eml-core 0.1.0

EML (exp-ln) universal function approximation — O(1) learned functions from data
Documentation 
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//! EML (exp-ln) universal function approximation.
//!
//! This crate provides the EML operator and learning machinery for
//! O(1) learned functions from data. Based on Odrzywolel 2026,
//! "All elementary functions from a single operator".
//!
//! # Core Idea
//!
//! The EML operator `eml(x, y) = exp(x) - ln(y)` is the continuous-
//! mathematics analog of the NAND gate: combined with the constant 1,
//! it can reconstruct all elementary functions.
//!
//! # Components
//!
//! - [`eml`] / [`eml_safe`] / [`softmax3`] — primitive operators
//! - [`EmlTree`] — depth-configurable evaluation tree
//! - [`EmlModel`] — multi-head model with training
//! - [`FeatureVector`] — trait for types that produce `&[f64]` inputs
//!
//! # Example
//!
//! ```
//! use eml_core::EmlModel;
//!
//! // Create a depth-4 model with 3 inputs and 1 output head
//! let mut model = EmlModel::new(4, 3, 1);
//!
//! // Record training data (y = x0 + x1 + x2)
//! for i in 0..100 {
//!     let x = [i as f64 / 100.0, i as f64 / 50.0, i as f64 / 200.0];
//!     let y = x[0] + x[1] + x[2];
//!     model.record(&x, &[Some(y)]);
//! }
//!
//! // Train
//! let _converged = model.train();
//!
//! // Predict
//! let prediction = model.predict_primary(&[0.5, 1.0, 0.25]);
//! assert!(prediction.is_finite());
//! ```

pub mod events;
pub mod features;
pub mod model;
pub mod operator;
pub mod tree;

#[cfg(feature = "experimental-attention")]
pub mod attention;
#[cfg(feature = "experimental-attention")]
pub mod baseline_attention;

// Re-export public API
pub use events::{EmlEvent, EmlEventLog};
pub use features::FeatureVector;
pub use model::EmlModel;
pub use operator::{eml, eml_safe, softmax3};
pub use tree::EmlTree;

#[cfg(feature = "experimental-attention")]
pub use attention::{
    run_benchmark, run_benchmark_with_trials, AttentionBenchmark, AttentionError,
    EndToEndTrainConfig, SafeTree, ScalingPoint, ToyEmlAttention, MAX_TOY_D_MODEL,
    MAX_TOY_SEQ_LEN,
};
#[cfg(feature = "experimental-attention")]
pub use baseline_attention::{
    compare_eml_vs_baseline, AttentionComparison, BaselineAttention,
};