Examples of MahalanobisDistanceMeasure

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

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  public void testDriverIterationsMahalanobisSeq() throws Exception {
    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as before
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description =
        new DistributionDescription(DistanceMeasureClusterDistribution.class.getName(),
                                    DenseVector.class.getName(),
                                    MahalanobisDistanceMeasure.class.getName(),
                                    2);

    Vector meanVector = new DenseVector(new double[] { 0.0, 0.0 });
    measure.setMeanVector(meanVector);
    Matrix m= new DenseMatrix(new double [][] {{0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);

    Path inverseCovarianceFile =
        new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.closeQuietly(out);
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as before

    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description =
        new DistributionDescription(DistanceMeasureClusterDistribution.class.getName(),
                                    DenseVector.class.getName(),
                                    MahalanobisDistanceMeasure.class.getName(),
                                    2);

    Vector meanVector = new DenseVector(new double[]{0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double [][] {{0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);

    Path inverseCovarianceFile =
        new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.closeQuietly(out);
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as
    // before
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description = new DistributionDescription(
        DistanceMeasureClusterDistribution.class.getName(), DenseVector.class.getName(),
        MahalanobisDistanceMeasure.class.getName(), 2);
   
    Vector meanVector = new DenseVector(new double[] {0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double[][] { {0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);
   
    Path inverseCovarianceFile = new Path(getTestTempDirPath("mahalanobis"),
        "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.close(out, true);
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, true, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as
    // before
   
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description = new DistributionDescription(
        DistanceMeasureClusterDistribution.class.getName(), DenseVector.class.getName(),
        MahalanobisDistanceMeasure.class.getName(), 2);
   
    Vector meanVector = new DenseVector(new double[] {0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double[][] { {0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);
   
    Path inverseCovarianceFile = new Path(getTestTempDirPath("mahalanobis"),
        "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.close(out, false);
View Full Code Here

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

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  public void testDriverIterationsMahalanobisSeq() throws Exception {
    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as before
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description =
        new DistributionDescription(DistanceMeasureClusterDistribution.class.getName(),
                                    DenseVector.class.getName(),
                                    MahalanobisDistanceMeasure.class.getName(),
                                    2);

    Vector meanVector = new DenseVector(new double[] { 0.0, 0.0 });
    measure.setMeanVector(meanVector);
    Matrix m= new DenseMatrix(new double [][] {{0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);

    Path inverseCovarianceFile =
        new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      out.close();
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as before

    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description =
        new DistributionDescription(DistanceMeasureClusterDistribution.class.getName(),
                                    DenseVector.class.getName(),
                                    MahalanobisDistanceMeasure.class.getName(),
                                    2);

    Vector meanVector = new DenseVector(new double[]{0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double [][] {{0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);

    Path inverseCovarianceFile =
        new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      out.close();
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 0, 0.5, 3.0);
    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as
    // before
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description = new DistributionDescription(
        DistanceMeasureClusterDistribution.class.getName(), DenseVector.class.getName(),
        MahalanobisDistanceMeasure.class.getName(), 2);
   
    Vector meanVector = new DenseVector(new double[] {0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double[][] { {0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);
   
    Path inverseCovarianceFile = new Path(getTestTempDirPath("mahalanobis"),
        "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.closeQuietly(out);
View Full Code Here

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

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    generateAsymmetricSamples(100, 0, 3, 0.3, 4.0);
    ClusteringTestUtils.writePointsToFile(sampleData, true, getTestTempFilePath("input/data.txt"), fs, conf);
    // Now run the driver using the run() method. Others can use runJob() as
    // before
   
    MahalanobisDistanceMeasure measure = new MahalanobisDistanceMeasure();
    DistributionDescription description = new DistributionDescription(
        DistanceMeasureClusterDistribution.class.getName(), DenseVector.class.getName(),
        MahalanobisDistanceMeasure.class.getName(), 2);
   
    Vector meanVector = new DenseVector(new double[] {0.0, 0.0});
    measure.setMeanVector(meanVector);
    Matrix m = new DenseMatrix(new double[][] { {0.5, 0.0}, {0.0, 4.0}});
    measure.setCovarianceMatrix(m);
   
    Path inverseCovarianceFile = new Path(getTestTempDirPath("mahalanobis"),
        "MahalanobisDistanceMeasureInverseCovarianceFile");
    conf.set("MahalanobisDistanceMeasure.inverseCovarianceFile", inverseCovarianceFile.toString());
    FileSystem fs = FileSystem.get(inverseCovarianceFile.toUri(), conf);
    MatrixWritable inverseCovarianceMatrix = new MatrixWritable(measure.getInverseCovarianceMatrix());
    DataOutputStream out = fs.create(inverseCovarianceFile);
    try {
      inverseCovarianceMatrix.write(out);
    } finally {
      Closeables.closeQuietly(out);
View Full Code Here
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