Gaussian processes¶

This module provides a collection of tools that are useful when performing Gaussian process regression.

Numpy¶

 abs_diff(x, xp) Gets matrix of differences between $$D$$-dimensional vectors x and xp i.e. exponential_squared(x, xp, sigmaf, l[, pspec]) Create exponential squared covariance function between $$D$$ dimensional vectors $$x$$ and $$x_p$$ i.e.
africanus.gps.abs_diff(x, xp)[source]

Gets matrix of differences between $$D$$-dimensional vectors x and xp i.e.

$X_{ij} = |x_i - x_j|$
Parameters: Array of inputs of shape (N, D). Array of inputs of shape (Np, D). Array of differences of shape (N, Np).
africanus.gps.exponential_squared(x, xp, sigmaf, l, pspec=False)[source]

Create exponential squared covariance function between $$D$$ dimensional vectors $$x$$ and $$x_p$$ i.e.

$k(x, x_p) = \sigma_f^2 \exp\left(-\frac{(x-x_p)^2}{2l^2}\right)$
Parameters: Array of shape (N, D). Array of shape (Np, D). sigmaf : float The signal variance hyper-parameter l : float The length scale hyper-parameter Array of shape (N, Np)