Error using classreg.l​earning.Li​near.prepa​reDataCR X and Y do not have the same number of observations.

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Abdelrahman
Abdelrahman 2023-8-11
回答： Drew 2023-9-1
I have faced this issue when I was implement a face recognition model using PLS method
Error using classreg.learning.Linear.prepareDataCR
X and Y do not have the same number of observations.
Error in RegressionLinear.prepareData (line 629)
[X,Y,W,dataSummary] = classreg.learning.Linear.prepareDataCR(...
Error in classreg.learning.FitTemplate/fit (line 246)
this.PrepareData(X,Y,this.BaseFitObjectArgs{:});
Error in RegressionLinear.fit (line 488)
[varargout{1:nargout}] = fit(temp,X,Y);
Error in fitrlinear (line 147)
[varargout{1:nargout}] = RegressionLinear.fit(X,y,RemainingArgs{:});
Error in FRM_PLS (line 39)
mdl = fitrlinear(trainFeaturesPLS, trainingLabels, 'Learner', 'leastsquares', 'Lambda', lambda);
although this is my original code
% PLS Method
% Load a dataset of grayscale face images
Dataset = imageDatastore('ExtendedYaleB', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
% Split the data into training and testing sets
[trainImgs, testImgs] = splitEachLabel(Dataset, 0.7, 'randomized');
% Extract local patches from the training images using the extractLBPFeatures function
numNeighbors = 8;
radius = 1;
trainFeatures = cell(numel(trainImgs.Files), 1);
for i = 1:numel(trainImgs.Files)
img = readimage(trainImgs, i);
trainFeatures{i} = extractLBPFeatures(img, 'NumNeighbors', numNeighbors, 'Radius', radius);
end
%% Perform PLS regression
numComponents = 59; % Number of PLS components to retain
trainingLabels = double(trainImgs.Labels);
trainFeaturesMat = cell2mat(trainFeatures);
[~,~,~,~,betaPLS,~] = plsregress(trainFeaturesMat, trainingLabels, numComponents);
%% Project train features onto PLS subspace
trainFeaturesPLS = cell(numel(trainImgs.Files), 1);
for i = 1:size(trainFeaturesMat, 2)
features = trainFeaturesMat(:, i);
trainFeaturesPLS{i} = features .* betaPLS';
end
% Convert train features to a consistent data type
trainFeaturesPLS = cellfun(@double, trainFeaturesPLS, 'UniformOutput', false);
% Convert train features to matrix
trainFeaturesPLS = cell2mat(trainFeaturesPLS);
%% Train the linear regression model on the modified LBP features
lambda = 0.1; % Set the value of lambda
mdl = fitrlinear(trainFeaturesPLS, trainingLabels, 'Learner', 'leastsquares', 'Lambda', lambda);
% Save the model to a file
save('FRM_PLS.mat', 'mdl', 'betaPLS');
% Extract local patches from the testing images and make predictions using the predict function
testFeatures = cell(numel(testImgs.Files), 1);
for i = 1:numel(testImgs.Files)
img = readimage(testImgs, i);
testFeatures{i} = extractLBPFeatures(img, 'NumNeighbors', numNeighbors, 'Radius', radius);
end
% Project test features onto PLS subspace
testFeaturesPLS = cell(numel(testImgs.Files), 1);
for i = 1:numel(testImgs.Files)
features = cell2mat(testFeatures(i));
testFeaturesPLS{i} = features' * betaPLS;
end
% Convert test features to matrix
testFeaturesPLS = cell2mat(testFeaturesPLS);
% Make predictions using the loaded model
load('my_Model.mat', 'mdl');
predictions = predict(mdl, testFeaturesPLS);
% Evaluate the performance of the model using the confusionmat and classificationReport functions
confMat = confusionmat(testImgs.Labels, predictions);
classificationReport = classificationReport(testImgs.Labels, predictions);
% Use the loaded model for prediction
testImg = imread('test_image.jpg');
testFeatures = extractLBPFeatures(testImg, 'NumNeighbors', numNeighbors, 'Radius', radius);
testFeaturesPLS = testFeatures' * betaPLS;
prediction = predict(mdl, testFeaturesPLS);
I want help
and this s the dataset if any one will run code
https://drive.google.com/file/d/1NiHF1NWM1kEBJTyNUAp4bw5h5Ty2Kt-O/view

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回答（1 个）

Drew
Drew 2023-9-1
The error message "X and Y do not have the same number of observations" indicates that, on line 39, which is
mdl = fitrlinear(trainFeaturesPLS, trainingLabels, 'Learner', 'leastsquares', 'Lambda', lambda),
trainFeaturesPLS and trainingLabels have a different number of rows. The observations are stored in rows, so those two variables need to have the same number of rows in order for the fitrlinear training to proceed.

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