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());