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SSGP code simple usage tutorial

Following these simple steps, the experimental section of the article "Sparse Spectrum Gaussian Processes for Regression" (submitted for review) can be easily reproduced. If you want to do that, you might also want to download the corresponding Regression datasets.


  1. Download the matlab code for SSGP regression.
  2. Unzip it to some directory of your choice.
  3. Within matlab, change to that directory (cd directory) or add it to the path (addpath directory).
  4. Format your input data adequately:
  5. Set m to the desired number of spectral points (one half the number of desired basis functions) and run the main function:

    [NMSE, mu, S2, NMLP, loghyper, convergence] = ssgprfixed_ui(X_tr, T_tr, X_tst, T_tst, m);

  6. The function selects the model using training data and makes predictions for test data. It returns the predictive mean (mu), variance (S2), error measures on test data NMSE (Normalized Mean Square Error) and NMLP (Negative Mean Log Probability), selected hyperparameters (loghyper) and convergence curve.


Using SSGP learning the spectral points

© Miguel Lázaro-Gredilla
Last modified: 2009-03-04, 18:47