The pooled covariance matrix of TRAINING must be positive definite.
5 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
sun rise
am 21 Jan. 2022
Kommentiert: sun rise
am 29 Jan. 2022
clc
clear all
load featurs_T
load featurs_S
load Group_Train
load Group_Test
cv_x=cov(Feat1);
[V,D] = eig(cv_x);
d=diag(D);
d=d(end:-1:1);
sm_d=cumsum(d) /sum(d);
idx=find(sm_d>0.99);
T=[V(:,end:-1:idx(1))]';
new_feat1=T*Feat1';
%TrainingSet= new_feat1';
new_feat2=T*Feat2';
%TestSet= new_feat2';
TrainingSet = new_feat1';
TestSet = new_feat2';
Group_Train1 = Group_Train1';
Group_Test1 = Group_Test1';
%------------------------
% result1= multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1);
result1= classify(TestSet,TrainingSet,Group_Train1,'linear');
testresult = result1;
Accuracy = mean(Group_Test1==result) * 100;
fprintf('Accuracy = %.2f\n', Accuracy);
fprintf('error rate = %.2f\n ', mean(result ~= Group_Test1 ) * 100);
Error using classify (line 233)
The pooled covariance matrix of TRAINING must be positive definite.
Error in HOG2 (line 31)
result1= classify(TestSet,TrainingSet,Group_Train1,'linear');
5 Kommentare
Akzeptierte Antwort
Matt J
am 23 Jan. 2022
I suggest you calculate the pooled covariance matrix and verify whether the error message is accurate.
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Dimensionality Reduction and Feature Extraction finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!