# [−][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.

## 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
- 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

## Inputs

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.

## Modules

gaussian_process | Gaussian process |

kernel | Kernels |

prior | Prior |

## Traits

Input | Implemented by |