int[][] order = SmileUtils.sort(attributes, x); List<TrainingTask> tasks = new ArrayList<>(); for (int i = 0; i < ntrees; i++) {
@Override public RBFNetwork<T> train(T[] x, double[] y) { @SuppressWarnings("unchecked") T[] centers = (T[]) java.lang.reflect.Array.newInstance(x.getClass().getComponentType(), m); GaussianRadialBasis gaussian = SmileUtils.learnGaussianRadialBasis(x, centers, distance); if (rbf == null) { return new RBFNetwork<>(x, y, distance, gaussian, centers, normalized); } else { return new RBFNetwork<>(x, y, distance, rbf, centers, normalized); } }
int[] oob = new int[n]; int[][] order = SmileUtils.sort(attributes, x); List<TrainingTask> tasks = new ArrayList<>(); for (int i = 0; i < ntrees; i++) {
@Override public RBFNetwork<T> train(T[] x, int[] y) { @SuppressWarnings("unchecked") T[] centers = (T[]) java.lang.reflect.Array.newInstance(x.getClass().getComponentType(), m); GaussianRadialBasis gaussian = SmileUtils.learnGaussianRadialBasis(x, centers, distance); if (rbf == null) { return new RBFNetwork<>(x, y, distance, gaussian, centers, normalized); } else { return new RBFNetwork<>(x, y, distance, rbf, centers, normalized); } }
int[][] order = SmileUtils.sort(attributes, x);
final int[][] order = SmileUtils.sort(_attributes, x); final RegressionTree.NodeOutput output = new L2NodeOutput(response);
int[][] order = SmileUtils.sort(attributes, x); trees = new RegressionTree[ntrees];
final double[][] response = new double[k][n]; // pseudo response. final int[][] order = SmileUtils.sort(_attributes, x); final RegressionTree.NodeOutput[] output = new LKNodeOutput[k]; for(int i = 0; i < k; i++) {
double[][] response = new double[k][n]; // pseudo response. int[][] order = SmileUtils.sort(attributes, x); forest = new RegressionTree[k][ntrees];
int[][] order = SmileUtils.sort(attributes, x); RegressionTree.NodeOutput output = new L2NodeOutput(response); trees = new RegressionTree[ntrees];