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It may be a problem with the training (and parameters) rather than with the code itself: if the SVM model is simply a horizontal line (e.g. if the epsilon is too large for your data) then it will always output the same value (the bias).
I dont remember off the bat but depending on the default parameters for SVM training, the model nay expect to have data that jumps around in the range of 0-1 instead of hundreds. Try rescaling your input to a 0-1 range, or try to play around with your training parameters (mostly the epsilon if you're doing epsilon SVM).
Good luck! Basilio
