Java Code Examples for org.apache.commons.math.linear.RealMatrix#walkInOptimizedOrder()
The following examples show how to use
org.apache.commons.math.linear.RealMatrix#walkInOptimizedOrder() .
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 |
|
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 2
| Source File: QRSolverTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
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 3
| Source File: QRSolverTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
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: QRDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
return 2.0 * r.nextDouble() - 1.0;
}
});
return m;
}
Example 5
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 6
| Source File: QRDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
return 2.0 * r.nextDouble() - 1.0;
}
});
return m;
}
Example 7
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 8
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 9
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 10
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 11
| Source File: QRSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
return 2.0 * r.nextDouble() - 1.0;
}
});
return m;
}
Example 12
| Source File: QRSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
return 2.0 * r.nextDouble() - 1.0;
}
});
return m;
}
Example 13
| Source File: QRSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
return 2.0 * r.nextDouble() - 1.0;
}
});
return m;
}
Example 14
| Source File: QRDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private void checkTrapezoidal(RealMatrix m) {
m.walkInOptimizedOrder(new DefaultRealMatrixPreservingVisitor() {
@Override
public void visit(int row, int column, double value) {
if (column > row) {
assertEquals(0.0, value, entryTolerance);
}
}
});
}
Example 15
| Source File: QRDecompositionImplTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private void checkTrapezoidal(RealMatrix m) {
m.walkInOptimizedOrder(new DefaultRealMatrixPreservingVisitor() {
@Override
public void visit(int row, int column, double value) {
if (column > row) {
assertEquals(0.0, value, entryTolerance);
}
}
});
}
Example 16
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 17
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
}
Example 18
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() throws Exception {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value) {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
Assert.assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
Assert.assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
}
Example 19
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() throws Exception {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value)
throws MatrixVisitorException {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
}
Example 20
| Source File: OLSMultipleLinearRegressionTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testPerfectFit() throws Exception {
double[] betaHat = regression.estimateRegressionParameters();
TestUtils.assertEquals(betaHat,
new double[]{ 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 },
1e-14);
double[] residuals = regression.estimateResiduals();
TestUtils.assertEquals(residuals, new double[]{0d,0d,0d,0d,0d,0d},
1e-14);
RealMatrix errors =
new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
@Override
public double visit(int row, int column, double value) {
if (row == 0) {
return s[column];
}
double x = s[row] * s[column];
return (row == column) ? 2 * x : x;
}
});
assertEquals(0.0,
errors.subtract(referenceVariance).getNorm(),
5.0e-16 * referenceVariance.getNorm());
assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
}
