Package org.apache.mahout.math

Examples of org.apache.mahout.math.SingularValueDecomposition

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    });
    // Verify all errors are nearly zero.
    assertEquals(0, columnNorms.norm(1) / columnNorms.size(), 0.1);

    // Verify that the centroids are a permutation of the original ones.
    SingularValueDecomposition svd = new SingularValueDecomposition(x);
    Vector s = svd.getS().viewDiagonal().assign(Functions.div(6));
    assertEquals(5, s.getLengthSquared(), 0.05);
    assertEquals(5, s.norm(1), 0.05);
  }
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  @Test
  public void testSingularValues() {
    Matrix A = lowRankMatrix();

    SequentialBigSvd s = new SequentialBigSvd(A, 8);
    SingularValueDecomposition svd = new SingularValueDecomposition(A);

    Vector reference = new DenseVector(svd.getSingularValues()).viewPart(0, 8);
    assertEquals(reference, s.getSingularValues());

    assertEquals(A, s.getU().times(new DiagonalMatrix(s.getSingularValues())).times(s.getV().transpose()));
  }
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  @Test
  public void testLeftVectors() {
    Matrix A = lowRankMatrix();

    SequentialBigSvd s = new SequentialBigSvd(A, 8);
    SingularValueDecomposition svd = new SingularValueDecomposition(A);

    // can only check first few singular vectors because once the singular values
    // go to zero, the singular vectors are not uniquely determined
    Matrix u1 = svd.getU().viewPart(0, 20, 0, 4).assign(Functions.ABS);
    Matrix u2 = s.getU().viewPart(0, 20, 0, 4).assign(Functions.ABS);
    assertEquals(0, u1.minus(u2).aggregate(Functions.PLUS, Functions.ABS), 1.0e-9);
  }
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  @Test
  public void testRightVectors() {
    Matrix A = lowRankMatrix();

    SequentialBigSvd s = new SequentialBigSvd(A, 6);
    SingularValueDecomposition svd = new SingularValueDecomposition(A);

    Matrix v1 = svd.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    Matrix v2 = s.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    assertEquals(v1, v2);
  }
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        b2.assign(bTmp.get().times(bTmp.get().transpose()), Functions.PLUS);
      }
    }
    l2 = new CholeskyDecomposition(b2);
    svd = new SingularValueDecomposition(l2.getL());
  }
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  @Test
  public void testLeftVectors() throws IOException {
    Matrix A = lowRankMatrixInMemory(20, 20);

    SequentialBigSvd s = new SequentialBigSvd(A, 6);
    SingularValueDecomposition svd = new SingularValueDecomposition(A);

    // can only check first few singular vectors
    Matrix u1 = svd.getU().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    Matrix u2 = s.getU().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    assertEquals(u1, u2);
  }
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  @Test
  public void testRightVectors() throws IOException {
    Matrix A = lowRankMatrixInMemory(20, 20);

    SequentialBigSvd s = new SequentialBigSvd(A, 6);
    SingularValueDecomposition svd = new SingularValueDecomposition(A);

    Matrix v1 = svd.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    Matrix v2 = s.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    assertEquals(v1, v2);
  }
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      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);
    Matrix sInv = svd.getS();
    // Inverse Diagonal Elems
    for (int i = 0; i < sInv.numRows(); i++) {
      double diagElem = sInv.get(i,i);
      if (diagElem > 0.0) {
        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
  }
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    // try to run the same thing without stochastic algo
    double[][] a = SSVDSolver.loadDistributedRowMatrix(fs, aPath, conf);

    // SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
    // Array2DRowRealMatrix(a));
    SingularValueDecomposition svd2 = new SingularValueDecomposition(
        new DenseMatrix(a));

    a = null;

    double[] svalues2 = svd2.getSingularValues();
    dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      Assert.assertTrue(Math.abs(svalues2[i] - stochasticSValues[i]) <= s_epsilon);
    }
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    // try to run the same thing without stochastic algo
    double[][] a = SSVDSolver.loadDistributedRowMatrix(fs, aPath, conf);

    // SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
    // Array2DRowRealMatrix(a));
    SingularValueDecomposition svd2 = new SingularValueDecomposition(new DenseMatrix(a));

    a = null;

    double[] svalues2 = svd2.getSingularValues();
    dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      Assert.assertTrue(Math.abs(svalues2[i] - stochasticSValues[i]) <= s_epsilon);
    }
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Related Classes of org.apache.mahout.math.SingularValueDecomposition

  • org.apache.mahout.clustering.streaming.cluster.BallKMeansTest
  • org.apache.mahout.common.distance.MahalanobisDistanceMeasure
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDPCADenseTest
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDSolverDenseTest
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDSolverSparseSequentialTest
  • org.apache.mahout.math.hadoop.stochasticsvd.SSVDPrototype
  • org.apache.mahout.math.solver.LSMRTest
  • org.apache.mahout.math.ssvd.SequentialBigSvd
  • org.apache.mahout.math.ssvd.SequentialBigSvdTest
  • org.apache.mahout.math.ssvd.SequentialOutOfCoreSvd
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