[−][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 sampleVec<f64> -> f64a single multidimensional sampleDMatrix<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 |