friedrich 0.6.0

Gaussian Process Regression.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

#![allow(dead_code)]

mod algebra;
mod conversion;
mod gaussian_process;
mod parameters;

use chrono::Duration;

use crate::gaussian_process::GaussianProcess;

fn main()
{
    {
        // Trains a gaussian process on a dataset of one dimension vectors.
        let training_inputs = vec![vec![0.8], vec![1.2], vec![3.8], vec![4.2]];
        let training_outputs = vec![3.0, 4.0, -2.0, -2.0];
        let mut gp = GaussianProcess::default(training_inputs, training_outputs);

        // Predicts the mean and variance of a single point.
        let input = vec![1.];
        let mean = gp.predict(&input);
        let var = gp.predict_variance(&input);
        println!("prediction: {} ± {}", mean, var.sqrt());

        // Computes the likelihood of the model.
        let likelihood = gp.likelihood();
        println!("likelihood of the current model : {}", likelihood);

        // Updates the model.
        let additional_inputs = vec![vec![0.], vec![1.], vec![2.], vec![5.]];
        let additional_outputs = vec![2.0, 3.0, -1.0, -2.0];
        let fit_prior = true;
        let fit_kernel = true;
        let max_iter = 100;
        let convergence_fraction = 0.05;
        let max_time = Duration::seconds(3600);
        gp.add_samples(&additional_inputs, &additional_outputs);
        gp.fit_parameters(fit_prior, fit_kernel, max_iter, convergence_fraction, max_time);
        println!("model is now updated.");

        // Makes several prediction.
        let inputs = vec![vec![1.0], vec![2.0], vec![3.0]];
        let outputs = gp.predict(&inputs);
        println!("predictions: {:?}", outputs);

        // Samples from the distribution.
        let new_inputs = vec![vec![1.0], vec![2.0]];
        let sampler = gp.sample_at(&new_inputs);
        let mut rng = rand::rng();
        for i in 1..=5
        {
            println!("sample {} : {:?}", i, sampler.sample(&mut rng));
        }
    }

    {
        // Trains a gaussian process on a dataset.
        let training_inputs = vec![vec![0.8, 0.1], vec![1.2, 0.2], vec![3.8, 0.3], vec![4.2, 0.5]];
        let training_outputs = vec![3.0, 4.0, -2.0, -2.0];
        let gp = GaussianProcess::default(training_inputs, training_outputs);

        // Predicts the mean and variance of a single point.
        let input = vec![1., 0.4];
        let mean = gp.predict(&input);
        let var = gp.predict_variance(&input);
        println!("prediction: {} ± {}", mean, var.sqrt());
    }
}