# revrand.basis_functions.FastFoodGM¶

class revrand.basis_functions.FastFoodGM(nbases, Xdim, mean=Parameter(value=0.0, bounds=Bound(lower=None, upper=None), shape=()), lenscale=Parameter(value=1.0, bounds=Positive(upper=None), shape=()), regularizer=None, random_state=None)

A mixture component from a Gaussian spectral mixture kernel approximation.

This implements a GM basis component from “A la Carte - Learning Fast Kernels”. This essentially learns the form of a kernel function, and so has no explicit kernel representation!

To fully implement a Gaussian spectral mixture, concatenate as many of these objects as desired (one per mixture component). Also remember to initialize all of the bases with different means.

Parameters: nbases (int) – a scalar for how many (unique) random bases to create approximately, this actually will be to the nearest larger two power. Xdim (int) – the dimension (d) of the observations (or the dimension of the slices if using apply_ind). mean (Parameter, optional) – A scalar or vector of shape (1,) or (d,) Parameter to bound and initialise the component frequency means for optimization. This will always initialise (d,) means if a scalr bound is given, it is applied to all means. lenscale (Parameter, optional) – A scalar or vector of shape (1,) or (d,) Parameter to bound and initialise the length scales for optimization. This will always initialise ARD length scales, if a scalr bound is given, it is applied to all length scales. regularizer (None, Parameter, optional) – The (initial) value of the regularizer/prior variance to apply to the regression weights of this basis function. The Parameter object must have a scalar value. If it is not set, it will take on a default value of Parameter(gamma(1.), Positive()). random_state (None, int or RandomState, optional) – random seed
__init__(nbases, Xdim, mean=Parameter(value=0.0, bounds=Bound(lower=None, upper=None), shape=()), lenscale=Parameter(value=1.0, bounds=Positive(upper=None), shape=()), regularizer=None, random_state=None)

See this class’s docstring.

Methods

 __init__(nbases, Xdim[, mean, bounds, ...]) See this class’s docstring. get_dim(X) Get the output dimensionality of this basis. grad(X[, mean, lenscale]) Get the gradients of this basis w.r.t.the mean and length scales. params_values() Get a list of the Parameter values if they have a value. regularizer_diagonal(X[, regularizer]) Get the diagonal of the prior variance on the weights (regularizer). transform(X[, mean, lenscale]) Apply the spectral mixture component basis to X.

Attributes

 params Get this basis’ Parameter types. regularizer Get the Parameter value of this basis’ regularizer.