Matlab SVM example -

i trying implement svm classification. goal output correct grid of origin of power signal (.wav file). grids titled a-i , there 93 total signals training set , 49 practice signals. have 93x10x36 matrix of feature vectors. know why errors shown? traincorrectgrid , training_cepstrum1 both have 93 rows don't understand problem is. appreciated.

my code shown here:

clc; clear; close all;  load('avg_fft_feature (4).mat'); %training feature vectors load('practice_fft_mag_all (2).mat'); %practice feauture vectors load('practice_gridorigin.mat'); %correct grids of origin practice data load practicecorrectgrid.mat; load training_cepstrum1; load practice_cepstrum1a; load fset1.mat %load in correct practice grids  traincorrectgrid=['a';'a';'a';'a';'a';'a';'a';'a';'a';'b';'b';'b';'b';'b';'b';'b';'b';'b';'b';'c';'c';'c';'c';'c';'c';'c';'c';'c';'c';'c';'d';'d';'d';'d';'d';'d';'d';'d';'d';'d';'d';'e';'e';'e';'e';'e';'e';'e';'e';'e';'e';'e';'f';'f';'f';'f';'f';'f';'f';'f';'g';'g';'g';'g';'g';'g';'g';'g';'g';'g';'g';'h';'h';'h';'h';'h';'h';'h';'h';'h';'h';'h';'i';'i';'i';'i';'i';'i';'i';'i';'i';'i';'i'];  %[results,u] = multisvm(avg_fft_feature, traincorrectgrid, avg_fft_feature_practice);%avg_fft_feature);  [results,u] = multisvm(training_cepstrum1(93,:,1), traincorrectgrid, practice_cepstrum1a(49,:,1));   disp('grids of origin (svm)');   %display svm results = 1:numel(u)     str = sprintf('%d: %s', i, u(i));     disp(str); end  %display percent correct numcorrect = 0; = 1:numel(u)     %if (strcmp(traincorrectgrid(i,1), u(i))==1); %compare training     %training     if (strcmp(practicecorrectgrid(i,1), u(i))==1); %compare practice data training         numcorrect = numcorrect + 1;      end end  numberofelements = numel(u); percentcorrect =  numcorrect / numberofelements * 100; % percentcorrect = round(percentcorrect, 2); disppercent = sprintf('percent correct = %0.3f%%', percentcorrect); disp(disppercent); 

error shown here

the multisvm function shown here:

function [result, u] = multisvm(trainingset,grouptrain,testset) %models given training set corresponding group vector ,  %classifies given test set using svm classifier according  %one vs. relation.  % %this code written cody neuburger cneuburg@fau.edu %florida atlantic university, florida usa , modified renny varghese %this code adapted , cleaned anand mishra's multisvm function %found @ http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/  u=unique(grouptrain); numclasses=length(u); result = zeros(length(testset(:,1)),1);  %build models k=1:numclasses     %vectorized statement binarizes group     %where 1 current class , 0 other classes     g1vall=(grouptrain==u(k));     models(k) = svmtrain(trainingset,g1vall); end  %classify test cases j=1:size(testset,1)     k=1:numclasses         if(svmclassify(models(k),testset(j,:)))              break;         end     end     result(j) = k; end  mapvalues = 'abcdefghi'; u = mapvalues(result); 

you state training_cepstrum1 has size [93,10,36]. when call multisvm, passing in training_cepstrum1(93,:,1) has size [1,10]. since traincorrectgrid has size [93,1], there mismatch in number of rows.

it looks make same error when passing in practice_cepstrum1a.

try replacing call multisvm with

[results,u] = multisvm(training_cepstrum1(:,:,1), traincorrectgrid, practice_cepstrum1a(:,:,1));   

this way training_cepstrum1(:,:,1) has size [93,10], same number of rows traincorrectgrid.