Editing this answer based on the comments below:
Ok, so it sounds like, for each test observation, you want the score of each output class, and you want those output class scores to be in the form of a probability.
Two steps:
(1) To get the scores, see https://www.mathworks.com/help/stats/export-classification-model-for-use-with-new-data.html#bu4764j-1
[yfit,scores] = C.predictFcn(T)
(2) Whether those scores represent probabilities depends on the type of classifier you have trained, and some other settings. See https://www.mathworks.com/matlabcentral/answers/395526-how-do-i-obtain-scores-as-probabilistic-estimates-using-the-predict-function-on-a-fitcensemble-model for more info for ensembles. Or, refer to the doc pages for the classifier that you have trained.
The initial answer: This appears to not be what you were asking, but if you want to get measures of accuracy over the test set on a per-class basis, you can use the confusionchart command https://www.mathworks.com/help/stats/confusionchart.html. You can use use the "ColumnSummary", "RowSummary" and "Normalization" name-value arguments to confusionchart to get absoluate counts or percentages with your choice of normalization.
