Use only one update function and pass in transpose of ratings matrix where appropriate

1 files changed, 3 insertions, 29 deletions

diff --git a/examples/src/main/scala/spark/examples/SparkALS.scala b/examples/src/main/scala/spark/examples/SparkALS.scala
index 4672812565..2766ad1702 100644
--- a/examples/src/main/scala/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/spark/examples/SparkALS.scala
@@ -43,7 +43,7 @@ object SparkALS {
     return sqrt(sumSqs / (M * U))
   }
 
-  def updateMovie(i: Int, m: DoubleMatrix1D, us: Array[DoubleMatrix1D],
+  def update(i: Int, m: DoubleMatrix1D, us: Array[DoubleMatrix1D],
     R: DoubleMatrix2D) : DoubleMatrix1D =
   {
     val U = us.size
@@ -69,32 +69,6 @@ object SparkALS {
     return solved2D.viewColumn(0)
   }
 
-  def updateUser(j: Int, u: DoubleMatrix1D, ms: Array[DoubleMatrix1D],
-    R: DoubleMatrix2D) : DoubleMatrix1D =
-  {
-    val M = ms.size
-    val F = ms(0).size
-    val XtX = factory2D.make(F, F)
-    val Xty = factory1D.make(F)
-    // For each movie that the user rated
-    for (i <- 0 until M) {
-      val m = ms(i)
-      // Add m * m^t to XtX
-      blas.dger(1, m, m, XtX)
-      // Add m * rating to Xty
-      blas.daxpy(R.get(i, j), m, Xty)
-    }
-    // Add regularization coefs to diagonal terms
-    for (d <- 0 until F) {
-      XtX.set(d, d, XtX.get(d, d) + LAMBDA * M)
-    }
-    // Solve it with Cholesky
-    val ch = new CholeskyDecomposition(XtX)
-    val Xty2D = factory2D.make(Xty.toArray, F)
-    val solved2D = ch.solve(Xty2D)
-    return solved2D.viewColumn(0)
-  }
-
   def main(args: Array[String]) {
     var host = ""
     var slices = 0
@@ -134,11 +108,11 @@ object SparkALS {
     for (iter <- 1 to ITERATIONS) {
       println("Iteration " + iter + ":")
       ms = spark.parallelize(0 until M, slices)
-                .map(i => updateMovie(i, msc.value(i), usc.value, Rc.value))
+                .map(i => update(i, msc.value(i), usc.value, Rc.value))
                 .toArray
       msc = spark.broadcast(ms) // Re-broadcast ms because it was updated
       us = spark.parallelize(0 until U, slices)
-                .map(i => updateUser(i, usc.value(i), msc.value, Rc.value))
+                .map(i => update(i, usc.value(i), msc.value, algebra.transpose(Rc.value)))
                 .toArray
       usc = spark.broadcast(us) // Re-broadcast us because it was updated
       println("RMSE = " + rmse(R, ms, us))