ml_kit 1.0.0

A Machine Learning library for Rust
Documentation 
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use std::fs::File;

use image::imageops::FilterType::Nearest;
use matrix_kit::dynamic::matrix::Matrix;
use ml_kit::math::activation::AFI;
use ml_kit::math::svd::{self, svd};
use ml_kit::models::neuralnet::NeuralNet;
//use ml_kit::training::learning_rate::FixedLR;
// use ml_kit::training::learning_rate::AdaGrad;
// use ml_kit::{
//     math::loss::LFI, training::sgd::SGDTrainer, utility::mnist::mnist_utility::load_mnist,
// };

fn main() {
    let relative_path: &'static str = "../data_sets";

    // let dataset = load_mnist(relative_path, "train");
    // let testing_ds = load_mnist(relative_path, "t10k");
    // let trainer: SGDTrainer<ml_kit::utility::mnist::mnist_utility::MNISTImage> =
    //     SGDTrainer::new(dataset, testing_ds, LFI::Squared);

    // let mut neuralnet = NeuralNet::random_network(
    //     vec![784, 16, 16, 10],
    //     vec![AFI::Sigmoid, AFI::Sigmoid, AFI::Sigmoid],
    // );

    // //let mut grad_update_sched = FixedLR::new(0.05);
    // let mut grad_update_sched = AdaGrad::new(0.01, neuralnet.parameter_count());
    // let epochs = 100;

    // let original_cost = trainer.cost(&neuralnet);
    // println!("Original cost: {}", original_cost);

    // trainer.train_sgd(&mut neuralnet, &mut grad_update_sched, epochs, 32, true);

    // let final_cost = trainer.cost(&neuralnet);

    // println!("Final cost: {}", final_cost);

    // // Now, let's go through and actually try it out!

    // trainer.display_behavior(&neuralnet, 10);

    // println!("Writing final network to testing folder.");

    // match File::create("testing/files/digits.mlk_nn") {
    //     Ok(mut f) => neuralnet.write_to_file(&mut f),
    //     Err(e) => println!("Error writing to file: {:?}", e),
    // }

    let channels = ml_kit::images::util::read_rgba_matrices("testing/files/sheep.png");

    // let channels: Vec<Matrix<f64>> = (0..4).map(|_|
    //     Matrix::random_normal(3, 4, 0.0, 1.0)
    // ).collect();

    println!(
        "Image is [{} x {}]",
        channels[0].row_count(),
        channels[0].col_count()
    );

    let image_svd_rgba: Vec<(Matrix<f64>, Matrix<f64>, Matrix<f64>)> =
        channels.iter().map(|channel| svd(channel)).collect();

    for i in 0..4 {
        let (u, v, s) = image_svd_rgba[i].clone();

        // We are going to write this as a neural network, because we already have code that can store it!

        let network = NeuralNet::new(
            vec![v.transpose(), s.clone(), u.clone()],
            vec![
                Matrix::new(v.col_count(), 1),
                Matrix::new(s.row_count(), 1),
                Matrix::new(u.row_count(), 1),
            ],
            vec![AFI::Identity; 3],
        );

        // Write this down!
        let path = format!("testing/files/rgba_sheep_{}.mlk_nn", i);
        let mut file = match File::create(path) {
            Ok(f) => f,
            Err(e) => panic!("Error opening file: {:?}", e),
        };

        network.write_to_file(&mut file);
    }

    // Can we recover?
}