Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseMatrix.numRows()

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    int desiredRank = 30;
    Matrix eigenVectors = new DenseMatrix(desiredRank, corpus.numCols());
    List<Double> eigenValues = new ArrayList<Double>();
    solver.solve(corpus, desiredRank, eigenVectors, eigenValues, symmetric);
    assertOrthonormal(eigenVectors);
    assertEigen(eigenVectors, corpus, eigenVectors.numRows() / 2, 0.01, symmetric);
  }

  public void testDistributedLanczosSolver() throws Exception {
  //  doTestDistributedLanczosSolver(false);
  //  TestCanopyCreation.rmr("testData");
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    for(int row = 0; row < eigenVectors.numRows(); row++) {
      Vector oldEigen = eigenVectors.viewRow(row);
      if(oldEigen == null) {
        break;
      }
      for(int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.viewRow(newRow);
        if(newEigen != null) {
          if(oldEigen.dot(newEigen) > 0.9) {
            oldEigensFound.add(row);
            break;
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    int desiredRank = 30;
    Matrix eigenVectors = new DenseMatrix(desiredRank, corpus.numCols());
    List<Double> eigenValues = new ArrayList<Double>();
    solver.solve(corpus, desiredRank, eigenVectors, eigenValues, symmetric);
    assertOrthonormal(eigenVectors);
    assertEigen(eigenVectors, corpus, eigenVectors.numRows() / 2, 0.01, symmetric);
  }

  @Test
  public void testDistributedLanczosSolver() throws Exception {
    doTestDistributedLanczosSolver(true);
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    for (int row = 0; row < eigenVectors.numRows(); row++) {
      Vector oldEigen = eigenVectors.viewRow(row);
      if (oldEigen == null) {
        break;
      }
      for (int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.viewRow(newRow);
        if (newEigen != null && oldEigen.dot(newEigen) > 0.9) {
          oldEigensFound.add(row);
          break;
        }
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    for(int row = 0; row < eigenVectors.numRows(); row++) {
      Vector oldEigen = eigenVectors.getRow(row);
      if(oldEigen == null) {
        break;
      }
      for(int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.getRow(newRow);
        if(newEigen != null) {
          if(oldEigen.dot(newEigen) > 0.9) {
            oldEigensFound.add(row);
            break;
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    for (int row = 0; row < eigenVectors.numRows(); row++) {
      Vector oldEigen = eigenVectors.viewRow(row);
      if (oldEigen == null) {
        break;
      }
      for (int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.viewRow(newRow);
        if (newEigen != null) {
          if (oldEigen.dot(newEigen) > 0.9) {
            oldEigensFound.add(row);
            break;
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    Matrix row_means_matrix = MatrixUtils.rowMeans(m); //m.rowMeans();
   
    assertEquals(1, row_means_matrix.numCols() );
    assertEquals(m.numRows(), row_means_matrix.numRows() );
   
    assertEquals( 6.0, row_means_matrix.get(0, 0), 0.0);
    assertEquals( 5.0, row_means_matrix.get(1, 0), 0.0);
   
    Matrix row_means_matrix_2 = MatrixUtils.mean(m, 1);
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    assertEquals( 5.0, row_means_matrix.get(1, 0), 0.0);
   
    Matrix row_means_matrix_2 = MatrixUtils.mean(m, 1);

    assertEquals(1, row_means_matrix_2.numCols() );
    assertEquals(m.numRows(), row_means_matrix_2.numRows() );
   
    assertEquals( 6.0, row_means_matrix_2.get(0, 0), 0.0);
    assertEquals( 5.0, row_means_matrix_2.get(1, 0), 0.0);

  }   
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    MatrixUtils.debug_print(v);
    MatrixUtils.debug_print(recon);
   
    // vector 0
    for ( int row = 0; row < v.numRows(); row++ ) {
   
      for ( int col = 0; col < v.numCols(); col++ ) {
     
        assertEquals( v.viewRow(row).get(col), recon.viewRow(row).get(col), 0.3 );
     
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    RandomGenerator rnd = new MersenneTwister(1234);
    //rnd.nextDouble();
   
    Matrix res = new DenseMatrix( rows, cols );
   
    for ( int r = 0; r < res.numRows(); r++ ) {
     
      for ( int c = 0; c < res.numCols(); c++ ) {
       
        res.set(r,  c, rnd.nextDouble());
        //System.out.println( "next: " + rnd.nextDouble() );
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