ODIn/spark-instrumented-optimizer
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Matei Zaharia 2013-06-22 18:08:52 -04:00
parent 6586c5e28b
commit 39684eafe3
2 changed files with 5 additions and 4 deletions
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2
ml/src/main/scala/spark/ml/recommendation/ALS.scala
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@ -365,7 +365,7 @@ object ALS {
def main(args: Array[String]) { def main(args: Array[String]) {
if (args.length != 5) { if (args.length != 5) {
println("Usage: ALS <master> <ratings_file> <rank> <num_iters> <output_dir>") println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir>")
System.exit(1) System.exit(1)
} }
val (master, ratingsFile, rank, iters, outputDir) = val (master, ratingsFile, rank, iters, outputDir) =

7
ml/src/main/scala/spark/ml/recommendation/MatrixFactorizationModel.scala
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@ -6,9 +6,10 @@ import spark.SparkContext._
import org.jblas._ import org.jblas._
class MatrixFactorizationModel( class MatrixFactorizationModel(
val rank: Int, val rank: Int,
val userFeatures: RDD[(Int, Array[Double])], val userFeatures: RDD[(Int, Array[Double])],
val productFeatures: RDD[(Int, Array[Double])]) val productFeatures: RDD[(Int, Array[Double])])
extends Serializable
{ {
/** Predict the rating of one user for one product. */ /** Predict the rating of one user for one product. */
def predict(user: Int, product: Int): Double = { def predict(user: Int, product: Int): Double = {