Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseMatrix

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      return r;
    }

    @Override
    public Matrix createInstance(Type type) {
      return new DenseMatrix();
    }
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                 Path outputTmpPath,
                 int numRows,
                 int numCols,
                 boolean isSymmetric,
                 int desiredRank) throws Exception {
    Matrix eigenVectors = new DenseMatrix(desiredRank, numCols);
    List<Double> eigenValues = new ArrayList<Double>();

    DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
    matrix.configure(new JobConf(getConf() != null ? getConf() : new Configuration()));
    solve(matrix, desiredRank, eigenVectors, eigenValues, isSymmetric);
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    double[][] matrix = { {0.7, 0.1, 0.1, 0.3}, {0.4, 0.4, 0.1, 0.1},
                         {0.1, 0.0, 0.8, 0.1}, {0.1, 0.1, 0.1, 0.7}};
    double[] labelSumArray = {1.2, 1.0, 1.0, 1.0};
    double[] featureSumArray = {1.3, 0.6, 1.1, 1.2};
   
    DenseMatrix weightMatrix = new DenseMatrix(matrix);
    DenseVector labelSum = new DenseVector(labelSumArray);
    DenseVector featureSum = new DenseVector(featureSumArray);
   
    double[] thetaNormalizerSum = {naiveBayesThetaWeight(0, weightMatrix, labelSum, featureSum),
                                   naiveBayesThetaWeight(1, weightMatrix, labelSum, featureSum),
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    double[][] matrix = { {0.7, 0.1, 0.1, 0.3}, {0.4, 0.4, 0.1, 0.1},
                         {0.1, 0.0, 0.8, 0.1}, {0.1, 0.1, 0.1, 0.7}};
    double[] labelSumArray = {1.2, 1.0, 1.0, 1.0};
    double[] featureSumArray = {1.3, 0.6, 1.1, 1.2};
   
    DenseMatrix weightMatrix = new DenseMatrix(matrix);
    DenseVector labelSum = new DenseVector(labelSumArray);
    DenseVector featureSum = new DenseVector(featureSumArray);
   
    double[] thetaNormalizerSum = {complementaryNaiveBayesThetaWeight(0, weightMatrix, labelSum, featureSum),
                                   complementaryNaiveBayesThetaWeight(1, weightMatrix, labelSum, featureSum),
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   *                         is used
   */
  public HmmModel(int nrOfHiddenStates, int nrOfOutputStates, long seed) {
    this.nrOfHiddenStates = nrOfHiddenStates;
    this.nrOfOutputStates = nrOfOutputStates;
    this.transitionMatrix = new DenseMatrix(nrOfHiddenStates, nrOfHiddenStates);
    this.emissionMatrix = new DenseMatrix(nrOfHiddenStates, nrOfOutputStates);
    this.initialProbabilities = new DenseVector(nrOfHiddenStates);
    // initialize a random, valid parameter set
    initRandomParameters(seed);
  }
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    }

    @Override
    public Matrix deserialize(JsonElement x, Type type, JsonDeserializationContext jsonDeserializationContext) {
      JsonObject data = x.getAsJsonObject();
      Matrix r = new DenseMatrix(data.get("rows").getAsInt(), data.get("cols").getAsInt());
      int i = 0;
      for (JsonElement row : data.get("data").getAsJsonArray()) {
        int j = 0;
        for (JsonElement element : row.getAsJsonArray()) {
          r.set(i, j, element.getAsDouble());
          j++;
        }
        i++;
      }
      return r;
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      return r;
    }

    @Override
    public Matrix createInstance(Type type) {
      return new DenseMatrix();
    }
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  public static Matrix getCumulativeTransitionMatrix(HmmModel model) {
    // fetch the needed parameters from the model
    int hiddenStates = model.getNrOfHiddenStates();
    Matrix transitionMatrix = model.getTransitionMatrix();
    // now compute the cumulative transition matrix
    Matrix resultMatrix = new DenseMatrix(hiddenStates, hiddenStates);
    for (int i = 0; i < hiddenStates; ++i) {
      double sum = 0;
      for (int j = 0; j < hiddenStates; ++j) {
        sum += transitionMatrix.get(i, j);
        resultMatrix.set(i, j, sum);
      }
      resultMatrix.set(i, hiddenStates - 1, 1.0);
      // make sure the last
      // state has always a
      // cumulative
      // probability of
      // exactly 1.0
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    // fetch the needed parameters from the model
    int hiddenStates = model.getNrOfHiddenStates();
    int outputStates = model.getNrOfOutputStates();
    Matrix outputMatrix = model.getEmissionMatrix();
    // now compute the cumulative output matrix
    Matrix resultMatrix = new DenseMatrix(hiddenStates, outputStates);
    for (int i = 0; i < hiddenStates; ++i) {
      double sum = 0;
      for (int j = 0; j < outputStates; ++j) {
        sum += outputMatrix.get(i, j);
        resultMatrix.set(i, j, sum);
      }
      resultMatrix.set(i, outputStates - 1, 1.0);
      // make sure the last
      // output state has
      // always a cumulative
      // probability of 1.0
    }
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        "--symmetric", "true"
    };
    new DistributedLanczosSolver().new DistributedLanczosSolverJob().run(args);

    Path rawEigenvectors = new Path(output, DistributedLanczosSolver.RAW_EIGENVECTORS);
    Matrix eigenVectors = new DenseMatrix(10, corpus.numCols());
    Configuration conf = new Configuration();

    FileSystem fs = FileSystem.get(rawEigenvectors.toUri(), conf);
    SequenceFile.Reader reader = new SequenceFile.Reader(fs, rawEigenvectors, conf);
    try {
      Writable key = reader.getKeyClass().asSubclass(Writable.class).newInstance();
      Writable value = reader.getValueClass().asSubclass(Writable.class).newInstance();
      int i = 0;
      while (reader.next(key, value)) {
        Vector v = ((VectorWritable) value).get();
        eigenVectors.assignRow(i, v);
        System.out.println("k=" + key.toString() + " V=" + AbstractCluster.formatVector(v, null));
        value = reader.getValueClass().asSubclass(Writable.class).newInstance();
        i++;
      }
      assertEquals("number of eigenvectors", 9, i);
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