diff --git a/src/main/scala/collaborativeFiltering/MatrixFactorization.scala b/src/main/scala/collaborativeFiltering/MatrixFactorization.scala
index 4cbdeda..abb7552 100644
--- a/src/main/scala/collaborativeFiltering/MatrixFactorization.scala
+++ b/src/main/scala/collaborativeFiltering/MatrixFactorization.scala
@@ -113,9 +113,9 @@ class MatrixFactorization extends Serializable{
         val testTimeStart = System.currentTimeMillis()
         val bc_test_itemFactors = ratingsByRow.context.broadcast(itemFactors)
         //training loss
-        val loss = ratingsByRow.mapPartitions {iter =>
+        val loss = ratingsByRow.mapPartitionsWithIndex {(index,iter) =>
           val localV = bc_test_itemFactors.value
-          val localU = MatrixFactorization.workerstore.get[Map[Int, Vector]]("userFactors")
+          val localU = MatrixFactorization.workerstore.get[Map[Int, Vector]](s"userFactors_$index")
           val reguV = localV.mapValues(v => lambda_v * v.dot(v))
           val reguU = localU.mapValues(u => lambda_u * u.dot(u))
           val ls = iter.foldLeft(0.0) { (l, r) =>
@@ -125,7 +125,7 @@ class MatrixFactorization extends Serializable{
             l + residual * residual + reguU.get(r.index_x).get + reguV.get(r.index_y).get
           }
           Iterator.single(ls)
-        }.reduce(_ + _) / numRatings
+        }.sum() / numRatings
         bc_test_itemFactors.unpersist()
         print(s"$loss\t")
         testTime += (System.currentTimeMillis() - testTimeStart)
@@ -160,7 +160,6 @@ class MatrixFactorization extends Serializable{
           val vj = localV.get(ranRating.index_y).get
           //update vj
           val residual = ranRating.rating - uh.dot(vj)
-          val rrr = stepsize * residual
           vj *= (1 - stepsize * lambda_v)
           vj.plusax(stepsize * residual, uh)
           loss += (residual * residual)
@@ -178,15 +177,15 @@ class MatrixFactorization extends Serializable{
       itemFactors.foreach(ui => ui._2 /= numParts.toDouble)
       bc_itemFactors.unpersist()
 
-      val approxLoss = lossSum / (numParts * numInnerIters)
-      if (i != 0) {
-        val oldLoss = lossList.last
-        if (approxLoss > oldLoss)
-          stepsize = stepsize * 0.5
-        else
-          stepsize *= 1.05
-      }
-      lossList.append(approxLoss)
+//      val approxLoss = lossSum / (numParts * numInnerIters)
+//      if (i != 0) {
+//        val oldLoss = lossList.last
+//        if (approxLoss > oldLoss)
+//          stepsize = stepsize * 0.5
+//        else
+//          stepsize *= 1.05
+//      }
+//      lossList.append(approxLoss)
 
 //      println(s"approximate loss: $approxLoss, time: ${System.currentTimeMillis() - startTime}")