Java Code Examples for org.apache.commons.math3.optim.PointVectorValuePair#getPointRef()
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org.apache.commons.math3.optim.PointVectorValuePair#getPointRef() .
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Example 1
| 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 2
| 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 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
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 5
| 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 6
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
}
}
}
Example 7
| Source File: CurveFitter.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Fit a curve.
* This method compute the coefficients of the curve that best
* fit the sample of observed points previously given through calls
* to the {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
*
* @param f parametric function to fit.
* @param initialGuess first guess of the function parameters.
* @param maxEval Maximum number of function evaluations.
* @return the fitted parameters.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of allowed evaluations is exceeded.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @since 3.0
*/
public double[] fit(int maxEval, T f,
final double[] initialGuess) {
// Prepare least squares problem.
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// Input to the optimizer: the model and its Jacobian.
final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);
// Perform the fit.
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(maxEval),
model.getModelFunction(),
model.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(initialGuess));
// Extract the coefficients.
return optimum.getPointRef();
}
Example 8
| Source File: CurveFitter.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Fit a curve.
* This method compute the coefficients of the curve that best
* fit the sample of observed points previously given through calls
* to the {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
*
* @param f parametric function to fit.
* @param initialGuess first guess of the function parameters.
* @param maxEval Maximum number of function evaluations.
* @return the fitted parameters.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of allowed evaluations is exceeded.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @since 3.0
*/
public double[] fit(int maxEval, T f,
final double[] initialGuess) {
// Prepare least squares problem.
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// Input to the optimizer: the model and its Jacobian.
final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);
// Perform the fit.
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(maxEval),
model.getModelFunction(),
model.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(initialGuess));
// Extract the coefficients.
return optimum.getPointRef();
}
Example 9
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
}
}
}
Example 10
| Source File: CurveFitter.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Fit a curve.
* This method compute the coefficients of the curve that best
* fit the sample of observed points previously given through calls
* to the {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
*
* @param f parametric function to fit.
* @param initialGuess first guess of the function parameters.
* @param maxEval Maximum number of function evaluations.
* @return the fitted parameters.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of allowed evaluations is exceeded.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @since 3.0
*/
public double[] fit(int maxEval, T f,
final double[] initialGuess) {
// Prepare least squares problem.
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// Input to the optimizer: the model and its Jacobian.
final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);
// Perform the fit.
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(maxEval),
model.getModelFunction(),
model.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(initialGuess));
// Extract the coefficients.
return optimum.getPointRef();
}
Example 11
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
}
}
}
Example 12
| Source File: CurveFitter.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Fit a curve.
* This method compute the coefficients of the curve that best
* fit the sample of observed points previously given through calls
* to the {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
*
* @param f parametric function to fit.
* @param initialGuess first guess of the function parameters.
* @param maxEval Maximum number of function evaluations.
* @return the fitted parameters.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of allowed evaluations is exceeded.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @since 3.0
*/
public double[] fit(int maxEval, T f,
final double[] initialGuess) {
// Prepare least squares problem.
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// Input to the optimizer: the model and its Jacobian.
final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);
// Perform the fit.
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(maxEval),
model.getModelFunction(),
model.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(initialGuess));
// Extract the coefficients.
return optimum.getPointRef();
}
Example 13
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
}
}
}
Example 14
| Source File: CurveFitter.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Fit a curve.
* This method compute the coefficients of the curve that best
* fit the sample of observed points previously given through calls
* to the {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
*
* @param f parametric function to fit.
* @param initialGuess first guess of the function parameters.
* @param maxEval Maximum number of function evaluations.
* @return the fitted parameters.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of allowed evaluations is exceeded.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @since 3.0
*/
public double[] fit(int maxEval, T f,
final double[] initialGuess) {
// Prepare least squares problem.
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// Input to the optimizer: the model and its Jacobian.
final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);
// Perform the fit.
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(maxEval),
model.getModelFunction(),
model.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(initialGuess));
// Extract the coefficients.
return optimum.getPointRef();
}
Example 15
| Source File: MinpackTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
}
}
}
Example 16
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30, 68);
circle.addPoint( 50, -6);
circle.addPoint(110, -20);
circle.addPoint( 35, 15);
circle.addPoint( 45, 97);
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= 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 }));
Assert.assertTrue(optimizer.getEvaluations() < 10);
double rms = optimizer.getRMS();
Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
}
double[] target = new double[circle.getN()];
Arrays.fill(target, 0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2);
optimum = optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 98.680, 47.345 }));
cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
}
Example 17
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30, 68);
circle.addPoint( 50, -6);
circle.addPoint(110, -20);
circle.addPoint( 35, 15);
circle.addPoint( 45, 97);
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= 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 }));
Assert.assertTrue(optimizer.getEvaluations() < 10);
double rms = optimizer.getRMS();
Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
}
double[] target = new double[circle.getN()];
Arrays.fill(target, 0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2);
optimum = optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 98.680, 47.345 }));
cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
}
Example 18
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30, 68);
circle.addPoint( 50, -6);
circle.addPoint(110, -20);
circle.addPoint( 35, 15);
circle.addPoint( 45, 97);
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= 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 }));
Assert.assertTrue(optimizer.getEvaluations() < 10);
double rms = optimizer.getRMS();
Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
}
double[] target = new double[circle.getN()];
Arrays.fill(target, 0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2);
optimum = optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 98.680, 47.345 }));
cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
}
Example 19
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30, 68);
circle.addPoint( 50, -6);
circle.addPoint(110, -20);
circle.addPoint( 35, 15);
circle.addPoint( 45, 97);
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= 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 }));
Assert.assertTrue(optimizer.getEvaluations() < 10);
double rms = optimizer.getRMS();
Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
}
double[] target = new double[circle.getN()];
Arrays.fill(target, 0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2);
optimum = optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 98.680, 47.345 }));
cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
}
Example 20
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30, 68);
circle.addPoint( 50, -6);
circle.addPoint(110, -20);
circle.addPoint( 35, 15);
circle.addPoint( 45, 97);
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= 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 }));
Assert.assertTrue(optimizer.getEvaluations() < 10);
double rms = optimizer.getRMS();
Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
}
double[] target = new double[circle.getN()];
Arrays.fill(target, 0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2);
optimum = optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 98.680, 47.345 }));
cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
}
