Java Code Examples for org.apache.commons.math.linear.MatrixUtils#createRealMatrix()
The following examples show how to use
org.apache.commons.math.linear.MatrixUtils#createRealMatrix() .
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Example 1
| Source File: VectorialCovariance.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Get the covariance matrix.
* @return covariance matrix
*/
public RealMatrix getResult() {
int dimension = sums.length;
RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);
if (n > 1) {
double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
int k = 0;
for (int i = 0; i < dimension; ++i) {
for (int j = 0; j <= i; ++j) {
double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
result.setEntry(i, j, e);
result.setEntry(j, i, e);
}
}
}
return result;
}
Example 2
| Source File: BiDiagonalTransformerTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testMatricesValues() {
BiDiagonalTransformer transformer =
new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testSquare));
final double s17 = Math.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());
}
Example 3
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test non positive definite matrix */
@Test(expected = NotPositiveDefiniteMatrixException.class)
public void testNotPositiveDefinite() throws MathException {
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(new double[][] {
{ 14, 11, 13, 15, 24 },
{ 11, 34, 13, 8, 25 },
{ 13, 13, 14, 15, 21 },
{ 15, 8, 15, 18, 23 },
{ 24, 25, 21, 23, 45 }
}));
}
Example 4
| Source File: LUDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test that U is upper triangular */
public void testUUpperTriangular() {
RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
RealMatrix u = new LUDecompositionImpl(matrix).getU();
for (int i = 0; i < u.getRowDimension(); i++) {
for (int j = 0; j < i; j++) {
assertEquals(u.getEntry(i, j), 0, entryTolerance);
}
}
}
Example 5
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test non-symmetric matrix */
@Test(expected = NotSymmetricMatrixException.class)
public void testNotSymmetricMatrixException() throws MathException {
double[][] changed = testData.clone();
changed[0][changed[0].length - 1] += 1.0e-5;
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(changed));
}
Example 6
| Source File: CorrelatedRandomVectorGeneratorTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
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 7
| Source File: SingularValueDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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());
}
Example 8
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test(expected = NotPositiveDefiniteMatrixException.class)
public void testMath274() throws MathException {
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(new double[][] {
{ 0.40434286, -0.09376327, 0.30328980, 0.04909388 },
{-0.09376327, 0.10400408, 0.07137959, 0.04762857 },
{ 0.30328980, 0.07137959, 0.30458776, 0.04882449 },
{ 0.04909388, 0.04762857, 0.04882449, 0.07543265 }
}));
}
Example 9
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test that L is lower triangular */
@Test
public void testLLowerTriangular() throws MathException {
RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
RealMatrix l = new CholeskyDecompositionImpl(matrix).getL();
for (int i = 0; i < l.getRowDimension(); i++) {
for (int j = i + 1; j < l.getColumnDimension(); j++) {
assertEquals(0.0, l.getEntry(i, j), 0.0);
}
}
}
Example 10
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test(expected = NotPositiveDefiniteMatrixException.class)
public void testMath274() throws MathException {
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(new double[][] {
{ 0.40434286, -0.09376327, 0.30328980, 0.04909388 },
{-0.09376327, 0.10400408, 0.07137959, 0.04762857 },
{ 0.30328980, 0.07137959, 0.30458776, 0.04882449 },
{ 0.04909388, 0.04762857, 0.04882449, 0.07543265 }
}));
}
Example 11
| Source File: QRDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test matrices values */
public void testMatricesValues() {
QRDecomposition qr =
new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3NonSingular));
RealMatrix qRef = MatrixUtils.createRealMatrix(new double[][] {
{ -12.0 / 14.0, 69.0 / 175.0, -58.0 / 175.0 },
{ -6.0 / 14.0, -158.0 / 175.0, 6.0 / 175.0 },
{ 4.0 / 14.0, -30.0 / 175.0, -165.0 / 175.0 }
});
RealMatrix rRef = MatrixUtils.createRealMatrix(new double[][] {
{ -14.0, -21.0, 14.0 },
{ 0.0, -175.0, 70.0 },
{ 0.0, 0.0, 35.0 }
});
RealMatrix hRef = MatrixUtils.createRealMatrix(new double[][] {
{ 26.0 / 14.0, 0.0, 0.0 },
{ 6.0 / 14.0, 648.0 / 325.0, 0.0 },
{ -4.0 / 14.0, 36.0 / 325.0, 2.0 }
});
// check values against known references
RealMatrix q = qr.getQ();
assertEquals(0, q.subtract(qRef).getNorm(), 1.0e-13);
RealMatrix qT = qr.getQT();
assertEquals(0, qT.subtract(qRef.transpose()).getNorm(), 1.0e-13);
RealMatrix r = qr.getR();
assertEquals(0, r.subtract(rRef).getNorm(), 1.0e-13);
RealMatrix h = qr.getH();
assertEquals(0, h.subtract(hRef).getNorm(), 1.0e-13);
// check the same cached instance is returned the second time
assertTrue(q == qr.getQ());
assertTrue(r == qr.getR());
assertTrue(h == qr.getH());
}
Example 12
| Source File: SingularValueDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test matrices values */
public void testMatricesValues2() {
RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
{ 0.0 / 5.0, 3.0 / 5.0, 0.0 / 5.0 },
{ -4.0 / 5.0, 0.0 / 5.0, -3.0 / 5.0 },
{ 0.0 / 5.0, 4.0 / 5.0, 0.0 / 5.0 },
{ -3.0 / 5.0, 0.0 / 5.0, 4.0 / 5.0 }
});
RealMatrix sRef = MatrixUtils.createRealMatrix(new double[][] {
{ 4.0, 0.0, 0.0 },
{ 0.0, 3.0, 0.0 },
{ 0.0, 0.0, 2.0 }
});
RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
{ 80.0 / 125.0, -60.0 / 125.0, 75.0 / 125.0 },
{ 24.0 / 125.0, 107.0 / 125.0, 60.0 / 125.0 },
{ -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());
}
Example 13
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test non positive definite matrix */
@Test(expected = NotPositiveDefiniteMatrixException.class)
public void testNotPositiveDefinite() throws MathException {
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(new double[][] {
{ 14, 11, 13, 15, 24 },
{ 11, 34, 13, 8, 25 },
{ 13, 13, 14, 15, 21 },
{ 15, 8, 15, 18, 23 },
{ 24, 25, 21, 23, 45 }
}));
}
Example 14
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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);
}
Example 15
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test(expected = NotPositiveDefiniteMatrixException.class)
public void testMath274() throws MathException {
new CholeskyDecompositionImpl(MatrixUtils.createRealMatrix(new double[][] {
{ 0.40434286, -0.09376327, 0.30328980, 0.04909388 },
{-0.09376327, 0.10400408, 0.07137959, 0.04762857 },
{ 0.30328980, 0.07137959, 0.30458776, 0.04882449 },
{ 0.04909388, 0.04762857, 0.04882449, 0.07543265 }
}));
}
Example 16
| Source File: SingularValueDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** 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());
}
Example 17
| Source File: LUDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test that L is lower triangular with unit diagonal */
public void testLLowerTriangular() {
RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
RealMatrix l = new LUDecompositionImpl(matrix).getL();
for (int i = 0; i < l.getRowDimension(); i++) {
assertEquals(l.getEntry(i, i), 1, entryTolerance);
for (int j = i + 1; j < l.getColumnDimension(); j++) {
assertEquals(l.getEntry(i, j), 0, entryTolerance);
}
}
}
Example 18
| Source File: CholeskyDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/** test that L is lower triangular */
@Test
public void testLLowerTriangular() throws MathException {
RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
RealMatrix l = new CholeskyDecompositionImpl(matrix).getL();
for (int i = 0; i < l.getRowDimension(); i++) {
for (int j = i + 1; j < l.getColumnDimension(); j++) {
assertEquals(0.0, l.getEntry(i, j), 0.0);
}
}
}
Example 19
| Source File: BiDiagonalTransformerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testSingularMatrix() {
BiDiagonalTransformer transformer =
new BiDiagonalTransformer(MatrixUtils.createRealMatrix(new double[][] {
{ 1.0, 2.0, 3.0 },
{ 2.0, 3.0, 4.0 },
{ 3.0, 5.0, 7.0 }
}));
final double s3 = Math.sqrt(3.0);
final double s14 = Math.sqrt(14.0);
final double s1553 = Math.sqrt(1553.0);
RealMatrix uRef = MatrixUtils.createRealMatrix(new double[][] {
{ -1.0 / s14, 5.0 / (s3 * s14), 1.0 / s3 },
{ -2.0 / s14, -4.0 / (s3 * s14), 1.0 / s3 },
{ -3.0 / s14, 1.0 / (s3 * s14), -1.0 / s3 }
});
RealMatrix bRef = MatrixUtils.createRealMatrix(new double[][] {
{ -s14, s1553 / s14, 0.0 },
{ 0.0, -87 * s3 / (s14 * s1553), -s3 * s14 / s1553 },
{ 0.0, 0.0, 0.0 }
});
RealMatrix vRef = MatrixUtils.createRealMatrix(new double[][] {
{ 1.0, 0.0, 0.0 },
{ 0.0, -23 / s1553, 32 / s1553 },
{ 0.0, -32 / s1553, -23 / s1553 }
});
// 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());
}
Example 20
| Source File: EigenSolverTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** test solve */
public void testSolve() {
RealMatrix m = MatrixUtils.createRealMatrix(new double[][] {
{ 91, 5, 29, 32, 40, 14 },
{ 5, 34, -1, 0, 2, -1 },
{ 29, -1, 12, 9, 21, 8 },
{ 32, 0, 9, 14, 9, 0 },
{ 40, 2, 21, 9, 51, 19 },
{ 14, -1, 8, 0, 19, 14 }
});
DecompositionSolver es = new EigenDecompositionImpl(m, MathUtils.SAFE_MIN).getSolver();
RealMatrix b = MatrixUtils.createRealMatrix(new double[][] {
{ 1561, 269, 188 },
{ 69, -21, 70 },
{ 739, 108, 63 },
{ 324, 86, 59 },
{ 1624, 194, 107 },
{ 796, 69, 36 }
});
RealMatrix xRef = MatrixUtils.createRealMatrix(new double[][] {
{ 1, 2, 1 },
{ 2, -1, 2 },
{ 4, 2, 3 },
{ 8, -1, 0 },
{ 16, 2, 0 },
{ 32, -1, 0 }
});
// using RealMatrix
assertEquals(0, es.solve(b).subtract(xRef).getNorm(), 2.0e-12);
// using double[]
for (int i = 0; i < b.getColumnDimension(); ++i) {
assertEquals(0,
new ArrayRealVector(es.solve(b.getColumn(i))).subtract(xRef.getColumnVector(i)).getNorm(),
2.0e-11);
}
// using Array2DRowRealMatrix
for (int i = 0; i < b.getColumnDimension(); ++i) {
assertEquals(0,
es.solve(b.getColumnVector(i)).subtract(xRef.getColumnVector(i)).getNorm(),
2.0e-11);
}
// using RealMatrix with an alternate implementation
for (int i = 0; i < b.getColumnDimension(); ++i) {
ArrayRealVectorTest.RealVectorTestImpl v =
new ArrayRealVectorTest.RealVectorTestImpl(b.getColumn(i));
assertEquals(0,
es.solve(v).subtract(xRef.getColumnVector(i)).getNorm(),
2.0e-11);
}
}
