Why do I get this error at the Classifier code line?? Please help

Question

Chidiebere Ike 2018-4-2

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/392219-why-do-i-get-this-error-at-the-classifier-code-line-please-help

评论： Walter Roberson 2020-3-26

%%Load Image Information from ORL Face Database Directory
faceDatabase = imageSet('orl_faces','recursive');
%%Display Montage of First Face
% (faceDatabase(1) means pull out all images belong to folder 1
figure;
montage(faceDatabase(1).ImageLocation);
title('Images of Single Face');

Display Query Image and Database Side-Side

personToQuery = 1; % Call the a set of images at position 1
galleryImage = read(faceDatabase(personToQuery),1);
figure;
for i=1:size(faceDatabase,2)
imageList(i) = faceDatabase(i).ImageLocation(5);
end
subplot(1,2,1);imshow(galleryImage);
subplot(1,2,2);montage(imageList);
diff = zeros(1,9);
%%Split Database into Training & Test Sets in the ratio 80% to 20%
[training,test] = partition(faceDatabase,[0.8 0.2]);
%%Extract and display Histogram of Oriented Gradient Features for single face 
person = 5;
[hogFeature, visualization]= ...
    extractHOGFeatures(read(training(person),1));
figure;
subplot(2,1,1);imshow(read(training(person),1));title('Input Face');
subplot(2,1,2);plot(visualization);title('HoG Feature');
%%Extract HOG Features for training set 
trainingFeatures = zeros(size(training,2)*training(1).Count,length(hogFeature));
featureCount = 1;
for i=1:size(training,2)
    for j = 1:training(i).Count
        trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j));
        trainingLabel{featureCount} = training(i).Description;
        featureCount = featureCount + 1;
    end
    personIndex{i} = training(i).Description;
end
%%Create 40 class classifier using fitcecoc 
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

Error message:

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 210)
X and Y do not have the same number of observations.
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 116)
            this = fit(temp,X,Y);
Error in fitcecoc (line 319)
    obj = ClassificationECOC.fit(X,Y,ecocArgs{:});
Error in SimpleFaceRecognition (line 49)
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

2 个评论
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Saira 2020-3-11

How can I solve this error?

Error:

Subscripted assignment dimension mismatch.

trainingLabel(featureCount,:) = TrainingimgSets(i).Description;

Thanks

Walter Roberson 2020-3-11

TrainingimgSets(i).Description is unlikely to happen to be a column vector of character or numbers or string objects. The Description field of a dataset is usually some text that talks about the purpose or origin of the data set.

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Answer 1

Walter Roberson 2018-4-2

0
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/392219-why-do-i-get-this-error-at-the-classifier-code-line-please-help#answer_313178

The most common cause of this message for the classifiers is that you need to transpose the first parameter, X.

36 个评论
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Chidiebere Ike 2018-4-2

编辑：Walter Roberson 2018-4-2

在 MATLAB Online 中打开

Thanks Walter. I will appreciate if you kindly guide me on how to do this. I did transposed my first parameter trainingFeatures using the lines of code below but still got errors.

My error message reads:

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 210)
X and Y do not have the same number of observations.
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 116)
            this = fit(temp,X,Y);
Error in fitcecoc (line 319)
    obj = ClassificationECOC.fit(X,Y,ecocArgs{:});
Error in SimpleFaceRecognition (line 50)
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);
%%Create 40 class classifier using fitcecoc
trainingFeatures = trainingFeatures';
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

Thank you

mekia 2020-2-13

在 MATLAB Online 中打开

am also get the same error message like above when am running genetic algorithm for feature selection on pima diabete.please any one have there to fix the broblem below

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 210)
X and Y do not have the same number of observations.
Error in classreg.learning.classif.FullClassificationModel.prepareData (line 487)
                classreg.learning.FullClassificationRegressionModel.prepareDataCR(...
Error in ClassificationKNN.prepareData (line 878)
                prepareData@classreg.learning.classif.FullClassificationModel(X,Y,varargin{:},'OrdinalIsCategorical',true);
Error in classreg.learning.FitTemplate/fit (line 213)
                    this.PrepareData(X,Y,this.BaseFitObjectArgs{:});
Error in ClassificationKNN.fit (line 863)
            this = fit(temp,X,Y);
Error in fitcknn (line 261)
    this = ClassificationKNN.fit(X,Y,RemainingArgs{:});
Error in jFitnessFunction>jwrapperKNN (line 20)
Model=fitcknn(feat,label,'NumNeighbors',k,'Distance','euclidean');
Error in jFitnessFunction (line 15)
fitness=jwrapperKNN(feat(:,X==1),label,k,kfold);
Error in jGA (line 39)
  fit(i)=fun(feat,label,X(i,:));
Error in Main (line 31)
[sFeat,Sf,Nf,curve]=jGA(feat,label,N,T,CR,MR);

mekia 2020-3-17

在 MATLAB Online 中打开

please debug this code. am try to split pima diabetes dataset with total of 768 into 538 instance for training set,115 for validation set and the rest 115 for test set but the below code cant split like that please help me i don no what is my mistake?

function data=LoadData()
    data=load('pima diabetes_dataset');
    Inputs=data.Inputs';
    Targets=data.Targets';
    Targets=Targets(:,1);
    
    nSample=size(Inputs,1);
    
    % Shuffle Data
    S=randperm(nSample);
    Inputs=Inputs(S,:);
    Targets=Targets(S,:);
    
    % Train Data
    pTrain=0.7;
    nTrain=round(pTrain*nSample);
    TrainInputs=Inputs(1:nTrain,:);
    TrainTargets=Targets(1:nTrain,:);
   % Validation Data
    pvalid=0.15;
    nvalid=round(pvalid*nSample);  
    ValInputs=Inputs(nvTrain+1:nvalid,:);
    ValTargets=Targets(nTrain+1:nvalid,:);
    
    % Test Data
    TestInputs=Inputs(nvalid+1:end,:);
    TestTargets=Targets(nvalid+1:end,:);
    
    % Export
    data.TrainInputs=TrainInputs;
    data.TrainTargets=TrainTargets;
    data.ValInputs=ValInputs;
    data.ValTargets=ValTargets;
    data.TestInputs=TestInputs;
    data.TestTargets=TestTargets;
end

mekia 2020-3-19

在 MATLAB Online 中打开

am try to plot confusion matrix for adaptive neuro-fuzzy inference system(anfis). i want to call plotconfusions function in to main class but i cant afford it and the code below can run with out any result or i mean never stop running unless am pause run. here i attach both class(main.m & plotconfusions.m) please help me by debug these code.thank you for you help sir.

%main.m
clear;
close all;
%% Load Data
data=LoadData();
%% Generate Basic FIS
fis=CreateInitialFIS(data,10);
%% Results
% Train Data
TrainOutputs=evalfis(data.TrainInputs,fis);
PlotResults(data.TrainTargets,TrainOutputs,'Train Data');
plotconfusions(data.TrainTargets,TrainOutputs,'Train Data');
% Valid Data
ValOutputs=evalfis(data.ValInputs,fis);
PlotResults(data.ValTargets,ValOutputs,'Valid Data');
Plotconfusions(data.ValTargets,ValOutputs,'Test Data');
% Test Data
TestOutputs=evalfis(data.TestInputs,fis);
PlotResults(data.TestTargets,TestOutputs,'Test Data');
Plotconfusions(data.TestTargets,TestOutputs,'Test Data');
%overall Data
overallOutputs=evalfis(data.Inputs,fis);
PlotcResults(data.Targets,overallOutputs,'overall Data');
Plotconfusions(data.Targets,overallOutputs,'overall Data');
%plotconfusions.m
close all
clear variable
clc
function plotconfusion(tTrn, yTrn,tval,yval, tTst, yTst,tAll,yAll)
    data=load('dmdataset');
    Inputs=data.Inputs';
    Targets=data.Targets';
    Targets=Targets(:,1);
% Training Confusion Plot Variables
yTrn = data.TrainOutputs;
tTrn = data.TrainTargets;
%validation confusion plot variables
yval = data.ValOutputs;
tval = data.ValTargets;
% Test Confusion Plot Variables
yTst = data.TestsOutput;
tTst = data.TestTarget;
%all data Confusion Plot Variables
yAll = Inputs;
tAll = Targets;
end

mekia 2020-3-20

在 MATLAB Online 中打开

still no plotting .i make both lower case and am changing the code like this:

clc;
clear;
close all;
%% Load Data
data=LoadData();
%% Generate Basic FIS
fis=CreateInitialFIS(data,10);
%% Results
% Train Data
TrainOutputs=evalfis(data.TrainInputs,fis);
PlotResults(data.TrainTargets,TrainOutputs,'Train Data');
Plotconfusions(data.TrainTargets,TrainOutputs,'Train Data');
% Valid Data
ValOutputs=evalfis(data.ValInputs,fis);
PlotResults(data.ValTargets,ValOutputs,'Valid Data');
Plotconfusions(data.ValTargets,ValOutputs,'valid Data');
% Test Data
TestOutputs=evalfis(data.TestInputs,fis);
plotResults(data.TestTargets,TestOutputs,'Test Data');
plotconfusions(data.TestTargets,TestOutputs,'Test Data');
% overall Data
overallOutputs=evalfis(data.Inputs,fis);
plotResults(data.Targets,overallOutputs,'overall Data');
plotconfusions(data.Targets,overallOutputs,'overall Data');
%plotconfusion.m
close all
clear variable
clc
function plotconfusion(Targets,Inputs,name)
    data=load('dmdataset');
% Training Confusion Plot Variables
Inputs=evalfis(data.TrainInputs,fis);
Targets = data.TrainTargets;
%validation confusion plot variables
Inputs=evalfis(data.ValInputs,fis);
Targets = data.ValTargets;
% Test Confusion Plot Variables
Inputs=evalfis(data.TestInputs,fis);
Targets = data.TestTarget;
%all data Confusion Plot Variables
Inputs=evalfis(data.TrainInputs,fis);
Targets=data.Targets;
end

Walter Roberson 2020-3-20

Are you aware that in MATLAB, if you have a variable name in the input parameters section, the right hand side after the function name, and inside the function you assign a value to the (entire) variable, that the change has no effect on the calling function? Inside functions, any change you make to an entire variable is made only locally inside the function and discarded when the function returns.

Furthermore, the same thing happens if you modify part of a variable that is input, with one exception:

If you pass the handle to an object on input, and you modify a property of the handle object, then you do affect the calling environment. For example, if you pass gca() to a function and inside the function you change the XTicks property of the handle, then the change is made permanently to the graphics object. However this does not apply unless you are passed a handle: it does not apply if you are passed a struct for example.

Now reread your function definition and try to figure out why you are ignoring the data passed in, and you are assigning to local variables that you do nothing with, and especially you need to decide why your function is not plotting anything.

Also, you still have two calls to Plotconfusions with a capital P

Walter Roberson 2020-3-21

编辑：Walter Roberson 2020-3-21

在 MATLAB Online 中打开

clc;
clear;
close all;
%% Load Data
data=LoadData();
%% Generate Basic FIS
fis=CreateInitialFIS(data,10);
%% Results
% Train Data
TrainOutputs=evalfis(data.TrainInputs,fis);
PlotResults(data.TrainTargets,TrainOutputs,'Train Data');
function_that_does_useless_things(data.TrainTargets,TrainOutputs,'Train Data');
% Valid Data
ValOutputs=evalfis(data.ValInputs,fis);
PlotResults(data.ValTargets,ValOutputs,'Valid Data');
function_that_does_useless_things(data.ValTargets,ValOutputs,'valid Data');
% Test Data
TestOutputs=evalfis(data.TestInputs,fis);
plotResults(data.TestTargets,TestOutputs,'Test Data');
function_that_does_useless_things(data.TestTargets,TestOutputs,'Test Data');
% overall Data
overallOutputs=evalfis(data.Inputs,fis);
plotResults(data.Targets,overallOutputs,'overall Data');
function_that_does_useless_things(data.Targets,overallOutputs,'overall Data');
%function_that_does_useless_things.m
%close all
%clear variable
%clc
function function_that_does_useless_things(Targets,Inputs,name)
    data=load('dmdataset');
% Training Confusion Plot Variables
Inputs=evalfis(data.TrainInputs,fis);
Targets = data.TrainTargets;
%validation confusion plot variables
Inputs=evalfis(data.ValInputs,fis);
Targets = data.ValTargets;
% Test Confusion Plot Variables
Inputs=evalfis(data.TestInputs,fis);
Targets = data.TestTarget;
%all data Confusion Plot Variables
Inputs=evalfis(data.TrainInputs,fis);
Targets=data.Targets;
end

mekia 2020-3-25

am working preprocessing using Pima datase (thttps://www.kaggle.com/kumargh/pimaindiansdiabetescsv) and also am using class based mean preprocessing method for each input variable with respect to class( o or 1)and getting some error such as:

Error using fillmissing/parseInputs (line 361)

Fill method must be 'constant', 'previous', 'next', 'nearest', 'linear', 'spline', 'pchip', 'movmean', or 'movmedian'.

Error in fillmissing (line 116)

[A,AisTable,intM,intConstOrWinSize,extM,x,dim,dataVars] = parseInputs(A,fillMethod,varargin{:});

Error in prep1 (line 4)

data.Pregnancies(class_1)=fillmissing(data.Pregnancies,(class_1),'constant',class_1_mean_Pregnancies);

the code are found in below please debug my mistake if you have a little bit time.thank you for kindly accept my request.

with regard!

load ('pimadatapre.mat');

class_1=categorical(data.Outcome)=="o";

class_1_mean_Pregnancies = mean(data.Pregnancies(class_1), 'omitnan');

data.Pregnancies(class_1)=fillmissing(data.Pregnancies,(class_1),'constant',class_1_mean_Pregnancies);