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. |
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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: x :
numpy.ndarray
Array of inputs of shape
(N, D)
.xp :
numpy.ndarray
Array of inputs of shape
(Np, D)
.Returns: XX :
numpy.ndarray
Array of differences of shape
(N, Np)
.
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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: x :
numpy.ndarray
Array of shape
(N, D)
.xp :
numpy.ndarray
Array of shape
(Np, D)
.sigmaf : float
The signal variance hyper-parameter
l : float
The length scale hyper-parameter
Returns: K :
numpy.ndarray
Array of shape
(N, Np)