Examples of CholeskyDecomposition

Jama.CholeskyDecomposition
Cholesky Decomposition.
For a symmetric, positive definite matrix A, the Cholesky decomposition is an lower triangular matrix L so that A = L*L'.
If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSPD() method.
cern.colt.matrix.linalg.CholeskyDecomposition
For a symmetric, positive definite matrix A, the Cholesky decomposition is a lower triangular matrix L so that A = L*L'; If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSymmetricPositiveDefinite() method.
org.apache.commons.math.linear.CholeskyDecomposition
nist.gov/javanumerics/jama/">JAMA library, with the following changes:
- a {@link #getLT() getLT} method has been added,
- the isspd method has been removed, the constructors of implementation classes being expected to throw {@link NotPositiveDefiniteMatrixException} when a matrix cannot be decomposed,
- a {@link #getDeterminant() getDeterminant} method has been added,
- the solve method has been replaced by a {@link #getSolver() getSolver} method and the equivalent method provided bythe returned {@link DecompositionSolver}.
@see MathWorld @see Wikipedia @version $Revision: 826627 $ $Date: 2009-10-19 12:27:47 +0200 (lun. 19 oct. 2009) $ @since 2.0
org.apache.commons.math3.linear.CholeskyDecomposition
nist.gov/javanumerics/jama/">JAMA library, with the following changes:
- a {@link #getLT() getLT} method has been added,
- the {@code isspd} method has been removed, since the constructor ofthis class throws a {@link NonPositiveDefiniteMatrixException} when amatrix cannot be decomposed,
- a {@link #getDeterminant() getDeterminant} method has been added,
- the {@code solve} method has been replaced by a {@link #getSolver() getSolver} method and the equivalent method provided by the returned{@link DecompositionSolver}.
@see MathWorld @see Wikipedia @since 2.0 (changed to concrete class in 3.0)
org.apache.mahout.math.CholeskyDecomposition
Cholesky decomposition shamelessly ported from JAMA.
A Cholesky decomposition of a semi-positive definite matrix A is a lower triangular matrix L such that L L^* = A. If A is full rank, L is unique. If A is real, then it must be symmetric and R will also be real.
org.encog.mathutil.matrices.decomposition.CholeskyDecomposition
nist.gov/javanumerics/jama/

Examples of Jama.CholeskyDecomposition

            errorCount = try_failure(errorCount, "solve()...", e1.getMessage());
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount, "solve()...", e.getMessage());
        }
        A = new Matrix(pvals);
        CholeskyDecomposition Chol = A.chol(); 
        Matrix L = Chol.getL();


        try {
            check(A, L.times(L.transpose()));
            try_success("CholeskyDecomposition...", "");
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount, "CholeskyDecomposition...",
                    "incorrect Cholesky decomposition calculation");
        }
        X = Chol.solve(Matrix.identity(3, 3));
        try {
            check(A.times(X), Matrix.identity(3, 3));
            try_success("CholeskyDecomposition solve()...", "");
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount,

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Examples of cern.colt.matrix.linalg.CholeskyDecomposition


   private void initL() {
      if (mu.length != sigma.rows() || mu.length != sigma.columns())
         throw new IllegalArgumentException
            ("Incompatible mean vector and covariance matrix");
      CholeskyDecomposition decomp = new CholeskyDecomposition (sigma);
      //if (!decomp.isSymmetricPositiveDefinite())
      //   throw new IllegalArgumentException
      //      ("The covariance matrix must be symmetric and positive-definite");
      sqrtSigma = decomp.getL();
   }

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Examples of cern.colt.matrix.linalg.CholeskyDecomposition

    */
   public void setSigma (DoubleMatrix2D sigma) {
      if (sigma.rows() != mu.length || sigma.columns() != mu.length)
         throw new IllegalArgumentException
            ("Invalid dimensions of covariance matrix");
      CholeskyDecomposition decomp = new CholeskyDecomposition (sigma);
      //if (!decomp.isSymmetricPositiveDefinite())
      //   throw new IllegalArgumentException
      //      ("The new covariance matrix must be symmetric and positive-definite");
      this.sigma.assign (sigma);
      this.sqrtSigma = decomp.getL();
   }

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Examples of cern.colt.matrix.linalg.CholeskyDecomposition


      if (mu.length != sigma.rows() ||
          mu.length != sigma.columns())
         throw new IllegalArgumentException
            ("Incompatible mean vector and covariance matrix dimensions");
      CholeskyDecomposition decomp = new CholeskyDecomposition (sigma);
      double[] temp = new double[mu.length];
      DoubleMatrix2D sqrtSigma = decomp.getL();
      for (int i = 0; i < temp.length; i++) {
         temp[i] = gen1.nextDouble();
         if (temp[i] == Double.NEGATIVE_INFINITY)
            temp[i] = -MYINF;
         if (temp[i] == Double.POSITIVE_INFINITY)

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Examples of org.apache.commons.math.linear.CholeskyDecomposition

    };


    /** test dimensions */
    @Test
    public void testDimensions() throws MathException {
        CholeskyDecomposition llt =
            new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(testData));
        assertEquals(testData.length, llt.getL().getRowDimension());
        assertEquals(testData.length, llt.getL().getColumnDimension());
        assertEquals(testData.length, llt.getLT().getRowDimension());
        assertEquals(testData.length, llt.getLT().getColumnDimension());
    }

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Examples of org.apache.commons.math.linear.CholeskyDecomposition


    /** test A = LLT */
    @Test
    public void testAEqualLLT() throws MathException {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        CholeskyDecomposition llt = new CholeskyDecompositionImpl(matrix);
        RealMatrix l  = llt.getL();
        RealMatrix lt = llt.getLT();
        double norm = l.multiply(lt).subtract(matrix).getNorm();
        assertEquals(0, norm, 1.0e-15);
    }

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Examples of org.apache.commons.math.linear.CholeskyDecomposition


    /** test that LT is transpose of L */
    @Test
    public void testLTTransposed() throws MathException {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        CholeskyDecomposition llt = new CholeskyDecompositionImpl(matrix);
        RealMatrix l  = llt.getL();
        RealMatrix lt = llt.getLT();
        double norm = l.subtract(lt.transpose()).getNorm();
        assertEquals(0, norm, 1.0e-15);
    }

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Examples of org.apache.commons.math.linear.CholeskyDecomposition

                {  2,  3,  0,  0,  0 },
                {  4,  5,  6,  0,  0 },
                {  7,  8,  9, 10,  0 },
                { 11, 12, 13, 14, 15 }
        });
       CholeskyDecomposition llt =
            new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(testData));


        // check values against known references
        RealMatrix l = llt.getL();
        assertEquals(0, l.subtract(lRef).getNorm(), 1.0e-13);
        RealMatrix lt = llt.getLT();
        assertEquals(0, lt.subtract(lRef.transpose()).getNorm(), 1.0e-13);


        // check the same cached instance is returned the second time
        assertTrue(l  == llt.getL());
        assertTrue(lt == llt.getLT());


    }

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Examples of org.apache.commons.math.linear.CholeskyDecomposition

    };


    /** test dimensions */
    @Test
    public void testDimensions() throws MathException {
        CholeskyDecomposition llt =
            new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(testData));
        assertEquals(testData.length, llt.getL().getRowDimension());
        assertEquals(testData.length, llt.getL().getColumnDimension());
        assertEquals(testData.length, llt.getLT().getRowDimension());
        assertEquals(testData.length, llt.getLT().getColumnDimension());
    }

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Examples of org.apache.commons.math.linear.CholeskyDecomposition


    /** test A = LLT */
    @Test
    public void testAEqualLLT() throws MathException {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        CholeskyDecomposition llt = new CholeskyDecompositionImpl(matrix);
        RealMatrix l  = llt.getL();
        RealMatrix lt = llt.getLT();
        double norm = l.multiply(lt).subtract(matrix).getNorm();
        assertEquals(0, norm, 1.0e-15);
    }

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