Java Code Examples for org.apache.commons.math3.optim.PointValuePair#getPoint()
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org.apache.commons.math3.optim.PointValuePair#getPoint() .
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Example 1
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param fLineTol Tolerance (relative error on the objective function)
* for the internal line search algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double fLineTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
Assert.assertTrue(optim.getIterations() > 0);
}
Example 2
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
}
Example 3
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
}
Example 4
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param fLineTol Tolerance (relative error on the objective function)
* for the internal line search algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double fLineTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
Assert.assertTrue(optim.getIterations() > 0);
}
Example 5
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
}
Example 6
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testMath293() {
LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, 10.0));
SimplexSolver solver = new SimplexSolver();
PointValuePair solution1 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(15.7143, solution1.getPoint()[0], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[1], .0001);
Assert.assertEquals(14.2857, solution1.getPoint()[2], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[3], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[4], .0001);
Assert.assertEquals(30.0, solution1.getPoint()[5], .0001);
Assert.assertEquals(40.57143, solution1.getValue(), .0001);
double valA = 0.8 * solution1.getPoint()[0] + 0.2 * solution1.getPoint()[1];
double valB = 0.7 * solution1.getPoint()[2] + 0.3 * solution1.getPoint()[3];
double valC = 0.4 * solution1.getPoint()[4] + 0.6 * solution1.getPoint()[5];
f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, valA));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, valB));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, valC));
PointValuePair solution2 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(40.57143, solution2.getValue(), .0001);
}
Example 7
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private static boolean validSolution(PointValuePair solution, List<LinearConstraint> constraints, double epsilon) {
double[] vals = solution.getPoint();
for (LinearConstraint c : constraints) {
double[] coeffs = c.getCoefficients().toArray();
double result = 0.0d;
for (int i = 0; i < vals.length; i++) {
result += vals[i] * coeffs[i];
}
switch (c.getRelationship()) {
case EQ:
if (!Precision.equals(result, c.getValue(), epsilon)) {
return false;
}
break;
case GEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) < 0) {
return false;
}
break;
case LEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) > 0) {
return false;
}
break;
}
}
return true;
}
Example 8
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private static boolean validSolution(PointValuePair solution, List<LinearConstraint> constraints, double epsilon) {
double[] vals = solution.getPoint();
for (LinearConstraint c : constraints) {
double[] coeffs = c.getCoefficients().toArray();
double result = 0.0d;
for (int i = 0; i < vals.length; i++) {
result += vals[i] * coeffs[i];
}
switch (c.getRelationship()) {
case EQ:
if (!Precision.equals(result, c.getValue(), epsilon)) {
return false;
}
break;
case GEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) < 0) {
return false;
}
break;
case LEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) > 0) {
return false;
}
break;
}
}
return true;
}
Example 9
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testMath293() {
LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, 10.0));
SimplexSolver solver = new SimplexSolver();
PointValuePair solution1 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(15.7143, solution1.getPoint()[0], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[1], .0001);
Assert.assertEquals(14.2857, solution1.getPoint()[2], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[3], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[4], .0001);
Assert.assertEquals(30.0, solution1.getPoint()[5], .0001);
Assert.assertEquals(40.57143, solution1.getValue(), .0001);
double valA = 0.8 * solution1.getPoint()[0] + 0.2 * solution1.getPoint()[1];
double valB = 0.7 * solution1.getPoint()[2] + 0.3 * solution1.getPoint()[3];
double valC = 0.4 * solution1.getPoint()[4] + 0.6 * solution1.getPoint()[5];
f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, valA));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, valB));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, valC));
PointValuePair solution2 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(40.57143, solution2.getValue(), .0001);
}
Example 10
| Source File: LPChecks.java From OSPREY3 with GNU General Public License v2.0 | 5 votes |
|
public static double[] getFeasiblePt(ArrayList<LinearConstraint> polytope){
//assuming polytope has a feasible pt, find one
if(polytope.isEmpty())
return new double[0];
SimplexSolver ss = new SimplexSolver();
LinearObjectiveFunction of = constrAsFunction(polytope.get(0));//obj func doesn't matter
PointValuePair ans = ss.optimize(of, new LinearConstraintSet(polytope), GoalType.MINIMIZE);
return ans.getPoint();
}
Example 11
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private static boolean validSolution(PointValuePair solution, List<LinearConstraint> constraints, double epsilon) {
double[] vals = solution.getPoint();
for (LinearConstraint c : constraints) {
double[] coeffs = c.getCoefficients().toArray();
double result = 0.0d;
for (int i = 0; i < vals.length; i++) {
result += vals[i] * coeffs[i];
}
switch (c.getRelationship()) {
case EQ:
if (!Precision.equals(result, c.getValue(), epsilon)) {
return false;
}
break;
case GEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) < 0) {
return false;
}
break;
case LEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) > 0) {
return false;
}
break;
}
}
return true;
}
Example 12
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testMath293() {
LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, 10.0));
SimplexSolver solver = new SimplexSolver();
PointValuePair solution1 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(15.7143, solution1.getPoint()[0], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[1], .0001);
Assert.assertEquals(14.2857, solution1.getPoint()[2], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[3], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[4], .0001);
Assert.assertEquals(30.0, solution1.getPoint()[5], .0001);
Assert.assertEquals(40.57143, solution1.getValue(), .0001);
double valA = 0.8 * solution1.getPoint()[0] + 0.2 * solution1.getPoint()[1];
double valB = 0.7 * solution1.getPoint()[2] + 0.3 * solution1.getPoint()[3];
double valC = 0.4 * solution1.getPoint()[4] + 0.6 * solution1.getPoint()[5];
f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, valA));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, valB));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, valC));
PointValuePair solution2 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(40.57143, solution2.getValue(), .0001);
}
Example 13
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testMath293() {
LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, 10.0));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, 10.0));
SimplexSolver solver = new SimplexSolver();
PointValuePair solution1 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(15.7143, solution1.getPoint()[0], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[1], .0001);
Assert.assertEquals(14.2857, solution1.getPoint()[2], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[3], .0001);
Assert.assertEquals(0.0, solution1.getPoint()[4], .0001);
Assert.assertEquals(30.0, solution1.getPoint()[5], .0001);
Assert.assertEquals(40.57143, solution1.getValue(), .0001);
double valA = 0.8 * solution1.getPoint()[0] + 0.2 * solution1.getPoint()[1];
double valB = 0.7 * solution1.getPoint()[2] + 0.3 * solution1.getPoint()[3];
double valC = 0.4 * solution1.getPoint()[4] + 0.6 * solution1.getPoint()[5];
f = new LinearObjectiveFunction(new double[] { 0.8, 0.2, 0.7, 0.3, 0.4, 0.6}, 0 );
constraints = new ArrayList<LinearConstraint>();
constraints.add(new LinearConstraint(new double[] { 1, 0, 1, 0, 1, 0 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0, 1, 0, 1, 0, 1 }, Relationship.EQ, 30.0));
constraints.add(new LinearConstraint(new double[] { 0.8, 0.2, 0.0, 0.0, 0.0, 0.0 }, Relationship.GEQ, valA));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.7, 0.3, 0.0, 0.0 }, Relationship.GEQ, valB));
constraints.add(new LinearConstraint(new double[] { 0.0, 0.0, 0.0, 0.0, 0.4, 0.6 }, Relationship.GEQ, valC));
PointValuePair solution2 = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
Assert.assertEquals(40.57143, solution2.getValue(), .0001);
}
Example 14
| Source File: SimplexSolverTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
private static boolean validSolution(PointValuePair solution, List<LinearConstraint> constraints, double epsilon) {
double[] vals = solution.getPoint();
for (LinearConstraint c : constraints) {
double[] coeffs = c.getCoefficients().toArray();
double result = 0.0d;
for (int i = 0; i < vals.length; i++) {
result += vals[i] * coeffs[i];
}
switch (c.getRelationship()) {
case EQ:
if (!Precision.equals(result, c.getValue(), epsilon)) {
return false;
}
break;
case GEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) < 0) {
return false;
}
break;
case LEQ:
if (Precision.compareTo(result, c.getValue(), epsilon) > 0) {
return false;
}
break;
}
}
return true;
}
Example 15
| Source File: CMAESOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Cf. MATH-867
*/
@Test
public void testFitAccuracyDependsOnBoundary() {
final CMAESOptimizer optimizer
= new CMAESOptimizer(30000, 0, true, 10,
0, new MersenneTwister(), false, null);
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 11.1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 1 };
// No bounds.
PointValuePair result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
SimpleBounds.unbounded(1),
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(new double[] { 1e-1 }),
new InitialGuess(start));
final double resNoBound = result.getPoint()[0];
// Optimum is near the lower bound.
final double[] lower = { -20 };
final double[] upper = { 5e16 };
final double[] sigma = { 10 };
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearLo = result.getPoint()[0];
// Optimum is near the upper bound.
lower[0] = -5e16;
upper[0] = 20;
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearHi = result.getPoint()[0];
// System.out.println("resNoBound=" + resNoBound +
// " resNearLo=" + resNearLo +
// " resNearHi=" + resNearHi);
// The two values currently differ by a substantial amount, indicating that
// the bounds definition can prevent reaching the optimum.
Assert.assertEquals(resNoBound, resNearLo, 1e-3);
Assert.assertEquals(resNoBound, resNearHi, 1e-3);
}
Example 16
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testIllConditioned() {
LinearProblem problem1 = new LinearProblem(new double[][] {
{ 10.0, 7.0, 8.0, 7.0 },
{ 7.0, 5.0, 6.0, 5.0 },
{ 8.0, 6.0, 10.0, 9.0 },
{ 7.0, 5.0, 9.0, 10.0 }
}, new double[] { 32, 23, 33, 31 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-13, 1e-13),
1e-15, 1e-15, 1);
PointValuePair optimum1
= optimizer.optimize(new MaxEval(200),
problem1.getObjectiveFunction(),
problem1.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 1, 2, 3 }));
Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-4);
Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-3);
Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-4);
Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-4);
LinearProblem problem2 = new LinearProblem(new double[][] {
{ 10.00, 7.00, 8.10, 7.20 },
{ 7.08, 5.04, 6.00, 5.00 },
{ 8.00, 5.98, 9.89, 9.00 },
{ 6.99, 4.99, 9.00, 9.98 }
}, new double[] { 32, 23, 33, 31 });
PointValuePair optimum2
= optimizer.optimize(new MaxEval(200),
problem2.getObjectiveFunction(),
problem2.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 1, 2, 3 }));
final double[] result2 = optimum2.getPoint();
final double[] expected2 = {-81, 137, -34, 22};
Assert.assertEquals(expected2[0], result2[0], 2);
Assert.assertEquals(expected2[1], result2[1], 4);
Assert.assertEquals(expected2[2], result2[2], 1);
Assert.assertEquals(expected2[3], result2[3], 1);
}
Example 17
| Source File: CMAESOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Cf. MATH-867
*/
@Test
public void testFitAccuracyDependsOnBoundary() {
final CMAESOptimizer optimizer
= new CMAESOptimizer(30000, 0, true, 10,
0, new MersenneTwister(), false, null);
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 11.1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 1 };
// No bounds.
PointValuePair result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
SimpleBounds.unbounded(1),
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(new double[] { 1e-1 }),
new InitialGuess(start));
final double resNoBound = result.getPoint()[0];
// Optimum is near the lower bound.
final double[] lower = { -20 };
final double[] upper = { 5e16 };
final double[] sigma = { 10 };
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearLo = result.getPoint()[0];
// Optimum is near the upper bound.
lower[0] = -5e16;
upper[0] = 20;
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearHi = result.getPoint()[0];
// System.out.println("resNoBound=" + resNoBound +
// " resNearLo=" + resNearLo +
// " resNearHi=" + resNearHi);
// The two values currently differ by a substantial amount, indicating that
// the bounds definition can prevent reaching the optimum.
Assert.assertEquals(resNoBound, resNearLo, 1e-3);
Assert.assertEquals(resNoBound, resNearHi, 1e-3);
}
Example 18
| Source File: CMAESOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Cf. MATH-867
*/
@Test
public void testFitAccuracyDependsOnBoundary() {
final CMAESOptimizer optimizer
= new CMAESOptimizer(30000, 0, true, 10,
0, new MersenneTwister(), false, null);
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 11.1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 1 };
// No bounds.
PointValuePair result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
SimpleBounds.unbounded(1),
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(new double[] { 1e-1 }),
new InitialGuess(start));
final double resNoBound = result.getPoint()[0];
// Optimum is near the lower bound.
final double[] lower = { -20 };
final double[] upper = { 5e16 };
final double[] sigma = { 10 };
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearLo = result.getPoint()[0];
// Optimum is near the upper bound.
lower[0] = -5e16;
upper[0] = 20;
result = optimizer.optimize(new MaxEval(100000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper));
final double resNearHi = result.getPoint()[0];
// System.out.println("resNoBound=" + resNoBound +
// " resNearLo=" + resNearLo +
// " resNearHi=" + resNearHi);
// The two values currently differ by a substantial amount, indicating that
// the bounds definition can prevent reaching the optimum.
Assert.assertEquals(resNoBound, resNearLo, 1e-3);
Assert.assertEquals(resNoBound, resNearHi, 1e-3);
}
Example 19
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testTwoSets() {
final double epsilon = 1.0e-7;
LinearProblem problem = new LinearProblem(new double[][] {
{ 2, 1, 0, 4, 0, 0 },
{ -4, -2, 3, -7, 0, 0 },
{ 4, 1, -2, 8, 0, 0 },
{ 0, -3, -12, -1, 0, 0 },
{ 0, 0, 0, 0, epsilon, 1 },
{ 0, 0, 0, 0, 1, 1 }
}, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
final Preconditioner preconditioner
= new Preconditioner() {
public double[] precondition(double[] point, double[] r) {
double[] d = r.clone();
d[0] /= 72.0;
d[1] /= 30.0;
d[2] /= 314.0;
d[3] /= 260.0;
d[4] /= 2 * (1 + epsilon * epsilon);
d[5] /= 4.0;
return d;
}
};
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-13, 1e-13),
1e-7, 1e-7, 1,
preconditioner);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
final double[] result = optimum.getPoint();
final double[] expected = {3, 4, -1, -2, 1 + epsilon, 1 - epsilon};
Assert.assertEquals(expected[0], result[0], 1.0e-7);
Assert.assertEquals(expected[1], result[1], 1.0e-7);
Assert.assertEquals(expected[2], result[2], 1.0e-9);
Assert.assertEquals(expected[3], result[3], 1.0e-8);
Assert.assertEquals(expected[4] + epsilon, result[4], 1.0e-6);
Assert.assertEquals(expected[5] - epsilon, result[5], 1.0e-6);
}
Example 20
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testTwoSets() {
final double epsilon = 1.0e-7;
LinearProblem problem = new LinearProblem(new double[][] {
{ 2, 1, 0, 4, 0, 0 },
{ -4, -2, 3, -7, 0, 0 },
{ 4, 1, -2, 8, 0, 0 },
{ 0, -3, -12, -1, 0, 0 },
{ 0, 0, 0, 0, epsilon, 1 },
{ 0, 0, 0, 0, 1, 1 }
}, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
final Preconditioner preconditioner
= new Preconditioner() {
public double[] precondition(double[] point, double[] r) {
double[] d = r.clone();
d[0] /= 72.0;
d[1] /= 30.0;
d[2] /= 314.0;
d[3] /= 260.0;
d[4] /= 2 * (1 + epsilon * epsilon);
d[5] /= 4.0;
return d;
}
};
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-13, 1e-13),
1e-7, 1e-7, 1,
preconditioner);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
final double[] result = optimum.getPoint();
final double[] expected = {3, 4, -1, -2, 1 + epsilon, 1 - epsilon};
Assert.assertEquals(expected[0], result[0], 1.0e-7);
Assert.assertEquals(expected[1], result[1], 1.0e-7);
Assert.assertEquals(expected[2], result[2], 1.0e-9);
Assert.assertEquals(expected[3], result[3], 1.0e-8);
Assert.assertEquals(expected[4] + epsilon, result[4], 1.0e-6);
Assert.assertEquals(expected[5] - epsilon, result[5], 1.0e-6);
}
