Don't build neighborhoods when clustersPerNeighborhood == 0

Author: iverase

muted-tests.yml

@@ -557,9 +557,6 @@ tests:
 - class: org.elasticsearch.compute.aggregation.TopIntAggregatorFunctionTests
   method: testManyInitialManyPartialFinalRunnerThrowing
   issue: https://github.com/elastic/elasticsearch/issues/130145
-- class: org.elasticsearch.index.codec.vectors.cluster.KMeansLocalTests
-  method: testKMeansNeighbors
-  issue: https://github.com/elastic/elasticsearch/issues/130258
 - class: org.elasticsearch.xpack.test.rest.XPackRestIT
   method: test {p0=esql/10_basic/basic with documents_found}
   issue: https://github.com/elastic/elasticsearch/issues/130256
@@ -587,9 +584,6 @@ tests:
 - class: org.elasticsearch.test.rest.yaml.RcsCcsCommonYamlTestSuiteIT
   method: test {p0=msearch/20_typed_keys/Multisearch test with typed_keys parameter for sampler and significant terms}
   issue: https://github.com/elastic/elasticsearch/issues/130472
-- class: org.elasticsearch.index.codec.vectors.cluster.HierarchicalKMeansTests
-  method: testHKmeans
-  issue: https://github.com/elastic/elasticsearch/issues/130497
 - class: org.elasticsearch.xpack.esql.action.EsqlActionBreakerIT
   method: testProjectWhere
   issue: https://github.com/elastic/elasticsearch/issues/130504

server/src/main/java/org/elasticsearch/index/codec/vectors/cluster/KMeansLocal.java

@@ -265,9 +265,10 @@ void cluster(FloatVectorValues vectors, KMeansIntermediate kMeansIntermediate) t
      */
     void cluster(FloatVectorValues vectors, KMeansIntermediate kMeansIntermediate, boolean neighborAware) throws IOException {
         float[][] centroids = kMeansIntermediate.centroids();
+        boolean computeNeighborhoods = neighborAware && clustersPerNeighborhood > 0;
 
         List<int[]> neighborhoods = null;
-        if (neighborAware) {
+        if (computeNeighborhoods) {
             int k = centroids.length;
             neighborhoods = new ArrayList<>(k);
             for (int i = 0; i < k; ++i) {
@@ -276,7 +277,7 @@ void cluster(FloatVectorValues vectors, KMeansIntermediate kMeansIntermediate, b
             computeNeighborhoods(centroids, neighborhoods, clustersPerNeighborhood);
         }
         cluster(vectors, kMeansIntermediate, neighborhoods);
-        if (neighborAware && clustersPerNeighborhood > 0) {
+        if (computeNeighborhoods) {
             int[] assignments = kMeansIntermediate.assignments();
             assert assignments != null;
             assert assignments.length == vectors.size();