Java源码示例：org.apache.commons.math3.analysis.MultivariateFunction
示例1
/**
* Compute and evaluate a new simplex.
*
* @param evaluationFunction Evaluation function.
* @param original Original simplex (to be preserved).
* @param coeff Linear coefficient.
* @param comparator Comparator to use to sort simplex vertices from best
* to poorest.
* @return the best point in the transformed simplex.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
private PointValuePair evaluateNewSimplex(final MultivariateFunction evaluationFunction,
final PointValuePair[] original,
final double coeff,
final Comparator<PointValuePair> comparator) {
final double[] xSmallest = original[0].getPointRef();
// Perform a linear transformation on all the simplex points,
// except the first one.
setPoint(0, original[0]);
final int dim = getDimension();
for (int i = 1; i < getSize(); i++) {
final double[] xOriginal = original[i].getPointRef();
final double[] xTransformed = new double[dim];
for (int j = 0; j < dim; j++) {
xTransformed[j] = xSmallest[j] + coeff * (xSmallest[j] - xOriginal[j]);
}
setPoint(i, new PointValuePair(xTransformed, Double.NaN, false));
}
// Evaluate the simplex.
evaluate(evaluationFunction, comparator);
return getPoint(0);
}
示例2
/**
* @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 MultivariateOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(1000, func, goal, 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);
}
}
示例3
MultivariateFunction generateLDPLObjectiveFunction(final GaussianProcessLDPLMeanModel model, final double[] initPoint){
MultivariateFunction negaLLfunc = new MultivariateFunction(){
@Override
public double value(double[] point){
double[] params = gpLDPL.getParams();
for(int i=0; i<initPoint.length; i++){
params[i] = point[i];
}
gpLDPL.setParams(params);
model.train(samples);
gpLDPL.updateByActiveBeaconList(activeBeaconList);
double logLikelihood = gpLDPL.looPredLogLikelihood();
double looMSE = gpLDPL.looMSE();
StringBuilder sb = new StringBuilder();
sb.append("optimizeForLDPL: n="+params[0]+",A="+params[1]+",fa="+params[2]+",fb="+params[3]);
sb.append(", LOOMSE="+looMSE);
sb.append(", logLikelihood="+(-logLikelihood));
System.out.println(sb.toString());
return -logLikelihood;
}
};
return negaLLfunc;
}
示例4
@Test
public void testRosenbrock() {
MultivariateFunction rosenbrock =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[1] - x[0] * x[0];
double b = 1.0 - x[0];
return 100 * a * a + b * b;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
}));
PointValuePair optimum =
optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 50);
Assert.assertTrue(optimizer.getEvaluations() < 100);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例5
MultivariateFunction generateGPIsoScaleLOOMSE(final GaussianProcessLDPLMeanModel model){
MultivariateFunction negaLLfunc = new MultivariateFunction(){
@Override
public double value(double[] point){
double lengthx = Math.pow(10, point[0]);
double lambda = Math.pow(10, point[1]);
double stdev = model.stdev;
double lz = model.lengthes[2];
model.lengthes = new double[]{lengthx, lengthx, lz};
model.sigmaN = Math.sqrt(lambda)*stdev;
model.train(samples);
gpLDPL.updateByActiveBeaconList(activeBeaconList);
double looMSE = gpLDPL.looMSE();
StringBuilder sb = new StringBuilder();
sb.append("optimizeForGPLOOError: lx=ly="+lengthx+",lz="+lz+",lambda="+lambda+",looMSE="+looMSE);
System.out.println(sb.toString());
return looMSE;
}
};
return negaLLfunc;
}
示例6
/**
* Compute and evaluate a new simplex.
*
* @param evaluationFunction Evaluation function.
* @param original Original simplex (to be preserved).
* @param coeff Linear coefficient.
* @param comparator Comparator to use to sort simplex vertices from best
* to poorest.
* @return the best point in the transformed simplex.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
private PointValuePair evaluateNewSimplex(final MultivariateFunction evaluationFunction,
final PointValuePair[] original,
final double coeff,
final Comparator<PointValuePair> comparator) {
final double[] xSmallest = original[0].getPointRef();
// Perform a linear transformation on all the simplex points,
// except the first one.
setPoint(0, original[0]);
final int dim = getDimension();
for (int i = 1; i < getSize(); i++) {
final double[] xOriginal = original[i].getPointRef();
final double[] xTransformed = new double[dim];
for (int j = 0; j < dim; j++) {
xTransformed[j] = xSmallest[j] + coeff * (xSmallest[j] - xOriginal[j]);
}
setPoint(i, new PointValuePair(xTransformed, Double.NaN, false));
}
// Evaluate the simplex.
evaluate(evaluationFunction, comparator);
return getPoint(0);
}
示例7
/**
* @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 MultivariateOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(1000, func, goal, 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);
}
}
示例8
/**
* @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 MultivariateOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(1000, func, goal, 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);
}
}
示例9
/**
* @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);
}
示例10
@Test
public void testMath864() {
final CMAESOptimizer optimizer = new CMAESOptimizer();
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 0 };
final double[] lower = { -1e6 };
final double[] upper = { 1.5 };
final double[] result = optimizer.optimize(10000, fitnessFunction, GoalType.MINIMIZE,
start, lower, upper).getPoint();
Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
result[0] <= upper[0]);
}
示例11
@Test
public void testPowell() {
MultivariateFunction powell =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[0] + 10 * x[1];
double b = x[2] - x[3];
double c = x[1] - 2 * x[2];
double d = x[0] - x[3];
return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(4));
PointValuePair optimum =
optimizer.optimize(1000, powell, GoalType.MINIMIZE, new double[] { 3, -1, 0, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 800);
Assert.assertTrue(optimizer.getEvaluations() < 900);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例12
@Test
public void testRosenbrock() {
MultivariateFunction rosenbrock =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[1] - x[0] * x[0];
double b = 1.0 - x[0];
return 100 * a * a + b * b;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
}));
PointValuePair optimum =
optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 50);
Assert.assertTrue(optimizer.getEvaluations() < 100);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例13
/**
* @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 MultivariateOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(1000, func, goal, 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);
}
}
示例14
@Test
public void testMath864() {
final CMAESOptimizer optimizer = new CMAESOptimizer();
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 0 };
final double[] lower = { -1e6 };
final double[] upper = { 1.5 };
final double[] result = optimizer.optimize(10000, fitnessFunction, GoalType.MINIMIZE,
start, lower, upper).getPoint();
Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
result[0] <= upper[0]);
}
示例15
@Test
public void testRosenbrock() {
MultivariateFunction rosenbrock =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[1] - x[0] * x[0];
double b = 1.0 - x[0];
return 100 * a * a + b * b;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
}));
PointValuePair optimum =
optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 50);
Assert.assertTrue(optimizer.getEvaluations() < 100);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例16
/**
* @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 MultivariateOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(1000, func, goal, 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);
}
}
示例17
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
PointValuePair expected) {
doTest(func,
startPoint,
boundaries,
goal,
fTol,
pointTol,
maxEvaluations,
0,
expected,
"");
}
示例18
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
PointValuePair expected) {
doTest(func,
startPoint,
boundaries,
goal,
fTol,
pointTol,
maxEvaluations,
0,
expected,
"");
}
示例19
/**
* @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);
}
示例20
@Test
public void testMath864() {
final CMAESOptimizer optimizer = new CMAESOptimizer();
final MultivariateFunction fitnessFunction = new MultivariateFunction() {
public double value(double[] parameters) {
final double target = 1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 0 };
final double[] lower = { -1e6 };
final double[] upper = { 1.5 };
final double[] result = optimizer.optimize(10000, fitnessFunction, GoalType.MINIMIZE,
start, lower, upper).getPoint();
Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
result[0] <= upper[0]);
}
示例21
/**
* Optimize an objective function.
*
* @param f Objective function.
* @param goalType Type of optimization goal: either
* {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
* @param startPoint Start point for optimization.
* @param maxEval Maximum number of function evaluations.
* @return the point/value pair giving the optimal value for objective
* function.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
* @throws org.apache.commons.math3.exception.NullArgumentException if
* any argument is {@code null}.
*/
protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType,
double[] startPoint) {
// Checks.
if (f == null) {
throw new NullArgumentException();
}
if (goalType == null) {
throw new NullArgumentException();
}
if (startPoint == null) {
throw new NullArgumentException();
}
// Reset.
evaluations.setMaximalCount(maxEval);
evaluations.resetCount();
// Store optimization problem characteristics.
function = f;
goal = goalType;
start = startPoint.clone();
// Perform computation.
return doOptimize();
}
示例22
@Test
public void testPowell() {
MultivariateFunction powell =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[0] + 10 * x[1];
double b = x[2] - x[3];
double c = x[1] - 2 * x[2];
double d = x[0] - x[3];
return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(4));
PointValuePair optimum =
optimizer.optimize(1000, powell, GoalType.MINIMIZE, new double[] { 3, -1, 0, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 800);
Assert.assertTrue(optimizer.getEvaluations() < 900);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例23
@Test
public void testRosenbrock() {
MultivariateFunction rosenbrock =
new MultivariateFunction() {
public double value(double[] x) {
++count;
double a = x[1] - x[0] * x[0];
double b = 1.0 - x[0];
return 100 * a * a + b * b;
}
};
count = 0;
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
}));
PointValuePair optimum =
optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 });
Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 50);
Assert.assertTrue(optimizer.getEvaluations() < 100);
Assert.assertTrue(optimum.getValue() > 1e-2);
}
示例24
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
PointValuePair expected) {
doTest(func,
startPoint,
boundaries,
goal,
fTol,
pointTol,
maxEvaluations,
0,
expected,
"");
}
示例25
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param additionalInterpolationPoints Number of interpolation to used
* in addition to the default (2 * dim + 1).
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
int additionalInterpolationPoints,
PointValuePair expected,
String assertMsg) {
// System.out.println(func.getClass().getName() + " BEGIN"); // XXX
int dim = startPoint.length;
// MultivariateOptimizer optim =
// new PowellOptimizer(1e-13, Math.ulp(1d));
// PointValuePair result = optim.optimize(100000, func, goal, startPoint);
final double[] lB = boundaries == null ? null : boundaries[0];
final double[] uB = boundaries == null ? null : boundaries[1];
final int numIterpolationPoints = 2 * dim + 1 + additionalInterpolationPoints;
BOBYQAOptimizer optim = new BOBYQAOptimizer(numIterpolationPoints);
PointValuePair result = optim.optimize(maxEvaluations, func, goal, startPoint, lB, uB);
// System.out.println(func.getClass().getName() + " = "
// + optim.getEvaluations() + " f(");
// for (double x: result.getPoint()) System.out.print(x + " ");
// System.out.println(") = " + result.getValue());
Assert.assertEquals(assertMsg, expected.getValue(), result.getValue(), fTol);
for (int i = 0; i < dim; i++) {
Assert.assertEquals(expected.getPoint()[i],
result.getPoint()[i], pointTol);
}
// System.out.println(func.getClass().getName() + " END"); // XXX
}
示例26
/** Simple constructor.
* @param bounded bounded function
* @param lower lower bounds for each element of the input parameters array
* (some elements may be set to {@code Double.NEGATIVE_INFINITY} for
* unbounded values)
* @param upper upper bounds for each element of the input parameters array
* (some elements may be set to {@code Double.POSITIVE_INFINITY} for
* unbounded values)
* @exception DimensionMismatchException if lower and upper bounds are not
* consistent, either according to dimension or to values
*/
public MultivariateFunctionMappingAdapter(final MultivariateFunction bounded,
final double[] lower, final double[] upper) {
// safety checks
MathUtils.checkNotNull(lower);
MathUtils.checkNotNull(upper);
if (lower.length != upper.length) {
throw new DimensionMismatchException(lower.length, upper.length);
}
for (int i = 0; i < lower.length; ++i) {
// note the following test is written in such a way it also fails for NaN
if (!(upper[i] >= lower[i])) {
throw new NumberIsTooSmallException(upper[i], lower[i], true);
}
}
this.bounded = bounded;
this.mappers = new Mapper[lower.length];
for (int i = 0; i < mappers.length; ++i) {
if (Double.isInfinite(lower[i])) {
if (Double.isInfinite(upper[i])) {
// element is unbounded, no transformation is needed
mappers[i] = new NoBoundsMapper();
} else {
// element is simple-bounded on the upper side
mappers[i] = new UpperBoundMapper(upper[i]);
}
} else {
if (Double.isInfinite(upper[i])) {
// element is simple-bounded on the lower side
mappers[i] = new LowerBoundMapper(lower[i]);
} else {
// element is double-bounded
mappers[i] = new LowerUpperBoundMapper(lower[i], upper[i]);
}
}
}
}
示例27
@Test
public void testMath864() {
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 = 1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 0 };
final double[] lower = { -1e6 };
final double[] upper = { 1.5 };
final double[] sigma = { 1e-1 };
final double[] result = optimizer.optimize(new MaxEval(10000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper)).getPoint();
Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
result[0] <= upper[0]);
}
示例28
public MultivariateFunction partialDerivative(final int k) {
return new MultivariateFunction() {
public double value(double[] point) {
return gradient(point)[k];
}
};
}
示例29
@Test
public void testMath864() {
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 = 1;
final double error = target - parameters[0];
return error * error;
}
};
final double[] start = { 0 };
final double[] lower = { -1e6 };
final double[] upper = { 1.5 };
final double[] sigma = { 1e-1 };
final double[] result = optimizer.optimize(new MaxEval(10000),
new ObjectiveFunction(fitnessFunction),
GoalType.MINIMIZE,
new CMAESOptimizer.PopulationSize(5),
new CMAESOptimizer.Sigma(sigma),
new InitialGuess(start),
new SimpleBounds(lower, upper)).getPoint();
Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
result[0] <= upper[0]);
}
示例30
public ObjectiveFunction getObjectiveFunction() {
return new ObjectiveFunction(new MultivariateFunction() {
public double value(double[] params) {
Vector2D center = new Vector2D(params[0], params[1]);
double radius = getRadius(center);
double sum = 0;
for (Vector2D point : points) {
double di = point.distance(center) - radius;
sum += di * di;
}
return sum;
}
});
}
