Andras Sereny created MATH-1152:
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Summary: Suboptimal implementation of
EnumeratedDistribution.sample()
Key: MATH-1152
URL: https://issues.apache.org/jira/browse/MATH-1152
Project: Commons Math
Issue Type: Bug
Affects Versions: 3.3
Reporter: Andras Sereny
org.apache.commons.math3.distribution.EnumeratedDistribution.sample() performs
a *linear* search to find the appropriate element in the probability space
(called singletons here) given a random double value. For large probability
spaces, this is not effective. Instead, we should cache the cumulative
probabilities and do a *binary* search.
Rough implementation:
{code:title=Bar.java|borderStyle=solid}
void computeCumulative() {
cumulative = new double[size];
double sum = 0;
for (int i = 1; i < weights.length - 1; i++) {
cumulative[i] = cumulative[i-1] + weights[i-1];
}
cumulative[size - 1] = 1;
}
{code}
and then
{code:title=Bar.java|borderStyle=solid}
int sampleIndex() {
double randomValue = random.nextDouble();
int result = Arrays.binarySearch(cumulative, randomValue);
if (result >= 0) return result;
int insertionPoint = -result-1;
return insertionPoint;
}
{code}
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