Java Code Examples for org.apache.commons.math.linear.RealMatrix#multiply()

The following examples show how to use org.apache.commons.math.linear.RealMatrix#multiply() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: QRSolverTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
public void testUnderdetermined() {
    final Random r    = new Random(42185006424567123l);
    int          p    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
    int          q    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
    RealMatrix   a    = createTestMatrix(r, p, q);
    RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);
    RealMatrix   b    = a.multiply(xRef);
    RealMatrix   x = new QRDecompositionImpl(a).getSolver().solve(b);

    // too many equations, the system cannot be solved at all
    assertTrue(x.subtract(xRef).getNorm() / (p * q) > 0.01);

    // the last unknown should have been set to 0
    assertEquals(0.0, x.getSubMatrix(p, q - 1, 0, x.getColumnDimension() - 1).getNorm());

}
Example 2
Source File: QRSolverTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
public void testUnderdetermined() {
    final Random r    = new Random(42185006424567123l);
    int          p    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
    int          q    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
    RealMatrix   a    = createTestMatrix(r, p, q);
    RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);
    RealMatrix   b    = a.multiply(xRef);
    RealMatrix   x = new QRDecompositionImpl(a).getSolver().solve(b);

    // too many equations, the system cannot be solved at all
    assertTrue(x.subtract(xRef).getNorm() / (p * q) > 0.01);

    // the last unknown should have been set to 0
    assertEquals(0.0, x.getSubMatrix(p, q - 1, 0, x.getColumnDimension() - 1).getNorm());

}
Example 3
Source File: QRSolverTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
public void testOverdetermined() {
    final Random r    = new Random(5559252868205245l);
    int          p    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
    int          q    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
    RealMatrix   a    = createTestMatrix(r, p, q);
    RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);

    // build a perturbed system: A.X + noise = B
    RealMatrix b = a.multiply(xRef);
    final double noise = 0.001;
    b.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return value * (1.0 + noise * (2 * r.nextDouble() - 1));
        }
    });

    // despite perturbation, the least square solution should be pretty good
    RealMatrix x = new QRDecompositionImpl(a).getSolver().solve(b);
    assertEquals(0, x.subtract(xRef).getNorm(), 0.01 * noise * p * q);

}
Example 4
Source File: QRSolverTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
public void testOverdetermined() {
    final Random r    = new Random(5559252868205245l);
    int          p    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
    int          q    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
    RealMatrix   a    = createTestMatrix(r, p, q);
    RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);

    // build a perturbed system: A.X + noise = B
    RealMatrix b = a.multiply(xRef);
    final double noise = 0.001;
    b.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return value * (1.0 + noise * (2 * r.nextDouble() - 1));
        }
    });

    // despite perturbation, the least square solution should be pretty good
    RealMatrix x = new QRDecompositionImpl(a).getSolver().solve(b);
    assertEquals(0, x.subtract(xRef).getNorm(), 0.01 * noise * p * q);

}
Example 5
Source File: QRSolverTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
public void testOverdetermined() {
    final Random r    = new Random(5559252868205245l);
    int          p    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
    int          q    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
    RealMatrix   a    = createTestMatrix(r, p, q);
    RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);

    // build a perturbed system: A.X + noise = B
    RealMatrix b = a.multiply(xRef);
    final double noise = 0.001;
    b.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return value * (1.0 + noise * (2 * r.nextDouble() - 1));
        }
    });

    // despite perturbation, the least square solution should be pretty good
    RealMatrix x = new QRDecompositionImpl(a).getSolver().solve(b);
    assertEquals(0, x.subtract(xRef).getNorm(), 0.01 * noise * p * q);

}
Example 6
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
@Test
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) {
            Assert.assertEquals(covariance.getEntry(i, j), bbt.getEntry(i, j), 1.0e-12);
        }
    }
}
Example 7
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
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);
        }
    }
}
Example 8
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
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);
        }
    }
}
Example 9
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
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);
        }
    }
}
Example 10
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
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);
        }
    }
}
Example 11
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
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);
        }
    }
}
Example 12
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
public CorrelatedRandomVectorGeneratorTest() {
    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);
        }
    }

    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(17399225432l);
    GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
    generator = new CorrelatedRandomVectorGenerator(mean,
                                                    covariance,
                                                    1.0e-12 * covariance.getNorm(),
                                                    rawGenerator);
}
Example 13
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
public CorrelatedRandomVectorGeneratorTest() {
    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);
        }
    }

    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(17399225432l);
    GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
    generator = new CorrelatedRandomVectorGenerator(mean,
                                                    covariance,
                                                    1.0e-12 * covariance.getNorm(),
                                                    rawGenerator);
}
Example 14
Source File: CorrelatedRandomVectorGeneratorTest.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
public CorrelatedRandomVectorGeneratorTest() {
    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);
        }
    }

    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(17399225432l);
    GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
    generator = new CorrelatedRandomVectorGenerator(mean,
                                                    covariance,
                                                    1.0e-12 * covariance.getNorm(),
                                                    rawGenerator);
}
Example 15
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance on the beta by OLS.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p> 
 * 
 * @return The beta variance
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}
Example 16
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance-covariance matrix of the regression parameters.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p>
 *
 * @return The beta variance-covariance matrix
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}
Example 17
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance on the beta by OLS.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p>
 *
 * @return The beta variance
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}
Example 18
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance on the beta by OLS.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p>
 *
 * @return The beta variance
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}
Example 19
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance on the beta by OLS.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p>
 *
 * @return The beta variance
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}
Example 20
Source File: OLSMultipleLinearRegression.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
/**
 * <p>Calculates the variance-covariance matrix of the regression parameters.
 * </p>
 * <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
 * </p>
 * <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
 * to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
 * R included, where p = the length of the beta vector.</p>
 *
 * @return The beta variance-covariance matrix
 */
@Override
protected RealMatrix calculateBetaVariance() {
    int p = X.getColumnDimension();
    RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
    RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
    return Rinv.multiply(Rinv.transpose());
}