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

org.apache.commons.math.linear.SingularValueDecompositionImpl
Calculates the compact Singular Value Decomposition of a matrix.
The Singular Value Decomposition of matrix A is a set of three matrices: U, Σ and V such that A = U × Σ × V^T. Let A be a m × n matrix, then U is a m × p orthogonal matrix, Σ is a p × p diagonal matrix with positive or null elements, V is a p × n orthogonal matrix (hence V^T is also orthogonal) where p=min(m,n).
@version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 2.0

        checkOrthogonal(new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare).transpose()).getU());
    }


    /** test that V is orthogonal */
    public void testVOrthogonal() {
        checkOrthogonal(new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testSquare)).getV());
        checkOrthogonal(new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare)).getV());
        checkOrthogonal(new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare).transpose()).getV());
    }

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    }


    /** test matrices values */
    public void testMatricesValues1() {
       SingularValueDecomposition svd =
            new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testSquare));
        RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
                { 3.0 / 5.0, -4.0 / 5.0 },
                { 4.0 / 5.0,  3.0 / 5.0 }
        });
        RealMatrix sRef = MatrixUtils.createRealMatrix(new double[][] {
                { 3.0, 0.0 },
                { 0.0, 1.0 }
        });
        RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
                { 4.0 / 5.0,  3.0 / 5.0 },
                { 3.0 / 5.0, -4.0 / 5.0 }
        });


        // check values against known references
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());
        assertTrue(s == svd.getS());
        assertTrue(v == svd.getV());
        
    }

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            { -93.0 / 125.0,  -24.0 / 125.0, 80.0 / 125.0 }
        });


        // check values against known references
        SingularValueDecomposition svd =
            new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare));
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());
        assertTrue(s == svd.getS());
        assertTrue(v == svd.getV());


    }

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    }


    /** test condition number */
    public void testConditionNumber() {
        SingularValueDecompositionImpl svd =
            new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testSquare));
        assertEquals(3.0, svd.getConditionNumber(), 1.0e-15);
    }

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  @Override
  public double getCondition(final Matrix<?> m) {
    Validate.notNull(m, "m");
    if (m instanceof DoubleMatrix2D) {
      final RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix2D) m);
      final SingularValueDecomposition svd = new SingularValueDecompositionImpl(temp);
      return svd.getConditionNumber();
    }
    throw new IllegalArgumentException("Can only find condition number of DoubleMatrix2D; have " + m.getClass());
  }

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  @Override
  public DoubleMatrix2D getInverse(final Matrix<?> m) {
    Validate.notNull(m, "matrix was null");
    if (m instanceof DoubleMatrix2D) {
      final RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix2D) m);
      final SingularValueDecomposition sv = new SingularValueDecompositionImpl(temp);
      final RealMatrix inv = sv.getSolver().getInverse();
      return CommonsMathWrapper.unwrap(inv);
    }
    throw new IllegalArgumentException("Can only find inverse of DoubleMatrix2D; have " + m.getClass());
  }

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    if (m instanceof DoubleMatrix1D) {
      final RealVector temp = CommonsMathWrapper.wrap((DoubleMatrix1D) m);
      return temp.getNorm();
    } else if (m instanceof DoubleMatrix2D) {
      final RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix2D) m);
      final SingularValueDecomposition svd = new SingularValueDecompositionImpl(temp);
      return svd.getNorm();
    }
    throw new IllegalArgumentException("Can only find norm2 of DoubleMatrix2D; have " + m.getClass());
  }

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  @Override
  public SVDecompositionResult evaluate(final DoubleMatrix2D x) {
    Validate.notNull(x);
    MatrixValidate.notNaNOrInfinite(x);
    final RealMatrix commonsMatrix = CommonsMathWrapper.wrap(x);
    final SingularValueDecomposition svd = new SingularValueDecompositionImpl(commonsMatrix);
    return new SVDecompositionCommonsResult(svd);
  }

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