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TestModelPredict.m
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TestModelPredict.m
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function [ TestResult ] = TestModelPredict(TrnValModelInfo,testDataSet)
%TESTMODELPREDICT Summary of this function goes here
% Detailed explanation goes here
M=length(testDataSet);
ValdInfo=TrnValModelInfo.ValdInfo;
nTrajectoryXY=testDataSet{1};
for i=2:M
TrajectoryXY=testDataSet{i};
nTrajectoryXY{1}=[nTrajectoryXY{1},TrajectoryXY{1}];
nTrajectoryXY{2}=[nTrajectoryXY{2},TrajectoryXY{2}];
end
flag=2;
ModelExtension;
M1= length(TrnValModelInfo.ModelInfo.gMats);
[N,T]=size(DirMat);
errOPTMat=zeros(N,T);
for itr=1:M1
errMats{itr}=zeros(N,T);
end
for ID=1:N
%filter=~( isnan(ModelInfo.ghinMat(ID,:))| isnan(ModelInfo.glocalMat(ID,:))|isnan(ModelInfo.gilMat(ID,:)) | isnan(ModelInfo.grMat(ID,:)) | isnan(ModelInfo.DirMat(ID,:)));
ActualVec=DirMat(ID,:)';
modelMat=[];
for itr=1:M1
modelMat=[modelMat,gMats{itr}(ID,:)'];
end
PredVec=ValdInfo.Wopt(ID,:)*modelMat';
errOPTMat(ID,:)=degDist(ActualVec',PredVec);
for itr=1:M1
selVec=zeros(1,M1);
selVec(itr)=1;
PredVec=selVec*modelMat';
errMats{itr}(ID,:) = degDist(ActualVec',PredVec);
end
end
TestResult.errOPTMat=errOPTMat;
TestResult.errMats=errMats;
end