I am a beginner in svm. Getting error when trying to run for the example given. Please lend me a hand. Thanks

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junnie vix
junnie vix 2017-10-27
Code: traindatasetDir = fullfile(toolboxdir('vision'), 'visiondata','digits','synthetic'); testdatasetDir = fullfile(toolboxdir('vision'), 'visiondata','digits','handwritten');
% imageDatastore recursively scans the directory tree containing the % images. Folder names are automatically used as labels for each image. trainingSet = imageDatastore(traindatasetDir, 'IncludeSubfolders', true, 'LabelSource', 'foldernames'); testSet = imageDatastore(testdatasetDir, 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
countEachLabel(trainingSet) countEachLabel(testSet)
figure;
subplot(2,3,1); imshow(trainingSet.Files{8});
subplot(2,3,2); imshow(trainingSet.Files{13});
subplot(2,3,3); imshow(trainingSet.Files{29});
subplot(2,3,4); imshow(testSet.Files{6});
subplot(2,3,5); imshow(testSet.Files{17});
subplot(2,3,6); imshow(testSet.Files{22});
% Show pre-processing results exTestImage = readimage(testSet,37); processedImage = imbinarize(rgb2gray(exTestImage));
figure;
subplot(1,2,1) imshow(exTestImage)
subplot(1,2,2) imshow(processedImage)
img = readimage(trainingSet, 3);
% Extract HOG features and HOG visualization [hog_2x2, vis2x2] = extractHOGFeatures(img,'CellSize',[2 2]); [hog_4x4, vis4x4] = extractHOGFeatures(img,'CellSize',[4 4]); [hog_8x8, vis8x8] = extractHOGFeatures(img,'CellSize',[8 8]);
% Show the original image figure; subplot(2,3,1:3); imshow(img);
% Visualize the HOG features subplot(2,3,4); plot(vis2x2); title({'CellSize = [2 2]'; ['Length = ' num2str(length(hog_2x2))]});
subplot(2,3,5); plot(vis4x4); title({'CellSize = [4 4]'; ['Length = ' num2str(length(hog_4x4))]});
subplot(2,3,6); plot(vis8x8); title({'CellSize = [8 8]'; ['Length = ' num2str(length(hog_8x8))]});
cellSize = [4 4]; hogFeatureSize = length(hog_4x4);
% Loop over the trainingSet and extract HOG features from each image. A % similar procedure will be used to extract features from the testSet.
numImages = numel(trainingSet.Files); trainingFeatures = cell(numImages, 1);
for i = 1:numImages img = readimage(trainingSet, i);
img = rgb2gray(img);
% Apply pre-processing steps
img = imbinarize(img);
trainingFeatures{i} = extractHOGFeatures(img, 'CellSize', cellSize);
end
% Get labels for each image. trainingLabels = trainingSet.Labels;
% fitcecoc uses SVM learners and a 'One-vs-One' encoding scheme. classifier = fitcecoc(trainingFeatures, trainingLabels); % Extract HOG features from the test set. The procedure is similar to what % was shown earlier and is encapsulated as a helper function for brevity. [testFeatures, testLabels] = helperExtractHOGFeaturesFromImageSet(testSet, hogFeatureSize, cellSize);
% Make class predictions using the test features. predictedLabels = predict(classifier, testFeatures);
% Tabulate the results using a confusion matrix. confMat = confusionmat(testLabels, predictedLabels);
helperDisplayConfusionMatrix(confMat)
Error: Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 171) X must be a numeric matrix.
Error in classreg.learning.classif.FullClassificationModel.prepareData (line 487) classreg.learning.FullClassificationRegressionModel.prepareDataCR(...
Error in classreg.learning.FitTemplate/fit (line 213) this.PrepareData(X,Y,this.BaseFitObjectArgs{:});
Error in ClassificationECOC.fit (line 115) this = fit(temp,X,Y);
Error in fitcecoc (line 289) obj = ClassificationECOC.fit(X,Y,ecocArgs{:});
Error in HOG_train (line 92) classifier = fitcecoc(trainingFeatures, trainingLabels);

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