Class GaussianProcessRegression.Trainer<T>

    • Constructor Detail

      • Trainer

        public Trainer​(MercerKernel<T> kernel,
                       double lambda)
        Constructor.
        Parameters:
        kernel - the Mercer kernel.
        lambda - the shrinkage/regularization parameter.
    • Method Detail

      • train

        public GaussianProcessRegression<T> train​(T[] x,
                                                  double[] y)
        Description copied from class: RegressionTrainer
        Learns a regression model with given training data.
        Specified by:
        train in class RegressionTrainer<T>
        Parameters:
        x - the training instances.
        y - the training response values.
        Returns:
        a trained regression model.
      • train

        public GaussianProcessRegression<T> train​(T[] x,
                                                  double[] y,
                                                  T[] t)
        Learns a Gaussian Process with given subset of regressors.
        Parameters:
        x - training samples.
        y - training labels in [0, k), where k is the number of classes.
        t - the inducing input, which are pre-selected or inducing samples acting as active set of regressors. Commonly, these can be chosen as the centers of k-means clustering.
        Returns:
        a trained Gaussian Process.