mdp_computePR(P,R)

function PR = mdp_computePR(P,R)


% mdp_computePR  Computes the reward for the system in one state 
%                chosing an action
% Arguments --------------------------------------------------------------
% Let S = number of states, A = number of actions
%   P(SxSxA)  = transition matrix 
%              P could be an array with 3 dimensions or 
%              a cell array (1xA), each cell containing a matrix (SxS) possibly sparse
%   R(SxSxA) or (SxA) = reward matrix
%              R could be an array with 3 dimensions (SxSxA) or 
%              a cell array (1xA), each cell containing a sparse matrix (SxS) or
%              a 2D array(SxA) possibly sparse  
% Evaluation -------------------------------------------------------------
%   PR(SxA)   = reward matrix

% MDPtoolbox: Markov Decision Processes Toolbox
% Copyright (C) 2009  INRA
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%      may be used to endorse or promote products derived from this software 
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if ndims(R)==2 & ~iscell(R)  % R has form R(SxA) 
    PR = R;
else % R has form R(SxSxA)
    PR = [];
    if iscell(P)
        A = length(P);
        if iscell(R)
            for a=1:A; PR(:,a) = sum(P{a}.*R{a},2); end;
        else
            for a=1:A; PR(:,a) = sum(P{a}.*R(:,:,a),2); end;
        end
    else
        A = size(P,3);
        if iscell(R)
            for a=1:A; PR(:,a) = sum(P(:,:,a).*R{a},2); end;
        else
            for a=1:A; PR(:,a) = sum(P(:,:,a).*R(:,:,a),2); end;
        end
    end
end;