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

org.apache.commons.math.linear.RealMatrix
Interface defining a real-valued matrix with basic algebraic operations.
Matrix element indexing is 0-based -- e.g., getEntry(0, 0) returns the element in the first row, first column of the matrix.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $


  @Test
  public void testLeastSquares2()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new double[] { 10.0, 0.1 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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  @Test
  public void testLeastSquares3()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new Array2DRowRealMatrix(new double [][] {
          { 1.0, 1.2 }, { 1.2, 2.0 }
      }));
      NelderMead optimizer = new NelderMead();

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    @Test
    public void testMatricesValues() {
       BiDiagonalTransformer transformer =
            new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testSquare));
       final double s17 = FastMath.sqrt(17.0);
        RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
                {  -8 / (5 * s17), 19 / (5 * s17) },
                { -19 / (5 * s17), -8 / (5 * s17) }
        });
        RealMatrix bRef = MatrixUtils.createRealMatrix(new double[][] {
                { -3 * s17 / 5, 32 * s17 / 85 },
                {      0.0,     -5 * s17 / 17 }
        });
        RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
                { 1.0,  0.0 },
                { 0.0, -1.0 }
        });


        // check values against known references
        RealMatrix u = transformer.getU();
        Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
        RealMatrix b = transformer.getB();
        Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
        RealMatrix v = transformer.getV();
        Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);


        // check the same cached instance is returned the second time
        Assert.assertTrue(u == transformer.getU());
        Assert.assertTrue(b == transformer.getB());
        Assert.assertTrue(v == transformer.getV());

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                { 1, 3, 2, 6 },
                { 3, 13, 16, 2 },
                { 2, 16, 38, -1 },
                { 6, 2, -1, 197 }
        };
        RealMatrix covRM = MatrixUtils.createRealMatrix(cov);
        JDKRandomGenerator jg = new JDKRandomGenerator();
        jg.setSeed(5322145245211l);
        NormalizedRandomGenerator rg = new GaussianRandomGenerator(jg);
        CorrelatedRandomVectorGenerator sg =
            new CorrelatedRandomVectorGenerator(mean, covRM, 0.00001, rg);

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        }


    }


    public void testRootMatrix() {
        RealMatrix b = generator.getRootMatrix();
        RealMatrix bbt = b.multiply(b.transpose());
        for (int i = 0; i < covariance.getRowDimension(); ++i) {
            for (int j = 0; j < covariance.getColumnDimension(); ++j) {
                assertEquals(covariance.getEntry(i, j), bbt.getEntry(i, j), 1.0e-12);
            }
        }
    }

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            meanStat.increment(v);
            covStat.increment(v);
        }


        double[] estimatedMean = meanStat.getResult();
        RealMatrix estimatedCovariance = covStat.getResult();
        for (int i = 0; i < estimatedMean.length; ++i) {
            assertEquals(mean[i], estimatedMean[i], 0.07);
            for (int j = 0; j <= i; ++j) {
                assertEquals(covariance.getEntry(i, j),
                        estimatedCovariance.getEntry(i, j),
                        0.1 * (1.0 + FastMath.abs(mean[i])) * (1.0 + FastMath.abs(mean[j])));
            }
        }


    }

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    @Override
    public void setUp() {
        try {
            mean = new double[] { 0.0, 1.0, -3.0, 2.3};


            RealMatrix b = MatrixUtils.createRealMatrix(4, 3);
            int counter = 0;
            for (int i = 0; i < b.getRowDimension(); ++i) {
                for (int j = 0; j < b.getColumnDimension(); ++j) {
                    b.setEntry(i, j, 1.0 + 0.1 * ++counter);
                }
            }
            RealMatrix bbt = b.multiply(b.transpose());
            covariance = MatrixUtils.createRealMatrix(mean.length, mean.length);
            for (int i = 0; i < covariance.getRowDimension(); ++i) {
                covariance.setEntry(i, i, bbt.getEntry(i, i));
                for (int j = 0; j < covariance.getColumnDimension(); ++j) {
                    double s = bbt.getEntry(i, j);
                    covariance.setEntry(i, j, s);
                    covariance.setEntry(j, i, s);
                }
            }

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                { 1, 3, 2, 6 },
                { 3, 13, 16, 2 },
                { 2, 16, 38, -1 },
                { 6, 2, -1, 197 }
        };
        RealMatrix covRM = MatrixUtils.createRealMatrix(cov);
        JDKRandomGenerator jg = new JDKRandomGenerator();
        jg.setSeed(5322145245211l);
        NormalizedRandomGenerator rg = new GaussianRandomGenerator(jg);
        CorrelatedRandomVectorGenerator sg =
            new CorrelatedRandomVectorGenerator(mean, covRM, 0.00001, rg);

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        }


    }


    public void testRootMatrix() {
        RealMatrix b = generator.getRootMatrix();
        RealMatrix bbt = b.multiply(b.transpose());
        for (int i = 0; i < covariance.getRowDimension(); ++i) {
            for (int j = 0; j < covariance.getColumnDimension(); ++j) {
                assertEquals(covariance.getEntry(i, j), bbt.getEntry(i, j), 1.0e-12);
            }
        }
    }

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            meanStat.increment(v);
            covStat.increment(v);
        }


        double[] estimatedMean = meanStat.getResult();
        RealMatrix estimatedCovariance = covStat.getResult();
        for (int i = 0; i < estimatedMean.length; ++i) {
            assertEquals(mean[i], estimatedMean[i], 0.07);
            for (int j = 0; j <= i; ++j) {
                assertEquals(covariance.getEntry(i, j),
                        estimatedCovariance.getEntry(i, j),
                        0.1 * (1.0 + Math.abs(mean[i])) * (1.0 + Math.abs(mean[j])));
            }
        }


    }

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