@Test public void test2DPlanes() { System.out.println("2dplanes"); MathEx.setSeed(19650218); double[] prediction = CrossValidation.regression(10, Planes.x, Planes.y, (xi, yi) -> RBFNetwork.fit(xi, yi, RBF.fit(xi, 20, 5.0))); double rmse = RMSE.of(Planes.y, prediction); System.out.println("RMSE = " + rmse); assertEquals(1.7161562336830596, rmse, 1E-4); }
public static <T> RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf) { return fit(x, y, rbf, false); }
RBFNetwork implements Regression<T> { public static <T> RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf) { return fit(x, y, rbf, false); } }
RBFNetwork implements Regression<T> { public static <T> RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf) { return fit(x, y, rbf, false); } RBFNetwork(RBF<T>[] rbf, double[] w, boolean normalized); }
RBFNetwork implements Regression<T> { public static <T> RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf) { return fit(x, y, rbf, false); } RBFNetwork(RBF<T>[] rbf, double[] w, boolean normalized); static RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf); static RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf, boolean normalized); boolean isNormalized(); @Override double predict(T x); }
RBFNetwork implements Regression<T> { public static <T> RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf) { return fit(x, y, rbf, false); } RBFNetwork(RBF<T>[] rbf, double[] w, boolean normalized); static RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf); static RBFNetwork<T> fit(T[] x, double[] y, RBF<T>[] rbf, boolean normalized); boolean isNormalized(); @Override double predict(T x);  }