Package org.apache.mahout.common.distance

Examples of org.apache.mahout.common.distance.EuclideanDistanceMeasure

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    clusterDumper.printClusters(termDictionary);
  }
 
  @Test
  public void testFuzzyKmeans() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, getTestTempDirPath("testdata"), output, measure, 8,
        4, false, true);
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    clusterDumper.printClusters(termDictionary);
  }
 
  // @Test
  public void testKmeansSVD() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration conf = new Configuration();
    solver.setConf(conf);
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    clusterDumper.printClusters(termDictionary);
  }
 
  // @Test
  public void testKmeansDSVD() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration config = new Configuration();
    solver.setConf(config);
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    clusterDumper.printClusters(termDictionary);
  }
 
  // @Test
  public void testKmeansDSVD2() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration config = new Configuration();
    solver.setConf(config);
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      Vector point = new DenseVector(doubles.size());
      int index = 0;
      for (Double d : doubles) {
        point.set(index++, d);
      }
      MeanShiftCanopy canopy = new MeanShiftCanopy(point, nextCanopyId++, new EuclideanDistanceMeasure());
      context.write(new Text(), canopy);
    }
  }
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  }
 
  public static void main(String[] args) throws Exception {
    t1 = 1.5;
    t2 = 0.5;
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    IKernelProfile kernelProfile = new TriangularKernelProfile();
    significance = 0.02;
   
    Path samples = new Path("samples");
    Path output = new Path("output");
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      log.info("Running with default arguments");
      Path output = new Path("output");
      Configuration conf = new Configuration();
      HadoopUtil.delete(conf, output);
      run(conf, new Path("testdata"), output,
          new EuclideanDistanceMeasure(), 80, 55, 10, (float) 2, 0.5);
    }
  }
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    } else {
      log.info("Running with default arguments");
      Path output = new Path("output");
      Configuration conf = new Configuration();
      HadoopUtil.delete(conf, output);
      run(conf, new Path("testdata"), output, new EuclideanDistanceMeasure(), 6, 0.5, 10);
    }
  }
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      log.info("Running with default arguments");
      Path output = new Path("output");
      Configuration conf = new Configuration();
      HadoopUtil.delete(conf, output);
      run(conf, new Path("testdata"), output,
          new EuclideanDistanceMeasure(), new TriangularKernelProfile(), 47.6,
          1, 0.5, 10);
    }
  }
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      ToolRunner.run(new Configuration(), new Job(), args);
    } else {
      log.info("Running with default arguments");
      Path output = new Path("output");
      HadoopUtil.delete(new Configuration(), output);
      run(new Path("testdata"), output, new EuclideanDistanceMeasure(), 80, 55);
    }
  }
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Related Classes of org.apache.mahout.common.distance.EuclideanDistanceMeasure

  • mia.clustering.ch07.SimpleKMeansClustering
  • mia.clustering.ch09.CanopyExample
  • mia.clustering.ch09.FuzzyKMeansExample
  • mia.clustering.ch09.KMeansExample
  • mia.clustering.ch09.NewsKMeansClustering
  • org.apache.mahout.benchmark.VectorBenchmarks
  • org.apache.mahout.clustering.canopy.TestCanopyCreation
  • org.apache.mahout.clustering.cdbw.TestCDbwEvaluator
  • org.apache.mahout.clustering.ClusteringUtils
  • org.apache.mahout.clustering.conversion.meanshift.InputMapper
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