From 536f36a1fce1bf7cbd0ad5e782176b4e28e61ea3 Mon Sep 17 00:00:00 2001
From: kunpeng <kunpeng.jiang@intel.com>
Date: Thu, 13 Apr 2023 14:22:47 +0800
Subject: [PATCH] Clean files

---
 .../spark/examples/ml/KMeansExample.scala       | 17 +++++------------
 .../java/com/intel/oap/mllib/LibLoader.java     |  7 +------
 .../spark/ml/clustering/spark321/KMeans.scala   |  2 --
 3 files changed, 6 insertions(+), 20 deletions(-)

diff --git a/examples/kmeans/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala b/examples/kmeans/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
index 0bccd4da9..37e26ebe2 100644
--- a/examples/kmeans/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
+++ b/examples/kmeans/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
@@ -21,7 +21,6 @@ package org.apache.spark.examples.ml
 
 // $example on$
 import org.apache.spark.ml.clustering.KMeans
-import org.apache.spark.ml.regression.LinearRegression
 import org.apache.spark.ml.evaluation.ClusteringEvaluator
 // $example off$
 import org.apache.spark.sql.SparkSession
@@ -49,27 +48,21 @@ object KMeansExample {
     dataset.cache()
 
     // Trains a k-means model.
-    println("seem good0")
     val kmeans = new KMeans().setK(2).setSeed(1L).setInitMode("random").setMaxIter(5)
-    val lr = new LinearRegression().setMaxIter(10).setRegParam(0.3).setElasticNetParam(0.8)
-    println("seem good1")
-    val lrModel = lr.fit(dataset)
-    println("seem good2")
     val model = kmeans.fit(dataset)
-    println("seem good3")
 
     // Make predictions
-    //val predictions = model.transform(dataset)
+    val predictions = model.transform(dataset)
 
     // Evaluate clustering by computing Silhouette score
-    //val evaluator = new ClusteringEvaluator()
+    val evaluator = new ClusteringEvaluator()
 
-    //val silhouette = evaluator.evaluate(predictions)
-    println(s"Silhouette with squared euclidean distance = ")
+    val silhouette = evaluator.evaluate(predictions)
+    println(s"Silhouette with squared euclidean distance = $silhouette")
 
     // Shows the result.
     println("Cluster Centers: ")
-    //model.clusterCenters.foreach(println)
+    model.clusterCenters.foreach(println)
     // $example off$
 
     spark.stop()
diff --git a/mllib-dal/src/main/java/com/intel/oap/mllib/LibLoader.java b/mllib-dal/src/main/java/com/intel/oap/mllib/LibLoader.java
index 165fc3873..1a14e4a99 100644
--- a/mllib-dal/src/main/java/com/intel/oap/mllib/LibLoader.java
+++ b/mllib-dal/src/main/java/com/intel/oap/mllib/LibLoader.java
@@ -49,12 +49,7 @@ public static synchronized void loadLibraries() throws IOException {
     if (isLoaded) {
       return;
     }
-/*
-    if (!loadLibSYCL()) {
-      log.debug("SYCL libraries are not available, will load CPU libraries only.");
-    }
-    loadLibCCL();
-	*/
+
     loadLibMLlibDAL();
 
     isLoaded = true;
diff --git a/mllib-dal/src/main/scala/org/apache/spark/ml/clustering/spark321/KMeans.scala b/mllib-dal/src/main/scala/org/apache/spark/ml/clustering/spark321/KMeans.scala
index ece970fc2..25b5d3ab1 100644
--- a/mllib-dal/src/main/scala/org/apache/spark/ml/clustering/spark321/KMeans.scala
+++ b/mllib-dal/src/main/scala/org/apache/spark/ml/clustering/spark321/KMeans.scala
@@ -58,8 +58,6 @@ class KMeans @Since("1.5.0") (
 
     instr.logPipelineStage(this)
     instr.logDataset(dataset)
-    System.exit(0)
-
     instr.logParams(this, featuresCol, predictionCol, k, initMode, initSteps, distanceMeasure,
       maxIter, seed, tol, weightCol)