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apache · Mar 20, 2014 · f4002d7 · f4002d7
1 parent bd53c7a
commit f4002d7
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Showing 2 changed files with 12 additions and 13 deletions.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala
@@ -22,8 +22,6 @@ import scala.util.Random
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
-import org.jblas.{DoubleMatrix, Singular, MatrixFunctions}
-
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
@@ -87,6 +85,7 @@ class PCASuite extends FunSuite with BeforeAndAfterAll {
   test("sparse matrix full rank matrix pca") {
     val m = 5
     val n = 3
+    // the entry that gets dropped is zero to test sparse support
     val dataarr = Array.tabulate(m,n){ (a, b) =>
       MatrixEntry(a, b, Math.sin(a+b+a*b)) }.flatten.drop(1)
     val data = sc.makeRDD(dataarr, 3)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala
@@ -80,8 +80,8 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val v = decomposed.V
     val s = decomposed.S
 
-    val densea = getDenseMatrix(a)
-    val svd = Singular.sparseSVD(densea)
+    val denseA = getDenseMatrix(a)
+    val svd = Singular.sparseSVD(denseA)
 
     val retu = getDenseMatrix(u)
     val rets = getDenseMatrix(s)
@@ -94,7 +94,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     assertMatrixEquals(retv, svd(2))
 
     // check multiplication guarantee
-    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), densea)  
+    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), denseA)  
   }
 
  test("dense full rank matrix svd") {
@@ -111,8 +111,8 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val v = decomposed.V
     val s = decomposed.S
 
-    val densea = getDenseMatrix(LAUtils.denseToSp(a))
-    val svd = Singular.sparseSVD(densea)
+    val denseA = getDenseMatrix(LAUtils.denseToSp(a))
+    val svd = Singular.sparseSVD(denseA)
 
     val retu = getDenseMatrix(u)
     val rets = DoubleMatrix.diag(new DoubleMatrix(s))
@@ -125,7 +125,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     assertMatrixEquals(retv, svd(2))
 
     // check multiplication guarantee
-    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), densea)
+    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), denseA)
   }
 
  test("rank one matrix svd") {
@@ -145,8 +145,8 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
 
     assert(retrank == 1, "rank returned not one")
 
-    val densea = getDenseMatrix(a)
-    val svd = Singular.sparseSVD(densea)
+    val denseA = getDenseMatrix(a)
+    val svd = Singular.sparseSVD(denseA)
 
     val retu = getDenseMatrix(u)
     val rets = getDenseMatrix(s)
@@ -158,7 +158,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     assertMatrixEquals(retv, svd(2).getColumn(0))
 
      // check multiplication guarantee
-    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), densea)  
+    assertMatrixEquals(retu.mmul(rets).mmul(retv.transpose), denseA)  
   }
 
  test("truncated with k") {
@@ -176,8 +176,8 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val v = decomposed.V
     val retrank = s.data.collect().length
 
-    val densea = getDenseMatrix(a)
-    val svd = Singular.sparseSVD(densea)
+    val denseA = getDenseMatrix(a)
+    val svd = Singular.sparseSVD(denseA)
 
     val retu = getDenseMatrix(u)
     val rets = getDenseMatrix(s)