diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
index b56c4b3bd7789..e0511eaec9cb5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
@@ -30,14 +30,15 @@ import org.apache.spark.mllib.linalg.Vectors
*/
object DenseGmmEM {
def main(args: Array[String]): Unit = {
- if (args.length != 3) {
+ if (args.length < 3) {
println("usage: DenseGmmEM ")
} else {
- run(args(0), args(1).toInt, args(2).toDouble)
+ 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) {
+ 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)
@@ -48,6 +49,7 @@ object DenseGmmEM {
val clusters = new GaussianMixtureModelEM()
.setK(k)
.setConvergenceTol(convergenceTol)
+ .setMaxIterations(maxIterations)
.run(data)
for (i <- 0 until clusters.k) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 734d67ea72a26..0285a847bd1b3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.stat.impl.MultivariateGaussian
* are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are
* the respective mean and covariance for each Gaussian distribution i=1..k.
*
- * @param weight Weights for each Gaussian distribution in the mixture, where mu(i) is
+ * @param weight Weights for each Gaussian distribution in the mixture, where weight(i) is
* the weight for Gaussian i, and weight.sum == 1
* @param mu Means for each Gaussian in the mixture, where mu(i) is the mean for Gaussian i
* @param sigma Covariance maxtrix for each Gaussian in the mixture, where sigma(i) is the