function [P,C,cpu_time]=gen_transition_matrix(file_name,VMAX,m,n,p,penalty,sp)global VMAX Map_Data Finish_Data Pos_Vector_Indexes Speed_Vector_Indexes% gn_sp_transition_matrix: this script generates a transition matrix %                          from the file_name file, stores it%                          in sparse format and writes it to the disk%                          in the form of a sparse transition matrix for each%                          action.% Arguments --------------------------------------------------------------%                          file_name = the location of the data file.%                          VMAX = the maximum allowed speed, in norm.%                          m,n = the dimensions of the data matrix%                          p = an action's probability of non-transmission%                          penalty = the penalty associated to an accident.%                          sp = boolean indicating whether the process will be run using%                               sparse matrices.% Evaluation -------------------------------------------------------------%                          cpu_time = the time needed to complete the program.%--------------------------------------------------------------------------% In verbose mode, there is no difference to silent mode.%--------------------------------------------------------------------------% MDP Toolbox, INRA, BIA Toulouse, France%--------------------------------------------------------------------------cpu_time = cputime;% Reading the data from the fileMap_Data=read_data(file_name,m,n);% Computing the indexes for position and speed[Pos_Vector_Indexes,Speed_Vector_Indexes]=index_computation;[Start_Indexes_Y Start_Indexes_X]=find(Map_Data==2);% Computing the start-line data (boundaries, center, coefficients; see% generate_starting_state.m for detailsy1=Start_Indexes_Y(1);x1=Start_Indexes_X(1);y2=Start_Indexes_Y(size(Start_Indexes_Y,1));x2=Start_Indexes_X(size(Start_Indexes_Y,1));yc=Start_Indexes_Y(floor(size(Start_Indexes_Y,1)/2));xc=Start_Indexes_X(floor(size(Start_Indexes_Y,1)/2));a=(y1-y2)/(x1-x2);b=y1-a*x1;Finish_Data=[y1 x1;y2 x2;yc xc;VMAX*VMAX 0;a b];% initialising the iteration countercurrent=0;% for each (s,t) action, the transition and cost matrices re computed in sparse format % (X,Y and Value vectors), and incorporated into the 3D matrix. if ~sp    P=[];    C=[];else    P=cell(9,1);    C=cell(9,1);endst_len=size(Pos_Vector_Indexes,1)*size(Speed_Vector_Indexes,1);for s=-1:1,    for t=-1:1,               [Y X V Cost]=transition_matrix(s,t,p);        Prob=sparse(Y,X,V,st_len+2,st_len+2);        CC=sparse(Y,X,-Cost,st_len+2,st_len+2);        courant=courant+1        if ~sp            P{courant}=Prob;            C{courant}=CC;           else            P(:,:,courant)=full(Prob);            C(:,:,courant)=full(CC);        end        endendcpu_time = cputime - cpu_time;