Why identical outputs despite different inputs to a machine learning models

why do I get the same predicted values despite having different inputs in a SVM model. For example, suppose the training data is matrixA, and the Two different Prediction data are MatrixC and MatrixD. Why is the predicted values identical?

A=trainedModel.predictFcn(matrixA);

B=trainedModel.predictFcn(matrixB);

MatrixB is a concatination of MatrixA with another Matrix---

I appreciate any help I can get?

Answer 1

[Bernhard Suhm]

Of course it could predict the same category for different kinds of inputs, especially if there aren't a lot of categories... Or for some reason, only the features represented by matrixA determined the final model, and the training of B ignored the additional features provided in the concatenated matrix. Or what am I missing?

[RICHARD BIGEGA]

My exercise is not classification. I am working on forecasting future values given a set of know past values X(x). I ampuzzled because when I train a model I get the same outputs in some instances dispite that the sets on which I am doing predictions are different. Below is an example of the matrix whicth the outputs in red