [ref=e296]:\n - generic [ref=e297]:\n - img \"Draft\" [ref=e298]\n - 'link \"[WIP] Implement U-fork fractal algorithm #68\" [ref=e300] [cursor=pointer]':\n - /url: https://github.com/konard/tsp/pull/68\n - text: \"[WIP] Implement U-fork fractal algorithm\"\n - generic [ref=e301]: \"#68\"\n - generic [ref=e302]:\n - link \"Sign up for free\" [ref=e303] [cursor=pointer]:\n - /url: /signup?return_to=https://github.com/konard/tsp/issues/65\n - generic [ref=e305]: Sign up for free\n - generic [ref=e306]:\n - strong [ref=e307]: to join this conversation on GitHub.\n - text: Already have an account?\n - link \"Sign in to comment\" [ref=e308] [cursor=pointer]:\n - /url: /login?return_to=https://github.com/konard/tsp/issues/65\n - generic [ref=e310]:\n - heading \"Metadata\" [level=2] [ref=e311]\n - generic [ref=e313]:\n - generic [ref=e315]:\n - generic [ref=e316]:\n - heading \"Assignees\" [level=3]\n - generic [ref=e317]: No one assigned\n - generic [ref=e318]:\n - generic [ref=e320]:\n - heading \"Labels\" [level=3]\n - generic [ref=e322]:\n - link \"documentation Improvements or additions to documentation\" [ref=e323] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues?q=state%3Aopen%20label%3A%22documentation%22\n - generic [ref=e325]: documentation\n - generic [ref=e326]: Improvements or additions to documentation\n - link \"enhancement New feature or request\" [ref=e327] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/issues?q=state%3Aopen%20label%3A%22enhancement%22\n - generic [ref=e329]: enhancement\n - generic [ref=e330]: New feature or request\n - generic [ref=e332]:\n - generic [ref=e333]:\n - heading \"Projects\" [level=3]\n - generic [ref=e334]: No projects\n - generic [ref=e336]:\n - generic [ref=e337]:\n - heading \"Milestone\" [level=3]\n - generic [ref=e338]: No milestone\n - generic [ref=e340]:\n - generic [ref=e341]:\n - heading \"Relationships\" [level=3]\n - generic [ref=e342]: None yet\n - generic [ref=e343]:\n - generic [ref=e345]:\n - heading \"Development\" [level=3]\n - generic [ref=e346]:\n - generic [ref=e348]:\n - button \"Code with agent mode\" [ref=e350] [cursor=pointer]:\n - generic [ref=e351]:\n - generic:\n - img\n - generic [ref=e352]: Code with agent mode\n - button \"Select code repository\" [ref=e355] [cursor=pointer]:\n - img [ref=e356]\n - list [ref=e358]:\n - listitem [ref=e359]:\n - link \"[WIP] Implement U-fork fractal algorithm\" [ref=e360] [cursor=pointer]:\n - /url: https://github.com/konard/tsp/pull/68\n - generic:\n - img \"Draft\"\n - generic [ref=e362]:\n - generic [ref=e363]: \"[WIP] Implement U-fork fractal algorithm\"\n - generic [ref=e364]: konard/tsp\n - generic [ref=e365]:\n - generic [ref=e367]:\n - heading \"Participants\" [level=3]\n - link \"@konard\" [ref=e371] [cursor=pointer]:\n - /url: /konard\n - img \"@konard\" [ref=e372]\n - heading \"Issue actions\" [level=2] [ref=e373]\n - list\n - contentinfo [ref=e374]:\n - heading \"Footer\" [level=2] [ref=e375]\n - generic [ref=e376]:\n - generic [ref=e377]:\n - link \"GitHub Homepage\" [ref=e378] [cursor=pointer]:\n - /url: https://github.com\n - img [ref=e379]\n - generic [ref=e381]: © 2026 GitHub, Inc.\n - navigation \"Footer\" [ref=e382]:\n - heading \"Footer navigation\" [level=3] [ref=e383]\n - list \"Footer navigation\" [ref=e384]:\n - listitem [ref=e385]:\n - link \"Terms\" [ref=e386] [cursor=pointer]:\n - /url: https://docs.github.com/site-policy/github-terms/github-terms-of-service\n - listitem [ref=e387]:\n - link \"Privacy\" [ref=e388] [cursor=pointer]:\n - /url: https://docs.github.com/site-policy/privacy-policies/github-privacy-statement\n - listitem [ref=e389]:\n - link \"Security\" [ref=e390] [cursor=pointer]:\n - /url: https://github.com/security\n - listitem [ref=e391]:\n - link \"Status\" [ref=e392] [cursor=pointer]:\n - /url: https://www.githubstatus.com/\n - listitem [ref=e393]:\n - link \"Community\" [ref=e394] [cursor=pointer]:\n - /url: https://git.521000.bestmunity/\n - listitem [ref=e395]:\n - link \"Docs\" [ref=e396] [cursor=pointer]:\n - /url: https://docs.github.com/\n - listitem [ref=e397]:\n - link \"Contact\" [ref=e398] [cursor=pointer]:\n - /url: https://support.github.com?tags=dotcom-footer\n - listitem [ref=e399]:\n - button \"Manage cookies\" [ref=e401] [cursor=pointer]\n - listitem [ref=e402]:\n - button \"Do not share my personal information\" [ref=e404] [cursor=pointer]\n```"
}
]
}
[2026-02-07T22:21:18.412Z] [INFO] {
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"id": "msg_01Xi3rWNSmgDb5Z6XszavG11",
"type": "message",
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"content": [
{
"type": "text",
"text": "Let me take a screenshot of the issue to see the image pattern."
}
],
"stop_reason": null,
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"usage": {
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[2026-02-07T22:21:19.111Z] [INFO] {
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"type": "tool_use",
"id": "toolu_01GyY7WorLQL4FJepSu8SGEy",
"name": "mcp__playwright__browser_click",
"input": {
"element": "Image link to see U-fork pattern",
"ref": "e206"
}
}
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[2026-02-07T22:21:20.266Z] [INFO] {
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{
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{
"type": "text",
"text": "### Ran Playwright code\n```js\nawait page.getByRole('link', { name: 'Image' }).click();\n```\n### Open tabs\n- 0: (current) [Implement U-fork fractal algorithm · Issue #65 · konard/tsp](https://github.com/konard/tsp/issues/65)\n- 1: [546653818-2aba7441-3e6e-412c-8686-832419947f6d.png (2732×2048)](https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE)\n### Page\n- Page URL: https://github.com/konard/tsp/issues/65\n- Page Title: Implement U-fork fractal algorithm · Issue #65 · konard/tsp\n### Snapshot\n```yaml\n- <changed> link \"Image\" [active] [ref=e206] [cursor=pointer]:\n - /url: https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE\n - ref=e207 [unchanged]\n```"
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"text": "### Ran Playwright code\n```js\nawait page.getByRole('link', { name: 'Image' }).click();\n```\n### Open tabs\n- 0: (current) [Implement U-fork fractal algorithm · Issue #65 · konard/tsp](https://github.com/konard/tsp/issues/65)\n- 1: [546653818-2aba7441-3e6e-412c-8686-832419947f6d.png (2732×2048)](https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE)\n### Page\n- Page URL: https://github.com/konard/tsp/issues/65\n- Page Title: Implement U-fork fractal algorithm · Issue #65 · konard/tsp\n### Snapshot\n```yaml\n- <changed> link \"Image\" [active] [ref=e206] [cursor=pointer]:\n - /url: https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzA1MDMxNzQsIm5iZiI6MTc3MDUwMjg3NCwicGF0aCI6Ii8xNDMxOTA0LzU0NjY1MzgxOC0yYWJhNzQ0MS0zZTZlLTQxMmMtODY4Ni04MzI0MTk5NDdmNmQucG5nP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDIwNyUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAyMDdUMjIyMTE0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9NTg3YWFiZDM3ODFhMTYwNjE1MzIyZWM0MWFjNTYxZmM4MDRlMGM3MjJmMGNkNDc0MjU2N2Y3ZTBjNTI1MDM5NCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE\n - ref=e207 [unchanged]\n```"
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[2026-02-07T22:21:22.399Z] [INFO] {
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[2026-02-07T22:21:22.438Z] [INFO] {
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"text": "### Result\n- 0: [Implement U-fork fractal algorithm · Issue #65 · konard/tsp](https://github.com/konard/tsp/issues/65)\n- 1: (current) [546653818-2aba7441-3e6e-412c-8686-832419947f6d.png (2732×2048)](https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzA1MDMxNzQsIm5iZiI6MTc3MDUwMjg3NCwicGF0aCI6Ii8xNDMxOTA0LzU0NjY1MzgxOC0yYWJhNzQ0MS0zZTZlLTQxMmMtODY4Ni04MzI0MTk5NDdmNmQucG5nP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDIwNyUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAyMDdUMjIyMTE0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9NTg3YWFiZDM3ODFhMTYwNjE1MzIyZWM0MWFjNTYxZmM4MDRlMGM3MjJmMGNkNDc0MjU2N2Y3ZTBjNTI1MDM5NCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE)\n### Events\n- New console entries: .playwright-mcp/console-2026-02-07T22-21-19-238Z.log#L1-L4\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Failed to load resource: the server respon...ivate-user-images.githubusercontent.com/favicon.ico:0"
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"text": "### Result\n- 0: [Implement U-fork fractal algorithm · Issue #65 · konard/tsp](https://github.com/konard/tsp/issues/65)\n- 1: (current) [546653818-2aba7441-3e6e-412c-8686-832419947f6d.png (2732×2048)](https://private-user-images.githubusercontent.com/1431904/546653818-2aba7441-3e6e-412c-8686-832419947f6d.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE)\n### Events\n- New console entries: .playwright-mcp/console-2026-02-07T22-21-19-238Z.log#L1-L4\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Applying inline style violates the followi...vc3QifQ.TMuTPzP9UXj36chsamI6hW7eliou7DQzmwaQjWRP1kE:0\n- [ERROR] Failed to load resource: the server respon...ivate-user-images.githubusercontent.com/favicon.ico:0"
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[2026-02-07T22:21:25.156Z] [INFO] {
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"id": "toolu_01W2d5WEZ292hrkrNTy9UFva",
"name": "mcp__playwright__browser_take_screenshot",
"input": {
"type": "png",
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[2026-02-07T22:21:25.516Z] [INFO] {
"type": "user",
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{
"tool_use_id": "toolu_01W2d5WEZ292hrkrNTy9UFva",
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{
"type": "text",
"text": "### Result\n- [Screenshot of viewport](u-fork-pattern.png)\n### Ran Playwright code\n```js\n// Screenshot viewport and save it as u-fork-pattern.png\nawait page.screenshot({\npath: 'u-fork-pattern.png',\n scale: 'css',\n type: 'png'\n});\n```"
},
{
"type": "image",
"source": {
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