Cell contents reference from a non-cell array object error

Question

Lester Lim 2013-1-23

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/59608-cell-contents-reference-from-a-non-cell-array-object-error

Error: Cell contents reference from a non-cell array object. I know that a input must be a cell array but thin is I don't know where went wrong when I put in a cell array...

Here's the code:

 function subset = GetClassSubsetIndexes(classes)
            subset=[];
            oldClassLabel = 'nekaLabela';
            for i=1 : 1 : length(classes)
                if oldClassLabel ~= classes{i}
                    oldClassLabel = classes{i};
                    subset = cat(1, subset, i);
                end
            end
            %now put end indicies
            for i=2 : 1 : size(subset,1)
                endIndex = subset(i, 1);
                subset(i-1, 2) = endIndex-1;
            end
            subset(size(subset,1), 2) = length(classes);           
        end

Need help Thanks!!! Ps: please don't close the question, it's quite important to me...

4 个评论
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Lester Lim 2013-1-23

For == it gives the same error, class(classes) gives the following error: For colon operator with char operands, first and last operands must be char.

Walter Roberson 2013-1-23

在 MATLAB Online 中打开

if ~strcmp(oldClassLabel, classes{i})

The bit about colon operators makes no sense unless the class() call itself has been shadowed.

Right after the "function" line, for the moment please put

which -all class
whos classes

and show us the output.

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Answer 1

Walter Roberson 2013-1-23

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/59608-cell-contents-reference-from-a-non-cell-array-object-error#answer_72083

Your code is written to assume that LDA is called with the second parameter being a cell array of strings, but you are instead calling it with the second parameter being a column vector of double (such as a class number.)

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Lester Lim 2013-1-23

编辑：Walter Roberson 2013-1-24

在 MATLAB Online 中打开

My bad, here it is. It was at the line :

projectedSamples = samples * vectors;

Code:

classdef LDA < handle 
  %inherit from handle otherwise we must store value (object) each time when we change some propertie value
  %function would need to return obj(this)
      properties
          Samples, %array of feature vectors
          Classes, %class labels for samples
          EigenVectors,
          EigenValues,
          BetweenScatter, %SB
          WithinScatter,  %SW
          NumberOfClasses
      end
      properties(Access = 'private')
          ClassSubsetIndexes %start and end indicies where one class begins and ends in variable "Classes"
          TotalMean,
          MeanPerClass
      end
      methods
          function this = LDA(samples, classes)
              this.Samples = samples;
              this.Classes = classes;
              this.ClassSubsetIndexes = LDA.GetClassSubsetIndexes(classes);
              this.NumberOfClasses = size(this.ClassSubsetIndexes, 1);
              [this.TotalMean this.MeanPerClass] = this.CalculateMean(samples);          
          end
          function Compute(this)
              SB = this.CalculateBetweenScatter();
              SW = this.CalculateWithinScatter();
              [eigVectors, eigValues] = eig(SB, SW);
              eigValues = diag(eigValues);
              %--------- sort eig values and vectors ---------%
              sortedValues = sort(eigValues,'descend');
              [c, ind] = sort(eigValues,'descend'); %store indicies
              sortedVectors = eigVectors(:,ind); % reorder columns 
              this.EigenVectors = sortedVectors;
              this.EigenValues = sortedValues;
              this.BetweenScatter = SB;
              this.WithinScatter = SW;
          end
          function projectedSamples = Transform(this, samples, numOfDiscriminants)
              vectors = this.EigenVectors(:, 1:numOfDiscriminants);
              %transformed sample is scalar (projection on a hyperplane)
              projectedSamples = samples * vectors; 
          end
          function measure = CalculateFLDMeasure(this, numOfDiscriminants)
              SB = this.BetweenScatter;
              SW = this.WithinScatter;
              vectors = this.EigenVectors(:, 1:numOfDiscriminants);
              measure = det(vectors' * SB * vectors) / det(vectors' * SW * vectors);
          end
      end
      methods(Access = 'private')
          function [totalMean meanPerClass] = CalculateMean(this, samples)
              for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                  startIdx = this.ClassSubsetIndexes(classIdx, 1);
                  endIdx = this.ClassSubsetIndexes(classIdx, 2);
                  meanPerClass(classIdx, :) = mean( samples(startIdx:endIdx, :), 1);
              end
              totalMean = mean(meanPerClass, 1); %global average value
          end
          function SW = CalculateWithinScatter(this)
              featureLength = size(this.Samples, 2);
              SW = zeros(featureLength, featureLength);
              for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                  startIdx = this.ClassSubsetIndexes(classIdx, 1);
                  endIdx = this.ClassSubsetIndexes(classIdx, 2);
                  classSamples = this.Samples(startIdx:endIdx, :);
                  classMean = this.MeanPerClass(classIdx, :);
                  Sw_Class = LDA.CalculateScatterMatrix(classSamples, classMean);
                  SW = SW + Sw_Class;
              end
          end
          function SB = CalculateBetweenScatter(this)
              featureLength = size(this.Samples, 2);
              SB = zeros(featureLength, featureLength);
               for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                   startIdx = this.ClassSubsetIndexes(classIdx, 1);
                   endIdx = this.ClassSubsetIndexes(classIdx, 2);
                   numberOfSamplesInClass = endIdx - startIdx + 1;
                   classMean = this.MeanPerClass(classIdx, :);
                   %because my vector is row-vector
                   Sb_class = (classMean - this.TotalMean)' * (classMean - this.TotalMean); 
                   Sb_class = numberOfSamplesInClass * Sb_class;
                   SB = SB + Sb_class;
               end
          end
      end
      methods(Static, Access = 'private')
          function subset = GetClassSubsetIndexes(classes)
              subset=[];
              oldClassLabel = NaN;
              for i=1 : 1 : length(classes)
                  if oldClassLabel ~= classes(i)
                      oldClassLabel = classes(i);
                      subset = cat(1, subset, i);
                  end
              end
              %now put end indicies
              for i=2 : 1 : size(subset,1)
                  endIndex = subset(i, 1);
                  subset(i-1, 2) = endIndex-1;
              end
              subset(size(subset,1), 2) = length(classes);           
          end
          function Sw_class = CalculateScatterMatrix(classSamples, classMean)
              featureLength = size(classSamples, 2);
              Sw_class = zeros(featureLength, featureLength);
              for sampleIdx=1 : 1 : size(classSamples, 1)
                  covariance = (classSamples(sampleIdx, :) - classMean);
                  covariance = covariance' * covariance; %because my vector is row-vector
                  Sw_class = Sw_class + covariance;
              end          
          end
      end
end

Lester Lim 2013-1-23

编辑：Lester Lim 2013-1-23

在 MATLAB Online 中打开

Maybe this might help:

function Apply_Callback(hObject, eventdata, handles) global I

data = importdata('LDA data.mat')

features=data(:,1:end-1); %split data without labels

lable = data(:,end); %get the labels from training data

trainSamples = features;%training samples

trainClasses = lable;%training labels

I2 = reshape(I,[],1);

testSamples = I2;%test samples

lableimage = reshape(handles.lableimage,[],1);

testClasses = lableimage;%test labels

mLDA = LDA(trainSamples, trainClasses);

mLDA.Compute();

transformedTrainSamples = mLDA.Transform(trainSamples, 1);

transformedTestSamples = mLDA.Transform(testSamples, 1);

calculatedClases = knnclassify(transformedTestSamples,

transformedTrainSamples, trainClasses);

simmilarity = [];

for i = 1 : 1 : length(testClasses)

similarity(i) = ( testClasses{i} == calculatedClases{i} );

end

accuracy = sum(similarity) / length(testClasses);

fprintf('Testing: Accuracy is: %f %%\n', accuracy*100);

guidata(hObject, handles);

% hObject handle to Apply (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

Lester Lim 2013-1-24

编辑：Walter Roberson 2013-1-24

在 MATLAB Online 中打开

---------------------LDA------------------------

classdef LDA < handle 
  %inherit from handle otherwise we must store value (object) each time when we change some propertie value
  %function would need to return obj(this)
      properties
          Samples, %array of feature vectors
          Classes, %class labels for samples
          EigenVectors,
          EigenValues,
          BetweenScatter, %SB
          WithinScatter,  %SW
          NumberOfClasses
      end
      properties(Access = 'private')
          ClassSubsetIndexes %start and end indicies where one class begins and ends in variable "Classes"
          TotalMean,
          MeanPerClass
      end
      methods
          function this = LDA(samples, classes)
              this.Samples = samples;
              this.Classes = classes;
              this.ClassSubsetIndexes = LDA.GetClassSubsetIndexes(classes);
              this.NumberOfClasses = size(this.ClassSubsetIndexes, 1);
              [this.TotalMean this.MeanPerClass] = this.CalculateMean(samples);          
          end
          function Compute(this)
              SB = this.CalculateBetweenScatter();
              SW = this.CalculateWithinScatter();
              [eigVectors, eigValues] = eig(SB, SW);
              eigValues = diag(eigValues);
              %--------- sort eig values and vectors ---------%
              sortedValues = sort(eigValues,'descend');
              [c, ind] = sort(eigValues,'descend'); %store indicies
              sortedVectors = eigVectors(:,ind); % reorder columns 
              this.EigenVectors = sortedVectors;
              this.EigenValues = sortedValues;
              this.BetweenScatter = SB;
              this.WithinScatter = SW;
          end
          function projectedSamples = Transform(this, samples, numOfDiscriminants)
              vectors = this.EigenVectors(:, 1:numOfDiscriminants);
              class(samples)
              class(vectors)
              size(samples)
              size(vectors)
              disp(samples)
              disp(vectors)
              %transformed sample is scalar (projection on a hyperplane)
              projectedSamples = samples * vectors; 
          end
          function measure = CalculateFLDMeasure(this, numOfDiscriminants)
              SB = this.BetweenScatter;
              SW = this.WithinScatter;
              vectors = this.EigenVectors(:, 1:numOfDiscriminants);
              measure = det(vectors' * SB * vectors) / det(vectors' * SW * vectors);
          end
      end
      methods(Access = 'private')
          function [totalMean meanPerClass] = CalculateMean(this, samples)
              for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                  startIdx = this.ClassSubsetIndexes(classIdx, 1);
                  endIdx = this.ClassSubsetIndexes(classIdx, 2);
                  meanPerClass(classIdx, :) = mean( samples(startIdx:endIdx, :), 1);
              end
              totalMean = mean(meanPerClass, 1); %global average value
          end
          function SW = CalculateWithinScatter(this)
              featureLength = size(this.Samples, 2);
              SW = zeros(featureLength, featureLength);
              for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                  startIdx = this.ClassSubsetIndexes(classIdx, 1);
                  endIdx = this.ClassSubsetIndexes(classIdx, 2);
                  classSamples = this.Samples(startIdx:endIdx, :);
                  classMean = this.MeanPerClass(classIdx, :);
                  Sw_Class = LDA.CalculateScatterMatrix(classSamples, classMean);
                  SW = SW + Sw_Class;
              end
          end
          function SB = CalculateBetweenScatter(this)
              featureLength = size(this.Samples, 2);
              SB = zeros(featureLength, featureLength);
               for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                   startIdx = this.ClassSubsetIndexes(classIdx, 1);
                   endIdx = this.ClassSubsetIndexes(classIdx, 2);
                   numberOfSamplesInClass = endIdx - startIdx + 1;
                   classMean = this.MeanPerClass(classIdx, :);
                   %because my vector is row-vector
                   Sb_class = (classMean - this.TotalMean)' * (classMean - this.TotalMean); 
                   Sb_class = numberOfSamplesInClass * Sb_class;
                   SB = SB + Sb_class;
               end
          end
      end
      methods(Static, Access = 'private')
          function subset = GetClassSubsetIndexes(classes)
              subset=[];
              oldClassLabel = NaN;
              for i=1 : 1 : length(classes)
                  if oldClassLabel ~= classes(i)
                      oldClassLabel = classes(i);
                      subset = cat(1, subset, i);
                  end
              end
              %now put end indicies
              for i=2 : 1 : size(subset,1)
                  endIndex = subset(i, 1);
                  subset(i-1, 2) = endIndex-1;
              end
              subset(size(subset,1), 2) = length(classes);    
          end
          function Sw_class = CalculateScatterMatrix(classSamples, classMean)
              featureLength = size(classSamples, 2);
              Sw_class = zeros(featureLength, featureLength);
              for sampleIdx=1 : 1 : size(classSamples, 1)
                  covariance = (classSamples(sampleIdx, :) - classMean);
                  covariance = covariance' * covariance; %because my vector is row-vector
                  Sw_class = Sw_class + covariance;
              end          
          end
      end
end

Code( As in GUI)

function Apply_Callback(hObject, eventdata, handles)
global I 
data = importdata('LDA data.mat')
features=data(:,1:end-1); %split data without labels
lable=data(:,end); %get the labels
trainSamples = features;%training samples
trainClasses = lable;%training labels
I2 = reshape(I,[],1);
testSamples = I2;%test samples
lableimage = reshape(handles.lableimage,[],1);
testClasses = lableimage;%test labels
mLDA = LDA(trainSamples, trainClasses);
mLDA.Compute();
transformedTrainSamples = mLDA.Transform(trainSamples, 1);
transformedTestSamples = mLDA.Transform(testSamples, 1);
calculatedClases = knnclassify(transformedTestSamples, 
transformedTrainSamples, trainClasses);
simmilarity = [];
for i = 1 : 1 : length(testClasses)
      similarity(i) = ( testClasses{i} == calculatedClases{i} );
end
accuracy = sum(similarity) / length(testClasses);
fprintf('Testing: Accuracy is: %f %%\n', accuracy*100);
guidata(hObject, handles);

Lester Lim 2013-1-24

Got another problem, hoping you could help, the dimensions don't agree but the double works. The testSample is supposed to be the picture I want to classify right?

Lester Lim 2013-1-24