@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; KMeans model = KMeans.fit(x, 10, 100, 4); 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(0.9063, r, 1E-4); assertEquals(0.5148, 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(0.8942, r, 1E-4); assertEquals(0.4540, r2, 1E-4); java.nio.file.Path temp = smile.data.Serialize.write(model); smile.data.Serialize.read(temp); }
public static KMeans fit(double[][] data, int k) { return fit(data, k, 100, 1E-4); }
KMeans extends CentroidClustering<double[], double[]> { public static KMeans fit(double[][] data, int k) { return fit(data, k, 100, 1E-4); } }
KMeans extends CentroidClustering<double[], double[]> { public static KMeans fit(double[][] data, int k) { return fit(data, k, 100, 1E-4); } KMeans(double distortion, double[][] centroids, int[] y); }
KMeans extends CentroidClustering<double[], double[]> { public static KMeans fit(double[][] data, int k) { return fit(data, k, 100, 1E-4); } KMeans(double distortion, double[][] centroids, int[] y); @Override double distance(double[] x, double[] y); static KMeans fit(double[][] data, int k); static KMeans fit(double[][] data, int k, int maxIter, double tol); static KMeans fit(BBDTree bbd, double[][] data, int k, int maxIter, double tol); static KMeans lloyd(double[][] data, int k); static KMeans lloyd(double[][] data, int k, int maxIter, double tol); }
KMeans extends CentroidClustering<double[], double[]> { public static KMeans fit(double[][] data, int k) { return fit(data, k, 100, 1E-4); } KMeans(double distortion, double[][] centroids, int[] y); @Override double distance(double[] x, double[] y); static KMeans fit(double[][] data, int k); static KMeans fit(double[][] data, int k, int maxIter, double tol); static KMeans fit(BBDTree bbd, double[][] data, int k, int maxIter, double tol); static KMeans lloyd(double[][] data, int k); static KMeans lloyd(double[][] data, int k, int maxIter, double tol);  }