diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala | 32 |
1 files changed, 31 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index f9a271f47e..c4bea7c2ca 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -51,6 +51,7 @@ import org.apache.spark.mllib.tree.loss.Losses import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel} import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest} import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.LinearDataGenerator import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel @@ -972,7 +973,7 @@ private[python] class PythonMLLibAPI extends Serializable { def estimateKernelDensity( sample: JavaRDD[Double], bandwidth: Double, points: java.util.ArrayList[Double]): Array[Double] = { - return new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate( + new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate( points.asScala.toArray) } @@ -991,6 +992,35 @@ private[python] class PythonMLLibAPI extends Serializable { List[AnyRef](model.clusterCenters, Vectors.dense(model.clusterWeights)).asJava } + /** + * Wrapper around the generateLinearInput method of LinearDataGenerator. + */ + def generateLinearInputWrapper( + intercept: Double, + weights: JList[Double], + xMean: JList[Double], + xVariance: JList[Double], + nPoints: Int, + seed: Int, + eps: Double): Array[LabeledPoint] = { + LinearDataGenerator.generateLinearInput( + intercept, weights.asScala.toArray, xMean.asScala.toArray, + xVariance.asScala.toArray, nPoints, seed, eps).toArray + } + + /** + * Wrapper around the generateLinearRDD method of LinearDataGenerator. + */ + def generateLinearRDDWrapper( + sc: JavaSparkContext, + nexamples: Int, + nfeatures: Int, + eps: Double, + nparts: Int, + intercept: Double): JavaRDD[LabeledPoint] = { + LinearDataGenerator.generateLinearRDD( + sc, nexamples, nfeatures, eps, nparts, intercept) + } } /** |