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LALSimulation 6.2.0.1-b246709
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GPRHyperParams Struct Reference

Detailed Description

Data used in a single GPR fit.

We follow sklearn closely. We use the kernel given in Eq.(S3) of arxiv:1809.09125 :

\[ K(x, x') = \sigma_k^2 exp(-1/2 \sum_i^D (x^{i} - x'^{i})^2/\sigma_i^2) \]

where D is the dimension of the model.

Note that Eq.(S3) also includes the WhiteKernel, which has a noise parameter, but we don't need that here since we only need to evaluate \( K_{x* x} \) when evaluating the fits (See the mean value in Eq.(S2) of same paper). The other term we need is alpha = \( K_{x x}^{-1} {\bf f}\), which involves the WhiteKernel, but is precomputed offline. alpha is a vector of size N, where N is the number of cases in the training data set.

Definition at line 60 of file LALSimNRHybSurUtilities.h.

Data Fields

REAL8 constant_value
 \( \sigma_k^2 \) in kernel. More...
 
REAL8 y_train_mean
 Mean value before GPR fit, usually zero. More...
 
gsl_vector * length_scale
 \( \sigma_i \) in kernel. More...
 
gsl_vector * alpha
 Precomputed \( K_{x x}^{-1} {\bf f}\). More...
 

Field Documentation

◆ constant_value

REAL8 GPRHyperParams::constant_value

\( \sigma_k^2 \) in kernel.

Definition at line 61 of file LALSimNRHybSurUtilities.h.

◆ y_train_mean

REAL8 GPRHyperParams::y_train_mean

Mean value before GPR fit, usually zero.

Definition at line 62 of file LALSimNRHybSurUtilities.h.

◆ length_scale

gsl_vector* GPRHyperParams::length_scale

\( \sigma_i \) in kernel.

Definition at line 63 of file LALSimNRHybSurUtilities.h.

◆ alpha

gsl_vector* GPRHyperParams::alpha

Precomputed \( K_{x x}^{-1} {\bf f}\).

Definition at line 64 of file LALSimNRHybSurUtilities.h.