Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/11844#discussion_r62798712
--- Diff:
examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java
---
@@ -48,26 +43,19 @@ public static void main(String[] args) {
.getOrCreate();
// $example on$
- List<Row> data = Arrays.asList(
- RowFactory.create(Vectors.dense(0.1, 0.1, 0.1)),
- RowFactory.create(Vectors.dense(0.3, 0.3, 0.25)),
- RowFactory.create(Vectors.dense(0.1, 0.1, -0.1)),
- RowFactory.create(Vectors.dense(20.3, 20.1, 19.9)),
- RowFactory.create(Vectors.dense(20.2, 20.1, 19.7)),
- RowFactory.create(Vectors.dense(18.9, 20.0, 19.7))
- );
-
- StructType schema = new StructType(new StructField[]{
- new StructField("features", new VectorUDT(), false,
Metadata.empty()),
- });
-
- Dataset<Row> dataset = spark.createDataFrame(data, schema);
+ // Loads data.
+ Dataset<Row> dataset =
spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
- BisectingKMeans bkm = new BisectingKMeans().setK(2);
+ // Trains a bisecting k-means model.
+ BisectingKMeans bkm = new BisectingKMeans().setK(2).setSeed(1);
BisectingKMeansModel model = bkm.fit(dataset);
- System.out.println("Compute Cost: " + model.computeCost(dataset));
+ // Evaluate clustering.
+ double cost = model.computeCost(dataset);
+ System.out.println("Compute Cost: " + cost);
--- End diff --
Good idea. I will do it.
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