Package for Gaussian Process Optimization

This package provides optimization functionality for hyperparameters of covariance functions pygp.covar given.

Package for Gaussian Process Optimization

This package provides optimization functionality for hyperparameters of covariance functions pygp.covar given.

pygp.optimize.optimize_base.checkgrad(f, fprime, x, verbose=True, hyper_names=None, tolerance=0.1, *args, **kw_args)

Analytical gradient calculation using a 3-point method

pygp.optimize.optimize_base.opt_hyper(gpr, hyperparams, Ifilter=None, maxiter=1000, gradcheck=False, bounds=None, callback=None, gradient_tolerance=1e-08, messages=True, *args, **kw_args)

Optimize hyperparemters of pygp.gp.basic_gp.GP gpr starting from given hyperparameters hyperparams.

Parameters:

gpr : pygp.gp.basic_gp

GP regression class

hyperparams : {‘covar’:logtheta, ...}

Dictionary filled with starting hyperparameters for optimization. logtheta are the CF hyperparameters.

Ifilter : [boolean]

Index vector, indicating which hyperparameters shall be optimized. For instance:

logtheta = [1,2,3]
Ifilter = [0,1,0]

means that only the second entry (which equals 2 in this example) of logtheta will be optimized and the others remain untouched.

bounds : [[min,max]]

Array with min and max value that can be attained for any hyperparameter

maxiter: int

maximum number of function evaluations

gradcheck: boolean

check gradients comparing the analytical gradients to their approximations

** argument passed onto LML**

priors : [pygp.priors]

non-default prior, otherwise assume first index amplitude, last noise, rest:lengthscales

pygp.optimize.optimize_base.param_dict_to_list(di, skeys=None)

convert from param dictionary to list

pygp.optimize.optimize_base.param_list_to_dict(li, param_struct, skeys)

convert from param dictionary to list param_struct: structure of parameter array