1 files changed, 80 insertions, 0 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/BucketedRandomProjectionLSHExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/BucketedRandomProjectionLSHExample.scala
new file mode 100644
index 0000000000..686cc39d3b
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/BucketedRandomProjectionLSHExample.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+// $example on$
+import org.apache.spark.ml.feature.BucketedRandomProjectionLSH
+import org.apache.spark.ml.linalg.Vectors
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+object BucketedRandomProjectionLSHExample {
+  def main(args: Array[String]): Unit = {
+    // Creates a SparkSession
+    val spark = SparkSession
+      .builder
+      .appName("BucketedRandomProjectionLSHExample")
+      .getOrCreate()
+
+    // $example on$
+    val dfA = spark.createDataFrame(Seq(
+      (0, Vectors.dense(1.0, 1.0)),
+      (1, Vectors.dense(1.0, -1.0)),
+      (2, Vectors.dense(-1.0, -1.0)),
+      (3, Vectors.dense(-1.0, 1.0))
+    )).toDF("id", "keys")
+
+    val dfB = spark.createDataFrame(Seq(
+      (4, Vectors.dense(1.0, 0.0)),
+      (5, Vectors.dense(-1.0, 0.0)),
+      (6, Vectors.dense(0.0, 1.0)),
+      (7, Vectors.dense(0.0, -1.0))
+    )).toDF("id", "keys")
+
+    val key = Vectors.dense(1.0, 0.0)
+
+    val brp = new BucketedRandomProjectionLSH()
+      .setBucketLength(2.0)
+      .setNumHashTables(3)
+      .setInputCol("keys")
+      .setOutputCol("values")
+
+    val model = brp.fit(dfA)
+
+    // Feature Transformation
+    model.transform(dfA).show()
+    // Cache the transformed columns
+    val transformedA = model.transform(dfA).cache()
+    val transformedB = model.transform(dfB).cache()
+
+    // Approximate similarity join
+    model.approxSimilarityJoin(dfA, dfB, 1.5).show()
+    model.approxSimilarityJoin(transformedA, transformedB, 1.5).show()
+    // Self Join
+    model.approxSimilarityJoin(dfA, dfA, 2.5).filter("datasetA.id < datasetB.id").show()
+
+    // Approximate nearest neighbor search
+    model.approxNearestNeighbors(dfA, key, 2).show()
+    model.approxNearestNeighbors(transformedA, key, 2).show()
+    // $example off$
+
+    spark.stop()
+  }
+}
+// scalastyle:on println