私はこの低次再帰関数をmatlabで取得しようとしていました. ステータスである初期確率があると仮定して、次の時間ステップでサイトのステータスの確率を計算したいと思います。
P= Probability
x= status(0,1)
Dij= probability to pick a site
P(Status of Site(i) being x at next time step)= Summation[P(Status of Site(i) being x at previous time step)*Dij]
そしてこれが私がやったことです！しかし、私のインデックスは常に行列の次元を超えています! これについて助けが必要です。
clear all;
clc;
%function [t,i]= CopyingInfluenceModel
%%Define constants
%% generate some random weights vectori.e. the transition matrix=C
% C=[0 (1,2) 0 (1,4) 0 0 0;
% (2,1) 0 (2,3) 0 0 0 0;
% 0 (3,2) 0 (3,4) 0 0 0;
% (1,4) 0 (4,3) 0 (4,5) 0 0;
% 0 0 0 (5,4) 0 (5,6) (5,7);
% 0 0 0 0 (6,5) 0 (6,7);
% 0 0 0 0 (7,5) (7,6) 0];
%copying probabilities=branch weights
onetwo=0.47;
twothree=0.47;
threefour=0.47;
onefour=0.47;
fourfive=0.023;
fivesix=0.47;
fiveseven=0.47;
sixseven=0.47;
selfweight1=0.06;
selfweight2=0.037;
% SourceNodes - a list of Nodes that are forced to be kept in one side of the cut.
% WeightedGraph - symetric matrix of edge weights. Wi,j is the edge
% connecting Nodes i,j use Wi,j=0 or Wi,j == inf to indicate unconnected Nodes
WeightedGraph=[0 onetwo 0 onefour 0 0 0;
onetwo 0 twothree 0 0 0 0;
0 twothree 0 threefour 0 0 0;
onefour 0 threefour 0 fourfive 0 0;
0 0 0 fourfive 0 fivesix fiveseven;
0 0 0 0 fivesix 0 sixseven;
0 0 0 0 fiveseven sixseven 0];
Dij=sparse(WeightedGraph);
% Initializing the variables
t=[];
i=[];
%assigining the initial conditions
t(1)=0;
p(1)= 0.003; %% initial probability of status
%set index no i to 1(initial condition for i=1)
i=1;
%repeating calculating new probabilities
%% If the probability is zero, terminate while loop
while p(i)>=0
%calculate at the next time step for given index no
t(i+1)= t(i);
%calculate the status_probability at given time t=(i+1)
[p(i+1)]=[p(i)]+sum([p(i)]*[Dij(i)]);
[NextStatus(i)]= [p(i+1)]
%index i increases by 1 to calculate next probability
i=i+1;
end
スタック トレースは次のとおりです。
%%??? Index exceeds matrix dimensions.
%%Error in ==> CopyingInfluenceModel at 54
%%[p(i+1)]=[p(i)]+sum([p(i)]*[Dij(i)]);