1. Software version

matlab2013b.

2. Theoretical knowledge of this algorithm

As you can see from the introduction above , The data format of the paper is as follows ：

	Time1	Time2	Time3	Time4	Time5	Time6	......
USER1	Pos1(1)	Pos1(2)	Pos1(3)	Pos1(4)	Pos1(5)	Pos1(6)	......
USER2	Pos2(1)	Pos2(2)	Pos2(3)	Pos2(4)	Pos2(5)	Pos2(6)	......
USER3	Pos3(1)	Pos3(2)	Pos3(3)	Pos3(4)	Pos3(5)	Pos3(6)	......
......	......	......	......	......	......	......	......
USERn	Posn(1)	Posn (2)	Posn (3)	Posn (4)	Posn (5)	Posn (6)	......

        That is, by collecting the positions of multiple users at different times , By algorithm , To predict the position behind （ In this paper, we predict the following 6 Hours , here , We set the parameters to be configurable , Predict the position at any time ）

        In paper 1 , speak of ：

        In the same way , For services , The same type of data （ Services are not covered in this paper ）

namely , The input data type of the model we use should be in the following format ：

	Time1	Time2	Time3	Time4	......
USER1	Pos1,Ser1(1)	Pos1,Ser1 (2)	Pos1,Ser1 (3)	Pos1,Ser1 (4)	......
USER2	Pos2,Ser2 (1)	Pos2,Ser2 (2)	Pos2,Ser2 (3)	Pos2,Ser2 (4)	......
USER3	Pos3,Ser3 (1)	Pos3,Ser3 (2)	Pos3,Ser3 (3)	Pos3,Ser3 (4)	......
......	......	......	......	......	......
USERn	Posn,Sern (1)	Posn ,Sern (2)	Posn,Sern (3)	Posn,Sern (4)	......

therefore , In demand , The model input you need , That is, data types similar to the above format .

The specific data is ：

3. Core code

clc;
clear;
close all;
warning off;
pack;
addpath 'func\'
load locs_realitymining2.mat

N1    = 100;% In order to prevent the continuous state from changing , here N1 Set it up bigger 
N2    = 6;
N     = N1 + N2; % front N1 For training , after N2 One for predicting 
Times = 1000;
% Through multiple cycles , Calculate the accuracy 
for Nu = 1:length(Locaiton_id3)
    UNo   = Nu;% User label 
    for tim = 1:Times
        Dat   = Locaiton_id3{1,UNo}(1+tim:N+tim);
        State = unique(Dat);
        %Counting the User Behavior Patterns
        %Counting the User Behavior Patterns
        Alpha = [];
        maps  = [];
        MAP   = [];
        [Alpha,maps,MAP] = func_find_alpha_table(Dat,Locaiton_id3,State);
        %Modeling the User Behaviors,the user’s behavior model can be built based on the resulting counting tables
        % State transition probability % Release probability 
        % Calculate in the corresponding algorithm step STATE Steps for , Calculation moving or steady？？
        [seq,states]         = func_cal_moving_steady(maps(1:N));% Here you need to map the address to a natural number 
        [TRANS_EST,EMIS_EST] = hmmestimate(seq,states);
        [r,c] = size(TRANS_EST);
        for p1 = 1:r
            for p2 = 1:c
                if TRANS_EST(p1,p2) == 0
                   TRANS_EST(p1,p2) = eps;
                end
                if TRANS_EST(p1,p2) == 1
                   TRANS_EST(p1,p2) = 1-eps;
                end                
            end
        end
        
        % adopt vertiber The algorithm calculates the probability 
        likelystates = hmmviterbi(seq,TRANS_EST,EMIS_EST);
        Ps = length(find(likelystates==1))/N;
        Pm = length(find(likelystates==2))/N;
        % Predict the position of the next time 
        likelihood_next_node = zeros(length(State),length(State));
        for i = 1:length(State)
            for j = 1:length(State)
                if i == j
                   likelihood_next_node(i,j) = Ps*Alpha{i,j}(1); 
                else
                   likelihood_next_node(i,j) = Pm*Alpha{i,j}(1);
                end
            end
        end
        Plikelihood_next_node = zeros(length(State),N2);
        for k = 1:N2
            for i = 1:length(State)
                Plikelihood_next_node(i,k) = likelihood_next_node(maps(N1+k-1),i)/(sum(likelihood_next_node(:,i))+eps);
            end
            [V,I]     = max(Plikelihood_next_node(:,k));
            for j = 1:size(MAP,1)
                if I == MAP(j,2);
                   POS(k)  = MAP(j,1);
                end
            end   
        end
        NNN(:,tim) = (Dat(N1+1:N)==POS(1:N2)')';
    end
    for k = 1:N2
        Precision1m(k,Nu) = sum(NNN(k,:))/Times;
    end
end
save tmps2.mat Precision1m
clear all;
load locs_realitymining2.mat
N1    = 100;
N2    = 6;
N     = N1 + N2;
Times = 200;
for i = 1:length(Locaiton_id3)
    L(i) = length(Locaiton_id3{i});
end
Len = min(L);
% In what we delineated , Compare the time period when there is user behavior 
Therehold = 20;
Flag      = 0;
ASS       = cell(length(Locaiton_id3),length(Locaiton_id3));
for k1 = 1:length(Locaiton_id3)
    for k2 = k1+1:length(Locaiton_id3)
        Flag = 0;
        for i = 1:Len
            if Locaiton3{k1}(i) == Locaiton3{k2}(i)% Suppose the duration is greater than 20 For the connection 
               Flag = Flag + 1;
            else
               Flag = 0;
            end
            if Flag > Therehold
               ASS{k1,k2} = [ASS{k1,k2},i];
               Flag = 0;
            end
        end
    end       
end
for Nu = 1:length(Locaiton_id3)
    UNo   = Nu;% User label 
    for tim = 1:Times
        UNo
        tim
        % Calculate the number of States 
        Dat   = Locaiton_id3{1,UNo}(1+tim:N+tim);
        State = unique(Dat);
        % Introduction to calculation status 
        P     = func_trans_prob(Dat,State,N);
        % Introduction to computing state transition 
        [P_tra,Map] = func_transition_matrix(Dat,State); 
        for j = 1:size(Map,1)
            if Dat(N1) == Map(j,1);
               Initial = Map(j,2);
            end
        end
        % Start to predict 
        TT          = zeros(1,length(State));
        TT(Initial) = 1;
        PState{1}= TT;
        for i = 2:N2+1
            PState{i} = PState{i-1}*P_tra;
            [V,I]     = max(PState{i});
            for j = 1:size(Map,1)
                if I == Map(j,2);
                   Pos(i-1)  = Map(j,1);
                end
            end
        end
        NNN(:,tim) = (Dat(N1+1:N)==Pos(1:N2)')';
    end
    for k = 1:N2
        Precision1(k,Nu) = sum(NNN(k,:))/Times;
    end
end
save tmps1.mat Precision1

load tmps1.mat
load tmps2.mat

% combination CPB The final probability of the result 
Precision2 = Precision1;
for Nu1 = 1:length(Locaiton_id3)
    for Nu2 = 1:length(Locaiton_id3)
        for tim = 1:Times
            if isempty(ASS{Nu1,Nu2}) == 0
            for k = 1:length(ASS{Nu1,Nu2})
                if ASS{Nu1,Nu2}(k) == tim
                   for k2 = 1:N2
                       Precision2(k2,Nu1) = max(max(Precision1(k2,Nu1),Precision1(k2,Nu2)),max(Precision1m(k2,Nu1),Precision1m(k2,Nu2)));  
                   end
                end
            end
            end
        end
    end
end
save Result.mat Precision2 
figure;
load Result1.mat
Views1 = [mean(Precision1(:,:),2)];
Views2 = [mean(Precision2(:,:),2)];
Views  = [Views1,Views2];
bar(Views);
axis([0,N2+1,0.4,0.8]);
xlabel('Times');
ylabel('Precision');
legend('Markov','MMUB CPB Markov');
 

 4. Operation steps and simulation conclusion

 

 

 

 

       This algorithm , We will combine CBP+MMUB+MarkovChain Build the prediction algorithm .

  From the simulation results, we can see , Compared with other algorithms, the improved algorithm has a certain performance improvement .

5. reference

A05-14

6. How to obtain the complete source code

The way 1： Wechat or QQ Contact bloggers
The way 2： subscribe , Get the tutorial case code and any of this blog for free 2 Complete source code