During the compilation process, a dependency analysis is run to pick up files that the code relies on. In this case, the dependent functions are specified as a value in the trained model struct loaded through a MAT file. This causes the analysis to miss the necessary files.
To work around this, we can add a "function pragma" to the function to make the dependency explicit:
function myAppCallback(app, event)
[ additional code ]
The "function pragma" was added based on the "ClassificationSVM" values in the trained model struct:
trainedModel =
struct with fields:
predictFcn: @(x)exportableModel.predictFcn(predictorExtractionFcn(x))
ClassificationSVM: [1×1 classreg.learning.classif.CompactClassificationECOC]
About: 'This struct is a trained model exported from Classification Learner R2017b.'
HowToPredict: 'To make predictions on a new predictor column matrix, X, use: ...
Multiple functions can be added to the pragma with spaces:
Additional information about the "function pragma" can be found in the following link (see example 3 in particular):
Some of the warnings will remain, but those are known and expected and will not affect the functionality of the application.
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