@Test
public void testRun() throws Exception {
Path input = getTestTempDirPath("input");
Path output = getTestTempDirPath("output");
Path seedsPath = getTestTempDirPath("seeds");
List<VectorWritable> points = getPointsWritable(REFERENCE);
List<VectorWritable> seeds = getPointsWritable(SEEDS);
Configuration conf = new Configuration();
ClusteringTestUtils.writePointsToFile(points, true, new Path(input, "file1"), fs, conf);
ClusteringTestUtils.writePointsToFile(seeds, true, new Path(seedsPath, "part-seeds"), fs, conf);
String[] args = {
optKey(DefaultOptionCreator.INPUT_OPTION),
input.toString(),
optKey(VectorDistanceSimilarityJob.SEEDS),
seedsPath.toString(),
optKey(DefaultOptionCreator.OUTPUT_OPTION),
output.toString(),
optKey(DefaultOptionCreator.DISTANCE_MEASURE_OPTION),
EuclideanDistanceMeasure.class.getName()
};
ToolRunner.run(new Configuration(), new VectorDistanceSimilarityJob(), args);
int expect = SEEDS.length * REFERENCE.length;
DummyOutputCollector<StringTuple, DoubleWritable> collector =
new DummyOutputCollector<StringTuple, DoubleWritable>();
//
for (Pair<StringTuple, DoubleWritable> record :
new SequenceFileIterable<StringTuple, DoubleWritable>(
new Path(output, "part-m-00000"), conf)) {
collector.collect(record.getFirst(), record.getSecond());
}
assertEquals(expect, collector.getData().size());
}
@Test
public void testSeqFileClusterIteratorKMeans() throws IOException {
Path pointsPath = getTestTempDirPath("points");
Path priorPath = getTestTempDirPath("prior");
Path outPath = getTestTempDirPath("output");
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
List<VectorWritable> points =
TestKmeansClustering.getPointsWritable(TestKmeansClustering.REFERENCE);
ClusteringTestUtils.writePointsToFile(points, new Path(pointsPath, "file1"), fs, conf);
Path path = new Path(priorPath, "priorClassifier");
ClusterClassifier prior = newClusterClassifier();
writeClassifier(prior, conf, path, fs);
assertEquals(3, prior.getModels().size());
System.out.println("Prior");
for (Cluster cluster : prior.getModels()) {
System.out.println(cluster.asFormatString(null));
}
ClusteringPolicy policy = new KMeansClusteringPolicy();
ClusterIterator iterator = new ClusterIterator(policy);
iterator.iterate(pointsPath, path, outPath, 5);
for (int i = 1; i <= 5; i++) {
System.out.println("Classifier-" + i);
ClusterClassifier posterior = readClassifier(conf, new Path(outPath, "classifier-" + i), fs);
assertEquals(3, posterior.getModels().size());
for (Cluster cluster : posterior.getModels()) {
System.out.println(cluster.asFormatString(null));
}
}
}
@Test
public void test() {
double threshold = 0.5;
Matrix topicTermCounts = ClusteringTestUtils.randomStructuredModel(20, 100);
Matrix sparseTopicTermCounts =
TopicModelUtils.sparsifyTopicTermCounts(topicTermCounts, threshold);
assertColumnNormsEqualOrZero(topicTermCounts, sparseTopicTermCounts);
assertFewerNonzeros(topicTermCounts, sparseTopicTermCounts);
}
