@Test(expected = Test.None.class) public void testUSPS() throws Exception { System.out.println("USPS"); MathEx.setSeed(19650218); double[][] x = USPS.x; int[] y = USPS.y; double[][] testx = USPS.testx; int[] testy = USPS.testy; DeterministicAnnealing model = DeterministicAnnealing.fit(x, 10, 0.8, 100, 1E-4, 1E-2); System.out.println(model); double r = RandIndex.of(y, model.y); double r2 = AdjustedRandIndex.of(y, model.y); System.out.format("Training rand index = %.2f%%, adjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2); assertEquals(0.8975, r, 1E-4); assertEquals(0.4701, r2, 1E-4); System.out.format("MI = %.2f%n", MutualInformation.of(y, model.y)); System.out.format("NMI.joint = %.2f%%%n", 100 * NormalizedMutualInformation.joint(y, model.y)); System.out.format("NMI.max = %.2f%%%n", 100 * NormalizedMutualInformation.max(y, model.y)); System.out.format("NMI.min = %.2f%%%n", 100 * NormalizedMutualInformation.min(y, model.y)); System.out.format("NMI.sum = %.2f%%%n", 100 * NormalizedMutualInformation.sum(y, model.y)); System.out.format("NMI.sqrt = %.2f%%%n", 100 * NormalizedMutualInformation.sqrt(y, model.y)); int[] p = new int[testx.length]; for (int i = 0; i < testx.length; i++) { p[i] = model.predict(testx[i]); } r = RandIndex.of(testy, p); r2 = AdjustedRandIndex.of(testy, p); System.out.format("Testing rand index = %.2f%%, adjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2); assertEquals(0.8995, r, 1E-4); assertEquals(0.4745, r2, 1E-4); java.nio.file.Path temp = smile.data.Serialize.write(model); smile.data.Serialize.read(temp); }
public static DeterministicAnnealing fit(double[][] data, int Kmax) { return fit(data, Kmax, 0.9, 100, 1E-4, 1E-2); }
DeterministicAnnealing extends CentroidClustering<double[], double[]> { public static DeterministicAnnealing fit(double[][] data, int Kmax) { return fit(data, Kmax, 0.9, 100, 1E-4, 1E-2); } }
DeterministicAnnealing extends CentroidClustering<double[], double[]> { public static DeterministicAnnealing fit(double[][] data, int Kmax) { return fit(data, Kmax, 0.9, 100, 1E-4, 1E-2); } DeterministicAnnealing(double distortion, double[][] centroids, int[] y); }
DeterministicAnnealing extends CentroidClustering<double[], double[]> { public static DeterministicAnnealing fit(double[][] data, int Kmax) { return fit(data, Kmax, 0.9, 100, 1E-4, 1E-2); } DeterministicAnnealing(double distortion, double[][] centroids, int[] y); @Override double distance(double[] x, double[] y); static DeterministicAnnealing fit(double[][] data, int Kmax); static DeterministicAnnealing fit(double[][] data, int Kmax, double alpha, int maxIter, double tol, double splitTol); }
DeterministicAnnealing extends CentroidClustering<double[], double[]> { public static DeterministicAnnealing fit(double[][] data, int Kmax) { return fit(data, Kmax, 0.9, 100, 1E-4, 1E-2); } DeterministicAnnealing(double distortion, double[][] centroids, int[] y); @Override double distance(double[] x, double[] y); static DeterministicAnnealing fit(double[][] data, int Kmax); static DeterministicAnnealing fit(double[][] data, int Kmax, double alpha, int maxIter, double tol, double splitTol);  }