[][src]Crate friedrich

Friedrich : Gaussian Process Regression

This libarie implements Gaussian Process Regression in Rust. Our goal is to provide a building block for other algorithms (such as Bayesian Optimization).

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

All of those properties make them 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 classical implementation unsuitable for large training datasets.


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
  • add additional samples and refit the process
  • predict the mean and variance and covariance matrix for given inputs
  • sample the distribution at a given position


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

  • Vec<Vec<f64>> -> Vec<f64> each inner vector is a multidimentional training sample
  • Vec<f64> -> f64 a single multidimensional sample
  • DMatrix<f64> -> DVector<f64> using a nalgebra matrix with one row per sample

See the Input trait if you want to add you own input type.



Gaussian process







Implemented by Input -> Output type pairs