Package org.apache.mahout.math

Examples of org.apache.mahout.math.CardinalityException

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   * @param m A covariance matrix.
   * @throws IllegalArgumentException if <tt>eigen values equal to 0 found</tt>.
   */
  public void setCovarianceMatrix(Matrix m) {
    if (m.numRows() != m.numCols()) {
      throw new CardinalityException(m.numRows(), m.numCols());
    }
    // See http://www.mlahanas.de/Math/svd.htm for details,
    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
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   * @param other   a DistributedRowMatrix
   * @return    a DistributedRowMatrix containing the product
   */
  public DistributedRowMatrix times(DistributedRowMatrix other) throws IOException {
    if (numRows != other.numRows()) {
      throw new CardinalityException(numRows, other.numRows());
    }
    Path outPath = new Path(outputTmpBasePath.getParent(), "productWith-" + (System.nanoTime() & 0xFF));
   
    Configuration initialConf = getConf() == null ? new Configuration() : getConf();
    Configuration conf =
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   *          of data- and labelset do not match, a CardinalityException is
   *          thrown
   */
  public void train(Vector labelset, Matrix dataset) throws TrainingException {
    if (labelset.size() != dataset.size()[1]) {
      throw new CardinalityException(labelset.size(), dataset.size()[1]);
    }
   
    boolean converged = false;
    int iteration = 0;
    while (!converged) {
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   * @return    a DistributedRowMatrix containing the product
   * @throws IOException
   */
  public DistributedRowMatrix times(DistributedRowMatrix other) throws IOException {
    if (numRows != other.numRows()) {
      throw new CardinalityException(numRows, other.numRows());
    }
    Path outPath = new Path(outputTmpBasePath.getParent(), "productWith-" + (System.nanoTime() & 0xFF));
    JobConf conf = MatrixMultiplicationJob.createMatrixMultiplyJobConf(rowPath, other.rowPath, outPath, other.numCols);
    JobClient.runJob(conf);
    DistributedRowMatrix out = new DistributedRowMatrix(outPath, outputTmpPath, numCols, other.numCols());
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  }

  @Override
  public double distance(Vector v1, Vector v2) {
    if (v1.size() != v2.size()) {
      throw new CardinalityException(v1.size(), v2.size());
    }
    return Math.sqrt(v1.minus(v2).dot(Algebra.mult(inverseCovarianceMatrix, v1.minus(v2))));
  }
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   * @param m A covariance matrix.
   * @throws IllegalArgumentException if <tt>eigen values equal to 0 found</tt>.
   */
  public void setCovarianceMatrix(Matrix m) {
    if (m.numRows() != m.numCols()) {
      throw new CardinalityException(m.numRows(), m.numCols());
    }
    // See http://www.mlahanas.de/Math/svd.htm for details,
    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
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  }

  @Override
  public double distance(Vector v1, Vector v2) {
    if (v1.size() != v2.size()) {
      throw new CardinalityException(v1.size(), v2.size());
    }
    return v1.aggregate(v2, Functions.PLUS, Functions.MINUS_ABS);
  }
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   * @param outPath path to write result to
   * @return    a DistributedRowMatrix containing the product
   */
  public DistributedRowMatrix times(DistributedRowMatrix other, Path outPath) throws IOException {
    if (numRows != other.numRows()) {
      throw new CardinalityException(numRows, other.numRows());
    }

    Configuration initialConf = getConf() == null ? new Configuration() : getConf();
    Configuration conf =
        MatrixMultiplicationJob.createMatrixMultiplyJobConf(initialConf,
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  }
 
  @Override
  public double distance(Vector v1, Vector v2) {
    if (v1.size() != v2.size()) {
      throw new CardinalityException(v1.size(), v2.size());
    }
    return v1.aggregate(v2, Functions.MAX_ABS, Functions.MINUS);
  }
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  }
 
  @Override
  public double distance(Vector v1, Vector v2) {
    if (v1.size() != v2.size()) {
      throw new CardinalityException(v1.size(), v2.size());
    }
    double lengthSquaredv1 = v1.getLengthSquared();
    double lengthSquaredv2 = v2.getLengthSquared();
   
    double dotProduct = v2.dot(v1);
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  • org.apache.mahout.classifier.discriminative.LinearTrainer
  • org.apache.mahout.common.distance.ChebyshevDistanceMeasure
  • org.apache.mahout.common.distance.CosineDistanceMeasure
  • org.apache.mahout.common.distance.MahalanobisDistanceMeasure
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  • org.apache.mahout.math.hadoop.DistributedRowMatrix
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