Summary of discriminant or predictive methods (discriminant analysis, neural networks, SVM for support vector machines, etc.)

Percent "input": s_train (Input sample data, number of rows is sample count, column dimension); S_group (training sample category); S_sample (data to be distinguished)
Percent "Output": Cla (Forecast category)

function Cla = Fun_panbie (S_train,s_group,s_sample,index)
Switch index
Case 1%classify
%[s_train,~]=mapminmax (s_train);% Standardized handling
%[s_sample,~]=mapminmax (s_sample);% Standardized handling
[Cla,err,posterior,~,~]=classify (S_sample,s_train,s_group, ' linear ', ' empirical ');%ss={' linear ', ' diaglinear ', ' Quadratic ', ' diagquadratic ', ' mahalanobis '};% discriminant function
Case 2%SVM
Net=svmtrain (S_train,s_group, ' kernel_function ', ' linear ');% kernel function ss={' linear ', ' quadratic ', ' polynomial ', ' RBF ', ' MLP ' }
Cla=svmclassify (net,s_sample);
Case 3%knnclassify
% slightly
Case 4%RBF
NET=NEWRB (S_train ', S_group ', 0.1,0.1);
Cla=int16 (NET (s_sample)) ';
Case 5%LVQ
T=ind2vec (S_group ' + 1);
NET=NEWLVQ (Minmax (S_train '), 5);
net.trainparam.showwindow=0;
Net=train (Net,s_train ', T);
Y=sim (Net,s_sample ');
Cla=vec2ind (y) '-1;
Case 6%elman
[Pn,minp,maxp,tn,mint,maxt]=premnmx (S_train ', s_group ');% data Normalization
p2= tramnmx (s_sample ', MINP,MAXP);
Net=newelm (Minmax (PN), [3,size (s_group,2)],{' Tansig ', ' Tansig '});% establishes a network model where parameters can be modified as required
net.trainparam.show=100;% shown 1 times per iteration 100 times
net.trainparam.epochs=1000;% Maximum number of iterations 2000
net.trainparam.goal=0.001;% Iteration Target
Net=init (NET);% Initialize Network
net.trainparam.showwindow=0;% Neural Network The window of the training process does not bounce out
[Net,tr]=train (NET,PN,TN);% Training Network
Cla=sim (net,s_sample ') ';% emulation
Cla=postmnmx (cla,mint,maxt); inverse normalization of simulation values
Case 7% Single-layer Perceptron
Yy=minmax (S_train ');
NET=NEWP (yy,1);
net.trainparam.epochs=20;
net.trainparam.showwindow=0;
Net=train (Net,s_train ', S_group ');
Cla=sim (net,s_sample) ';
Case 8% Linear Neural network
T=repmat (S_group,1,size (S_train ', 2));
Net=newlin (Minmax (S_train '), size (t,1), 10,0.05);
net.trainparam.epochs=500;
net.trainparam.goal=0.0001;
net.trainparam.showwindow=0;
Net=train (Net,s_train ', T);
Y=sim (Net,s_sample ');
Cla=y (:, 1);
Case 9% single layer competitive neural network
Mm=s_train (:);
Mm=minmax (mm ');
Q=repmat (Mm,size (s_train,2), 1);
NET=NEWC (q,2,0.1);
Net=init (NET);
net.trainparam.showwindow=0;
net.trainparam.epochs=20;
Net=train (Net,s_train ');
A=sim (Net,s_train ');
Cla=vec2ind (s_sample ') ';
Case 10%BP Neural Network
Mm=s_train (:);
Mm=minmax (mm ');
NET=NEWFF (Repmat (Mm,size (S_train ', 1), 1), [5,3,3,1],{' Tansig ', ' tansig ', ' tansig ', ' Purelin '}, ' Traingd ');
net.trainparam.epochs=300;
net.trainparam.lr=0.05;
net.trainparam.show=50;
net.trainparam.goal=1e-5;
net.trainparam.showwindow=0;
[Net,tr]=train (Net,s_train ', S_group ');
Cla=sim (net,s_sample ') ';
otherwise

End

End

Summary of discriminant or predictive methods (discriminant analysis, neural networks, SVM for support vector machines, etc.)