diff options
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala | 22 |
1 files changed, 17 insertions, 5 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 2a77e1a9ef..0cf9a7f909 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -472,13 +472,15 @@ class ALS private ( // Compute the XtX and Xy values for each user by adding products it rated in each product // block for (productBlock <- 0 until numBlocks) { - for (p <- 0 until blockFactors(productBlock).length) { + var p = 0 + while (p < blockFactors(productBlock).length) { val x = wrapDoubleArray(blockFactors(productBlock)(p)) tempXtX.fill(0.0) dspr(1.0, x, tempXtX) val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p) - for (i <- 0 until us.length) { - if (implicitPrefs) { + if (implicitPrefs) { + var i = 0 + while (i < us.length) { // Extension to the original paper to handle rs(i) < 0. confidence is a function // of |rs(i)| instead so that it is never negative: val confidence = 1 + alpha * abs(rs(i)) @@ -489,11 +491,17 @@ class ALS private ( if (rs(i) > 0) { SimpleBlas.axpy(confidence, x, userXy(us(i))) } - } else { + i += 1 + } + } else { + var i = 0 + while (i < us.length) { userXtX(us(i)).addi(tempXtX) SimpleBlas.axpy(rs(i), x, userXy(us(i))) + i += 1 } } + p += 1 } } @@ -502,7 +510,11 @@ class ALS private ( // Compute the full XtX matrix from the lower-triangular part we got above fillFullMatrix(userXtX(index), fullXtX) // Add regularization - (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda) + var i = 0 + while (i < rank) { + fullXtX.data(i * rank + i) += lambda + i += 1 + } // Solve the resulting matrix, which is symmetric and positive-definite if (implicitPrefs) { Solve.solvePositive(fullXtX.addi(YtY.get.value), userXy(index)).data |