1 files changed, 0 insertions, 75 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala
deleted file mode 100644
index 90b817b23e..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * 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.mllib
-
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.mllib.clustering.GaussianMixture
-import org.apache.spark.mllib.linalg.Vectors
-
-/**
- * An example Gaussian Mixture Model EM app. Run with
- * {{{
- * ./bin/run-example mllib.DenseGaussianMixture <input> <k> <convergenceTol>
- * }}}
- * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
- */
-object DenseGaussianMixture {
-  def main(args: Array[String]): Unit = {
-    if (args.length < 3) {
-      println("usage: DenseGmmEM <input file> <k> <convergenceTol> [maxIterations]")
-    } else {
-      val maxIterations = if (args.length > 3) args(3).toInt else 100
-      run(args(0), args(1).toInt, args(2).toDouble, maxIterations)
-    }
-  }
-
-  private def run(inputFile: String, k: Int, convergenceTol: Double, maxIterations: Int) {
-    val conf = new SparkConf().setAppName("Gaussian Mixture Model EM example")
-    val ctx = new SparkContext(conf)
-
-    val data = ctx.textFile(inputFile).map { line =>
-      Vectors.dense(line.trim.split(' ').map(_.toDouble))
-    }.cache()
-
-    val clusters = new GaussianMixture()
-      .setK(k)
-      .setConvergenceTol(convergenceTol)
-      .setMaxIterations(maxIterations)
-      .run(data)
-
-    for (i <- 0 until clusters.k) {
-      println("weight=%f\nmu=%s\nsigma=\n%s\n" format
-        (clusters.weights(i), clusters.gaussians(i).mu, clusters.gaussians(i).sigma))
-    }
-
-    println("The membership value of each vector to all mixture components (first <= 100):")
-    val membership = clusters.predictSoft(data)
-    membership.take(100).foreach { x =>
-      print(" " + x.mkString(","))
-    }
-    println()
-    println("Cluster labels (first <= 100):")
-    val clusterLabels = clusters.predict(data)
-    clusterLabels.take(100).foreach { x =>
-      print(" " + x)
-    }
-    println()
-  }
-}
-// scalastyle:on println