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Posted to commits@commons.apache.org by er...@apache.org on 2021/08/22 00:33:30 UTC
[commons-math] 06/13: sonar fix: Ensure checkFeasableCount is not
negative.
This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 30aa597f1edec81de7514e638bc8d713ca281edc
Author: Alex Herbert <ah...@apache.org>
AuthorDate: Sat Aug 21 08:16:20 2021 +0100
sonar fix: Ensure checkFeasableCount is not negative.
Change loop condition to 'i <= checkFeasableCount' from 'i <
checkFeasableCount + 1'
This ensures the loop to identify a new feasible column (RealMatrix
arxk) always executes at least once even with checkFeasibleCount at the
limit of 0 or Integer.MAX_VALUE.
---
.../math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
index a5b8e35..13e7480 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
@@ -236,7 +236,7 @@ public class CMAESOptimizer
this.stopFitness = stopFitness;
this.isActiveCMA = isActiveCMA;
this.diagonalOnly = diagonalOnly;
- this.checkFeasableCount = checkFeasableCount;
+ this.checkFeasableCount = Math.max(0, checkFeasableCount);
this.random = new NormalDistribution(0, 1).createSampler(rng);
this.generateStatistics = generateStatistics;
}
@@ -398,7 +398,7 @@ public class CMAESOptimizer
// generate random offspring
for (int k = 0; k < lambda; k++) {
RealMatrix arxk = null;
- for (int i = 0; i < checkFeasableCount + 1; i++) {
+ for (int i = 0; i <= checkFeasableCount; i++) {
if (diagonalOnly <= 0) {
arxk = xmean.add(BD.multiply(arz.getColumnMatrix(k))
.scalarMultiply(sigma)); // m + sig * Normal(0,C)