diff options
Diffstat (limited to 'inst/core/stk_predict.m')
| -rw-r--r-- | inst/core/stk_predict.m | 62 |
1 files changed, 45 insertions, 17 deletions
diff --git a/inst/core/stk_predict.m b/inst/core/stk_predict.m index e91aff7..abda5ae 100644 --- a/inst/core/stk_predict.m +++ b/inst/core/stk_predict.m @@ -1,11 +1,20 @@ -% STK_PREDICT performs a kriging prediction from data +% STK_PREDICT performs a kriging prediction % +% CALL: ZP = stk_predict (MODEL, XP) % CALL: ZP = stk_predict (MODEL, XI, ZI, XP) % -% performs a kriging prediction at the points XP, given the observations -% (XI, ZI) and the prior MODEL. The input arguments XI, ZI, and XP can be -% either numerical matrices or dataframes. More precisely, on a factor space -% of dimension DIM, +% performs a kriging prediction at the points XP, given the MODEL and, +% if available, the data (XI, ZI). +% +% The MODEL argument can be either a prior model structure (as provided +% by stk_model) or a model object (for instance, a posterior model +% represented by an stk_model_gpposterior object). If MODEL is already +% a posterior object and some additional data (XI, ZI) is provided, the +% model is first updated with the data before the prediction is actually +% carried out. +% +% The input arguments XI, ZI, and XP can be either numerical matrices or +% dataframes. More precisely, on an input space of dimension DIM, % % * XI must have size NI x DIM, % * ZI must have size NI x 1, @@ -14,8 +23,8 @@ % where NI is the number of observations and NP the number of prediction % points. The output ZP is a dataframe of size NP x 2, with: % -% * the kriging predictor in the first column (ZP.mean), and -% * the kriging variance in the second column (ZP.var). +% * the prediction mean in the first column (ZP.mean), and +% * the prediction variance in the second column (ZP.var). % % From a Bayesian point of view, ZP.mean and ZP.var are respectively the % posterior mean and variance of the Gaussian process prior MODEL given the @@ -23,12 +32,12 @@ % (posterior) variance of the latent Gaussian process, not the variance of a % future noisy observation at location XP. % -% CALL: [ZP, LAMBDA, MU] = stk_predict (MODEL, XI, ZI, XP) +% CALL: [ZP, LAMBDA, MU] = stk_predict (MODEL, ...) % % also returns the matrix of kriging weights LAMBDA and the matrix of % Lagrange multipliers MU. % -% CALL: [ZP, LAMBDA, MU, K] = stk_predict (MODEL, XI, ZI, XP) +% CALL: [ZP, LAMBDA, MU, K] = stk_predict (MODEL, ...) % % also returns the posterior covariance matrix K at the locations XP (this is % an NP x NP covariance matrix). From a frequentist point of view, K can be @@ -42,7 +51,7 @@ % Copyright Notice % -% Copyright (C) 2015, 2016, 2018 CentraleSupelec +% Copyright (C) 2015, 2016, 2018, 2020 CentraleSupelec % Copyright (C) 2011-2014 SUPELEC % % Authors: Julien Bect <julien.bect@centralesupelec.fr> @@ -53,7 +62,7 @@ % This file is part of % % STK: a Small (Matlab/Octave) Toolbox for Kriging -% (http://sourceforge.net/projects/kriging) +% (https://github.com/stk-kriging/stk/) % % STK is free software: you can redistribute it and/or modify it under % the terms of the GNU General Public License as published by the Free @@ -68,12 +77,31 @@ % You should have received a copy of the GNU General Public License % along with STK. If not, see <http://www.gnu.org/licenses/>. -function varargout = stk_predict (M_prior, x_obs, z_obs, x_prd) +function varargout = stk_predict (model, varargin) -M_post = stk_model_gpposterior (M_prior, x_obs, z_obs); +% Support for several syntaxes +switch nargin + + case {0, 1} + stk_error ('Not enough input arguments.', 'NotEnoughInputArgs'); + + case 2 % CALL: [...] = stk_predict (MODEL, X_PRD) + x_prd = varargin{1}; + + case 3 % CALL: [...] = stk_predict (MODEL, DATA, X_PRD) + stk_error ('This syntax is not implemented yet.', 'NotImplemented'); + model = stk_model_gpposterior (model, varargin{1}); + x_prd = varargin{2}; + + case 4 % CALL: [...] = stk_predict (MODEL, X_OBS, Z_OBS, X_PRD) + model = stk_model_gpposterior (model, varargin{1}, varargin{2}); + x_prd = varargin{3}; + + +end % switch varargout = cell (1, max (1, nargout)); -[varargout{:}] = stk_predict (M_post, x_prd); +[varargout{:}] = stk_predict_ (model, x_prd); end % function @@ -93,13 +121,13 @@ end % function %! z_obs = sin (double (x_obs)); %! x_prd = x0(idx_prd); %! -%! model = stk_model ('stk_materncov32_iso'); +%! model = stk_model (@stk_materncov32_iso); %! model.param = log ([1.0; 2.1]); %!error y_prd1 = stk_predict (); %!error y_prd1 = stk_predict (model); -%!error y_prd1 = stk_predict (model, x_obs); -%!error y_prd1 = stk_predict (model, x_obs, z_obs); +%!test y_prd1 = stk_predict (model, x_prd); +%!error y_prd1 = stk_predict (model, data, x_prd); %!test y_prd1 = stk_predict (model, x_obs, z_obs, x_prd); %!error y_prd1 = stk_predict (model, [x_obs; x_obs], [z_obs; z_obs], x_prd); |