How to use

SeedFromKMeans_F64

in

org.ddogleg.clustering.gmm

/**
 * <p>
 * High level interface for creating GMM cluster.  If more flexibility is needed (e.g. custom seeds)
 * then create and instance of {@link ExpectationMaximizationGmm_F64} directly
 * </p>
 *
 * <p>WARNING: DEVELOPMENTAL AND IS LIKELY TO FAIL HORRIBLY</p>
 *
 * @param maxIterations Maximum number of iterations it will perform.
 * @param maxConverge Maximum iterations allowed before convergence.  Re-seeded if it doesn't converge.
 * @param convergeTol Distance based convergence tolerance.  Try 1e-8
 * @return ExpectationMaximizationGmm_F64
 */
public static ExpectationMaximizationGmm_F64 gaussianMixtureModelEM_F64(
    int maxIterations, int maxConverge , double convergeTol) {
  StandardKMeans_F64 kmeans = kMeans_F64(null,maxIterations,maxConverge,convergeTol);
  SeedFromKMeans_F64 seeds = new SeedFromKMeans_F64(kmeans);
  return new ExpectationMaximizationGmm_F64(maxIterations,convergeTol,seeds);
}

SeedFromKMeans_F64 alg = new SeedFromKMeans_F64(createKMeans());
alg.init(2,234234);
seeds.add( new GaussianGmm_F64(2));
alg.selectSeeds(points,seeds);

/**
 * <p>
 * High level interface for creating GMM cluster.  If more flexibility is needed (e.g. custom seeds)
 * then create and instance of {@link ExpectationMaximizationGmm_F64} directly
 * </p>
 *
 * <p>WARNING: DEVELOPMENTAL AND IS LIKELY TO FAIL HORRIBLY</p>
 *
 * @param maxIterations Maximum number of iterations it will perform.
 * @param maxConverge Maximum iterations allowed before convergence.  Re-seeded if it doesn't converge.
 * @param convergeTol Distance based convergence tolerance.  Try 1e-8
 * @return ExpectationMaximizationGmm_F64
 */
public static ExpectationMaximizationGmm_F64 gaussianMixtureModelEM_F64(
    int maxIterations, int maxConverge , double convergeTol) {
  StandardKMeans_F64 kmeans = kMeans_F64(null,maxIterations,maxConverge,convergeTol);
  SeedFromKMeans_F64 seeds = new SeedFromKMeans_F64(kmeans);
  return new ExpectationMaximizationGmm_F64(maxIterations,convergeTol,seeds);
}

  @Override
  public ComputeClusters<double[]> createClustersAlg( boolean hint ) {

    if( hint ) {
      return new ExpectationMaximizationGmm_F64(1000, 1e-8, seeds);
    } else {
      InitializeStandard_F64 kseeds = new InitializeStandard_F64();
      StandardKMeans_F64 kmeans = new StandardKMeans_F64(1000,1000,1e-8,kseeds);
      SeedFromKMeans_F64 seeds = new SeedFromKMeans_F64(kmeans);
      return new ExpectationMaximizationGmm_F64(1000, 1e-8, seeds);
    }
  }
}