diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala | 39 |
1 files changed, 23 insertions, 16 deletions
diff --git a/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala index 8637d27cd0..1d2b5c89f0 100644 --- a/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala +++ b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala @@ -24,25 +24,32 @@ import org.jblas.DoubleMatrix import spark.{RDD, SparkContext} import spark.mllib.util.MLUtils +/** +* Generate RDD(s) containing data for Matrix Factorization. +* +* This method samples training entries according to the oversampling factor +* 'tr_samp_fact', which is a multiplicative factor of the number of +* degrees of freedom of the matrix: rank*(m+n-rank). +* +* It optionally samples entries for a testing matrix using +* 'te_samp_fact', the percentage of the number of training entries +* to use for testing. +* +* This method takes the following inputs: +* sparkMaster (String) The master URL. +* outputPath (String) Directory to save output. +* m (Int) Number of rows in data matrix. +* n (Int) Number of columns in data matrix. +* rank (Int) Underlying rank of data matrix. +* tr_samp_fact (Double) Oversampling factor. +* noise (Boolean) Whether to add gaussian noise to training data. +* sigma (Double) Standard deviation of added gaussian noise. +* test (Boolean) Whether to create testing RDD. +* te_samp_fact (Double) Percentage of training data to use as test data. +*/ object MFDataGenerator{ - /** - * Generate RDD(s) containing data for Matrix Factorization. This function chooses - * positive labels with probability `probOne` and scales positive examples by `eps`. - * - * @param sc SparkContext to use for creating the RDD. - * @param outputPath Directory to save output. - * @param m Number of rows in data matrix. - * @param n Number of columns in data matrix. - * @param rank Underlying rank of data matrix. - * @param tr_samp_fact Oversampling factor. - * @param noise Boolean value - whether to add gaussian noise to training data. - * @param sigma Standard deviation of added gaussian noise. - * @param test Boolean value - whether to create testing RDD. - * @param te_samp_fact Percentage of training data to use as test data. - */ - def main(args: Array[String]) { if (args.length != 10) { println("Usage: MFGenerator " + |