% Mean functions to be use by Gaussian process functions. There are two
% different kinds of mean functions: simple and composite:
%
% Simple mean functions:
%
% meanZero - zero mean function
% meanOne - one mean function
% meanConst - constant mean function
% meanLinear - linear mean function
% meanPoly - polynomial mean function
% meanDiscrete - precomputed mean for discrete data
% meanGP - predictive mean of another GP
% meanGPexact - predictive mean of a regression GP
% meanNN - nearest neighbor mean function
% meanWSPC - weighted sum of projected cosines
%
% Composite mean functions (see explanation at the bottom):
%
% meanScale - scaled version of a mean function
% meanSum - sum of mean functions
% meanProd - product of mean functions
% meanPow - power of a mean function
% meanMask - mask some dimensions of the data
% meanPref - difference mean for preference learning
% meanWarp - warped mean function
%
% Naming convention: all mean functions are named "mean/mean*.m".
%
%
% 1) With no or only a single input argument:
%
% s = meanNAME or s = meanNAME(hyp)
%
% The mean function returns a string s telling how many hyperparameters hyp it
% expects, using the convention that "D" is the dimension of the input space.
% For example, calling "meanLinear" returns the string 'D'.
%
% 2) With two input arguments and one output argument:
%
% m = meanNAME(hyp, x)
%
% The function computes and returns the mean vector m with components
% m(i) = m(x(i,:)) where hyp are the hyperparameters and x is an n by D matrix
% of data, where D is the dimension of the input space. The returned mean
% vector m is of size n by 1.
%
% 3) With two input arguments and two output arguments:
%
% [m,dm] = meanNAME(hyp, x)
%
% The function computes and returns the mean vector m as in 2) above.
% In addition to that, the (linear) directional derivative function dm is
% returned. The call dhyp = dm(q) for a direction vector q of size n by 1
% returns a vector of directional derivatives dhyp = d (q'*m(x)) / d hyp of
% the same size as the hyperparameter vector hyp. The components of dhyp are
% defined as follows: dhyp(i) = q'*( d m(x) / d hyp(i) ).
%
% See also doc/usageMean.m.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2018-06-15.
% File automatically generated using noweb.