fill_mean_vector - Fills a mean vector for a Gaussian process


#include "gp/gp_gaussian_processes.h"

Example compile flags (system dependent):
   -L/home/kobus/misc/load/linux_x86_64_opteron -L/usr/lib/x86_64-linux-gnu
  -lKJB                               -lfftw3  -lgsl -lgslcblas -ljpeg  -lSVM -lstdc++                    -lpthread -lSLATEC -lg2c    -lacml -lacml_mv -lblas -lg2c      -lncursesw 

int fill_mean_vector
	Vector **mean,
	const Vector_vector *indices,
	int (*mean_func)(Vector **,const Vector *,int),
	int d


This routine creates a mean vector for a Gaussian process where the dimension of the indices is given by the dimension of the vectors of indices, and the dimension of the variables is d. Then, it fills *mean with subvectors that are the result of calling the given mean function for that index. For example, the subvector of *mean of elements 0,1,...,d is v, where v is the vector gotten in the call mean_func(&v, indices->elements[0], d). Naturally, all of the vectors of indices must have equal lengths. Also, mean_func must "return" d-vector (there are some predefined mean functions in the library). If the routine succeeds, mean will be of length indices->elements[0]->length * d. If the vector pointed to by mean is NULL, then a vector of the appropriate size is created. If it exists, but is the wrong size, then it is recreated. Otherwise, the storage is recycled.


If the routine fails (due to storage allocation or an error in the mean function), then ERROR is returned with and error message being set. Otherwise NO_ERROR is returned.




This software is not adequatedly tested. It is recomended that results are checked independantly where appropriate.


Ernesto Brau


Ernesto Brau


fill_covariance_matrix , sample_from_gaussian_process_prior , sample_from_gaussian_process_prior_i , sample_from_gaussian_process_predictive , sample_from_gaussian_process_predictive_i , get_gaussian_process_predictive_distribution , get_gaussian_process_predictive_distribution_i , get_gaussian_process_posterior_distribution , get_gaussian_process_posterior_distribution_i , compute_gaussian_process_likelihood , compute_gaussian_process_likelihood_i , compute_gaussian_process_marginal_likelihood , compute_gaussian_process_marginal_likelihood_i , compute_gaussian_process_marginal_log_likelihood , compute_gaussian_process_marginal_log_likelihood_i