…efs #74)

Implement three advanced synthetic data generation components:

- MixUp generator: Zhang et al. 2018 embedding interpolation with Beta
  distribution sampling and configurable alpha parameter (24 tests)
- WeakSupervision generator: Snorkel-style programmatic labeling with
  LabelingFunction trait, multiple aggregation strategies (MajorityVote,
  WeightedVote, Unanimous, Any), and built-in LFs (29 tests)
- SyntheticCache: LRU eviction memoization for avoiding redundant
  generation during AutoML hyperparameter search (18 tests)

Total: 71 new tests, 2030 tests passing

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Add comprehensive model bundling and memory paging support:

## Model Bundling (.apbundle format)
- Binary format with magic bytes, version, and manifest
- BundleReader/BundleWriter for efficient file I/O
- ModelBundle API for creating, saving, and loading bundles
- Builder pattern for flexible bundle construction
- Support for multiple models with metadata

## Memory-Mapped File Support
- MappedRegion for efficient memory access
- MemoryMappedFile with region caching
- PageTable for LRU/LFU tracking

## LRU Paging
- PagedBundle for memory-constrained environments
- Configurable max_memory and eviction strategies
- LRU (Least Recently Used) and LFU (Least Frequently Used) eviction
- Automatic page eviction when memory limit exceeded

## Pre-fetching
- Access pattern tracking for predictive loading
- Configurable prefetch_count
- Hint API for explicit prefetch requests

## Also included:
- Synthetic data integration tests (15 tests)
- Synthetic data generation example
- Updated spec status to "Implemented (Phases 1-4)"

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…74)

Update spec status to reflect complete implementation.

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- Add PagedMarkovModel using aprender's bundle module for memory-efficient storage
- Implement LRU-based on-demand segment loading
- Add --memory-limit CLI flag to train, suggest, and stats commands
- Add 13 comprehensive tests for paged model functionality
- Fix doctest in synthetic/mixup.rs (missing Clone derive)

The paged model stores n-gram segments separately and loads them
on-demand, enabling handling of shell histories that exceed RAM.

Refs #74

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- Add comprehensive case study for bundle module
- Update shell-completion chapter with paging documentation
- Add bundle_trace_demo example for renacer tracing
- Update SUMMARY.md with new chapter

Refs #74

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Add comprehensive guide for using renacer syscall tracer to profile
and optimize memory paging behavior in ML model loading.

Content includes:
- Renacer usage patterns (-e trace=file, -T, -c, -s flags)
- Syscall analysis for detecting evictions and cache misses
- Pre-fetch effectiveness measurement
- JSON output for programmatic analysis
- Optimization patterns (reduce seeks, right-size memory, pre-fetching)
- Troubleshooting guide with symptom/fix table

Also adds book chapters for bundle_trace_demo and synthetic_data_generation
examples to satisfy EXTREME TDD requirements.

Allows clippy::large_stack_arrays lint for ML test data arrays.

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…77)

Implements two new synthetic data components for code analysis:

CodeEDA (GH-76):
- Code-specific EDA (Easy Data Augmentation) implementing SyntheticGenerator
- Variable renaming with synonym dictionary
- Comment insertion (Rust/Python/Generic modes)
- Statement reordering for independent statements
- Dead code removal (comments and whitespace)
- Quality scoring via token overlap
- 23 unit tests

CodeFeatureExtractor (GH-77):
- 8-dimensional commit feature extraction for defect prediction
- CommitFeatures: defect_category, files_changed, lines_added/deleted,
  complexity_delta, timestamp, hour_of_day, day_of_week
- Keyword-based commit classification (bug/security/perf/refactor)
- Batch extraction and normalization support
- 22 unit tests

References:
- Wei & Zou (2019) EDA paper
- D'Ambros et al. (2012) defect prediction benchmark

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…76, Refs #77)

- Add --use-code-eda flag to Augment command for code-aware augmentation
- Add new Analyze command using CodeFeatureExtractor
  - Shows command categories (bug/security/performance/refactor/general)
  - Displays top base commands with visual bar charts
  - Shows sample commands by category
  - Reports complexity metrics (avg tokens, max tokens, unique bases)
  - Identifies developer workflow (git, cargo, npm, docker usage)
- Add 3 integration tests for new features

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…Refs #74)

Benchmarks (modeled after bashrs patterns):
- parse_history: History file parsing throughput
- train_model: N-gram model training (small/medium/large fixtures)
- suggest_latency: Suggestion performance for common prefixes
- partial_completion: Partial token completion benchmarks
- serialization: JSON and file save/load benchmarks
- end_to_end: Complete workflow benchmarks
- synthetic_generation: CodeEDA augmentation benchmarks

Fixtures (aligned with bashrs):
- small_history.txt: ~50 commands (basic developer workflow)
- medium_history.txt: ~265 commands (full developer workflow)
- large_history.txt: ~3800 commands (production scale)

Real-world tests (19 new tests):
- REAL_001-003: Small/Medium/Large history training and suggestions
- REAL_004: Cross-validation testing
- REAL_005: Data augmentation with CodeEDA
- REAL_006: Analysis command testing
- REAL_007: Export/import roundtrip
- REAL_008: Paged model for large histories
- REAL_009: Incremental updates
- REAL_010: End-to-end user workflow

Architecture changes:
- Added lib.rs to expose modules for benchmarks
- Refactored main.rs to use library imports

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…rks (Refs #74)

Sub-10ms Verification Benchmark Suite:

Performance Results (vs 10ms target):
- Small model (50 cmds):  437ns - 1.5µs (6,500-22,000x faster)
- Medium model (500 cmds): 530ns - 10.6µs (940-18,800x faster)
- Large model (5000 cmds): 670ns - 15µs (660-14,900x faster)

Benchmark Groups:
- suggestion_latency: Core latency verification by model size
- partial_completion: Mid-word completion (git co → git commit)
- training_throughput: Commands/second during training
- cold_start: Model load + first suggestion latency
- serialization: JSON serialize/deserialize performance
- scalability: Latency growth with model size (O(1) verified)
- paged_model: Memory-constrained model performance

Industry Comparison:
- GitHub Copilot: 100-500ms → aprender 10,000-50,000x faster
- Fish completion: 5-20ms → aprender 500-2,000x faster
- Zsh compinit: 10-50ms → aprender 1,000-5,000x faster

Run: cargo bench --package aprender-shell --bench recommendation_latency

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#74)

Updated shell-completion.md:
- Added "Performance: Sub-10ms Verification" section
- Detailed benchmark results table (437ns - 14.6µs latency)
- Industry comparison (600-22,000x faster than alternatives)
- "Why So Fast?" explanation (O(1) trie, no neural overhead)
- Benchmark suite overview

New chapter: shell-completion-benchmarks.md
- Comprehensive benchmark analysis
- trueno-style criterion patterns
- Scalability analysis (sub-linear O(log n))
- Training throughput metrics
- Cold start verification (<3ms)
- Fixture design documentation
- Custom benchmark extension guide
- CI integration example

Key results documented:
- Worst case: 14.6 µs (685x under 10ms target)
- Best case: 437 ns (22,883x under 10ms target)
- Scales sub-linearly with model size

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Add dedicated book chapters for the new code-aware synthetic data modules:

- CodeEDA: Syntax-aware data augmentation for source code
  - Variable renaming, comment insertion, statement reorder
  - Language-specific reserved keyword handling (Rust, Python)
  - Quality and diversity metrics

- CodeFeatureExtractor: 8-dimensional commit feature extraction
  - Defect category classification (bug, security, perf, refactor)
  - Complexity estimation, time-based features
  - Normalization for ML pipelines

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Change alimentar from local path dependency to crates.io v0.1.0
for publishing compatibility.

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- Change aprender dependency from path to crates.io v0.10.0
- Add README.md for crate documentation

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## Metaheuristics (Refs #80)
- Add src/metaheuristics/ module with Differential Evolution (DE)
- SearchSpace enum for continuous/discrete/mixed optimization
- ComputeBudget for resource-aware optimization
- PerturbativeMetaheuristic trait following Toyota Way principles
- Book documentation for DE and metaheuristics fundamentals

## aprender-shell Enhancements (Refs #87, #88, #96)
- Fish shell widget support (fish-widget command)
- Uninstall command for clean widget removal
- ZSH widget v2 with toggle, timeout, ShellCheck fixes
- New CLI integration tests

## AutoML Enhancements
- Expanded search.rs with advanced hyperparameter optimization
- Grid search, random search, and TPE improvements
- Fixed clippy warnings (range contains, format strings)

## Documentation
- aprender-shell-harden-plan.md spec (16 issues, Toyota Way, 10 refs)
- metaheuristics-spec.md with CEC benchmarks
- Updated roadmap.yaml

## Quality
- 382 tests passing
- 92.66% coverage
- Clippy clean (-D warnings)
- PMAT: A+ (151/134), TDG: A+ (99/100)

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… unsafe)

POLICY: We will NEVER use unsafe code. If HE crypto primitives are needed,
we will implement them from scratch in safe Rust.

Additions:
- docs/specifications/homomorphic-encryption-spec.md (10 peer-reviewed citations)
- book/src/examples/shell-encryption-tiers.md (4-tier protection guide)
- src/format/homomorphic.rs (28 tests: types, traits, API design)
- Shell Tier 2 compression: save_compressed() (5 tests)
- Shell Tier 2+3 combo: save_compressed_encrypted()

4-Tier Model Protection:
- Tier 1: Plain (.apr)
- Tier 2: Compressed (zstd, 14x smaller)
- Tier 3: At-rest encrypted (AES-256-GCM)
- Tier 4: Homomorphic (API ready, crypto deferred)

Test counts:
- Core aprender: 2,292 tests (with format-homomorphic)
- aprender-shell: 127 tests (+5 compression)

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- Add src/ensemble/ module with MoE, SoftmaxGating, MoeConfig
- Add ModelType::MixtureOfExperts (0x0040) to format
- Add examples/mixture_of_experts.rs runnable example
- Add book/src/examples/mixture-of-experts.md documentation
- Update model-format.md with MoE section and model type
- Fix Makefile coverage (move config before clean for sccache)
- Add docs/specifications/more-learning-specs.md (34 sections)
  - GAN, VAE, Diffusion, Contrastive, GNN, Meta-learning
  - Transfer learning for transpiler ecosystem
  - Distillation ingestion from entrenar
  - Code-specific ML for depyler oracle

Refs #101

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100 test cases covering:
- Installation (5)
- train command (17)
- update command (8)
- suggest command (14)
- stats command (6)
- export/import (10)
- validate command (10)
- augment command (8)
- analyze command (6)
- tune command (6)
- zsh-widget (4)
- Edge cases (6)
- Performance benchmarks (5)
- Platform compatibility (5)

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New features:
- Mixture of Experts (MoE) ensemble module
- ModelType::MixtureOfExperts (0x0040)
- Future ML specs (34 sections)

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- Update aprender dependency from path to crates.io v0.11
- Ready for v0.2.0 release

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- Fix trivial cast lint error in mmap.rs:611 that broke CI
- Update hero image: 17 → 18 model types (MoE added)
- Update hero image version: v0.9 → v0.11

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- hf_hub/mod.rs: Replace unwrap() with expect() (disallowed-methods)
- hf_hub/mod.rs: Use char '.' instead of string "." (single_char_pattern)
- stopwords.rs: Remove redundant is_empty check (const_is_empty)
- format/mod.rs: Fix large file tests using Compression::None and unique values

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Without --all-features, feature-gated examples fail to compile,
causing coverage to show 0%.

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This flag keeps getting accidentally removed, causing 0% coverage.

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Prevents the recurring bug where coverage shows 0% because
--all-features gets accidentally removed from the coverage target.

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