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

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

[Walter Roberson]

A common source of this kind of problem is if either the X or the Y needs to be transposed. More often the X.

[Abdelrahman]

How can I transpose that and withany value?

Answer 1

[Drew]

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.