diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala | 6 |
1 files changed, 0 insertions, 6 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 28489410f8..03b71301e9 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -49,7 +49,6 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // Test if we can accurately learn Y = 10*X1 + 10*X2 on streaming data test("parameter accuracy") { - // create model val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(0.0, 0.0)) @@ -82,7 +81,6 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // Test that parameter estimates improve when learning Y = 10*X1 on streaming data test("parameter convergence") { - // create model val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(0.0)) @@ -113,12 +111,10 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { assert(deltas.forall(x => (x._1 - x._2) <= 0.1)) // check that error shrunk on at least 2 batches assert(deltas.map(x => if ((x._1 - x._2) < 0) 1 else 0).sum > 1) - } // Test predictions on a stream test("predictions") { - // create model initialized with true weights val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(10.0, 10.0)) @@ -142,7 +138,5 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // compute the mean absolute error and check that it's always less than 0.1 val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints) assert(errors.forall(x => x <= 0.1)) - } - } |