Friedrich : Gaussian Process Regression

This library implements Gaussian Process Regression, also known as Kriging, in Rust. Our goal is to provide a solid and well featured building block for other algorithms (such as Bayesian Optimization).

Gaussian processes have both the ability to extract a lot of information from their training data and to return a prediction and an uncertainty value on their prediction. Furthermore, they can handle non-linear phenomena, take uncertainty on the inputs into account, and encode a prior on the output.

All of those properties make it an algorithm of choice to perform regression when data is scarce or when having uncertainty bars on the ouput is a desirable property.

However, the O(n^3) complexity of the algorithm makes the classic implementations unsuitable for large datasets.

Functionalities

This implementation lets you:

• define a gaussian process with default parameters or using the builder pattern
• train it on multidimensional data
• fit the parameters (kernel, prior and noise) on the training data
• introduce an optional cholesky_epsilon to make the Cholesky decomposition infallible in case of badly conditioned problem
• add additional samples efficiently (O(n^2)) and refit the process
• predict the mean, variance and covariance matrix for given inputs
• sample the distribution at a given position
• save and load a trained model with serde

(See the todo file to get up-to-date informations on current developements.)

Code sample

use friedrich::gaussian_process::GaussianProcess;

// trains a gaussian process on a dataset of one-dimensional 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 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());

// 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::thread_rng();
println!("samples: {:?}", sampler.sample(&mut rng));

Inputs

Most methods of this library can currently work with the following input -> ouput pairs :

• Vec<f64> -> f64 a single, multidimensional, sample
• Vec<Vec<f64>> -> Vec<f64> each inner vector is a training sample
• DMatrix<f64> -> DVector<f64> using a nalgebra matrix with one row per sample
• ArrayBase<f64, Ix1> -> f64 a single sample stored in a ndarray array (using the friedrich_ndarray feature)
• ArrayBase<f64, Ix2> -> Array1<f64> each row is a sample (using the friedrich_ndarray feature)

The Input trait is provided to add your own pairs.

Why call it Friedrich ?

Gaussian Process are named after the Gaussian distribution which is itself named after Carl Friedrich Gauss.