usage.md

# Usage

In this visualizer you can visualize gaussian processes with different kernels on different datasets.

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### Dataset 
Generate or upload a dataset to train on. If generating, enter a function and the visualizer will sample points uniformly in the domain based on the settings.

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### Model
Here you can specify the gaussian process kernel, and whether to use the prior or posterior. For specifying kernels, you must use the sci-kit Learn API - see <https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes>

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### Kernel examples
* RBF()
* RBF(length_scale=1, length_scale_bounds="fixed")
* RBF(length_scale=100, length_scale_bounds="fixed") + WhiteKernel()
* ConstantKernel()*DotProduct(sigma_0=0, sigma_0_bounds="fixed") + WhiteKernel()