"Rust Model Inference Example"

gistfile1.txt

use ndarray::prelude::*;
use ndarray_rand::RandomExt;
use ndarray_rand::rand::distributions::Uniform;

fn sigmoid(x: &Array2<f64>) -> Array2<f64> {
    x.mapv(|v| 1.0 / (1.0 + (-v).exp()))
}

// Pretrained weights
struct Model {
    weights: Array2<f64>,
}

impl Model {
    fn predict(&self, input: &Array2<f64>) -> Array2<f64> {
        sigmoid(&input.dot(&self.weights))
    }
}

fn main() {
    // Example: 3 input features
    let x = array![
        [0.0, 0.0, 1.0],
        [0.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [1.0, 1.0, 1.0]
    ];

    let unseen_patterns = array![
    [0.0, 1.0, 0.0], // feature3 flipped
    [1.0, 1.0, 0.0],
    [1.0, 0.0, 0.0],
    [0.0, 0.0, 0.0]  // all zeros
];

let edge_cases = array![
    [1.0, 1.0, 1.0], // all ones
    [0.0, 0.0, 0.0], // all zeros
    [0.5, 0.5, 0.5], // intermediate values
    [1.0, 0.0, 1.0]  // training sample
];

let noisy_inputs = array![
    [0.1, 0.0, 1.0],
    [0.0, 0.9, 1.0],
    [0.95, 0.05, 1.0],
    [0.5, 0.5, 1.0]
];
    // Load pretrained weights (random for demo)
    let weights = Array::random((3,1), Uniform::new(-1.0, 1.0));
    let model = Model { weights };

    let output = model.predict(&x);
    let unseen_output = model.predict(&unseen_patterns);
    let edge_output = model.predict(&edge_cases);
    let noisy_output = model.predict(&noisy_inputs);
    

    println!("Inference output:\n{:?}", output);
    println!("Unseen patterns output:\n{:?}", unseen_output); 
    println!("Edge cases output:\n{:?}", edge_output);
    println!("Noisy inputs output:\n{:?}", noisy_output);

    let all_samples = vec![output, unseen_patterns, edge_cases, noisy_inputs];

for (i, batch) in all_samples.iter().enumerate() {
    let output = model.predict(batch);
    println!("Batch {} prediction:\n{:?}", i + 1, output);
}

}

Had an interesting observation explanation through claude :