README

PHP-IR is a modern, research-oriented Information Retrieval (IR) and Vector Space Modeling library for PHP, focused on correctness, transparency, and theoretical grounding.

It provides low-level, composable primitives for text representation, weighting, similarity, clustering, and evaluation, designed for engineers who need full control and explainability, not opaque ML abstractions.

Why PHP-IR exists

The PHP ecosystem has historically lacked serious IR tooling beyond thin wrappers around search engines. PHP-IR fills that gap by offering:

• Explicit vector space modeling
• Reproducible term weighting pipelines
• Deterministic clustering algorithms
• Quantitative cluster quality metrics
• APIs aligned with Information Retrieval literature

The goal is not convenience-first APIs, but scientifically correct and inspectable IR workflows.

Core capabilities

Text processing

• Tokenization (regex, whitespace)
• Text normalization (lowercasing, accent folding, composition)
• Stop-word filtering with language support (English, Spanish)

Vocabulary & statistics

• Vocabulary construction
• Document frequency tracking
• IDF computation (per-term and vectorized)
• Corpus-level statistics via dedicated façades (no core pollution)

Vectorization

• Sparse and dense vector representations
• Term Frequency (TF)
• TF-IDF weighting
• Spherical (L2-normalized) vector spaces
• Explicit densification for algorithms that require fixed dimensions

Similarity

• Cosine similarity
• Pluggable similarity interfaces

Clustering

• Spherical K-Means
• Spherical K-Medians (robust to outliers)
• Deterministic centroid update strategies
• Explicit iteration control
• Centroid initialization and update policies

Cluster evaluation

• Intra-cluster cohesion
• Inter-cluster separation
• Global quality score aligned with IR theory
• Metrics designed for algorithm comparison, not just reporting

Design philosophy

PHP-IR is intentionally not:

• A search engine
• A machine learning framework
• A black-box clustering toolkit

Instead, it provides clear, inspectable building blocks that let you:

• Reason about every step of the IR pipeline
• Swap strategies without side effects
• Validate theoretical assumptions with executable code
• Compare algorithms using quantitative invariants

If you are familiar with TF-IDF, cosine similarity, and clustering theory, PHP-IR should feel predictable and rigorous.

Theoretical foundation

The library is grounded in classical and modern IR research, including:

• Introduction to Information Retrieval - Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze

• Spherical k-means clustering - I. S. Dhillon and D. S. Modha

• Spherical K - Medians - Rafael E. Espinosa Santiesteban

Current status

• Actively developed
• API stabilized through real-world usage
• Strong test coverage with invariant-based tests
• English and Spanish corpora used for validation
• Designed to evolve without breaking theoretical guarantees

Detailed documentation, examples, and usage guides will be added incrementally.

Roadmap (high level)

• Advanced convergence criteria beyond fixed iteration limits
• Additional robustness heuristics for clustering
• Optional serialization of evaluation artifacts
• Extended language tooling and corpora support

License

MIT License.
Use it, extend it, and build on it responsibly.