Module friedrich::gaussian_process

Gaussian process

// 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());

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

// 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 fit_noise = true;
let max_iter = 100;
let convergence_fraction = 0.01;
gp.add_samples(&additional_inputs, &additional_outputs);
gp.fit_parameters(fit_prior, fit_kernel, fit_noise, max_iter, convergence_fraction);

// 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();
for i in 1..=5
{
  println!("sample {} : {:?}", i, sampler.sample(&mut rng));
}

Structs

GaussianProcess	A Gaussian process that can be used to make predictions based on its training data
GaussianProcessBuilder	Builder to set the parameters of a gaussian process.
MultivariateNormal	Multivariate Normal Distribution