1.软件版本
matlab2013b。
2.本算法理论知识
从上面的介绍可以知道，论文的数据格式为如下：
| 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) |
...... |
即通过采集多个用户在不同时刻的位置，通过算法，来预测后面的位置（论文中为预测后面6个小时，这里，我们设置为参数可配置，预测任意时刻位置）
在论文一中，讲到：
同样道理，对于服务，也是相同类型的数据（这篇论文中没有涉及到服务）
即，我们所使用的模型的输入数据类型应该为如下的格式：
| 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) |
...... |
因此，要求中，你所需要的模型的输入，即类同如上格式的数据类型。
具体数据为：
3.核心代码
clc;
clear;
close all;
warning off;
pack;
addpath 'func\'
load locs_realitymining2.mat
N1 = 100;%为了防止出现连续状态不变得情况，这里N1设置大点
N2 = 6;
N = N1 + N2; %前N1个用于训练，后N2个用于预测
Times = 1000;
%通过多次循环，计算正确率
for Nu = 1:length(Locaiton_id3)
UNo = Nu;%用户标号
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
%状态转移概率%释放概率
%对应算法步骤中计算STATE的步骤，计算moving or steady？？
[seq,states] = func_cal_moving_steady(maps(1:N));%这里需要地址映射为自然数
[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
%通过vertiber算法计算概率
likelystates = hmmviterbi(seq,TRANS_EST,EMIS_EST);
Ps = length(find(likelystates==1))/N;
Pm = length(find(likelystates==2))/N;
%预测后面时刻的位置
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);
%在我们划定的，都存在用户行为的时间段内进行对比
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)%假设持续时间大于20为关联
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;%用户标号
for tim = 1:Times
UNo
tim
%计算状态数
Dat = Locaiton_id3{1,UNo}(1+tim:N+tim);
State = unique(Dat);
%计算状态概论
P = func_trans_prob(Dat,State,N);
%计算状态转移概论
[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
%开始预测
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
%结合CPB结果最后的概率
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.操作步骤与仿真结论
本算法，我们将结合CBP+MMUB+MarkovChain进行预测算法的构建。
从仿真结果可以看到，改进后的算法较其他几种算法均有一定的性能提升。
5.参考文献
A05-14
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