# rust-fuzzylogic
A Rust crate that provides building blocks for authoring fuzzy inference systems.
The project aims to make it easy to describe linguistic variables, membership functions, and rule
bases while keeping room for experimentation with multiple aggregation operators and inference
strategies.
This Project is still WIP and any form of feedback or contribution is very much appreciated!
## Project status
The repository currently focuses on laying down the module structure of the crate.
At this stage, it implements Type‑1 fuzzy sets and a Mamdani inference engine. The project also plans to add Takagi–Sugeno inference for Type‑1 sets.
Several source files exist as stubs and will be fleshed out in future iterations. Even though the public API is not ready for consumption yet, the layout already highlights the intended responsibilities of each
module:
- `membership`: utilities for defining membership functions (triangular, trapezoidal, gaussian, …).
- `variable`: strongly-typed linguistic variables composed of terms and membership functions.
- `term`: basic linguistic terms that bind membership functions to human-readable labels.
- `rulespace`: abstractions for authoring rule bases and connecting antecedents to consequents.
- `antecedent`: helpers to compose fuzzy predicates out of linguistic terms.
- `aggregate`: algorithms to combine the contribution of multiple rules.
- `defuzz`: defuzzification routines that convert fuzzy outputs into crisp values.
- `mamdani`: reference implementation of a Mamdani-style inference engine.
- `ops`: configurable T-norm/T-conorm operators (minimum, product, Łukasiewicz, …).
- `sampler`: sampling utilities for visualisation or numerical integration tasks.
- `system`: high-level ergonomics for constructing complete systems.
- `error`: error types returned by the crate.
- `prelude`: convenient re-exports for end users.
## Getting started
The library is published on crates.io: https://crates.io/crates/rust-fuzzylogic
You can import the library as a dependency in `Cargo.toml` from Git or crates.io:
crates.io:
```toml
[dependencies]
rust-fuzzylogic = "0.2.0"
```
Git (unreleased features):
```toml
[dependencies]
rust-fuzzylogic = { git = "https://github.com/joushuakento/rust-fuzzylogic" }
```
## Documentation
You can find the documentation of the latest release here:
https://docs.rs/rust-fuzzylogic/latest
Alternatively, If you are working from the unreleased version in the GitHub repository,
you can run `cargo doc` locally to generate documentation.
You can see the changelog here: https://github.com/JoshuaKento/rust-fuzzylogic/blob/main/CHANGELOG.md
## Development
If you would like to contribute, clone the repository and open it with your favourite editor:
```bash
git clone https://github.com/joushuakento/rust-fuzzylogic.git
cd rust-fuzzylogic
```
The crate targets Rust 1.74+ (edition 2021). Standard Cargo commands apply:
- `cargo check` — make sure the crate compiles.
- `cargo fmt` — format the codebase.
- `cargo test` — run the test suite.
### Feature flags
The crate is organised around a set of feature flags to enable or disable components at compile
time:
- `f32` / `f64` — choose the floating-point precision used throughout the inference engine.
- `serde` — derive serialisation support for configuration data structures.
- `parallel` — enable rayon-powered parallel execution for suitable workloads.
- `ops-minmax`, `ops-product`, `ops-lukasiewicz` — opt into specific operator families.
- `ops-dyn` — use dynamic dispatch for selecting operators at runtime.
## Roadmap
- [ ] Make some example scripts under examples/, benches/.
- [ ] Implement comprehensive tests.
- [x] Document the system APIs and provide a quick start guide.
- [x] Publish the crate and examples for real-world testing.
---
This README will evolve alongside the code. Contributions, bug reports, and ideas are warmly
welcome!