Package org.apache.mahout.math

Examples of org.apache.mahout.math.MatrixWritable

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      ParameteredGeneralizations.configureParameters(this, jobConf);
    }
    try {
      if (inverseCovarianceFile.get() != null) {
        FileSystem fs = FileSystem.get(inverseCovarianceFile.get().toUri(), jobConf);
        MatrixWritable inverseCovarianceMatrix = (MatrixWritable) vectorClass.get().newInstance();
        if (!fs.exists(inverseCovarianceFile.get())) {
          throw new FileNotFoundException(inverseCovarianceFile.get().toString());
        }
        DataInputStream in = fs.open(inverseCovarianceFile.get());
        try {
          inverseCovarianceMatrix.readFields(in);
        } finally {
          in.close();
        }
        this.inverseCovarianceMatrix = inverseCovarianceMatrix.get();
      }
     
      if (meanVectorFile.get() != null) {
        FileSystem fs = FileSystem.get(meanVectorFile.get().toUri(), jobConf);
        VectorWritable meanVector = (VectorWritable) vectorClass.get().newInstance();
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      ParameteredGeneralizations.configureParameters(this, jobConf);
    }
    try {
      if (inverseCovarianceFile.get() != null) {
        FileSystem fs = FileSystem.get(inverseCovarianceFile.get().toUri(), jobConf);
        MatrixWritable inverseCovarianceMatrix =
            ClassUtils.instantiateAs((Class<? extends MatrixWritable>) matrixClass.get(), MatrixWritable.class);
        if (!fs.exists(inverseCovarianceFile.get())) {
          throw new FileNotFoundException(inverseCovarianceFile.get().toString());
        }
        DataInputStream in = fs.open(inverseCovarianceFile.get());
        try {
          inverseCovarianceMatrix.readFields(in);
        } finally {
          Closeables.closeQuietly(in);
        }
        this.inverseCovarianceMatrix = inverseCovarianceMatrix.get();
        Preconditions.checkArgument(this.inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
      }

      if (meanVectorFile.get() != null) {
        FileSystem fs = FileSystem.get(meanVectorFile.get().toUri(), jobConf);
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    }
    return 0;
  }
 
  private static void exportText(Path inputPath, PrintStream out) throws IOException {
    MatrixWritable mw = new MatrixWritable();
    Text key = new Text();
    readSeqFile(inputPath, key, mw);
    Matrix m = mw.get();
    ConfusionMatrix cm = new ConfusionMatrix(m);
    out.println(cm.toString());
  }
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    ConfusionMatrix cm = new ConfusionMatrix(m);
    out.println(cm.toString());
  }
 
  private static void exportTable(Path inputPath, PrintStream out, boolean wrapHtml) throws IOException {
    MatrixWritable mw = new MatrixWritable();
    Text key = new Text();
    readSeqFile(inputPath, key, mw);
    String fileName = inputPath.getName();
    fileName = fileName.substring(fileName.lastIndexOf('/') + 1, fileName.length());
    Matrix m = mw.get();
    ConfusionMatrix cm = new ConfusionMatrix(m);
    if (wrapHtml) {
      printHeader(out, fileName);
    }
    out.println("<p/>");
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    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);
    }

    Path meanVectorFile = new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureMeanVectorFile");
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    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);
    }

    Path meanVectorFile = new Path(getTestTempDirPath("mahalanobis"), "MahalanobisDistanceMeasureMeanVectorFile");
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          new Path(params.get("confusionMatrix")), Text.class, MatrixWritable.class);
      String name = params.get("confusionMatrix");
      // embed file name as sequence key- useful for tuning classifiers
      name = name.substring(name.lastIndexOf('/') + 1, name.length());
      try {
        writer.append(new Text(name), new MatrixWritable(matrix.getMatrix()));
      } finally {
        Closeables.closeQuietly(writer);
      }
    }
  }
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      ParameteredGeneralizations.configureParameters(this, jobConf);
    }
    try {
      if (inverseCovarianceFile.get() != null) {
        FileSystem fs = FileSystem.get(inverseCovarianceFile.get().toUri(), jobConf);
        MatrixWritable inverseCovarianceMatrix =
            ClassUtils.instantiateAs((Class<? extends MatrixWritable>) matrixClass.get(), MatrixWritable.class);
        if (!fs.exists(inverseCovarianceFile.get())) {
          throw new FileNotFoundException(inverseCovarianceFile.get().toString());
        }
        DataInputStream in = fs.open(inverseCovarianceFile.get());
        try {
          inverseCovarianceMatrix.readFields(in);
        } finally {
          Closeables.close(in, true);
        }
        this.inverseCovarianceMatrix = inverseCovarianceMatrix.get();
        Preconditions.checkArgument(this.inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
      }

      if (meanVectorFile.get() != null) {
        FileSystem fs = FileSystem.get(meanVectorFile.get().toUri(), jobConf);
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  private LossyHmmSerializer() {
  }

  static void serialize(HmmModel model, DataOutput output) throws IOException {
    MatrixWritable matrix = new MatrixWritable(model.getEmissionMatrix());
    matrix.write(output);
    matrix.set(model.getTransitionMatrix());
    matrix.write(output);

    VectorWritable vector = new VectorWritable(model.getInitialProbabilities());
    vector.write(output);
  }
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    VectorWritable vector = new VectorWritable(model.getInitialProbabilities());
    vector.write(output);
  }

  static HmmModel deserialize(DataInput input) throws IOException {
    MatrixWritable matrix = new MatrixWritable();
    matrix.readFields(input);
    Matrix emissionMatrix = matrix.get();

    matrix.readFields(input);
    Matrix transitionMatrix = matrix.get();

    VectorWritable vector = new VectorWritable();
    vector.readFields(input);
    Vector initialProbabilities = vector.get();
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Related Classes of org.apache.mahout.math.MatrixWritable

  • org.apache.mahout.classifier.bayes.mapreduce.bayes.BayesClassifierDriver
  • org.apache.mahout.classifier.ConfusionMatrixDumper
  • org.apache.mahout.classifier.sequencelearning.hmm.LossyHmmSerializer
  • org.apache.mahout.clustering.dirichlet.TestMapReduce
  • org.apache.mahout.common.distance.MahalanobisDistanceMeasure
  • org.apache.mahout.h2obindings.drm.H2OBCast
  • org.apache.mahout.math.list.IntArrayList
  • org.apache.mahout.math.ssvd.SequentialOutOfCoreSvd
  • org.apache.mahout.math.ssvd.SequentialOutOfCoreSvdTest
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