private List<Vector2D> makeBlobs(int centers, double clusterStd, double min, double max) {
NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd, 1e-9);
double range = max - min;
Vector2D[] centerPoints = new Vector2D[centers];
for (int i = 0; i < centers; i++) {
centerPoints[i] =
new Vector2D(random.nextDouble() * range + min, random.nextDouble() * range + min);
}
int[] nSamplesPerCenter = new int[centers];
int count = samples / centers;
Arrays.fill(nSamplesPerCenter, count);
for (int i = 0; i < samples % centers; i++) {
nSamplesPerCenter[i]++;
}
List<Vector2D> points = new ArrayList<>();
for (int i = 0; i < centers; i++) {
for (int j = 0; j < nSamplesPerCenter[i]; j++) {
points.add(new Vector2D(dist.sample(), dist.sample()).add(centerPoints[i]));
}
}
return points;
}
/** {@inheritDoc} */
@Override
protected UnivariatePointValuePair doOptimize() {
// Remove all instances of "MaxEval" and "SearchInterval" from the
// array that will be passed to the internal optimizer.
// The former is to enforce smaller numbers of allowed evaluations
// (according to how many have been used up already), and the latter
// to impose a different start value for each start.
for (int i = 0; i < optimData.length; i++) {
if (optimData[i] instanceof MaxEval) {
optimData[i] = null;
maxEvalIndex = i;
continue;
}
if (optimData[i] instanceof SearchInterval) {
optimData[i] = null;
searchIntervalIndex = i;
continue;
}
}
if (maxEvalIndex == -1) {
throw new MathIllegalStateException();
}
if (searchIntervalIndex == -1) {
throw new MathIllegalStateException();
}
RuntimeException lastException = null;
optima = new UnivariatePointValuePair[starts];
totalEvaluations = 0;
final int maxEval = getMaxEvaluations();
final double min = getMin();
final double max = getMax();
final double startValue = getStartValue();
// Multi-start loop.
for (int i = 0; i < starts; i++) {
// CHECKSTYLE: stop IllegalCatch
try {
// Decrease number of allowed evaluations.
optimData[maxEvalIndex] = new MaxEval(maxEval - totalEvaluations);
// New start value.
final double s = (i == 0) ? startValue : min + generator.nextDouble() * (max - min);
optimData[searchIntervalIndex] = new SearchInterval(min, max, s);
// Optimize.
optima[i] = optimizer.optimize(optimData);
} catch (RuntimeException mue) {
lastException = mue;
optima[i] = null;
}
// CHECKSTYLE: resume IllegalCatch
totalEvaluations += optimizer.getEvaluations();
}
sortPairs(getGoalType());
if (optima[0] == null) {
throw lastException; // Cannot be null if starts >= 1.
}
// Return the point with the best objective function value.
return optima[0];
}
