org.apache.commons.math3.optim.nonlinear.vector.Target Java Examples
The following examples show how to use
org.apache.commons.math3.optim.nonlinear.vector.Target.
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Example #1
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #2
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingGoodInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
}
Example #3
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingBadInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { -12, -12 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertTrue(optimizer.getEvaluations() < 25);
Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
Assert.assertEquals( 0.292235, circle.getRadius(center), 1e-6);
Assert.assertEquals(-0.151738, center.getX(), 1e-6);
Assert.assertEquals( 0.2075001, center.getY(), 1e-6);
}
Example #4
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #5
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #6
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #7
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #8
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
LevenbergMarquardtOptimizer optimizer
= new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
FastMath.sqrt(2.22044604926e-16),
2.22044604926e-16);
try {
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
function.getModelFunction(),
function.getModelFunctionJacobian(),
new Target(function.getTarget()),
new Weight(function.getWeight()),
new InitialGuess(function.getStartPoint()));
Assert.assertFalse(exceptionExpected);
function.checkTheoreticalMinCost(optimizer.getRMS());
function.checkTheoreticalMinParams(optimum);
} catch (TooManyEvaluationsException e) {
Assert.assertTrue(exceptionExpected);
}
}
Example #9
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void doTestStRD(final StatisticalReferenceDataset dataset,
final double errParams,
final double errParamsSd) {
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] w = new double[dataset.getNumObservations()];
Arrays.fill(w, 1);
final double[][] data = dataset.getData();
final double[] initial = dataset.getStartingPoint(0);
final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(data[1]),
new Weight(w),
new InitialGuess(initial));
final double[] actual = optimum.getPoint();
for (int i = 0; i < actual.length; i++) {
double expected = dataset.getParameter(i);
double delta = FastMath.abs(errParams * expected);
Assert.assertEquals(dataset.getName() + ", param #" + i,
expected, actual[i], delta);
}
}
Example #10
| Source File: LSQFitter.java From thunderstorm with GNU General Public License v3.0 | 6 votes |
|
protected Molecule fit(ILsqFunctions functions) {
// init
double[] weights = functions.calcWeights(useWeighting);
double[] observations = functions.getObservations();
// fit
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(
new SimplePointChecker<PointVectorValuePair>(10e-10, 10e-10, maxIter));
PointVectorValuePair pv;
pv = optimizer.optimize(
MaxEval.unlimited(),
new MaxIter(MAX_ITERATIONS + 1),
new ModelFunction(functions.getValueFunction()),
new ModelFunctionJacobian(functions.getJacobianFunction()),
new Target(observations),
new InitialGuess(psfModel.transformParametersInverse(functions.getInitialParams())),
new Weight(weights));
// estimate background and return an instance of the `Molecule`
fittedParameters = pv.getPointRef();
if (bkgStdColumn >= 0) {
fittedParameters[bkgStdColumn] = VectorMath.stddev(sub(observations, functions.getValueFunction().value(fittedParameters)));
}
return psfModel.newInstanceFromParams(psfModel.transformParameters(fittedParameters), functions.getImageUnits(), true);
}
Example #11
| Source File: AbstractLeastSquaresOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testGetChiSquare() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1.0);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double expected = dataset.getResidualSumOfSquares();
final double actual = optimizer.getChiSquare();
Assert.assertEquals(dataset.getName(), expected, actual,
1E-11 * expected);
}
Example #12
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
LevenbergMarquardtOptimizer optimizer
= new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
FastMath.sqrt(2.22044604926e-16),
2.22044604926e-16);
try {
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
function.getModelFunction(),
function.getModelFunctionJacobian(),
new Target(function.getTarget()),
new Weight(function.getWeight()),
new InitialGuess(function.getStartPoint()));
Assert.assertFalse(exceptionExpected);
function.checkTheoreticalMinCost(optimizer.getRMS());
function.checkTheoreticalMinParams(optimum);
} catch (TooManyEvaluationsException e) {
Assert.assertTrue(exceptionExpected);
}
}
Example #13
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void doTestStRD(final StatisticalReferenceDataset dataset,
final double errParams,
final double errParamsSd) {
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] w = new double[dataset.getNumObservations()];
Arrays.fill(w, 1);
final double[][] data = dataset.getData();
final double[] initial = dataset.getStartingPoint(0);
final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(data[1]),
new Weight(w),
new InitialGuess(initial));
final double[] actual = optimum.getPoint();
for (int i = 0; i < actual.length; i++) {
double expected = dataset.getParameter(i);
double delta = FastMath.abs(errParams * expected);
Assert.assertEquals(dataset.getName() + ", param #" + i,
expected, actual[i], delta);
}
}
Example #14
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #15
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingBadInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { -12, -12 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertTrue(optimizer.getEvaluations() < 25);
Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
Assert.assertEquals( 0.292235, circle.getRadius(center), 1e-6);
Assert.assertEquals(-0.151738, center.getX(), 1e-6);
Assert.assertEquals( 0.2075001, center.getY(), 1e-6);
}
Example #16
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingGoodInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
}
Example #17
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #18
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingBadInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { -12, -12 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertTrue(optimizer.getEvaluations() < 25);
Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
Assert.assertEquals( 0.292235, circle.getRadius(center), 1e-6);
Assert.assertEquals(-0.151738, center.getX(), 1e-6);
Assert.assertEquals( 0.2075001, center.getY(), 1e-6);
}
Example #19
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingGoodInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
}
Example #20
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
public void doTestStRD(final StatisticalReferenceDataset dataset,
final double errParams,
final double errParamsSd) {
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] w = new double[dataset.getNumObservations()];
Arrays.fill(w, 1);
final double[][] data = dataset.getData();
final double[] initial = dataset.getStartingPoint(0);
final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(data[1]),
new Weight(w),
new InitialGuess(initial));
final double[] actual = optimum.getPoint();
for (int i = 0; i < actual.length; i++) {
double expected = dataset.getParameter(i);
double delta = FastMath.abs(errParams * expected);
Assert.assertEquals(dataset.getName() + ", param #" + i,
expected, actual[i], delta);
}
}
Example #21
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
LevenbergMarquardtOptimizer optimizer
= new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
FastMath.sqrt(2.22044604926e-16),
2.22044604926e-16);
try {
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
function.getModelFunction(),
function.getModelFunctionJacobian(),
new Target(function.getTarget()),
new Weight(function.getWeight()),
new InitialGuess(function.getStartPoint()));
Assert.assertFalse(exceptionExpected);
function.checkTheoreticalMinCost(optimizer.getRMS());
function.checkTheoreticalMinParams(optimum);
} catch (TooManyEvaluationsException e) {
Assert.assertTrue(exceptionExpected);
}
}
Example #22
| Source File: AbstractLeastSquaresOptimizerTestValidation.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @return the normalized chi-square.
*/
private double getChi2N(AbstractLeastSquaresOptimizer optim,
StraightLineProblem problem,
double[] params) {
final double[] t = problem.target();
final double[] w = problem.weight();
optim.optimize(new MaxEval(Integer.MAX_VALUE),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(t),
new Weight(w),
new InitialGuess(params));
return optim.getChiSquare() / (t.length - params.length);
}
Example #23
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #24
| Source File: AbstractLeastSquaresOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testGetChiSquare() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1.0);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double expected = dataset.getResidualSumOfSquares();
final double actual = optimizer.getChiSquare();
Assert.assertEquals(dataset.getName(), expected, actual,
1E-11 * expected);
}
Example #25
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #26
| Source File: AbstractLeastSquaresOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testGetChiSquare() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1.0);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double expected = dataset.getResidualSumOfSquares();
final double actual = optimizer.getChiSquare();
Assert.assertEquals(dataset.getName(), expected, actual,
1E-11 * expected);
}
Example #27
| Source File: AbstractLeastSquaresOptimizerTestValidation.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @return the normalized chi-square.
*/
private double getChi2N(AbstractLeastSquaresOptimizer optim,
StraightLineProblem problem,
double[] params) {
final double[] t = problem.target();
final double[] w = problem.weight();
optim.optimize(new MaxEval(Integer.MAX_VALUE),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(t),
new Weight(w),
new InitialGuess(params));
return optim.getChiSquare() / (t.length - params.length);
}
Example #28
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #29
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
LevenbergMarquardtOptimizer optimizer
= new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
FastMath.sqrt(2.22044604926e-16),
2.22044604926e-16);
try {
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
function.getModelFunction(),
function.getModelFunctionJacobian(),
new Target(function.getTarget()),
new Weight(function.getWeight()),
new InitialGuess(function.getStartPoint()));
Assert.assertFalse(exceptionExpected);
function.checkTheoreticalMinCost(optimizer.getRMS());
function.checkTheoreticalMinParams(optimum);
} catch (TooManyEvaluationsException e) {
Assert.assertTrue(exceptionExpected);
}
}
Example #30
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingGoodInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
}
