Java源码示例：org.apache.commons.math3.optim.univariate.UnivariatePointValuePair
示例1
/**
* Find the minimum of the function {@code f(p + alpha * d)}.
*
* @param p Starting point.
* @param d Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] p, final double[] d) {
final int n = p.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = p[i] + alpha * d[i];
}
final double obj = PowellOptimizer.this.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = PowellOptimizer.this.getGoalType();
bracket.search(f, goal, 0, 1);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例2
/**
* Find the minimum of the function {@code f(p + alpha * d)}.
*
* @param p Starting point.
* @param d Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] p, final double[] d) {
final int n = p.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = p[i] + alpha * d[i];
}
final double obj = PowellOptimizer.this.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = PowellOptimizer.this.getGoalType();
bracket.search(f, goal, 0, 1);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例3
/**
* Find the minimum of the function {@code f(p + alpha * d)}.
*
* @param p Starting point.
* @param d Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] p, final double[] d) {
final int n = p.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = p[i] + alpha * d[i];
}
final double obj = PowellOptimizer.this.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = PowellOptimizer.this.getGoalType();
bracket.search(f, goal, 0, 1);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例4
/**
* Find the minimum of the function {@code f(p + alpha * d)}.
*
* @param p Starting point.
* @param d Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] p, final double[] d) {
final int n = p.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = p[i] + alpha * d[i];
}
final double obj = PowellOptimizer.this.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = PowellOptimizer.this.getGoalType();
bracket.search(f, goal, 0, 1);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例5
/**
* Find the minimum of the function {@code f(p + alpha * d)}.
*
* @param p Starting point.
* @param d Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] p, final double[] d) {
final int n = p.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = p[i] + alpha * d[i];
}
final double obj = PowellOptimizer.this.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = PowellOptimizer.this.getGoalType();
bracket.search(f, goal, 0, 1);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例6
/**
* Finds the number {@code alpha} that optimizes
* {@code f(startPoint + alpha * direction)}.
*
* @param startPoint Starting point.
* @param direction Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] startPoint,
final double[] direction) {
final int n = startPoint.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = startPoint[i] + alpha * direction[i];
}
final double obj = mainOptimizer.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = mainOptimizer.getGoalType();
bracket.search(f, goal, 0, initialBracketingRange);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return lineOptimizer.optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例7
/**
* Finds the number {@code alpha} that optimizes
* {@code f(startPoint + alpha * direction)}.
*
* @param startPoint Starting point.
* @param direction Search direction.
* @return the optimum.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the number of evaluations is exceeded.
*/
public UnivariatePointValuePair search(final double[] startPoint,
final double[] direction) {
final int n = startPoint.length;
final UnivariateFunction f = new UnivariateFunction() {
public double value(double alpha) {
final double[] x = new double[n];
for (int i = 0; i < n; i++) {
x[i] = startPoint[i] + alpha * direction[i];
}
final double obj = mainOptimizer.computeObjectiveValue(x);
return obj;
}
};
final GoalType goal = mainOptimizer.getGoalType();
bracket.search(f, goal, 0, initialBracketingRange);
// Passing "MAX_VALUE" as a dummy value because it is the enclosing
// class that counts the number of evaluations (and will eventually
// generate the exception).
return lineOptimizer.optimize(new MaxEval(Integer.MAX_VALUE),
new UnivariateObjectiveFunction(f),
goal,
new SearchInterval(bracket.getLo(),
bracket.getHi(),
bracket.getMid()));
}
示例8
private static double calculateContamination(final double errorRate, final List<List<PileupSummary>> segments, final List<Double> mafs) {
final DoubleUnaryOperator objective = c -> modelLogLikelihood(segments, c, errorRate, mafs);
final List<UnivariatePointValuePair> optima = CONTAMINATION_INITIAL_GUESSES.stream()
.map(initial -> OptimizationUtils.max(objective, 0, 0.5, initial, 1.0e-4, 1.0e-4, 30))
.collect(Collectors.toList());
return Collections.max(optima, Comparator.comparingDouble(UnivariatePointValuePair::getValue)).getPoint();
}
示例9
/**
* Maximizes a univariate function using a grid search followed by Brent's algorithm.
*
* @param fn the likelihood function to minimize
* @param gridStart the lower bound for the grid search
* @param gridEnd the upper bound for the grid search
* @param gridStep step size for the grid search
* @param relErr relative error tolerance for Brent's algorithm
* @param absErr absolute error tolerance for Brent's algorithm
* @param maxIter maximum # of iterations to perform in Brent's algorithm
* @param maxEval maximum # of Likelihood function evaluations in Brent's algorithm
*
* @return the value of the parameter that maximizes the function
*/
public static double maximize(UnivariateFunction fn, double gridStart, double gridEnd,
double gridStep, double relErr, double absErr, int maxIter, int maxEval) {
Interval interval = gridSearch(fn, gridStart, gridEnd, gridStep);
BrentOptimizer bo = new BrentOptimizer(relErr, absErr);
UnivariatePointValuePair max = bo.optimize(
new MaxIter(maxIter),
new MaxEval(maxEval),
new SearchInterval(interval.getInf(), interval.getSup()),
new UnivariateObjectiveFunction(fn),
GoalType.MAXIMIZE);
return max.getPoint();
}
