Examples of MatrixWritable

de.jungblut.writable.MatrixWritable
Writable class for dense and sparse matrices.
org.apache.hama.commons.io.MatrixWritable
Majorly designed for dense matrices, can be extended for sparse ones as well.
org.apache.hama.ml.writable.MatrixWritable
Majorly designed for dense matrices, can be extended for sparse ones as well.
org.apache.mahout.math.MatrixWritable

Examples of org.apache.hama.ml.writable.MatrixWritable

    // write the number of neurons for each layer
    for (int i = 0; i < this.numberOfLayers; ++i) {
      output.writeInt(this.layerSizeArray[i]);
    }
    for (int i = 0; i < numberOfLayers - 1; ++i) {
      MatrixWritable matrixWritable = new MatrixWritable(this.weightMatrice[i]);
      matrixWritable.write(output);
    }
  }

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Examples of org.apache.mahout.math.MatrixWritable

      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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Examples of org.apache.mahout.math.MatrixWritable

      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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Examples of org.apache.mahout.math.MatrixWritable

    }
    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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Examples of org.apache.mahout.math.MatrixWritable

    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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Examples of org.apache.mahout.math.MatrixWritable


    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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Examples of org.apache.mahout.math.MatrixWritable


    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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Examples of org.apache.mahout.math.MatrixWritable

          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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Examples of org.apache.mahout.math.MatrixWritable

      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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Examples of org.apache.mahout.math.MatrixWritable


  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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