Error - Unable to use a value of type 'cell' as an index.

I am using MATLAB's classification learner app to train a new classification model.

When I run the code in MATLAB, it execute without error. However, I am interfacing this classification model with LabVIEW, and if I run the same code through the LabVIEW interface I get an error;

"Error occurred while executing script. Error message from server: ??? Unable to use a value of type 'cell' as an index.

Error in trainClassifier (line 48)

predictors = inputTable(:, predictorNames);"

In LabVIEW, I am reading a table of 'trainingData' directly from Excel, which is used as the input for the trainClassifer function. I have verified that this data has been imported into LabVIEW in the correct format.

I would appreciate if anyone could shed some light on what this error may be caused by, and how to correct it!

Thanks

The full code is as follows (exported directly from Classification Learner):

function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)

% [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)

% returns a trained classifier and its accuracy. This code recreates the

% classification model trained in Classification Learner app. Use the

% generated code to automate training the same model with new data, or to

% learn how to programmatically train models.

%

% Input:

% trainingData: a table containing the same predictor and response

% columns as imported into the app.

%

% Output:

% trainedClassifier: a struct containing the trained classifier. The

% struct contains various fields with information about the trained

% classifier.

%

% trainedClassifier.predictFcn: a function to make predictions on new

% data.

%

% validationAccuracy: a double containing the accuracy in percent. In

% the app, the History list displays this overall accuracy score for

% each model.

%

% Use the code to train the model with new data. To retrain your

% classifier, call the function from the command line with your original

% data or new data as the input argument trainingData.

%

% For example, to retrain a classifier trained with the original data set

% T, enter:

% [trainedClassifier, validationAccuracy] = trainClassifier(T)

%

% To make predictions with the returned 'trainedClassifier' on new data T2,

% use

% yfit = trainedClassifier.predictFcn(T2)

%

% T2 must be a table containing at least the same predictor columns as used

% during training. For details, enter:

% trainedClassifier.HowToPredict

% Auto-generated by MATLAB on 22-Jul-2019 08:31:22

% Extract predictors and response

% This code processes the data into the right shape for training the

% model.

inputTable = trainingData;

predictorNames = {'VarName1', 'VarName2', 'VarName3', 'VarName4', 'VarName5', 'VarName6', 'VarName7', 'VarName8', 'VarName9', 'VarName10', 'VarName11', 'VarName12', 'VarName13', 'VarName14', 'VarName15', 'VarName16', 'VarName17', 'VarName18', 'VarName19', 'VarName20', 'VarName21', 'VarName22', 'VarName23', 'VarName24', 'VarName25', 'VarName26', 'VarName27', 'VarName28', 'VarName29', 'VarName30', 'VarName31', 'VarName32', 'VarName33', 'VarName34', 'VarName35', 'VarName36', 'VarName37', 'VarName38', 'VarName39', 'VarName40', 'VarName41', 'VarName42', 'VarName43', 'VarName44', 'VarName45', 'VarName46', 'VarName47', 'VarName48', 'VarName49', 'VarName50'};

predictors = inputTable(:, predictorNames);

response = inputTable.VarName51;

isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];

% Train a classifier

% This code specifies all the classifier options and trains the classifier.

classificationKNN = fitcknn(...

predictors, ...

response, ...

'Distance', 'Euclidean', ...

'Exponent', [], ...

'NumNeighbors', 10, ...

'DistanceWeight', 'SquaredInverse', ...

'Standardize', true, ...

'ClassNames', [0; 1; 2; 3; 4]);

% Create the result struct with predict function

predictorExtractionFcn = @(t) t(:, predictorNames);

knnPredictFcn = @(x) predict(classificationKNN, x);

trainedClassifier.predictFcn = @(x) knnPredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct

trainedClassifier.RequiredVariables = {'VarName1', 'VarName10', 'VarName11', 'VarName12', 'VarName13', 'VarName14', 'VarName15', 'VarName16', 'VarName17', 'VarName18', 'VarName19', 'VarName2', 'VarName20', 'VarName21', 'VarName22', 'VarName23', 'VarName24', 'VarName25', 'VarName26', 'VarName27', 'VarName28', 'VarName29', 'VarName3', 'VarName30', 'VarName31', 'VarName32', 'VarName33', 'VarName34', 'VarName35', 'VarName36', 'VarName37', 'VarName38', 'VarName39', 'VarName4', 'VarName40', 'VarName41', 'VarName42', 'VarName43', 'VarName44', 'VarName45', 'VarName46', 'VarName47', 'VarName48', 'VarName49', 'VarName5', 'VarName50', 'VarName6', 'VarName7', 'VarName8', 'VarName9'};

trainedClassifier.ClassificationKNN = classificationKNN;

trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2019a.';

trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');

% Extract predictors and response

% This code processes the data into the right shape for training the

% model.

inputTable = trainingData;

predictorNames = {'VarName1', 'VarName2', 'VarName3', 'VarName4', 'VarName5', 'VarName6', 'VarName7', 'VarName8', 'VarName9', 'VarName10', 'VarName11', 'VarName12', 'VarName13', 'VarName14', 'VarName15', 'VarName16', 'VarName17', 'VarName18', 'VarName19', 'VarName20', 'VarName21', 'VarName22', 'VarName23', 'VarName24', 'VarName25', 'VarName26', 'VarName27', 'VarName28', 'VarName29', 'VarName30', 'VarName31', 'VarName32', 'VarName33', 'VarName34', 'VarName35', 'VarName36', 'VarName37', 'VarName38', 'VarName39', 'VarName40', 'VarName41', 'VarName42', 'VarName43', 'VarName44', 'VarName45', 'VarName46', 'VarName47', 'VarName48', 'VarName49', 'VarName50'};

predictors = inputTable(:, predictorNames);

response = inputTable.VarName51;

isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];

% Perform cross-validation

partitionedModel = crossval(trainedClassifier.ClassificationKNN, 'KFold', 4);

% Compute validation predictions

[validationPredictions, validationScores] = kfoldPredict(partitionedModel);

% Compute validation accuracy

validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

[ziyang huang]

I've actually encountered the same problem. I solved this problem by importing the .mat from the current workspace at the beginning. In that way the generated model or function will not require the cell type input, which can avoid the error you've mentioned.