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| author | Erich Schubert <erich@debian.org> | 2012-12-14 20:45:15 +0100 |
|---|---|---|
| committer | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:35 +0000 |
| commit | 357b2761a2c0ded8cad5e4d3c1e667b7639ff7a6 (patch) | |
| tree | 3dd8947bb70a67c221adc3cd4359ba1d385e2f3c /test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java | |
| parent | 4343785ebed9d4145f417d86d581f18a0d31e4ac (diff) | |
| parent | b7b404fd7a726774d442562d11659d7b5368cdb9 (diff) | |
Import Debian changes 0.5.5-1
elki (0.5.5-1) unstable; urgency=low
* New upstream release: 0.5.5 interim release.
Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java')
| -rw-r--r-- | test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java index 589be8ad..bfe57052 100644 --- a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java +++ b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansResults.java @@ -62,11 +62,11 @@ public class TestKMeansResults extends AbstractSimpleAlgorithmTest implements JU ListParameterization params = new ListParameterization(); params.addParameter(KMeans.K_ID, 5); params.addParameter(KMeans.SEED_ID, 3); - AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params); + AbstractKMeans<DoubleVector, DoubleDistance, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params); testParameterizationOk(params); // run KMeans on database - Clustering<MeanModel<DoubleVector>> result = kmeans.run(db); + Clustering<? extends MeanModel<DoubleVector>> result = kmeans.run(db); testFMeasure(db, result, 0.998005); testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); } @@ -85,11 +85,11 @@ public class TestKMeansResults extends AbstractSimpleAlgorithmTest implements JU ListParameterization params = new ListParameterization(); params.addParameter(KMeans.K_ID, 5); params.addParameter(KMeans.SEED_ID, 3); - AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansMacQueen.class, params); + AbstractKMeans<DoubleVector, DoubleDistance, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansMacQueen.class, params); testParameterizationOk(params); // run KMeans on database - Clustering<MeanModel<DoubleVector>> result = kmeans.run(db); + Clustering<? extends MeanModel<DoubleVector>> result = kmeans.run(db); testFMeasure(db, result, 0.998005); testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); } @@ -108,11 +108,11 @@ public class TestKMeansResults extends AbstractSimpleAlgorithmTest implements JU ListParameterization params = new ListParameterization(); params.addParameter(KMeans.K_ID, 5); params.addParameter(KMeans.SEED_ID, 3); - AbstractKMeans<DoubleVector, DoubleDistance> kmedians = ClassGenericsUtil.parameterizeOrAbort(KMediansLloyd.class, params); + AbstractKMeans<DoubleVector, DoubleDistance, ?> kmedians = ClassGenericsUtil.parameterizeOrAbort(KMediansLloyd.class, params); testParameterizationOk(params); // run KMedians on database - Clustering<MeanModel<DoubleVector>> result = kmedians.run(db); + Clustering<? extends MeanModel<DoubleVector>> result = kmedians.run(db); testFMeasure(db, result, 0.998005); testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 }); } |