wrong size of matrix

HI guys when i run this function it gives me an error This is F

F

 fileClassification('audio.wav', 1, 'auto5.mat')
Error using pdist2 (line 309)
The covariance matrix for the Mahalanobis metric must be a square matrix with the same number of columns
as X. And it must be symmetric and positive definite.

Error in classifyKNN_D_Multi (line 54)
                d{i} =  pdist2(F{i}.', testSample, 'mahalanobis');   %56

Error in fileClassification (line 43)
[P, label] = classifyKNN_D_Multi(Features, ...
 
>> 





function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype )
% function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype);
% 
% This function is used for classifying an unknown sample using the kNN
% algorithm, in its multi-class form.
%
% ARGUMENTS:
% - F: an CELL array that contains the feature values for each class. I.e.,
%      F{1} is a matrix of size numOfDimensions x numofSamples FOR THE FIRST
%      CLASS, etc.
%
% - testSample: the input sample to be classified
% - k: the kNN parameter
% - NORMALIZE: use class priors to weight results
% - useL1distance: use L1 instead of L2 distance
%
% RETURNS:
% - Ps: an array that contains the classification probabilities for each class
% - winnerClass: the label of the winner class
  %%error(nargchk(4,5,nargin))
  switch nargin
      case 4
           distancetype = 1; % euclidean distance if 4 variables included
      case 5
          distancetype = 3;
      otherwise
          disp('error')
  end
numOfClasses = length(F);
if (size(testSample, 2)==1)
    testSample = testSample';
end
% initilization of distance vectors:
numOfDims = zeros( 1, numOfClasses );
numOfTrainSamples = zeros( 1, numOfClasses );
d = cell(numOfClasses,1);
% d{i} is a vector, whose elements represent the distance of the testing
% sample from all the samples of i-th class
testSample(isnan(testSample)) = 0.0;
for i=1:numOfClasses
    [ numOfDims(i), numOfTrainSamples(i) ] = size( F{i} );
    d{i} = inf*ones(max(numOfTrainSamples), 1); % we fill it with inf values
    F{i}(isnan(F{i})) = 0.0;    
end
if (length(testSample)>1)
    for i=1:numOfClasses % for each class:
        if (numOfTrainSamples(i)>0)
            if ( distancetype == 1 )                
              %  d{i} = sum( abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}'),2); % L1
                d{i} =  pdist2(F{i}.', testSample, 'euclidean');
            elseif ( distancetype == 2)
                d{i} = pdist2(F{i}.', testSample, 'cityblock');    %L1
            else
                d{i} =  pdist2(F{i}.', testSample, 'mahalanobis');   %56
            end
            d{i} = sort(d{i});            
            d{i}(end+1:max(numOfTrainSamples)) = inf;                                    
        else
            d{i} = inf;
        end        
    end
else % single dimension (NO SUM required!!!)
    for i=1:numOfClasses
        if (numOfTrainSamples(i)>0)
            d{i}   = (abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}')'); 
            d{i} = sort(d{i});
            d{i}(end+1:max(numOfTrainSamples)) = inf;            
        else
            d{i} = inf;
        end
    end    
end
kAll = zeros(numOfClasses, 1);
for j=1:k
    curArray = zeros(numOfClasses, 1);
    for i=1:numOfClasses
        curArray(i) = d{i}(kAll(i)+1);
    end    
    [MIN, IMIN] = min(curArray);
    kAll(IMIN) = kAll(IMIN) + 1;
end
if ( NORMALIZE == 0 )
    Ps = (kAll ./ k);
else
    Ps = kAll ./ numOfTrainSamples';
    Ps = Ps / sum(Ps);
end
[MAX, IMAX] = max(Ps);
winnerClass = IMAX;

[dpb]

What is testsample?

From function comments doesn't look like it would necessarily be the matching Y to go with X which seems to be what the error is telling you.

Answer 1

[Kamil Kacer]

[Features, classNames, MEAN, STD, ...
    stWin, stStep] = kNN_model_load(modelFileName);


[x, fs] = audioread(wavFileName);         % read wav file
% short-term feature extraction:
ShortFeatures = stFeatureExtraction(x, fs, stWin, stStep);      
 %mtWinRatio = mtWin / stWin; mtStepRatio =  mtStep / stStep;
% mid-term feature statistic calculation:
%[mtFeatures] = mtFeatureExtraction(...
 %   stF, mtWinRatio, mtStepRatio, Statistics);
% long term averaging of the mid-term statistics:
ShortFeatures = mean(ShortFeatures,2);
% kNN classification
% % [P, label] = classifyKNN_D_Multi(Features, ...
% %     (mtFeatures - MEAN') ./ STD', kNN, 1);
[P, label] = classifyKNN_D_Multi(Features, ...
    (ShortFeatures - MEAN') ./ STD', kNN,1,1);

ShortFeatures is test sample