ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

fixing formatting

Browse source
This commit is contained in:
Ginger Smith 2013-08-05 11:22:18 -07:00
parent 4ab4df5edb
commit 8c8947e2b6
1 changed files with 23 additions and 16 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

39
mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala
View file

@ -24,25 +24,32 @@ import org.jblas.DoubleMatrix
import spark.{RDD, SparkContext} import spark.{RDD, SparkContext}
import spark.mllib.util.MLUtils 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{ 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]) { def main(args: Array[String]) {
if (args.length != 10) { if (args.length != 10) {
println("Usage: MFGenerator " + println("Usage: MFGenerator " +